diff --git a/Scripts/Evaluation/predictions_eval.ipynb b/Scripts/Evaluation/predictions_eval.ipynb
index e8a667c8df6c211f1a3d44fff0c0ee55d059e84e..b8a75cdaa918b53c5fa39ba30ad8087fe1fd377f 100644
--- a/Scripts/Evaluation/predictions_eval.ipynb
+++ b/Scripts/Evaluation/predictions_eval.ipynb
@@ -2,14 +2,4954 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "\n"
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import numpy as np\n",
+    "import scipy as sci\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "import ndjson\n",
+    "import jsonlines\n",
+    "import os\n",
+    "import sys\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import re\n",
+    "sys.path.append('..')\n",
+    "from ML import ml_utils, plottings\n",
+    "path = os.path.join(*ml_utils.split_path_unix_win('../../Data/pan19-celebrity-profiling-training-dataset-2019-01-31/predictions/workset/creator'))\n",
+    "agen = 'age'\n",
+    "gendern = 'gender'\n",
+    "order = ['dist', 'char', 'asis', 'pos', 'tag', 'dep', 'lemma', 'word','emoticon_c','polarity', 'num']\n",
+    "stackdirs = ['_'.join(order[:i]) for i in range(2, len(order)+1)] + ['dist_char_asis_lemma','dist_char_asis_lemma_word']\n",
+    "dirs = order + ['_'.join(order[:i]) for i in range(2, len(order)+1)] + ['dist_char_asis_lemma', 'dist_char_asis_lemma_word']\n",
+    "\n",
+    "authors = [50, 150, 500, 1000]\n",
+    "lens = [100, 250, 500]\n",
+    "models = ['', 'direct', 'dynAA']\n",
+    "\n",
+    "targets = ['age', 'gender']\n",
+    "\n",
+    "featuretyp_dic = {'emoticon': [[100, 250, 500], [1], [1000, 500, 150, 50]],\n",
+    "                  'char': [[100, 250, 500], list(range(2, 5 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'word': [[100, 250, 500], list(range(1, 2 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'tag': [[100, 250, 500], list(range(1, 3 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'dep': [[100, 250, 500], list(range(1, 3 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'asis': [[100, 250, 500], list(range(2, 5 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'lemma': [[100, 250, 500], list(range(1, 2 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'dist': [[100, 250, 500], list(range(2, 5 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'pos': [[100, 250, 500], list(range(1, 3 + 1)), [1000, 500, 150, 50]],\n",
+    "                  'num': [[100, 250, 500], [1], [1000, 500, 150, 50]],\n",
+    "                  'polarity': [[100, 250, 500], [1], [1000, 500, 150, 50]]\n",
+    "                      }\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "dats = {}\n",
+    "for target in targets:\n",
+    "    dats[target] = {}\n",
+    "    for minTw in [str(el) for el in lens]:\n",
+    "        dats[target][minTw]  = {}\n",
+    "        for model in models:\n",
+    "            if model in ['direct', 'dynAA']:\n",
+    "\n",
+    "                tmpdirs = stackdirs\n",
+    "                settyp = ['test']\n",
+    "                key = 'stacked' if model == 'dynAA' else 'cumulated'\n",
+    "                filebeg = '{}_'.format(model)\n",
+    "                dats[target][minTw][key] = {}\n",
+    "            else:\n",
+    "                tmpdirs = order\n",
+    "                settyp = ['val', 'test']\n",
+    "                key = 'baseline'\n",
+    "                dats[target][minTw][key] = {}\n",
+    "                filebeg = ''\n",
+    "            for dir in tmpdirs:\n",
+    "                dir = re.sub('emoticon_c', 'emoticon', dir)\n",
+    "                part = dir.split('_')[-1]\n",
+    "                dir = re.sub('emoticon', 'emoticon_c', dir)\n",
+    "                grams = featuretyp_dic[part][1]\n",
+    "                dats[target][minTw][key][dir] = {}\n",
+    "                for subgram in [grams[0:i] for i in range(1, len(grams)+1)]:\n",
+    "                    sg = '_'.join([str(el) for el in subgram])\n",
+    "                    dats[target][minTw][key][dir][sg] = {}\n",
+    "                    for author in authors:\n",
+    "                        dats[target][minTw][key][dir][sg][str(author)] = []\n",
+    "                        for subset in settyp:\n",
+    "                            file = \"{0}pred_{1}_{2}_{3}_{4}_{5}_{6}.json\".format(filebeg,\n",
+    "                                                                                                                      subset,\n",
+    "                                                                                                                      target,\n",
+    "                                                                                                                      dir,\n",
+    "                                                                                                                      sg,\n",
+    "                                                                                                                      minTw,\n",
+    "                                                                                                                      author)\n",
+    "                            dats[target][minTw][key][dir][sg][str(author)].append(os.path.join(path, target, dir, minTw, file))\n",
+    "\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:51: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:59: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:187: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n",
+      "<ipython-input-5-dfe456c928e7>:35: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "<ipython-input-5-dfe456c928e7>:42: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+      "C:\\Users\\schubert\\Documents\\attributionfeatures\\Scripts\\ML\\plottings.py:101: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(1, figsize=(18,4))\n"
+     ]
+    }
+   ],
+   "source": [
+    "savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/figures'))\n",
+    "redo =False\n",
+    "backend_ =  mpl.get_backend()\n",
+    "mpl.use(\"Agg\")\n",
+    "for target in dats.keys():\n",
+    "    for mT in dats[target].keys():\n",
+    "        minTw = int(mT)\n",
+    "        os.makedirs(os.path.join(savedir, target, mT, 'proportions'), exist_ok=True)\n",
+    "        os.makedirs(os.path.join(savedir, target, mT, 'confusion'), exist_ok=True)\n",
+    "        for mode in dats[target][mT].keys():\n",
+    "            for dir in dats[target][mT][mode].keys():\n",
+    "                for grams in dats[target][mT][mode][dir].keys():\n",
+    "                    df = None\n",
+    "                    targCol = 'gender' if target == 'gender' else 'centered_age'\n",
+    "                    predCol = target + '_predicted_cat'\n",
+    "\n",
+    "                    fp = os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                             'confusion_{}_{}_{}_{}_mode_major_only_false.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                    if os.path.exists(fp) and not redo:\n",
+    "                        continue\n",
+    "\n",
+    "                    for author in sorted([int(el) for el in list(dats[target][mT][mode][dir][grams].keys())]):\n",
+    "                        author = str(author)\n",
+    "                        for file in dats[target][mT][mode][dir][grams][author]:\n",
+    "                            try:\n",
+    "                                tmp = pd.read_json(file)\n",
+    "                            except:\n",
+    "                                print('Loading file failed : {}'.format(file))\n",
+    "                                continue\n",
+    "                            sb = 'test' if 'test' in file else 'val'\n",
+    "                            tmp['# Authors'] =  int(author)\n",
+    "                            tmp['subset'] =  sb\n",
+    "                            if type(df) == type(None):\n",
+    "                                df = tmp\n",
+    "                            else:\n",
+    "                                df = pd.concat((df, tmp), axis=0)\n",
+    "                    df.reset_index(inplace=True, drop=True)\n",
+    "                    df['correct'] = df[targCol] == df[predCol]\n",
+    "                    fig, ax = plottings.plot_distrib_graph(df=df, to_plot='True_False', absolute=False)\n",
+    "\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+    "                                             'proportions_{}_{}_{}_{}_color_pred.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                                dpi=fig.dpi,transparent=True, bbox_inches='tight')\n",
+    "\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+    "                                             'proportions_{}_{}_{}_{}_color_pred.png'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                                dpi=fig.dpi,transparent=True, bbox_inches='tight')\n",
+    "\n",
+    "                    fig, ax = plottings.plot_distrib_graph(df=df, to_plot='False', absolute=False,\n",
+    "                                                           color=targCol)\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+    "                                             'proportions_{}_{}_{}_{}_color_{}.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT,\n",
+    "                                                                                           targCol)),\n",
+    "                                dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'proportions' ,\n",
+    "                                             'proportions_{}_{}_{}_{}_color_{}.png'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT,\n",
+    "                                                                                           targCol)),\n",
+    "                                dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "                    #plot basic confusion matrix\n",
+    "                    fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                 major_wrong = False, normalize='true')\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                             'confusion_{}_{}_{}_{}_mode_basic.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                                dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "\n",
+    "\n",
+    "\n",
+    "                    fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                 major_wrong = True, normalize='all')\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                             'confusion_{}_{}_{}_{}_mode_major_false.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                                dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "\n",
+    "                    fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                 major_wrong = 'wrong', normalize='all')\n",
+    "                    fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                             'confusion_{}_{}_{}_{}_mode_major_only_false.pdf'.format(mode,\n",
+    "                                                                                          dir,\n",
+    "                                                                                          grams,\n",
+    "                                                                                          mT)),\n",
+    "                                dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "                    plt.clf()\n",
+    "\n",
+    "mpl.use(backend_) # Reset backend"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n",
+     "is_executing": true
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Doing the stuff including pred_test_age_dist_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_4_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_4_500_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_4_500_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_2_3_500_1000.json\n",
+      "Doing the stuff including pred_test_age_lemma_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_lemma_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_word_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_word_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_emoticon_c_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_polarity_1_500_1000.json\n",
+      "Doing the stuff including pred_test_age_num_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_4_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_4_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_2_3_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_2_3_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_1_2_3_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_lemma_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_lemma_1_2_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_lemma_word_1_500_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_lemma_word_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_3_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_3_4_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_2_3_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_2_3_4_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_2_3_4_5_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_1_2_3_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_1_2_3_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_1_2_3_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_lemma_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_lemma_1_2_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_lemma_word_1_500_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_lemma_word_1_2_500_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dist_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_age_char_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_age_asis_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_pos_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_tag_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_dep_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_age_lemma_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_word_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_word_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_age_emoticon_c_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_polarity_1_250_1000.json\n",
+      "Doing the stuff including pred_test_age_num_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_4_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_4_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_1_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_1_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_1_2_250_1000.json\n",
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+      "Doing the stuff including direct_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_250_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_3_250_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_1_250_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_250_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_lemma_word_1_2_250_1000.json\n",
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+      "Doing the stuff including pred_test_age_emoticon_c_1_100_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_2_100_1000.json\n",
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+      "Doing the stuff including dynAA_pred_test_age_dist_char_asis_pos_tag_1_100_1000.json\n",
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+      "Doing the stuff including pred_test_gender_dist_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_dist_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_4_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_2_3_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_lemma_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_word_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_word_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_emoticon_c_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_polarity_1_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_num_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_4_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_4_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_3_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_word_1_250_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_word_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_4_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_4_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_4_5_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_2_3_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_2_3_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_3_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_1_2_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_word_1_250_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_word_1_2_250_1000.json\n",
+      "Doing the stuff including pred_test_gender_dist_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dist_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dist_2_3_4_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dist_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_4_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_char_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_4_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_asis_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_pos_1_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_tag_1_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_dep_1_2_3_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_lemma_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_lemma_1_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_word_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_word_1_2_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_emoticon_c_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_polarity_1_100_1000.json\n",
+      "Doing the stuff including pred_test_gender_num_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_4_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_4_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_1_2_3_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_1_2_3_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_3_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_1_2_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_word_1_100_1000.json\n",
+      "Doing the stuff including direct_pred_test_gender_dist_char_asis_lemma_word_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_4_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_4_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_2_3_4_5_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_1_2_3_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_1_2_3_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_1_2_3_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_1_2_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_word_1_100_1000.json\n",
+      "Doing the stuff including dynAA_pred_test_gender_dist_char_asis_lemma_word_1_2_100_1000.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/figures'))\n",
+    "redo =False\n",
+    "idx = [\"500\", \"250\", \"100\"]\n",
+    "only_do = ['age','gender']\n",
+    "backend_ =  mpl.get_backend()\n",
+    "mpl.use(\"Agg\")\n",
+    "for target in dats.keys():\n",
+    "    if target in only_do:\n",
+    "        tweets = sorted(list(dats[target].keys()), key=idx.index)\n",
+    "        for mT in tweets:\n",
+    "            minTw = int(mT)\n",
+    "            os.makedirs(os.path.join(savedir, target, mT, 'bars'), exist_ok=True)\n",
+    "            os.makedirs(os.path.join(savedir, target, mT, 'confusion'), exist_ok=True)\n",
+    "            for mode in dats[target][mT].keys():\n",
+    "                for dir in dats[target][mT][mode].keys():\n",
+    "                    for grams in dats[target][mT][mode][dir].keys():\n",
+    "                        df = None\n",
+    "                        targCol = 'gender' if target == 'gender' else 'centered_age'\n",
+    "                        predCol = target + '_predicted_cat'\n",
+    "\n",
+    "                        # fp = os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                        #                          'confusion_{}_{}_{}_{}_mode_major_only_false.pdf'.format(mode,\n",
+    "                        #                                                                       dir,\n",
+    "                        #                                                                       grams,\n",
+    "                        #                                                                       mT)),\n",
+    "                        # if os.path.exists(fp) and not redo:\n",
+    "                        #     continue\n",
+    "\n",
+    "                        for author in sorted([int(el) for el in list(dats[target][mT][mode][dir][grams].keys())]):\n",
+    "                            author = str(author)\n",
+    "                            for file in dats[target][mT][mode][dir][grams][author]:\n",
+    "                                try:\n",
+    "                                    tmp = pd.read_json(file)\n",
+    "                                except:\n",
+    "                                    print('Loading file failed : {}'.format(os.path.basename(file)))\n",
+    "                                    continue\n",
+    "                                sb = 'test' if 'test' in file else 'val'\n",
+    "                                tmp['# Authors'] =  int(author)\n",
+    "                                tmp['subset'] =  sb\n",
+    "                                if type(df) == type(None):\n",
+    "                                    df = tmp\n",
+    "                                else:\n",
+    "                                    df = pd.concat((df, tmp), axis=0)\n",
+    "                        print('Doing the stuff including {}'.format(os.path.basename(file)))\n",
+    "                        df.reset_index(inplace=True, drop=True)\n",
+    "                        df['correct'] = df[targCol] == df[predCol]\n",
+    "                        # fig, ax = plottings.plot_distrib_graph(df=df, to_plot='True_False', absolute=False, line=True)\n",
+    "                        #\n",
+    "                        # fig.savefig(os.path.join(savedir, target,mT,'bars' ,\n",
+    "                        #                          'proportions_{}_{}_{}_{}_color_pred.pdf'.format(mode,\n",
+    "                        #                                                                       dir,\n",
+    "                        #                                                                       grams,\n",
+    "                        #                                                                       mT)),\n",
+    "                        #             dpi=fig.dpi,transparent=True, bbox_inches='tight')\n",
+    "                        #\n",
+    "                        # fig.savefig(os.path.join(savedir, target,mT,'bars' ,\n",
+    "                        #                          'proportions_{}_{}_{}_{}_color_pred.png'.format(mode,\n",
+    "                        #                                                                       dir,\n",
+    "                        #                                                                       grams,\n",
+    "                        #                                                                       mT)),\n",
+    "                        #             dpi=fig.dpi,transparent=True, bbox_inches='tight')\n",
+    "                        #\n",
+    "                        # fig, ax = plottings.plot_distrib_graph(df=df, to_plot='False', absolute=False,\n",
+    "                        #                                        color=targCol, line=True)\n",
+    "                        # fig.savefig(os.path.join(savedir, target,mT,'bars' ,\n",
+    "                        #                          'proportions_{}_{}_{}_{}_color_{}.pdf'.format(mode,\n",
+    "                        #                                                                       dir,\n",
+    "                        #                                                                       grams,\n",
+    "                        #                                                                       mT,\n",
+    "                        #                                                                        targCol)),\n",
+    "                        #             dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "                        #\n",
+    "                        # fig.savefig(os.path.join(savedir, target,mT,'bars' ,\n",
+    "                        #                          'proportions_{}_{}_{}_{}_color_{}.png'.format(mode,\n",
+    "                        #                                                                       dir,\n",
+    "                        #                                                                       grams,\n",
+    "                        #                                                                       mT,\n",
+    "                        #                                                                        targCol)),\n",
+    "                        #             dpi=fig.dpi, transparent=True, bbox_inches='tight')\n",
+    "                        #plot basic confusion matrix\n",
+    "                        fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                     major_wrong = False, normalize='true')\n",
+    "                        fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                                 'confusion_{}_{}_{}_{}_mode_basic.pdf'.format(mode,\n",
+    "                                                                                              dir,\n",
+    "                                                                                              grams,\n",
+    "                                                                                              mT)),\n",
+    "                                    dpi=fig.dpi, bbox_inches='tight')\n",
+    "\n",
+    "\n",
+    "\n",
+    "                        fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                     major_wrong = True, normalize='all')\n",
+    "                        fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                                                 'confusion_{}_{}_{}_{}_mode_major_false.pdf'.format(mode,\n",
+    "                                                                                              dir,\n",
+    "                                                                                              grams,\n",
+    "                                                                                              mT)),\n",
+    "                                    dpi=fig.dpi, bbox_inches='tight')\n",
+    "\n",
+    "                        fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol,\n",
+    "                                                     major_wrong = 'wrong', normalize='all')\n",
+    "                        #fig.savefig(os.path.join(savedir, target,mT,'confusion' ,\n",
+    "                        #                         'confusion_{}_{}_{}_{}_mode_major_only_false.pdf'.format(mode,\n",
+    "                        #                                                                      dir,\n",
+    "                        #                                                                      grams,\n",
+    "                        #                                                                      mT)),\n",
+    "                        #            dpi=fig.dpi, bbox_inches='tight')\n",
+    "                        plt.clf()\n",
+    "\n",
+    "\n",
+    "mpl.use(backend_) # Reset backend"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "target = 'gender'\n",
+    "minTw = '100'\n",
+    "dir = 'asis'\n",
+    "mode = 'baseline'\n",
+    "grams = '2_3_4_5'\n",
+    "df = None\n",
+    "targCol = 'gender' if target == 'gender' else 'centered_age'\n",
+    "predCol = target + '_predicted_cat'\n",
+    "for author in sorted([int(el) for el in list(dats[target][minTw][mode][dir][grams].keys())]):\n",
+    "    author = str(author)\n",
+    "    for file in dats[target][minTw][mode][dir][grams][author]:\n",
+    "        tmp = pd.read_json(file)\n",
+    "        sb = 'test' if 'test' in file else 'val'\n",
+    "        tmp['# Authors'] =  int(author)\n",
+    "        tmp['subset'] =  sb\n",
+    "        if type(df) == type(None):\n",
+    "            df = tmp\n",
+    "        else:\n",
+    "            df = pd.concat((df, tmp), axis=0)\n",
+    "df.reset_index(inplace=True, drop=True)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "tmp = df[0:100000].copy(deep=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "           gender  centered_age\n# Authors                      \n50            0.5          0.17\n150           0.5          0.13\n500           0.5          0.17\n1000          0.5          0.17",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>gender</th>\n      <th>centered_age</th>\n    </tr>\n    <tr>\n      <th># Authors</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>0.5</td>\n      <td>0.17</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>0.5</td>\n      <td>0.13</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>0.5</td>\n      <td>0.17</td>\n    </tr>\n    <tr>\n      <th>1000</th>\n      <td>0.5</td>\n      <td>0.17</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['correct'] = df[targCol] == df[predCol]\n",
+    "def prop(x):\n",
+    "    labs = x.unique()\n",
+    "\n",
+    "    return round(sum(x == labs[0])/len(x),2)\n",
+    "df.groupby('# Authors').agg({'gender':prop,\n",
+    "                             'centered_age':prop})\n",
+    "#tmp['correct'] = tmp[targCol] == tmp[predCol]\n",
+    "\n",
+    "#fig, ax = plottings.plot_distrib_graph(df=df, to_plot='True_False', absolute=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1296x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax= plottings.plot_distrib_graph(df=df, to_plot='True_False', absolute=False, line=True, color='correct')\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gender\n",
+      "..\\..\\tex\\figures\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 1296x288 with 3 Axes>",
+      "image/png": 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IiIiIiF/QdVBFRERERETEJ+g6qCIiIiIiIuITnNbp7RDypQRVRERERETED6gHVURERERERHyCElQRERERERHxCaVhkSRdZkZERERERMQPWGsLfCsIY8wgY8xmY8w2Y8y4XPZXN8b8YIxZbYxZb4y5Mb861YMqIiIiIiLiB4pykSRjTAAwERgAxADLjDEzrbUbMhUbA2yw1l5ijKkNbDbGfGmtPZlXvepBFRERERER8QNF3IPaFdhmrd2RkXBOAYZlPyRQ1RhjgCDgEJB+ukqVoIqIiIiIiPiBwiSoxpjRxpjlmW6js1UXBuzJtB2TcV9mbwMtgX3AWuBua63rdDFqiK+IiIiIiIgfcJ0+N8zCWjsJmHSaIia3h2XbvghYBfQDmgHzjDELrbVJeVWqHlQRERERERE/YAvxrwBigAaZtsNx95RmdiMwzbptA6KBc09XqRJUERERERERP1DEc1CXARHGmCbGmPJAJDAzW5ndQH8AY0xd4Bxgx+kq1RBfERERERERP+B0FXyIb36stenGmLHAz0AA8LG1dr0x5raM/e8BTwGTjTFrcQ8JftBae+B09SpBFRERERER8QMFvb5pIeqbDczOdt97mf7eBwwsTJ1KUEVERERERPyApeh6UIuLElQRERERERE/4CriHtTioARVRERERETEDxT1EN/ioFV8RURERERExCeoB1VERERERMQPuIpwFd/iogRVRERERETED7isElQRERERERHxARbfn4OqBFVERERERMQPlIZFkpSgioiIiIiI+AElqCIiIiIiIuITnFokSURERERERHyB1SJJIiIiIiIi4gs0xFdERERERER8glbxFREREREREZ/gUg+qiIiIiIiI+AIN8RURERERERGf4NIqviIiIiIiIuILNMTXj+xas4OFX83Huly06t2OTkPOz7J/5ewlbPl7A+D+5eLwvoPc9NadBJYvx7TnvsKZno51umjW5Ry6De/ljVMoc3q178Ej/3mQAIeDqfOnMWn6x1n2D+11MbcM/w8Ax44fY8Kkp9m0awtN6jfmjf++6CnXoG44b0x5h8mzvijR+Mua3Wuj+fOrX3FZS6tebej4725Z9kfNWcqWxRsBsC4Xh/cd4sY37iCwQjm+f34KzjQnLpeLZp1b0PXSHt44hTKlV/sLePjGBwhwOPh2/nQmff9Jlv2X9LyYWy69AYBjqcd5/INnMtpHI16/N1P7qBPGG9+8y6ezvyzJ8MukiNpNubjVv3AYByv2rGLB9sU5yjSp0dBdxuHg2MnjfLTY/bzfd+HtnEg/ibUWl3Xx7qLJJRt8GdS9VVf+N+JOHMbB93/N4tO5X2XZ36dtD2675CZcLhdOl5NX/u9tVm9fC8DMp6ZwLPU4TpcTp8vJ9S/c6o1TKHOa12rCxS37Y4yDlTGrWbhjSY4yjWs0YHDL/gSYAI6lHePjJV8DUDGwAsPaDKZOUC0Avl87mz1H9pVo/GXNBa268r8r7ybA4WD6oh+Z/HPWz4E+7XpyxyU347LuNvLy1DdZldFGABzGwRfjPyDhyAHufufBkg7fb+kyM37C5XLxx+fzGHb/VQTVqMrUJz6lSYfm1Air5SnT8eJudLzY/YU8Omobq+Yuo2JQJay1XPpgJOUrlseZ7mTas1/SqE1TQpuHeet0ygSHw8HjtzzEDU+OJvZgHN+98DW/LvudbTE7PGX2xO/lmkdvJOloMr079OTp2yZwxfhriN63k6H/u9JTz5+TfmHu0vneOpUyweVyseCLX7jkvhEE1ajK/z35BY3bN8vSRjoM7kqHwV0B2LlqO6vnLve0kWH3X0m5jDYy/bmvadimCaHN6nvrdEo9h8PBhJvGc+NTtxF7KI7vnvuS+cv/YHum9hETv5drJ9zkbh/te/DUrY8y4qHriN63i2H3X+WpZ+H7c5m39FdvnUqZYTBc0nognyyZQlJqErf1vIGNcVtJSDnoKVMxsAKXnHcRny79hsTUJKqUr5yljo8Xf8WxtOMlHXqZ5DAOHrzqHsa8eR9xRxL47MH3WbBmEdGxuzxllm5eyR9rFgHQPKwpz9/0OFc8eb1n/62v30Pi0cQSj72sMhiGtB7Ap0u/ISk1mVsvGMWm+G052siQ1gP5fNlUElOTs7SRwS37szVhB99EfU+AcVAuoJw3TqPMcBgHD478L3e8cS9xhxP4YvwH/LFmEdH7d3rKLN20gj9W/wlARFgznr/lCS5//FrP/pH9RhAdu4ugilVKOHr/VhrmoDq8HUBZELdjP9XrBlO9TjABgQFEdGvJjqiteZbfsmQDLbq1BMAYQ/mK5QFwOV24nC4wpkTiLsvaNj+PXbG72RO3l7T0dGb9+RP9u1yYpUzU5tUkHU0GYNWW1dStWSdHPRe06cbuuD3sS9hfInGXVfE7YqleJ8TTRpp3O5foVdvzLL91yUYiMrWRctnaiEFt5Gy428ce9sRntI9FP/Ovzn2zlInakql9bF1DaM26Oerpfl43dsfGsO+A2sfZCg+uz8Fjhzl8/AhO62Ltvo20rNsiS5m2Ya3ZELuZxNQkAI6ePOaNUP1C68Yt2ZOwl70H95PuTGfuil/p065nljLHT/zzY0Cl8pVKwYUbSrfw4HocOnqEw8cT3W1k/0bOrRORpUyb+q3YGLuFxFT3e9epNlIhsDyNazRgZcwaAJzWRWr6iZI9gTLmvMYtiYnfy94D7jby87L59G17ujZSETIlRnWCa9OrTXe+X/RjicUsbrYQ/7xFPahF4OjhZKrWqObZDgqpStyO3L+wpZ1IY/faaPpcO8Bzn8vlYuqET0mMP0yb/h3VM1QEQmvUZf+BOM927KE42kW0ybP8iP6XsSBqUY77/91jED/+OadYYvQnR48kE1Sjqmc7KCTo9G1k3U56XdPfc5/L5eLbJz4nMf4Ibfq1p26zesUec1lWt0YdYg/Gerbzax9X9BvOgqg/c9z/7x4XMWuR2kdRqFYxiMTjSZ7tpNRkwoOzfhbUqlIDh3Fw0/lXUz6wPH9HL2fV3nWe/Td0i8RiWbZrFcv3rCqx2MuiOsG1iDsc79mOP5zAeY1b5ijXt10vxg67hZCqIdzzzjjP/dbCxDtfxmKZtvAHpi/6oUTiLsuqVqzq+XEGTrWRrJ8Fp9rIjV1HUiGwPH/vXM7qfesJqRTM0ZPHGN7mYkKr1WFfYiyzN84nzZlW0qdRZtQOqU1s5jZyJIHzmuRsIxe278XYS2+lRtUQ7n77Ac/9/7vyLt6Y9g6VK1bO8RgpXqWhB7XEE1RjjLHZnpk87hsNjAa46oHr6XFpnxKMspAK8TrvXLWNes3DqBhUyXOfw+Eg8qkbOXE0ldlvTedgTAI1w2sXQ6B+JJcOtrwaZLfzujCi/3AiHx6V5f5ygYH069KXl798ozgi9Cu5PfUmj5ECO1dvJ7R5/Rxt5KonRnHiWCpz3p6hNnKWcuuBzrN9tO7MiH6XMvLRG7PcXy4wkP6d+/DKV28WS4z+J5fXJNuHi8M4CKseysdLvqacI5DRPa5nz5F9HDx6iEl/fU7yiRSqlK/MDd0iOXD0IDsP7Smp4Mug3F6PnH5fvZDfVy+kQ/O23HbJfxjz5n0A3PTKGA4kHiQkKJiJd73CzrhdRG1bU8wxl225fWJkf9tyGEP96qFMXjqFco5Abul+LTFH9uEwDupVC2X2hl+ISdzP4Jb96dX0fH7durBEYi+LCvJ6APy2aiG/rVpIx+btuH3ozdz+xr30anMBh5IPs3H3Fjq1aF/ssUpWpSFBLdEhvpkTUWNMf2NMZ2NMK2utNdm+rVprJ1lrO1trO/t0cgpUqVGV5EP//KqXcjiZKiFBuZbdumQjEee3ynVfhSoVCTu3AbvW7sh1vxRc7ME46tX6Z0hiaI26xB9KyFHunEYRPHv749z2/N0cSck6V6h3h55s2LGRg4mHij3esi4opCoph5I92ymHU6gcnHsb2bZkk2d4b3YVKlck7JwG7F63szjC9Buxh+IIrRnq2c6zfTSM4JnbJnD7i/fkbB/te7I+epPaRxFJSk2meqV/RuJUq1iV5NSUbGWS2JqwgzRnGsfSjrPr0B5Cq7qnJiSfcJc9evIYG2O3EBasUQZnI/5IAnVD/pn2USekNgmJB/IsH7VtDeG1wqhepToABxLd8yIPpxzh99ULaZ1L76sUTlJqMtUrZmsjJ7K3kWS2ZWojOw/FEFqtDkmpySSlJhOT6B65syF2M/Wr5Zy2IAUXfziB0MxtJLg2CUfybiMrt60mvHZ9gqtUp12zNvRp24Mfn5nKczc9TudzO/L0jY+WRNiCexXfgt68pUQT1EzJ6RjgKaAH8Icxpln2HtTSpG6TeiTGHSYp4QjOdCdbl2ykSYfmOcqdOHaCvZv30LTjP/uOJx3jxNFUANJPprFnwy5C6tUssdjLqrXb1tO4XiPC64RRLjCQf/ccxPzlv2cpU69WKBPvf43/vfkQO/fvylHHkJ6DNby3iNRpEpqljWxbsokm7ZvlKHfi2An2bYmhSYd/9h1POsaJY/+0kZgNuwgJrVFisZdF7vbRkPA69d3to8dFzF/+R5Yy9WqF8vb9r3D/W4+wc//uHHUM6TmIH//8qaRCLvP2Ju6jZpUQQipVJ8A4aFO/JZvisq5lsDFuK41qNMBhDOUcgYQH1ych5QDlAspRPsA9T7tcQDma125CfHLeXxQlfxt2baJBnXDq1wwlMCCQgZ36sWBN1mkg4bX/WczwnAYRlAsMJPFoIhXLV6RyBfcIkIrlK9KtZRe274su0fjLor2J+6lRJYTgU22kXks2xW/LUmZj3DYahYRnaiP1SEg5SMrJoySlJlGzivuzo2nNRsSnqI2cjfWeNlKPwIBALurSnz/WZJ0K0iBTGzm3QQvKBZbjyNFE3v7+fQaPv5whD1/J+I8eZ/mmlTzyyVMlfQp+y1pb4Ju3lMgQX2NMTWvtwYy/2wGXAP8C7gRWANHGmABrrbMk4ilqjgAHva8dwIyXp2Jd7kto1AyrzbpfowA4r18HAHas2ELD1o0pV6G857FHE1P45YNZWJf7P0LzrufSpH3O5FYKx+ly8sSHz/Lxo+8S4Ajg/379nm17tjNy4AgAvp77LWNH3EZw1WCeuOVhANKdTi57cCTg/lLRo113Hn1fb5hFwRHgoNe1/fnh1e+wLhfn9mxDjbBarPvNPU/uvAvdQ3yiV26lQetG2drIUX79aI77wtLW0qzLOTTOJbmVgnO6nDz50fN89PC7BDgc/N9vM9gWs53IAVcAMGXe/zH2itEEBwXz+C0PAZDuTOfycdcA7vZxQdvzeXTS0147h7LGZS0/rpvHqK6ROIxhRcwa4lMO0KWh+/Nj2e4oElIOsjVhB2N73YzFsnz3auJTDhBSKZirO18GuIcBr9m3ga0JGolzNpwuJy998zpvjX2ZAIeDmX/PZsf+nVzeaygA3y2cSf/2vbm420WkO9M5kXaS8R89AUDNqiG8dKu7bQQ4Avh5+S/8vWGpt06lzHBZy6wN87i+y5U4jGFlzFoSUg7QuYH782P5nlUcOHqQrQeiuaPnf7DWsjKjHQHM2vALV7QbQoAJ4PDxI0xfM9ubp1PqOV1OXvjmNSbe9QoOh4OZf83KaCPDAPhu4Qz6dejDkPMHZbSRE4z7YIJ3gxagdAzxNcUdpDGmKTAG+MVaO8cYUx/33NKTQC/gMmvtcWPMtcDP1toc48ze+vtj338m/cibL7/u7RAkw9bvss5pemPRB16KRDKb+PpEb4cgGbZ8m3WxoEdmPeelSCSzn2ap991XLH8n6+iJx+a84KVIJLMfZ2h1W1+z8r2FZeISAo/NeaHAedWTgx/0yjmXRA9qKnAU6GOMOQn8AQwEmltr6wBkJKf/AeaWQDwiIiIiIiJ+x+VyeTuEfBVbgnpqQSRr7T5jzPvADbiH9iYCw4GfjDEfAgeBfsB/rLXxeVYoIiIiIiIiZ6w0DPEtlkWSsq3WW95auxd4F4gHrgMaAX2A5cAW4Gpr7driiEVERERERETAhS3wzVuKpQc1U3I6GrjAGBMFzAPeBO4CRgIzrLXvFcfxRUREREREJCu/7UEFMMbcAVwDTAYuB54G2gOv456TOsAYU6W4ji8iIiIiIiL/8NvLzJHDgfYAACAASURBVBhj6gH1gCG45546gYXAf4GXgGeAytbao8VxfBEREREREcnKb3pQjTFZliC21u4HXsA913SotfZC4MeM7VsyyhwsimOLiIiIiIhI/lwuV4Fv3lIkPajZ5pzWw7340W9AGlA9o1grYCXwkLX2eFEcV0RERERERAqmzPegZu45Ncb8C7gZKA9cDNxrrd0M7DDGLAJeBN6w1iaczTFFRERERESk8Ip6DqoxZpAxZrMxZpsxZlweZfoaY1YZY9YbY/7Ir84z7kHNdimZZkBL3EnpImPMAGCoMeZe3Cv2hgEndZ1TERERERER73DZohu6a4wJACYCA4AYYJkxZqa1dkOmMsHAO8Aga+1uY0yd/Oo94wQ1U3J6F3A9UAuYBSwC5gMWuBr4n7X2pTM9joiIiIiIiJy9Ih7i2xXYZq3dAWCMmQIMAzZkKnM1MM1auzvj+Pl2WJ7tEN+BQB+ge0YwA4wxY6y1LtxzUD/LuImIiIiIiIgXFWaRJGPMaGPM8ky30dmqCwP2ZNqOybgvsxZAiDHmd2PMCmPM9fnFeDZDfOsBl+Ee2htirV1tjLkOmGyMqWStfRn4/UzrFxERERERkaJTmB5Ua+0kYNJpiphc7st+gECgE9AfqAT8bYxZbK3dklelBepBNW6OTNsBGZeSeQP3kN7/GmPqWWuX4F4oaaQxJqQgdYuIiIiIiEipEwM0yLQdDuzLpcxP1tqj1toDwAKg3ekqLegQ3yoZw3YxxtwDvGGM+RxIxT2E1wB3GWPCrbWLgB7W2sMFrFtERERERESKWRGv4rsMiDDGNDHGlAcigZnZyswAehljAo0xlYFuwMbTVZpvgmqMGYq7pxRjzLW455o+AFwI/MdauxD4HggGbs5YzelkQc5IRERERERESkZRJqjW2nRgLPAz7qRzqrV2vTHmNmPMbRllNgI/AWuApcCH1tp1p6v3tHNQjTE1gbuAu40xDYHOwP3ADcBa4OmMA/9tjEkB4q21znzPRkREREREREpUEa/ii7V2NjA7233vZdt+CSjwVV3yWyTpJJAOTAACgE3As0AKMNRam2aMeQxIs9Y+V9CDioiIiIiISMkqyuugFpfTDvG11ibjvqbpxcBi4HWgITANqGWMicS9ku/3xRyniIiIiIiInIUinoNaLApymZmpQBTwNrAXuAX3WOMhuOedXpsxtlhERERERER8lDcTz4LKN0G11u4CdhljrgG+AR4Frsbd+1rZWptYvCGKiIiIiIjI2SoTCeop1toVxpjLcQ/5DbHWvgMoORURERERESkFylSCCmCtXW2M6QscL55wREREREREpDiUuQQVIL/r1oiIiIiIiIjvcbl8fxXfQieoIiIiIiIiUvqUyR5UERERERERKX2UoIqIiIiIiIhPUIIqIiIiIiIiPkEJqoiIiIiIiPgELZIkIiIiIiIiPkE9qCIiIiIiIuITlKCKiIiIiIiITygNCarD2wGIiIiIiIiIgHpQRURERERE/EJp6EFVgioiIiIiIuIHtIqviIiIiIiI+AT1oIqIiIiIiIhPUIIqIiIiIiIiPkEJqoiIiIiIiPgEJagiIiIiIiLiE5SgioiIiIiIiE9wObWKr4iIiIiIiPgA9aCKiIiIiIiIT1CCWkSevPsxb4cgmXzy5WRvhyB5eP3517wdggAPTXjY2yFIHr6fOt3bIQhwzahrvR2C5GHOj3O8HYIAg4cM9nYIUkYpQRURERERERGfoARVREREREREfILLpUWSRERERERExBf4fgeqElQRERERERF/oCG+IiIiIiIi4hOUoIqIiIiIiIhPKA0JqsPbAYiIiIiIiIiAelBFRERERET8QmlYxVc9qCIiIiIiIn7AWlvgW0EYYwYZYzYbY7YZY8adplwXY4zTGHNFfnWqB1VERERERMQPFOUcVGNMADARGADEAMuMMTOttRtyKfcC8HNB6lUPqoiIiIiIiD9w2YLf8tcV2Gat3WGtPQlMAYblUu5O4DsgviCVKkEVERERERHxA4UZ4muMGW2MWZ7pNjpbdWHAnkzbMRn3eRhjwoDhwHsFjVFDfEVERERERPxAYYb4WmsnAZNOU8Tk9rBs268DD1prncbkVjwnJagiIiIiIiJ+wFWwobsFFQM0yLQdDuzLVqYzMCUjOa0FXGyMSbfWfp9XpUpQRURERERE/EBRLpIELAMijDFNgL1AJHB1tuM1OfW3MWYy8OPpklNQgioiIiIiIuIXijJBtdamG2PG4l6dNwD42Fq73hhzW8b+As87zUwJqoiIiIiIiD8o2h5UrLWzgdnZ7ss1MbXW3lCQOpWgioiIiIiI+IEiHuJbLJSgioiIiIiI+AGX0+XtEPKlBFVERERERMQPqAdVREREREREfEIpyE+VoIqIiIiIiPiFUpChKkEVERERERHxAxriKyIiIiIiIj5BCaqIiIiIiIj4BCWoIiIiIiIi4huUoIqIiIiIiIgvsC4lqCIiIiIiIuIDNMRXREREREREfEIpyE+VoIqIiIiIiPgD63J5O4R8KUEVERERERHxAxriKyIiIiIiIj5BCaqIiIiIiIj4BK3iKyIiIiIiIr7B9/NTJagiIiIiIiL+QEN8RURERERExCe4tIqviIiIiIiI+AT1oIqIiIiIiIgvsL7fgaoEVURERERExB9oDqqIiIiIiIj4BiWoIiIiIiIi4gvUgyoiIiIiIiI+weX0/UmoSlBFRERERET8ge93oOLwdgAiIiIiIiIioB5UERERERERv6A5qH6k3/l9eea+JwhwBPDFjK9587OJWfY3b9SMNx97lbbnnMez777IO1++D0Czhk358Nl3PeUa1W/IC5Ne5v0pH5Vo/GXRphXr+X7S/+Fyueg2sAf9RwzMsv/40eN89fJkDiccxuVy0nf4v+g6oDtpJ9OY+OBrpKel43I5adujA4OuGeKlsyg7enfsyYRbxuNwBPDNvP/jvf/7MMv+YX2GcNvlNwFwNPUYj77zJBt3bvbsdzgczHz1W2IPxXHzk3eUaOxlUfTqbfz6+c9Yl4s2fTvQbWjPLPuX/vgXGxetBcDlcnFo7wHueO9/VAqqROrRVH7+4AcOxsSDMQwafQn1Ixp44zTKlB5tzmfcNfcR4HDw3R8z+GjWZ1n2X9ihN3defisul8XpcvL8l68StXU1AE/d9Ai92/fkUNJhhj880hvhlzkx63exdOqfWOsiokcr2l7UKUeZ/Vv2svTbhViniwpBlRj83+EAbPh1NVv+3ABYInq0pnX/diUcfdnUvVVX7rtiLA5HADMWzeLTeV9l2d+7bQ9uG/IfrLWkO528+t3brN7ufh+b8eQUjqUew2VdpDudjHrxVm+cQpkSUbsp/249AIcxLN+9mgXb/85RpknNhvy71QAcDgfHTh7nw7+/AOB//e7gRPpJrLW4rIt3/vykpMP3W0pQ/YTD4eD5B55mxNir2Re/n7mfzuKnhXPZEr3VU+ZI0hEeevkxLu57UZbHbt+9gwuvvchTz9pZy5n1+08lGn9Z5HK6mPbuVG59+k6q1wzm9XtfpHW3NoQ2rOcps2jWH9RtWI+bJtxOSmIyz9/6JB37diGwXCC3P3sXFSpVxJnu5O0HXqFlp9Y0OreJF8+odHM4HDx52yNc9+jNxB6MY8ar3/DLkt/Ytme7p8yeuBiuGj+KpKNJ9OnUi2fHPsHw/0V69t94yXVsi9lOUOUgb5xCmeJyufhl8hxGjL+WqjWq8cWjH9Ks4znUCq/tKdN1yAV0HXIBANtXbmb5nCVUCqoEwK+f/0STds0Yds8InOlO0k6keeU8yhKHcfDI9Q9wy4tjiT0UzzePf8pvUQvZsS/aU2bxhmX8FrUAgBYNmvPyHc8ydPyVAHz/5yy++uVbnh39uDfCL3NcLhdLpixg4F1DqRwSxI/Pf0vDtk0IrlfDU+bEsRMs/voPBtx5CUE1qnI86RgAh/ceZMufGxgy7gocAQHMe+sHGrRpRLU6wd46nTLBYRw8cOXdjH3rf8QdSeDTB95jwdpFRMfu8pRZtnklC9YsAqB5/aY8d9PjjHjqes/+2964l8SjiSUee1lkMFxy3kV8suRrko4ncXuvG9kYt5WElAOeMhUDKzD0vEFMXjKFxNQkqpSvnKWOj/7+kmNpx0s6dL9nnb6foGoOahHo2Lo9O2N2smvfbtLS0/h+7gwG987aW3fg8EFWbVxNWnp6nvX07tKTnTG7iIndW9whl3m7t+ykZr3a1AytRWC5QDr07sT6xWuylDEYThxPxVrLieMnqFy1Mo4AB8YYKlSqCIAz3YnT6QLjjbMoO9pFtGHX/t3siYshLT2NHxbMYUC3flnKrNy0iqSjSQBEbVpNaK26nn2hNetyYZc+fDP3uxKNu6yK3b6XkLohBNcJISAwgHPPb832FZvzLL/xr/W07H4e4P5SHrNpN236dgAgIDCAilUqlkjcZVmbpq3ZHRdDTMI+0p3pzFkyl34de2cpc/zEP1/kKpWvROaVLlZsjiIxo/3I2TuwM56qtatTtXZ1AgIDaNI5gt2ro7OUiV62hUbtmxJUoyoAlaq5v3wnxh6mdpO6BJYvhyPAQWiL+uxataPEz6Gsad34XPYk7GXvwf2kO9OZt+JX+rTtkaVMljZSoSK2NKwGU0qFB9fn0NHDHD52BKd1sWbvBlrWjchSpl1Ya9bHbiYx1f3edPTkMW+EKtlYawt88xb1oBaBerXrsTduv2d7X3wsnVp3KHQ9wwcMZdrcGUUZmt9KPHiE4Nohnu3qtYLZvXlnljI9hvTh46fe44nrH+LE8VSue/AmHA73bzYup4vX7nmeA/sT6PHvPjQ6R72nZyO0Zl32H4j1bMcejKV9i7Z5lr9q4OX8sWKhZ/uxW8bx/CcvU6VSlWKN018kH0qmas3qnu2gGtXYvz33H8bSTqSxc802+t8wGIDE+MNUrlqZn96fScLuOOo2qceF111E+YrlSyT2sqpOSG1iD8V5tuMOxdOmWesc5fp36svdV9xBzWoh3PHqf0syRL9y7EgKVUL+Ga1RJSSIhOi4LGUS445gnS7mvDqd9NQ0WvZrS/PzzyW4fg1WzlxMakoqgeUDiFm3i5qN6pT0KZQ5tYNrE3c4wbMddySB8xq3ylGub7uejBk6mpCqwdz77jjP/dZa3h77EhbL9D9/YPqiH0sk7rKqWqWqnsQTICk1mQYh9bOUqVmlBgGOAG7qfg0VAsrzV/QyVu1dB7h/Xrvx/JFYa1m2O4plu1eVZPj+TUN8c2eMMTZbWm6McVhrXZm2RwOjAYIaBVOxju9+MTW59K4V9le7coHluKj3QJ5+5/kiikpyyPZCbV65gbCm4dz+7N0c3J/A+4++TdPWzahYuRKOAAf3vfUQx1OO8ckzk9i/cx/1GtfPo2LJj8mlkeT1/nh+m65cOeAyRjx4LQD9uvThQOIh1m3fQLfzuhRnmH4tt/cxgO0rt1C/RQPP8F6Xy0Xczv30HzWIes3D+fWzn1j6wyJ6jriwBKMtewraRuav+J35K36n0zkdGHv5rdzy4tgSiM4P5fb+lO0lsi7Lgd0JXHTPMJxp6cx68TtqNwkluF4NzhvYkblvzqBchXKEhNfy/PgpZy63t6jcenh+X/0nv6/+kw7N23LbkJsY89Z9ANz86lgOJB4kJCiYt+98mZ1xu4natibH46Vgcn89sm4HOBzUrx7Kx4u/opwjkFt7jmLPkX0cPHqISYs+I/lEClXKV+bG80eSkHKQnYf2lEjs/q40zEEt8XfMzMmpMWaQMeZSY0zzzMkpgLV2krW2s7W2sy8npwD74vcTVvefuY3164QSmxB7mkfk1P+CC1mzaS0Jhw7kX1jyVb1mMEcSDnu2Ew8coXqN6lnKLPtlMW26t8cYQ636dahRtybxe7L+Ql4pqDLN2kSwaeWGEom7rNp/IJZ6tUI926E1Q4k7FJ+j3LmNW/D8nU8y+umxHEl2zxPq1LIj/+p6IQs/nMdbD7zCBW278dp/Xyix2MuiqjWqknzwn3lYKYeSCAqummvZTYvXeYb3uh9bjao1qlGveTgALbq2JG7n/lwfKwUXdyie0Br/DGuvW6MOCUcS8iy/YnMUDeqEExxUPc8ycuYqhwRx9HCKZ/vo4RQqV6+SrUwVwlo3pFyFclQMqkRoRH0Ox7g/w1v0aMXQh65i8H2XUaFyBarV0et0tuKPJFA35J958nWDa3MgMe/vTFHb1hBWqz7Vq7if+wOJBwE4nHKE31f/SetGLYs34DIu8Xgy1StW82xXq1iVpNTkHGW2JuwgzZnGsbTj7Dy4m3rV3KMJkk+429fRk8fYELuF8GB1ApSU0jDE12s/6Rlj7gAeA1oAUcaYzt6K5WxFbVhNkwZNaFi/AeUCy3HpwGH8tHBeoeq4bOAwpmt4b5Fp0KIRB/bFczD2AOlp6UQtWEHrbm2ylAmuHcLW1e55d8mHk4iPiaNGaC1SEpM5nuKeJ5F24iRbV22mbnjdHMeQgluzdR2N6zcivG4Y5QLLcUnvwfyy9LcsZerXrse749/kv6+OI3rfP4tevPTZa1xwYz963TyAO1+8j7/WLOHeVx8s6VMoU0KbhnE49hBH4g/jTHeyafF6mnVqkaPciWOpxGzcRbNO53juqxIcRNWa1Ti0z/3FcNf6aGqG1c7xWCmcddEbaFi3AWG16hMYEMjgbgP5LWphljIN6oR7/m7Z6BzKBQZyJEULvhSHWo3qkBSfSPKBJJzpTqKXb6VB28ZZyjRs24T4bftwOV2kn0wjITqO6qHuqSWnFkxKOZTMrlU7aNI5IvshpJA27NpMwzrh1K8ZSmBAIAM69WPB2r+ylAmvHeb5+5wGEZQLDCTxaCIVy1ekcgX3KJCK5StyfsvObN+fdU6xFM7exH3UrBJCSKXqBBgHbcNasSlua5YyG+O20LhGAxzGUM4RSIPgMOJTDlIuoBzlA9zTQsoFlKN5rSbEJef9g5wUMVchbgWQ0eG42RizzRgzLpf91xhj1mTc/jLG5LuseYkN8TXGNLDW7rHWWmNMS2BAxm0ksBRYmamsI3uPqi9zOp2Mf+lRpr75JQ6Hg69/+IbNO7Yw6jL3EMVPp31BnZq1mTd5NlWrBOGyLm6NvJkekReScjSFShUq0qdbb+57LsdrKmcoICCAy267kkmPTcS6XHQd0J3QRvX5a7b7C98FF/diQORgprz+OS+NeQasZciNlxJUPYh90Xv5+rXPsC4X1mVp16sjrbq2yeeIcjpOl5MJ7z3DZ098gMPh4NtfprN19zauHnQVAF/99A13Rd5OSLXqPHX7YwCkO9MZ9t8rvRl2meUIcND/hsF898KXuFyWNn3aUyu8Dqt+WQ5A+3+5fy/cumwTjdo0yzG/tP/1g5n1znSc6U6C64Qw6NahJX4OZY3T5eTZz1/i/fvfJMDhYPqCH9i+dwdXXngZAFN/m8aAzv0Y2vNi0tPTSU07wf8mPux5/Iu3P0WXczsRHBTML6/9wDvTP2DagpneOp1SzxHg4PzIXsx7aybWZWl+QUtC6tdk0wL3/Llze59HcL0ahLVqyIynp2CMIaJHK0LCagLw26SfOHE0NaOe3lTQQmJnzely8uLUN3hzzEsEOBzM/HsOO/bv5LKe7vefaX/OpF/73vy720DSnU5ST57goY+fBKBm1RBeHP0UAIEBAfy0bD5/b1jqrVMpE1zW8sP6udzQLRJjHKzcs5r4lAN0beheg2Xp7igSUg6yJX47d/a+BWsty/esIj45gZDKwVzT+XLAvTrzmr3r2ZqghcRKistVdCmWMSYAmIg7p4sBlhljZlprMw89jAb6WGsPG2MGA5OAbqettyS6b40xNYGPgfnW2jeNMRWB/wINgKbAEGttmjHmTuBra22WMRu1u4b7/mBpP/LJl5O9HYJkGBLxryzbTS7JuWCElLyHJjycfyEpEbd0vibL9nmjunopEsnsmlHXejsEyTC+311ZtruM6eudQCSLgYMH5l9IStQzQx4qE9d0CLuwaYHzqr2/7TjtORtjugOPW2svytgeD2CtfS6P8iHAOmttWG77TympIb5HcWfLvYwxt1trU4FzgZ7ApRnJ6ZXAf4DKp6lHREREREREzoC1Bb8ZY0YbY5Znuo3OVl0YkHl1q5iM+/JyEzAnvxiLdYjvqQWRrLWpxphfAScwxhhzCBgLzATeMsZUAFoD11lrdxdnTCIiIiIiIv7Iugo+MNVaOwl3J2Necl3QOdeCxlyIO0Htmd9xiy1BzbZab0XghLX2J+NeS38skAYMAroCtYFHrbU7iyseERERERERv1a00ztjcE/ZPCUc2Je9kDGmLfAhMNhaezC/SostQc2UnN4FdAeOGWO+stbOybje221APWvtxOKKQURERERERNyKeP2hZUCEMaYJsBeIBK7OXMAY0xCYhnuk7JaCVFqsc1CNMWOAy4CHgJrAR8aYy6y1c3AvmtTDGBNicrtCuYiIiIiIiBQZ67QFvuVbl7XpuEfG/gxsBKZaa9cbY24zxtyWUewx3HngO8aYVcaY5fnVW6Q9qJkvD5Mxr9QJXA7cgPtqOg8ALxtjXNba740xv1hrjxZlDCIiIiIiIpJTUV/BxVo7G5id7b73Mv19M3BzYeos0gQ1U3I6DDgOrAdSgEuAEdbahIzVn57KSE5TivL4IiIiIiIikocSuMTo2SqSIb6Zh+gaYyKB94F+wKu4xyEvBeoZY24AlgP/UnIqIiIiIiIimZ11D2q21Xob4V5auIe1drsx5mpgHFAOOAGMxH3d07izPa6IiIiIiIgUXCnoQD27BDVbcjoGuA6oBrxqjNlrrf3KGJMCTARWAi9Za5PONmgREREREREpHOt0eTuEfJ1VgpopOR0GdMCdoN4CtAHON8b8aa2dmXEd1NVKTkVERERERLyjNPSgnvUcVGNMGPAWgLV2K+6lhJNwr957oTEm0Fo71Vq742yPJSIiIiIiImfI2oLfvOSsE1Rr7V7gHuBiY8xIa20q8ASQBlwElD/bY4iIiIiIiMjZsS5b4Ju3FMllZqy104wxJ4DnjDFYa782xjwAhFhrjxXFMUREREREROQslIIxvkV2HVRr7SxjjAuYZIxJt9Z+CyQUVf0iIiIiIiJy5kpBflp0CSqAtXaOMeY/wPairFdERERERETOTplfxTc31tp5RV2niIiIiIiInB2/60EVERERERERH1UKMlQlqCIiIiIiIn7Am6vzFpQSVBEREREREX+gHlQRERERERHxBaUgP1WCKiIiIiIi4g/8chVfERERERER8UHqQRURERERERFfoEWSRERERERExDeUgkmoSlBFRERERET8QCnIT5WgioiIiIiI+IVSkKEqQRUREREREfEDmoMqIiIiIiIivsH381MlqCIiIiIiIv7AaoiviIiIiIiI+AQN8RURERERERFfUAo6UJWgioiIiIiI+APrdHk7hHwpQRUREREREfEH6kEVERERERERX6BFkkRERERERMQ3+P4IXyWoIiIiIiIi/kA9qCIiIiIiIuIblKCKiIiIiIiIL7BOJagiIiIiIiLiC1xKUEVERERERMQHlIIRvji8HYCIiIiIiIiUAGsLfisAY8wgY8xmY8w2Y8y4XPYbY8ybGfvXGGM65lenElQRERERERE/YF0Fv+XHGBMATAQGA62AkcaYVtmKDQYiMm6jgXfzq1cJqoiIiIiIiD8o2h7UrsA2a+0Oa+1JYAowLFuZYcBn1m0xEGyMqXe6SkvFHNSEpTHG2zEUFWPMaGvtJG/HIW5l7fWI/mGDt0M4a2XtNSntytrrse7Tpd4O4ayUtdejLChrr8myib97O4SzUtZej7JAr4lvsdYWOK8yxozG3et5yqRsr2UYsCfTdgzQLVs1uZUJA/bndVz1oJa80fkXkRKk18P36DXxLXo9fIteD9+j18S36PXwPXpNSilr7SRrbedMt+w/NOSW7Gbvei1ImSyUoIqIiIiIiEhhxQANMm2HA/vOoEwWSlBFRERERESksJYBEcaYJsaY8kAkMDNbmZnA9Rmr+Z4PJFpr8xzeC6VkDur/t3f3IHJVcRjGn9cYFJMY8aOVRQla+NkYsEoRBC2MIlaxWBEhSCIi6UQIJCjY2KaQRYtUEggmiEGigiQoEmTdLIkfsDZiawwIQsixmKu7kRk3M5vrPXf2+cGBM3fOnL13/ryznDt3ZqaM1+DXxXrUx5rUxXrUxXrUx5rUxXrUx5pMqVLK5SR7gZPABmCulLKYZE9z/2HgY+Ap4CfgD+DF1eZN6cOvtUqSJEmSpp6X+EqSJEmSquACVZIkSZJUBReoY0ryapLzSY60NP+BJPvbmFurS7IjyYmu96PPzMj0Mh9rZz6mmxlZOzMy3cyIroVfkjS+V4AnSylLXe+IVCkzIo1mPqT/Zkakdc53UMeQ5DBwD/BRkjeSzCX5Jsm3SXY1Y2aTHEtyPMlSkr1JXm/GfJXk9mbcy81j55McTXLLkL93b5JPkpxN8mWS+//fI+6nJDNJLiR5L8m5JEeS7ExyOsmPSR5r2pmmLmeS3Ddknk3DaqzRzEj9zEd3zEc/mJHumJF+MCNqXSnFNkYDfgbuBN4CXmi23Qb8AGwCZhl8jfIW4C7gIrCnGfcu8FrTv2PFnIeAfU3/ALC/6Z8CtjX97cBnXR9/HxowA1wGHmRwEuYsMAcE2AUcA24FbmzG7wSONv0dwImmP7TGXR9f7c2M1N3MR+fPv/movJmRzp9/M1J5MyO2tpuX+E7uCeDpLH+O4Wbg7qb/eSnlEnApyUXgeLN9AXio6T+Q5BCDQG5m8PtB/0iyGXgc+DDJ35tvauNAptRSKWUBIMkicKqUUpIsMHhh3Qp8kGQbUICNQ+YYVePzbe/8lDAj9TIf3TMfdTMj3TMjdTMjao0L1MkFeK6U8v1VG5PtwJ8rNl1ZcfsKy8/5+8AzpZT5JLMMziitdAPwQDGBkwAAAQhJREFUWynlkeu72+vGajU4yOAf3LNJZoAvhswxtMa6ZmakXuaje+ajbmake2akbmZErfEzqJM7CexLc9otyaNjPn4L8GuSjcDuf99ZSvkdWEryfDN/kjy8xn3Wsq3AL01/dsSYtdZ4vTMj/WU+2mc++s2MtM+M9JsZ0cRcoE7uIIPLFb5Lcq65PY43ga+BT4ELI8bsBl5KMg8sMriuX9fHO8DbSU4DG0aMWWuN1zsz0l/mo33mo9/MSPvMSL+ZEU0spZSu90GSJEmSJN9BlSRJkiTVwQWqJEmSJKkKLlAlSZIkSVVwgSpJkiRJqoILVEmSJElSFVygSpIkSZKq4AJVkiRJklSFvwC0s057q1ReMAAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plottings.plot_heatmap(df=df, target=targCol, pred=predCol, major_wrong = False, normalize='true')\n",
+    "\n",
+    "fig.savefig(os.path.join(savedir, target,str(100),'confusion' ,'test.pdf'),\n",
+    "                                    dpi=fig.dpi, bbox_inches='tight')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "             ID\n# Authors      \n50           50\n150         150\n500         500\n1000       1000",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n    </tr>\n    <tr>\n      <th># Authors</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>150</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>500</td>\n    </tr>\n    <tr>\n      <th>1000</th>\n      <td>1000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def counter(x):\n",
+    "    return len(x.unique())\n",
+    "df.groupby(by='# Authors').agg({'ID':counter})\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {
diff --git a/Scripts/Evaluation/result_evaluations.ipynb b/Scripts/Evaluation/result_evaluations.ipynb
index a8f853946a3cae0142f639d621a866b1f683c22c..636ec3b6f69be42ae54ce608b8100fd3bf50064c 100644
--- a/Scripts/Evaluation/result_evaluations.ipynb
+++ b/Scripts/Evaluation/result_evaluations.ipynb
@@ -41,7 +41,299 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 21,
+   "outputs": [],
+   "source": [
+    "path = os.path.join(*ml_utils.split_path_unix_win('../../Data/pan19-celebrity-profiling-training-dataset-2019-01-31/results/workset/creator'))\n",
+    "agen = 'age'\n",
+    "gendern = 'gender'\n",
+    "order = ['dist', 'char', 'asis', 'pos', 'tag', 'dep', 'lemma', 'word','emoticon_c','polarity', 'num']\n",
+    "order2 = []\n",
+    "for i in range(1, len(order)):\n",
+    "    order2.append('_'.join(order[0:i]))\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Data Set Distributions\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [],
+   "source": [
+    "prepr = re.sub('results', 'preprocessed', path)\n",
+    "def transformer(x):\n",
+    "    x['init_sum'] = sum(x['init_len'])\n",
+    "    x['n_tweets'] = len(x['init_len'])\n",
+    "    x['prepr_sum'] = sum(x['prepr_len'])\n",
+    "\n",
+    "    return x\n",
+    "import gc\n",
+    "gc.collect()\n",
+    "if os.path.exists(os.path.join(prepr, 'ids_total.json')):\n",
+    "    ids = pd.read_json(os.path.join(prepr, 'ids_total.json'))\n",
+    "else:\n",
+    "    ids = None\n",
+    "    for tar in ['age', 'gender']:\n",
+    "        for leng in [100, 250, 500]:\n",
+    "            for aut in [50, 150, 500, 1000]:\n",
+    "                tmp_num = None\n",
+    "                for st in ['train', 'val', 'test']:\n",
+    "                    nfile = os.path.join(prepr, 'num', '{}'.format(leng), '{}'.format(tar),\n",
+    "                                         st, '{}_num_{}_{}_authors.ndjson'.format(st, leng, aut))\n",
+    "\n",
+    "                    if type(tmp_num) == type(None):\n",
+    "                        with open(nfile, 'r', encoding='utf-8') as r:\n",
+    "                            tmp_num = pd.DataFrame(ndjson.load(r))\n",
+    "                            tmp_num.drop(labels=['mentions', 'tags', 'urls','times', 'tweetID',\n",
+    "                                                 'emotic_num', 'emojis_num', 'numericals', 'ID'], inplace=True, axis=1)\n",
+    "\n",
+    "                            tmp_num['set'] = st\n",
+    "                            tmp_num = tmp_num.apply(func=transformer, axis=1)\n",
+    "                    else:\n",
+    "                        with open(nfile, 'r', encoding='utf-8') as r:\n",
+    "                            tmp = pd.DataFrame(ndjson.load(r))\n",
+    "                            tmp.drop(labels=['mentions', 'tags', 'urls','times', 'tweetID',\n",
+    "                                                 'emotic_num', 'emojis_num', 'numericals', 'ID'], inplace=True, axis=1)\n",
+    "\n",
+    "                            tmp['set'] = st\n",
+    "                            tmp = tmp.apply(func=transformer, axis=1)\n",
+    "                            tmp_num = pd.concat([tmp_num, tmp], axis=0)\n",
+    "\n",
+    "\n",
+    "                file = os.path.join(prepr, '{}_ids'.format(tar),\n",
+    "                                                'ids_{}_{}_{}_authors_balanced.json').format(tar, leng, aut)\n",
+    "                print(file)\n",
+    "                tmp = pd.read_json(file)\n",
+    "                tmp['target'] = tar\n",
+    "                tmp['No. of Authors'] = aut\n",
+    "                tmp['No. of Characters'] = leng\n",
+    "                tmp = tmp.merge(tmp_num, how='left', left_on='uID', right_on='uID')\n",
+    "                if type(ids)==type(None):\n",
+    "                    ids = tmp\n",
+    "                else:\n",
+    "                    ids = pd.concat([ids, tmp], axis=0)\n",
+    "    ids = ids.reset_index(drop=True)\n",
+    "\n",
+    "    ids.to_json(os.path.join(prepr, 'ids_total.json'))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      ID               tweetID                uID  centered_age gender  \\\n0  14544             [14544_5]            14544_5          1985   male   \n1  14544             [14544_6]            14544_6          1985   male   \n2  14544   [14544_9, 14544_10]   14544_9|14544_10          1985   male   \n3  14544  [14544_11, 14544_12]  14544_11|14544_12          1985   male   \n4  14544            [14544_13]           14544_13          1985   male   \n\n   birthyear      strat target  No. of Authors  No. of Characters  init_len  \\\n0       1984  1985_male    age              50                100     [142]   \n1       1984  1985_male    age              50                100     [140]   \n2       1984  1985_male    age              50                100  [47, 53]   \n3       1984  1985_male    age              50                100  [99, 70]   \n4       1984  1985_male    age              50                100     [116]   \n\n    prepr_len    set  init_sum  n_tweets  prepr_sum  \n0       [159]   test       142         1        159  \n1       [165]   test       140         1        165  \n2    [29, 74]  train       100         2        103  \n3  [132, 103]   test       169         2        235  \n4       [144]    val       116         1        144  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>tweetID</th>\n      <th>uID</th>\n      <th>centered_age</th>\n      <th>gender</th>\n      <th>birthyear</th>\n      <th>strat</th>\n      <th>target</th>\n      <th>No. of Authors</th>\n      <th>No. of Characters</th>\n      <th>init_len</th>\n      <th>prepr_len</th>\n      <th>set</th>\n      <th>init_sum</th>\n      <th>n_tweets</th>\n      <th>prepr_sum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>14544</td>\n      <td>[14544_5]</td>\n      <td>14544_5</td>\n      <td>1985</td>\n      <td>male</td>\n      <td>1984</td>\n      <td>1985_male</td>\n      <td>age</td>\n      <td>50</td>\n      <td>100</td>\n      <td>[142]</td>\n      <td>[159]</td>\n      <td>test</td>\n      <td>142</td>\n      <td>1</td>\n      <td>159</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>14544</td>\n      <td>[14544_6]</td>\n      <td>14544_6</td>\n      <td>1985</td>\n      <td>male</td>\n      <td>1984</td>\n      <td>1985_male</td>\n      <td>age</td>\n      <td>50</td>\n      <td>100</td>\n      <td>[140]</td>\n      <td>[165]</td>\n      <td>test</td>\n      <td>140</td>\n      <td>1</td>\n      <td>165</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>14544</td>\n      <td>[14544_9, 14544_10]</td>\n      <td>14544_9|14544_10</td>\n      <td>1985</td>\n      <td>male</td>\n      <td>1984</td>\n      <td>1985_male</td>\n      <td>age</td>\n      <td>50</td>\n      <td>100</td>\n      <td>[47, 53]</td>\n      <td>[29, 74]</td>\n      <td>train</td>\n      <td>100</td>\n      <td>2</td>\n      <td>103</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>14544</td>\n      <td>[14544_11, 14544_12]</td>\n      <td>14544_11|14544_12</td>\n      <td>1985</td>\n      <td>male</td>\n      <td>1984</td>\n      <td>1985_male</td>\n      <td>age</td>\n      <td>50</td>\n      <td>100</td>\n      <td>[99, 70]</td>\n      <td>[132, 103]</td>\n      <td>test</td>\n      <td>169</td>\n      <td>2</td>\n      <td>235</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14544</td>\n      <td>[14544_13]</td>\n      <td>14544_13</td>\n      <td>1985</td>\n      <td>male</td>\n      <td>1984</td>\n      <td>1985_male</td>\n      <td>age</td>\n      <td>50</td>\n      <td>100</td>\n      <td>[116]</td>\n      <td>[144]</td>\n      <td>val</td>\n      <td>116</td>\n      <td>1</td>\n      <td>144</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ids.rename(columns={'# Authors':'No. of Authors',\n",
+    "                    '# Chars in Input': 'No. of Characters'}, inplace=True)\n",
+    "ids.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "def aggregate(x):\n",
+    "    d = {}\n",
+    "    tot =  x['init_sum'].sum()\n",
+    "    tsum = x['n_tweets'].sum()\n",
+    "    d['avg_tweet'] = round(tot/tsum,2)\n",
+    "    d['avg_instance'] = round(tot/x.shape[0],2)\n",
+    "    d['avg_tweet_per_instance'] = round(tsum/x.shape[0],2)\n",
+    "    return pd.Series(d, index=['avg_instance', 'avg_tweet', 'avg_tweet_per_instance'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                 avg_instance         avg_tweet          \\\ntarget                                    age  gender       age  gender   \nNo. of Characters No. of Authors                                          \n100               50                   160.30  162.94    109.58  111.92   \n                  150                  160.52  162.37    107.38  112.94   \n                  500                  160.78  161.63    109.21  111.86   \n                  1000                 160.07  160.99    109.28  111.84   \n250               50                   313.16  315.73    109.05  112.02   \n                  150                  313.25  315.58    107.43  112.84   \n                  500                  313.26  314.66    109.09  111.67   \n                  1000                 313.30  314.34    109.18  111.84   \n500               50                   565.60  568.76    109.12  111.87   \n                  150                  565.89  568.71    107.48  112.84   \n                  500                  566.13  567.54    109.15  111.70   \n                  1000                 566.10  567.38    109.17  111.85   \n\n                                 avg_tweet_per_instance         \ntarget                                              age gender  \nNo. of Characters No. of Authors                                \n100               50                               1.46   1.46  \n                  150                              1.49   1.44  \n                  500                              1.47   1.44  \n                  1000                             1.46   1.44  \n250               50                               2.87   2.82  \n                  150                              2.92   2.80  \n                  500                              2.87   2.82  \n                  1000                             2.87   2.81  \n500               50                               5.18   5.08  \n                  150                              5.27   5.04  \n                  500                              5.19   5.08  \n                  1000                             5.19   5.07  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th colspan=\"2\" halign=\"left\">avg_instance</th>\n      <th colspan=\"2\" halign=\"left\">avg_tweet</th>\n      <th colspan=\"2\" halign=\"left\">avg_tweet_per_instance</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>target</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>gender</th>\n    </tr>\n    <tr>\n      <th>No. of Characters</th>\n      <th>No. of Authors</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">100</th>\n      <th>50</th>\n      <td>160.30</td>\n      <td>162.94</td>\n      <td>109.58</td>\n      <td>111.92</td>\n      <td>1.46</td>\n      <td>1.46</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>160.52</td>\n      <td>162.37</td>\n      <td>107.38</td>\n      <td>112.94</td>\n      <td>1.49</td>\n      <td>1.44</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>160.78</td>\n      <td>161.63</td>\n      <td>109.21</td>\n      <td>111.86</td>\n      <td>1.47</td>\n      <td>1.44</td>\n    </tr>\n    <tr>\n      <th>1000</th>\n      <td>160.07</td>\n      <td>160.99</td>\n      <td>109.28</td>\n      <td>111.84</td>\n      <td>1.46</td>\n      <td>1.44</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">250</th>\n      <th>50</th>\n      <td>313.16</td>\n      <td>315.73</td>\n      <td>109.05</td>\n      <td>112.02</td>\n      <td>2.87</td>\n      <td>2.82</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>313.25</td>\n      <td>315.58</td>\n      <td>107.43</td>\n      <td>112.84</td>\n      <td>2.92</td>\n      <td>2.80</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>313.26</td>\n      <td>314.66</td>\n      <td>109.09</td>\n      <td>111.67</td>\n      <td>2.87</td>\n      <td>2.82</td>\n    </tr>\n    <tr>\n      <th>1000</th>\n      <td>313.30</td>\n      <td>314.34</td>\n      <td>109.18</td>\n      <td>111.84</td>\n      <td>2.87</td>\n      <td>2.81</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">500</th>\n      <th>50</th>\n      <td>565.60</td>\n      <td>568.76</td>\n      <td>109.12</td>\n      <td>111.87</td>\n      <td>5.18</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>565.89</td>\n      <td>568.71</td>\n      <td>107.48</td>\n      <td>112.84</td>\n      <td>5.27</td>\n      <td>5.04</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>566.13</td>\n      <td>567.54</td>\n      <td>109.15</td>\n      <td>111.70</td>\n      <td>5.19</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <th>1000</th>\n      <td>566.10</td>\n      <td>567.38</td>\n      <td>109.17</td>\n      <td>111.85</td>\n      <td>5.19</td>\n      <td>5.07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped = ids.groupby(by=['target', 'No. of Characters', 'No. of Authors']).apply(aggregate)\n",
+    "grouped = grouped.reset_index()\n",
+    "grouped = pd.pivot(index=[ 'No. of Characters','No. of Authors'], columns=['target'],\n",
+    "                   values=['avg_instance', 'avg_tweet', 'avg_tweet_per_instance'], data=grouped)\n",
+    "\n",
+    "\n",
+    "\n",
+    "grouped"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/tables'))\n",
+    "table = grouped.to_latex(sparsify=True,\n",
+    "                         multirow=True,\n",
+    "                         multicolumn=True,\n",
+    "                        caption='''Statistics of the Dataset''',\n",
+    "                         label='tab:dataset',\n",
+    "                         float_format=\"%.2f\")\n",
+    "table = re.sub(r'begin{table}', 'begin{table}[!ht]', table)\n",
+    "table = re.sub('& target &', '& Target &', table)\n",
+    "table = re.sub('& tweetLen &', r'& Min. \\\\No. of Characters &', table)\n",
+    "table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
+    "table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
+    "\n",
+    "with open(os.path.join(savedir, 'dataset.tex'), 'w') as w:\n",
+    "    w.write(table)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    set target  No. of Characters  No. of Authors  avg_instance  avg_tweet  \\\n0  test    age                100              50        159.93     109.42   \n1  test    age                100             150        161.05     107.75   \n2  test    age                100             500        160.74     109.27   \n3  test    age                100            1000        160.09     109.28   \n4  test    age                250              50        313.19     108.30   \n\n   avg_tweet_per_instance  \n0                    1.46  \n1                    1.49  \n2                    1.47  \n3                    1.47  \n4                    2.89  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>set</th>\n      <th>target</th>\n      <th>No. of Characters</th>\n      <th>No. of Authors</th>\n      <th>avg_instance</th>\n      <th>avg_tweet</th>\n      <th>avg_tweet_per_instance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>test</td>\n      <td>age</td>\n      <td>100</td>\n      <td>50</td>\n      <td>159.93</td>\n      <td>109.42</td>\n      <td>1.46</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>test</td>\n      <td>age</td>\n      <td>100</td>\n      <td>150</td>\n      <td>161.05</td>\n      <td>107.75</td>\n      <td>1.49</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>test</td>\n      <td>age</td>\n      <td>100</td>\n      <td>500</td>\n      <td>160.74</td>\n      <td>109.27</td>\n      <td>1.47</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>test</td>\n      <td>age</td>\n      <td>100</td>\n      <td>1000</td>\n      <td>160.09</td>\n      <td>109.28</td>\n      <td>1.47</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>test</td>\n      <td>age</td>\n      <td>250</td>\n      <td>50</td>\n      <td>313.19</td>\n      <td>108.30</td>\n      <td>2.89</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped = ids.groupby(by=['set','target', 'No. of Characters', 'No. of Authors']).apply(aggregate)\n",
+    "grouped = grouped.reset_index()\n",
+    "grouped.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [],
+   "source": [
+    "for st in ['train', 'val', 'test']:\n",
+    "    pv = pd.pivot(index=[ 'No. of Characters','No. of Authors'], columns=['target'],\n",
+    "                       values=['avg_instance', 'avg_tweet', 'avg_tweet_per_instance'],\n",
+    "                       data=grouped.loc[grouped.set == st,:])\n",
+    "\n",
+    "\n",
+    "\n",
+    "    savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/tables'))\n",
+    "    table = pv.to_latex(sparsify=True,\n",
+    "                             multirow=True,\n",
+    "                             multicolumn=True,\n",
+    "                            caption='''Statistics of the Dataset''',\n",
+    "                             label='tab:dataset_{}'.format(st),\n",
+    "                             float_format=\"%.2f\")\n",
+    "    table = re.sub(r'begin{table}', 'begin{table}[!ht]', table)\n",
+    "    table = re.sub('& target &', '& Target &', table)\n",
+    "    table = re.sub('& tweetLen &', r'& Min. No. of Characters &', table)\n",
+    "    table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
+    "    table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
+    "\n",
+    "    with open(os.path.join(savedir, '{}_dataset.tex'.format(st)), 'w') as w:\n",
+    "        w.write(table)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "508"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "del ids\n",
+    "gc.collect()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
    "outputs": [
     {
      "name": "stdout",
@@ -97,19 +389,12 @@
       "text/plain": "  featuretypes  # Authors subgrams  modeltype    name  tweetLen target   rank  \\\n0         dist        150        2  cumulated  dist 2       500    age     02   \n1         dist        150      2-3  cumulated  dist 3       500    age    023   \n2         dist        150    2-3-4  cumulated  dist 4       500    age   0234   \n3         dist        150  2-3-4-5  cumulated  dist 5       500    age  02345   \n4    dist_char        150        2    stacked  char 2       500    age    012   \n\n   Average Spearman's Rho (ext.)  Average Spearman's Rho (red.)  \\\n0                       0.187245                       0.072675   \n1                       0.114232                       0.035884   \n2                       0.026844                       0.015722   \n3                       0.021929                       0.006432   \n4                       0.073347                       0.022784   \n\n                    set  set_enum              set_feat  \n0  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n1  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n2  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n3  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n4  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  ",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>featuretypes</th>\n      <th># Authors</th>\n      <th>subgrams</th>\n      <th>modeltype</th>\n      <th>name</th>\n      <th>tweetLen</th>\n      <th>target</th>\n      <th>rank</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>set</th>\n      <th>set_enum</th>\n      <th>set_feat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2</td>\n      <td>cumulated</td>\n      <td>dist 2</td>\n      <td>500</td>\n      <td>age</td>\n      <td>02</td>\n      <td>0.187245</td>\n      <td>0.072675</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3</td>\n      <td>cumulated</td>\n      <td>dist 3</td>\n      <td>500</td>\n      <td>age</td>\n      <td>023</td>\n      <td>0.114232</td>\n      <td>0.035884</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3-4</td>\n      <td>cumulated</td>\n      <td>dist 4</td>\n      <td>500</td>\n      <td>age</td>\n      <td>0234</td>\n      <td>0.026844</td>\n      <td>0.015722</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3-4-5</td>\n      <td>cumulated</td>\n      <td>dist 5</td>\n      <td>500</td>\n      <td>age</td>\n      <td>02345</td>\n      <td>0.021929</td>\n      <td>0.006432</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dist_char</td>\n      <td>150</td>\n      <td>2</td>\n      <td>stacked</td>\n      <td>char 2</td>\n      <td>500</td>\n      <td>age</td>\n      <td>012</td>\n      <td>0.073347</td>\n      <td>0.022784</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 34,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "path = os.path.join(*ml_utils.split_path_unix_win('../../Data/pan19-celebrity-profiling-training-dataset-2019-01-31/results/workset/creator'))\n",
-    "agen = 'age'\n",
-    "gendern = 'gender'\n",
-    "order = ['dist', 'char', 'asis', 'pos', 'tag', 'dep', 'lemma', 'word','emoticon_c','polarity', 'num']\n",
-    "order2 = []\n",
-    "for i in range(1, len(order)):\n",
-    "    order2.append('_'.join(order[0:i]))\n",
     "\n",
     "age_files = {'acc':[], 'dist': []}\n",
     "gender_files = {'acc':[], 'dist': []}\n",
@@ -200,7 +485,7 @@
     "age_dist.drop_duplicates(colnames, inplace=True)\n",
     "print(age_dist.shape)\n",
     "print(age_acc.shape)\n",
-    "#age = age_acc.merge(age_dist.loc[:, ['set', 'tweetLen', '# Authors','rank',\n",
+    "#age = age_acc.merge(age_dist.loc[:, ['set', 'tweetLen', 'No. of Authors','rank',\n",
     "#                             'featuretypes', 'modeltype', 'target',\n",
     "#                             \"Average Spearman's Rho (ext.)\",\n",
     " #                            \"Average Spearman's Rho (red.)\"]], how='left')\n",
@@ -224,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "outputs": [
     {
      "name": "stdout",
@@ -282,7 +567,7 @@
       "text/plain": "  featuretypes  # Authors subgrams  modeltype    name  tweetLen  target  \\\n0         dist        150        2  cumulated  dist 2       500  gender   \n1         dist        150      2-3  cumulated  dist 3       500  gender   \n2         dist        150    2-3-4  cumulated  dist 4       500  gender   \n3         dist        150  2-3-4-5  cumulated  dist 5       500  gender   \n4    dist_char        150        2    stacked  char 2       500  gender   \n\n    rank  Average Spearman's Rho (ext.)  Average Spearman's Rho (red.)  \\\n0     02                       0.085755                       0.136993   \n1    023                       0.153513                       0.113376   \n2   0234                       0.118509                      -0.005042   \n3  02345                       0.090370                       0.054982   \n4    012                       0.127436                       0.053515   \n\n                    set  set_enum              set_feat  \n0  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n1  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n2  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n3  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  \n4  dist-char-word-lemma         2  DIST-CHAR-WORD-LEMMA  ",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>featuretypes</th>\n      <th># Authors</th>\n      <th>subgrams</th>\n      <th>modeltype</th>\n      <th>name</th>\n      <th>tweetLen</th>\n      <th>target</th>\n      <th>rank</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>set</th>\n      <th>set_enum</th>\n      <th>set_feat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2</td>\n      <td>cumulated</td>\n      <td>dist 2</td>\n      <td>500</td>\n      <td>gender</td>\n      <td>02</td>\n      <td>0.085755</td>\n      <td>0.136993</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3</td>\n      <td>cumulated</td>\n      <td>dist 3</td>\n      <td>500</td>\n      <td>gender</td>\n      <td>023</td>\n      <td>0.153513</td>\n      <td>0.113376</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3-4</td>\n      <td>cumulated</td>\n      <td>dist 4</td>\n      <td>500</td>\n      <td>gender</td>\n      <td>0234</td>\n      <td>0.118509</td>\n      <td>-0.005042</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dist</td>\n      <td>150</td>\n      <td>2-3-4-5</td>\n      <td>cumulated</td>\n      <td>dist 5</td>\n      <td>500</td>\n      <td>gender</td>\n      <td>02345</td>\n      <td>0.090370</td>\n      <td>0.054982</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dist_char</td>\n      <td>150</td>\n      <td>2</td>\n      <td>stacked</td>\n      <td>char 2</td>\n      <td>500</td>\n      <td>gender</td>\n      <td>012</td>\n      <td>0.127436</td>\n      <td>0.053515</td>\n      <td>dist-char-word-lemma</td>\n      <td>2</td>\n      <td>DIST-CHAR-WORD-LEMMA</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 35,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -366,7 +651,7 @@
     "\n",
     "print(gender_dist.shape)\n",
     "print(gender_acc.shape)\n",
-    "#gender = gender_acc.merge(gender_dist.loc[:, ['set', 'tweetLen', '# Authors','rank',\n",
+    "#gender = gender_acc.merge(gender_dist.loc[:, ['set', 'tweetLen', 'No. of Authors','rank',\n",
     "#                             'featuretypes', 'modeltype', 'target',\n",
     "#                             \"Average Spearman's Rho (ext.)\",\n",
     "#                             \"Average Spearman's Rho (red.)\"]], how='left')\n",
@@ -389,11 +674,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 32,
    "outputs": [],
    "source": [
     "dist = pd.concat([age_dist, gender_dist])\n",
-    "acc = pd.concat([age_acc, gender_acc])"
+    "acc = pd.concat([age_acc, gender_acc])\n",
+    "dist.rename(columns={'# Authors':'No. of Authors'}, inplace=True)\n",
+    "acc.rename(columns={'# Authors':'No. of Authors'}, inplace=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['featuretypes', 'No. of Authors', 'subgrams', 'modeltype', 'name',\n       'tweetLen', 'target', 'rank', 'Average Spearman's Rho (ext.)',\n       'Average Spearman's Rho (red.)', 'set', 'set_enum', 'set_feat'],\n      dtype='object')"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dist.columns"
    ],
    "metadata": {
     "collapsed": false,
@@ -404,14 +714,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 86,
    "outputs": [
     {
      "data": {
-      "text/plain": "target                                                                         age  \\\ntweetLen                                                                       500   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.187245   \n                                            2-3                           0.114232   \n                                            2-3-4                         0.026844   \n                                            2-3-4-5                       0.021929   \n                               dist_char    2                             0.136581   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.088622   \n                                            1-2                           0.017799   \n                                            1-2-3                         0.082513   \n                               word         1                            -0.058514   \n                                            1-2                          -0.011166   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.041580   \n                                            2-3                          -0.022333   \n                                            2-3-4                         0.002778   \n                                            2-3-4-5                       0.004039   \n                               dist_char    2                             0.061984   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.018548   \n                                            1-2                           0.045190   \n                                            1-2-3                         0.040029   \n                               word         1                            -0.118380   \n                                            1-2                          -0.068614   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.034811   \n                                            2-3                          -0.005109   \n                                            2-3-4                        -0.003334   \n                                            2-3-4-5                       0.021338   \n                               dist_char    2                             0.001302   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.113389   \n                                            1-2                           0.064695   \n                                            1-2-3                         0.033945   \n                               word         1                             0.026580   \n                                            1-2                          -0.010872   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.008349   \n                                            2-3                           0.027542   \n                                            2-3-4                         0.050173   \n                                            2-3-4-5                       0.037073   \n                               dist_char    2                             0.024354   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.071146   \n                                            1-2                          -0.010918   \n                                            1-2-3                         0.038633   \n                               word         1                             0.042761   \n                                            1-2                           0.001316   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.009855   \n                                            2-3                          -0.006344   \n                                            2-3-4                         0.024525   \n                                            2-3-4-5                      -0.009718   \n                               dist_char    2                             0.007699   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.060031   \n                                            1-2                           0.069371   \n                                            1-2-3                         0.036958   \n                               word         1                             0.027012   \n                                            1-2                           0.003212   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.007215   \n                                            2-3                          -0.013204   \n                                            2-3-4                         0.025758   \n                                            2-3-4-5                       0.027035   \n                               dist_char    2                             0.012189   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.045259   \n                                            1-2                           0.047406   \n                                            1-2-3                         0.009889   \n                               word         1                            -0.002993   \n                                            1-2                           0.014187   \n\ntarget                                                                              \\\ntweetLen                                                                             \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.015321   \n                                            2-3                           0.008644   \n                                            2-3-4                         0.027900   \n                                            2-3-4-5                      -0.007913   \n                               dist_char    2                             0.018137   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.025578   \n                                            1-2                           0.046400   \n                                            1-2-3                         0.001714   \n                               word         1                            -0.008936   \n                                            1-2                          -0.003547   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.019163   \n                                            2-3                           0.047782   \n                                            2-3-4                         0.009939   \n                                            2-3-4-5                       0.015407   \n                               dist_char    2                             0.004790   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.067977   \n                                            1-2                           0.044056   \n                                            1-2-3                         0.047400   \n                               word         1                             0.015079   \n                                            1-2                           0.011849   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                            -0.014053   \n                                            2-3                           0.012595   \n                                            2-3-4                         0.001973   \n                                            2-3-4-5                      -0.020033   \n                               dist_char    2                             0.004727   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.052828   \n                                            1-2                           0.034712   \n                                            1-2-3                         0.000329   \n                               word         1                             0.004566   \n                                            1-2                          -0.000954   \n\ntarget                                                                      gender  \\\ntweetLen                                                                       500   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.085755   \n                                            2-3                           0.153513   \n                                            2-3-4                         0.118509   \n                                            2-3-4-5                       0.090370   \n                               dist_char    2                             0.091084   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.008557   \n                                            1-2                           0.154573   \n                                            1-2-3                         0.051351   \n                               word         1                            -0.030364   \n                                            1-2                          -0.000287   \n\ntarget                                                ...  \\\ntweetLen                                              ...   \n# Authors                                             ...   \nScore                                                 ...   \nset                  modeltype featuretypes subgrams  ...   \ndist-char-word-lemma cumulated dist         2         ...   \n                                            2-3       ...   \n                                            2-3-4     ...   \n                                            2-3-4-5   ...   \n                               dist_char    2         ...   \n...                                                   ...   \nsingular             baseline  tag          1         ...   \n                                            1-2       ...   \n                                            1-2-3     ...   \n                               word         1         ...   \n                                            1-2       ...   \n\ntarget                                                                         age  \\\ntweetLen                                                                       100   \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.017371   \n                                            2-3                           0.045819   \n                                            2-3-4                         0.020676   \n                                            2-3-4-5                      -0.032071   \n                               dist_char    2                             0.032844   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.052828   \n                                            1-2                           0.049368   \n                                            1-2-3                         0.002219   \n                               word         1                            -0.006523   \n                                            1-2                           0.000597   \n\ntarget                                                                      gender  \\\ntweetLen                                                                       500   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.136993   \n                                            2-3                           0.113376   \n                                            2-3-4                        -0.005042   \n                                            2-3-4-5                       0.054982   \n                               dist_char    2                             0.026470   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.008557   \n                                            1-2                           0.132701   \n                                            1-2-3                         0.057714   \n                               word         1                             0.028155   \n                                            1-2                           0.025200   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.194533   \n                                            2-3                           0.196929   \n                                            2-3-4                         0.170263   \n                                            2-3-4-5                       0.084400   \n                               dist_char    2                             0.025105   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.027320   \n                                            1-2                           0.079936   \n                                            1-2-3                        -0.000031   \n                               word         1                            -0.016220   \n                                            1-2                           0.048501   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \n# Authors                                                                     150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.288756   \n                                            2-3                           0.048504   \n                                            2-3-4                         0.047140   \n                                            2-3-4-5                      -0.006098   \n                               dist_char    2                            -0.028486   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.029294   \n                                            1-2                           0.173908   \n                                            1-2-3                        -0.019950   \n                               word         1                             0.077121   \n                                            1-2                          -0.030074   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.093845   \n                                            2-3                           0.145157   \n                                            2-3-4                         0.010895   \n                                            2-3-4-5                       0.017548   \n                               dist_char    2                             0.085429   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.203978   \n                                            1-2                          -0.104392   \n                                            1-2-3                         0.003267   \n                               word         1                             0.023986   \n                                            1-2                           0.013636   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                            -0.025917   \n                                            2-3                           0.007511   \n                                            2-3-4                         0.094241   \n                                            2-3-4-5                      -0.018271   \n                               dist_char    2                            -0.062807   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.051619   \n                                            1-2                          -0.123443   \n                                            1-2-3                         0.019267   \n                               word         1                             0.032958   \n                                            1-2                           0.005569   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \n# Authors                                                                     500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.265406   \n                                            2-3                           0.104863   \n                                            2-3-4                         0.061039   \n                                            2-3-4-5                       0.064423   \n                               dist_char    2                             0.046563   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.146045   \n                                            1-2                          -0.027975   \n                                            1-2-3                        -0.014083   \n                               word         1                            -0.011463   \n                                            1-2                           0.017931   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.258740   \n                                            2-3                           0.111348   \n                                            2-3-4                         0.090751   \n                                            2-3-4-5                       0.067456   \n                               dist_char    2                             0.060031   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.043825   \n                                            1-2                          -0.008411   \n                                            1-2-3                        -0.028449   \n                               word         1                             0.025321   \n                                            1-2                          -0.007934   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \n# Authors                                                                     1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.283375   \n                                            2-3                           0.187528   \n                                            2-3-4                         0.035410   \n                                            2-3-4-5                       0.070322   \n                               dist_char    2                             0.088929   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.004342   \n                                            1-2                           0.109083   \n                                            1-2-3                         0.121241   \n                               word         1                             0.052303   \n                                            1-2                          -0.008001   \n\ntarget                                                                              \ntweetLen                                                                       100  \n# Authors                                                                     1000  \nScore                                                Average Spearman's Rho (red.)  \nset                  modeltype featuretypes subgrams                                \ndist-char-word-lemma cumulated dist         2                            -0.078502  \n                                            2-3                           0.061935  \n                                            2-3-4                        -0.026524  \n                                            2-3-4-5                       0.066062  \n                               dist_char    2                             0.049725  \n...                                                                            ...  \nsingular             baseline  tag          1                            -0.096762  \n                                            1-2                           0.040120  \n                                            1-2-3                         0.040217  \n                               word         1                             0.016749  \n                                            1-2                           0.028786  \n\n[108 rows x 36 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"9\" halign=\"left\">age</th>\n      <th>gender</th>\n      <th>...</th>\n      <th>age</th>\n      <th colspan=\"9\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>500</th>\n      <th>100</th>\n      <th colspan=\"2\" halign=\"left\">250</th>\n      <th>500</th>\n      <th>100</th>\n      <th>500</th>\n      <th>...</th>\n      <th>100</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n      <th>500</th>\n      <th>250</th>\n      <th>500</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th># Authors</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>150</th>\n      <th>...</th>\n      <th>1000</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>...</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist</th>\n      <th>2</th>\n      <td>0.187245</td>\n      <td>0.041580</td>\n      <td>0.034811</td>\n      <td>0.008349</td>\n      <td>0.009855</td>\n      <td>0.007215</td>\n      <td>0.015321</td>\n      <td>0.019163</td>\n      <td>-0.014053</td>\n      <td>0.085755</td>\n      <td>...</td>\n      <td>0.017371</td>\n      <td>0.136993</td>\n      <td>0.194533</td>\n      <td>0.288756</td>\n      <td>0.093845</td>\n      <td>-0.025917</td>\n      <td>0.265406</td>\n      <td>0.258740</td>\n      <td>0.283375</td>\n      <td>-0.078502</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.114232</td>\n      <td>-0.022333</td>\n      <td>-0.005109</td>\n      <td>0.027542</td>\n      <td>-0.006344</td>\n      <td>-0.013204</td>\n      <td>0.008644</td>\n      <td>0.047782</td>\n      <td>0.012595</td>\n      <td>0.153513</td>\n      <td>...</td>\n      <td>0.045819</td>\n      <td>0.113376</td>\n      <td>0.196929</td>\n      <td>0.048504</td>\n      <td>0.145157</td>\n      <td>0.007511</td>\n      <td>0.104863</td>\n      <td>0.111348</td>\n      <td>0.187528</td>\n      <td>0.061935</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.026844</td>\n      <td>0.002778</td>\n      <td>-0.003334</td>\n      <td>0.050173</td>\n      <td>0.024525</td>\n      <td>0.025758</td>\n      <td>0.027900</td>\n      <td>0.009939</td>\n      <td>0.001973</td>\n      <td>0.118509</td>\n      <td>...</td>\n      <td>0.020676</td>\n      <td>-0.005042</td>\n      <td>0.170263</td>\n      <td>0.047140</td>\n      <td>0.010895</td>\n      <td>0.094241</td>\n      <td>0.061039</td>\n      <td>0.090751</td>\n      <td>0.035410</td>\n      <td>-0.026524</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.021929</td>\n      <td>0.004039</td>\n      <td>0.021338</td>\n      <td>0.037073</td>\n      <td>-0.009718</td>\n      <td>0.027035</td>\n      <td>-0.007913</td>\n      <td>0.015407</td>\n      <td>-0.020033</td>\n      <td>0.090370</td>\n      <td>...</td>\n      <td>-0.032071</td>\n      <td>0.054982</td>\n      <td>0.084400</td>\n      <td>-0.006098</td>\n      <td>0.017548</td>\n      <td>-0.018271</td>\n      <td>0.064423</td>\n      <td>0.067456</td>\n      <td>0.070322</td>\n      <td>0.066062</td>\n    </tr>\n    <tr>\n      <th>dist_char</th>\n      <th>2</th>\n      <td>0.136581</td>\n      <td>0.061984</td>\n      <td>0.001302</td>\n      <td>0.024354</td>\n      <td>0.007699</td>\n      <td>0.012189</td>\n      <td>0.018137</td>\n      <td>0.004790</td>\n      <td>0.004727</td>\n      <td>0.091084</td>\n      <td>...</td>\n      <td>0.032844</td>\n      <td>0.026470</td>\n      <td>0.025105</td>\n      <td>-0.028486</td>\n      <td>0.085429</td>\n      <td>-0.062807</td>\n      <td>0.046563</td>\n      <td>0.060031</td>\n      <td>0.088929</td>\n      <td>0.049725</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.088622</td>\n      <td>-0.018548</td>\n      <td>0.113389</td>\n      <td>0.071146</td>\n      <td>-0.060031</td>\n      <td>0.045259</td>\n      <td>0.025578</td>\n      <td>-0.067977</td>\n      <td>-0.052828</td>\n      <td>-0.008557</td>\n      <td>...</td>\n      <td>-0.052828</td>\n      <td>-0.008557</td>\n      <td>0.027320</td>\n      <td>-0.029294</td>\n      <td>0.203978</td>\n      <td>0.051619</td>\n      <td>-0.146045</td>\n      <td>0.043825</td>\n      <td>0.004342</td>\n      <td>-0.096762</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.017799</td>\n      <td>0.045190</td>\n      <td>0.064695</td>\n      <td>-0.010918</td>\n      <td>0.069371</td>\n      <td>0.047406</td>\n      <td>0.046400</td>\n      <td>0.044056</td>\n      <td>0.034712</td>\n      <td>0.154573</td>\n      <td>...</td>\n      <td>0.049368</td>\n      <td>0.132701</td>\n      <td>0.079936</td>\n      <td>0.173908</td>\n      <td>-0.104392</td>\n      <td>-0.123443</td>\n      <td>-0.027975</td>\n      <td>-0.008411</td>\n      <td>0.109083</td>\n      <td>0.040120</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.082513</td>\n      <td>0.040029</td>\n      <td>0.033945</td>\n      <td>0.038633</td>\n      <td>0.036958</td>\n      <td>0.009889</td>\n      <td>0.001714</td>\n      <td>0.047400</td>\n      <td>0.000329</td>\n      <td>0.051351</td>\n      <td>...</td>\n      <td>0.002219</td>\n      <td>0.057714</td>\n      <td>-0.000031</td>\n      <td>-0.019950</td>\n      <td>0.003267</td>\n      <td>0.019267</td>\n      <td>-0.014083</td>\n      <td>-0.028449</td>\n      <td>0.121241</td>\n      <td>0.040217</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>-0.058514</td>\n      <td>-0.118380</td>\n      <td>0.026580</td>\n      <td>0.042761</td>\n      <td>0.027012</td>\n      <td>-0.002993</td>\n      <td>-0.008936</td>\n      <td>0.015079</td>\n      <td>0.004566</td>\n      <td>-0.030364</td>\n      <td>...</td>\n      <td>-0.006523</td>\n      <td>0.028155</td>\n      <td>-0.016220</td>\n      <td>0.077121</td>\n      <td>0.023986</td>\n      <td>0.032958</td>\n      <td>-0.011463</td>\n      <td>0.025321</td>\n      <td>0.052303</td>\n      <td>0.016749</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>-0.011166</td>\n      <td>-0.068614</td>\n      <td>-0.010872</td>\n      <td>0.001316</td>\n      <td>0.003212</td>\n      <td>0.014187</td>\n      <td>-0.003547</td>\n      <td>0.011849</td>\n      <td>-0.000954</td>\n      <td>-0.000287</td>\n      <td>...</td>\n      <td>0.000597</td>\n      <td>0.025200</td>\n      <td>0.048501</td>\n      <td>-0.030074</td>\n      <td>0.013636</td>\n      <td>0.005569</td>\n      <td>0.017931</td>\n      <td>-0.007934</td>\n      <td>-0.008001</td>\n      <td>0.028786</td>\n    </tr>\n  </tbody>\n</table>\n<p>108 rows × 36 columns</p>\n</div>"
+      "text/plain": "target                                                                         age  \\\ntweetLen                                                                       500   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.187245   \n                                            2-3                           0.114232   \n                                            2-3-4                         0.026844   \n                                            2-3-4-5                       0.021929   \n                               dist_char    2                             0.136581   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.088622   \n                                            1-2                           0.017799   \n                                            1-2-3                         0.082513   \n                               word         1                            -0.058514   \n                                            1-2                          -0.011166   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.041580   \n                                            2-3                          -0.022333   \n                                            2-3-4                         0.002778   \n                                            2-3-4-5                       0.004039   \n                               dist_char    2                             0.061984   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.018548   \n                                            1-2                           0.045190   \n                                            1-2-3                         0.040029   \n                               word         1                            -0.118380   \n                                            1-2                          -0.068614   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.034811   \n                                            2-3                          -0.005109   \n                                            2-3-4                        -0.003334   \n                                            2-3-4-5                       0.021338   \n                               dist_char    2                             0.001302   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.113389   \n                                            1-2                           0.064695   \n                                            1-2-3                         0.033945   \n                               word         1                             0.026580   \n                                            1-2                          -0.010872   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.008349   \n                                            2-3                           0.027542   \n                                            2-3-4                         0.050173   \n                                            2-3-4-5                       0.037073   \n                               dist_char    2                             0.024354   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.071146   \n                                            1-2                          -0.010918   \n                                            1-2-3                         0.038633   \n                               word         1                             0.042761   \n                                            1-2                           0.001316   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.009855   \n                                            2-3                          -0.006344   \n                                            2-3-4                         0.024525   \n                                            2-3-4-5                      -0.009718   \n                               dist_char    2                             0.007699   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.060031   \n                                            1-2                           0.069371   \n                                            1-2-3                         0.036958   \n                               word         1                             0.027012   \n                                            1-2                           0.003212   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.007215   \n                                            2-3                          -0.013204   \n                                            2-3-4                         0.025758   \n                                            2-3-4-5                       0.027035   \n                               dist_char    2                             0.012189   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.045259   \n                                            1-2                           0.047406   \n                                            1-2-3                         0.009889   \n                               word         1                            -0.002993   \n                                            1-2                           0.014187   \n\ntarget                                                                              \\\ntweetLen                                                                             \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.015321   \n                                            2-3                           0.008644   \n                                            2-3-4                         0.027900   \n                                            2-3-4-5                      -0.007913   \n                               dist_char    2                             0.018137   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.025578   \n                                            1-2                           0.046400   \n                                            1-2-3                         0.001714   \n                               word         1                            -0.008936   \n                                            1-2                          -0.003547   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.019163   \n                                            2-3                           0.047782   \n                                            2-3-4                         0.009939   \n                                            2-3-4-5                       0.015407   \n                               dist_char    2                             0.004790   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.067977   \n                                            1-2                           0.044056   \n                                            1-2-3                         0.047400   \n                               word         1                             0.015079   \n                                            1-2                           0.011849   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                            -0.014053   \n                                            2-3                           0.012595   \n                                            2-3-4                         0.001973   \n                                            2-3-4-5                      -0.020033   \n                               dist_char    2                             0.004727   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.052828   \n                                            1-2                           0.034712   \n                                            1-2-3                         0.000329   \n                               word         1                             0.004566   \n                                            1-2                          -0.000954   \n\ntarget                                                                      gender  \\\ntweetLen                                                                       500   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (ext.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.085755   \n                                            2-3                           0.153513   \n                                            2-3-4                         0.118509   \n                                            2-3-4-5                       0.090370   \n                               dist_char    2                             0.091084   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.008557   \n                                            1-2                           0.154573   \n                                            1-2-3                         0.051351   \n                               word         1                            -0.030364   \n                                            1-2                          -0.000287   \n\ntarget                                                ...  \\\ntweetLen                                              ...   \nNo. of Authors                                        ...   \nScore                                                 ...   \nset                  modeltype featuretypes subgrams  ...   \ndist-char-word-lemma cumulated dist         2         ...   \n                                            2-3       ...   \n                                            2-3-4     ...   \n                                            2-3-4-5   ...   \n                               dist_char    2         ...   \n...                                                   ...   \nsingular             baseline  tag          1         ...   \n                                            1-2       ...   \n                                            1-2-3     ...   \n                               word         1         ...   \n                                            1-2       ...   \n\ntarget                                                                         age  \\\ntweetLen                                                                       100   \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.017371   \n                                            2-3                           0.045819   \n                                            2-3-4                         0.020676   \n                                            2-3-4-5                      -0.032071   \n                               dist_char    2                             0.032844   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.052828   \n                                            1-2                           0.049368   \n                                            1-2-3                         0.002219   \n                               word         1                            -0.006523   \n                                            1-2                           0.000597   \n\ntarget                                                                      gender  \\\ntweetLen                                                                       500   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.136993   \n                                            2-3                           0.113376   \n                                            2-3-4                        -0.005042   \n                                            2-3-4-5                       0.054982   \n                               dist_char    2                             0.026470   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.008557   \n                                            1-2                           0.132701   \n                                            1-2-3                         0.057714   \n                               word         1                             0.028155   \n                                            1-2                           0.025200   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.194533   \n                                            2-3                           0.196929   \n                                            2-3-4                         0.170263   \n                                            2-3-4-5                       0.084400   \n                               dist_char    2                             0.025105   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.027320   \n                                            1-2                           0.079936   \n                                            1-2-3                        -0.000031   \n                               word         1                            -0.016220   \n                                            1-2                           0.048501   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \nNo. of Authors                                                                150    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.288756   \n                                            2-3                           0.048504   \n                                            2-3-4                         0.047140   \n                                            2-3-4-5                      -0.006098   \n                               dist_char    2                            -0.028486   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.029294   \n                                            1-2                           0.173908   \n                                            1-2-3                        -0.019950   \n                               word         1                             0.077121   \n                                            1-2                          -0.030074   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.093845   \n                                            2-3                           0.145157   \n                                            2-3-4                         0.010895   \n                                            2-3-4-5                       0.017548   \n                               dist_char    2                             0.085429   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.203978   \n                                            1-2                          -0.104392   \n                                            1-2-3                         0.003267   \n                               word         1                             0.023986   \n                                            1-2                           0.013636   \n\ntarget                                                                              \\\ntweetLen                                                                       100   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                            -0.025917   \n                                            2-3                           0.007511   \n                                            2-3-4                         0.094241   \n                                            2-3-4-5                      -0.018271   \n                               dist_char    2                            -0.062807   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.051619   \n                                            1-2                          -0.123443   \n                                            1-2-3                         0.019267   \n                               word         1                             0.032958   \n                                            1-2                           0.005569   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \nNo. of Authors                                                                500    \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.265406   \n                                            2-3                           0.104863   \n                                            2-3-4                         0.061039   \n                                            2-3-4-5                       0.064423   \n                               dist_char    2                             0.046563   \n...                                                                            ...   \nsingular             baseline  tag          1                            -0.146045   \n                                            1-2                          -0.027975   \n                                            1-2-3                        -0.014083   \n                               word         1                            -0.011463   \n                                            1-2                           0.017931   \n\ntarget                                                                              \\\ntweetLen                                                                       250   \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.258740   \n                                            2-3                           0.111348   \n                                            2-3-4                         0.090751   \n                                            2-3-4-5                       0.067456   \n                               dist_char    2                             0.060031   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.043825   \n                                            1-2                          -0.008411   \n                                            1-2-3                        -0.028449   \n                               word         1                             0.025321   \n                                            1-2                          -0.007934   \n\ntarget                                                                              \\\ntweetLen                                                                       500   \nNo. of Authors                                                                1000   \nScore                                                Average Spearman's Rho (red.)   \nset                  modeltype featuretypes subgrams                                 \ndist-char-word-lemma cumulated dist         2                             0.283375   \n                                            2-3                           0.187528   \n                                            2-3-4                         0.035410   \n                                            2-3-4-5                       0.070322   \n                               dist_char    2                             0.088929   \n...                                                                            ...   \nsingular             baseline  tag          1                             0.004342   \n                                            1-2                           0.109083   \n                                            1-2-3                         0.121241   \n                               word         1                             0.052303   \n                                            1-2                          -0.008001   \n\ntarget                                                                              \ntweetLen                                                                       100  \nNo. of Authors                                                                1000  \nScore                                                Average Spearman's Rho (red.)  \nset                  modeltype featuretypes subgrams                                \ndist-char-word-lemma cumulated dist         2                            -0.078502  \n                                            2-3                           0.061935  \n                                            2-3-4                        -0.026524  \n                                            2-3-4-5                       0.066062  \n                               dist_char    2                             0.049725  \n...                                                                            ...  \nsingular             baseline  tag          1                            -0.096762  \n                                            1-2                           0.040120  \n                                            1-2-3                         0.040217  \n                               word         1                             0.016749  \n                                            1-2                           0.028786  \n\n[108 rows x 36 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"9\" halign=\"left\">age</th>\n      <th>gender</th>\n      <th>...</th>\n      <th>age</th>\n      <th colspan=\"9\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>500</th>\n      <th>100</th>\n      <th colspan=\"2\" halign=\"left\">250</th>\n      <th>500</th>\n      <th>100</th>\n      <th>500</th>\n      <th>...</th>\n      <th>100</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n      <th>500</th>\n      <th>250</th>\n      <th>500</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>No. of Authors</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>150</th>\n      <th>...</th>\n      <th>1000</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>...</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist</th>\n      <th>2</th>\n      <td>0.187245</td>\n      <td>0.041580</td>\n      <td>0.034811</td>\n      <td>0.008349</td>\n      <td>0.009855</td>\n      <td>0.007215</td>\n      <td>0.015321</td>\n      <td>0.019163</td>\n      <td>-0.014053</td>\n      <td>0.085755</td>\n      <td>...</td>\n      <td>0.017371</td>\n      <td>0.136993</td>\n      <td>0.194533</td>\n      <td>0.288756</td>\n      <td>0.093845</td>\n      <td>-0.025917</td>\n      <td>0.265406</td>\n      <td>0.258740</td>\n      <td>0.283375</td>\n      <td>-0.078502</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.114232</td>\n      <td>-0.022333</td>\n      <td>-0.005109</td>\n      <td>0.027542</td>\n      <td>-0.006344</td>\n      <td>-0.013204</td>\n      <td>0.008644</td>\n      <td>0.047782</td>\n      <td>0.012595</td>\n      <td>0.153513</td>\n      <td>...</td>\n      <td>0.045819</td>\n      <td>0.113376</td>\n      <td>0.196929</td>\n      <td>0.048504</td>\n      <td>0.145157</td>\n      <td>0.007511</td>\n      <td>0.104863</td>\n      <td>0.111348</td>\n      <td>0.187528</td>\n      <td>0.061935</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.026844</td>\n      <td>0.002778</td>\n      <td>-0.003334</td>\n      <td>0.050173</td>\n      <td>0.024525</td>\n      <td>0.025758</td>\n      <td>0.027900</td>\n      <td>0.009939</td>\n      <td>0.001973</td>\n      <td>0.118509</td>\n      <td>...</td>\n      <td>0.020676</td>\n      <td>-0.005042</td>\n      <td>0.170263</td>\n      <td>0.047140</td>\n      <td>0.010895</td>\n      <td>0.094241</td>\n      <td>0.061039</td>\n      <td>0.090751</td>\n      <td>0.035410</td>\n      <td>-0.026524</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.021929</td>\n      <td>0.004039</td>\n      <td>0.021338</td>\n      <td>0.037073</td>\n      <td>-0.009718</td>\n      <td>0.027035</td>\n      <td>-0.007913</td>\n      <td>0.015407</td>\n      <td>-0.020033</td>\n      <td>0.090370</td>\n      <td>...</td>\n      <td>-0.032071</td>\n      <td>0.054982</td>\n      <td>0.084400</td>\n      <td>-0.006098</td>\n      <td>0.017548</td>\n      <td>-0.018271</td>\n      <td>0.064423</td>\n      <td>0.067456</td>\n      <td>0.070322</td>\n      <td>0.066062</td>\n    </tr>\n    <tr>\n      <th>dist_char</th>\n      <th>2</th>\n      <td>0.136581</td>\n      <td>0.061984</td>\n      <td>0.001302</td>\n      <td>0.024354</td>\n      <td>0.007699</td>\n      <td>0.012189</td>\n      <td>0.018137</td>\n      <td>0.004790</td>\n      <td>0.004727</td>\n      <td>0.091084</td>\n      <td>...</td>\n      <td>0.032844</td>\n      <td>0.026470</td>\n      <td>0.025105</td>\n      <td>-0.028486</td>\n      <td>0.085429</td>\n      <td>-0.062807</td>\n      <td>0.046563</td>\n      <td>0.060031</td>\n      <td>0.088929</td>\n      <td>0.049725</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.088622</td>\n      <td>-0.018548</td>\n      <td>0.113389</td>\n      <td>0.071146</td>\n      <td>-0.060031</td>\n      <td>0.045259</td>\n      <td>0.025578</td>\n      <td>-0.067977</td>\n      <td>-0.052828</td>\n      <td>-0.008557</td>\n      <td>...</td>\n      <td>-0.052828</td>\n      <td>-0.008557</td>\n      <td>0.027320</td>\n      <td>-0.029294</td>\n      <td>0.203978</td>\n      <td>0.051619</td>\n      <td>-0.146045</td>\n      <td>0.043825</td>\n      <td>0.004342</td>\n      <td>-0.096762</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.017799</td>\n      <td>0.045190</td>\n      <td>0.064695</td>\n      <td>-0.010918</td>\n      <td>0.069371</td>\n      <td>0.047406</td>\n      <td>0.046400</td>\n      <td>0.044056</td>\n      <td>0.034712</td>\n      <td>0.154573</td>\n      <td>...</td>\n      <td>0.049368</td>\n      <td>0.132701</td>\n      <td>0.079936</td>\n      <td>0.173908</td>\n      <td>-0.104392</td>\n      <td>-0.123443</td>\n      <td>-0.027975</td>\n      <td>-0.008411</td>\n      <td>0.109083</td>\n      <td>0.040120</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.082513</td>\n      <td>0.040029</td>\n      <td>0.033945</td>\n      <td>0.038633</td>\n      <td>0.036958</td>\n      <td>0.009889</td>\n      <td>0.001714</td>\n      <td>0.047400</td>\n      <td>0.000329</td>\n      <td>0.051351</td>\n      <td>...</td>\n      <td>0.002219</td>\n      <td>0.057714</td>\n      <td>-0.000031</td>\n      <td>-0.019950</td>\n      <td>0.003267</td>\n      <td>0.019267</td>\n      <td>-0.014083</td>\n      <td>-0.028449</td>\n      <td>0.121241</td>\n      <td>0.040217</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>-0.058514</td>\n      <td>-0.118380</td>\n      <td>0.026580</td>\n      <td>0.042761</td>\n      <td>0.027012</td>\n      <td>-0.002993</td>\n      <td>-0.008936</td>\n      <td>0.015079</td>\n      <td>0.004566</td>\n      <td>-0.030364</td>\n      <td>...</td>\n      <td>-0.006523</td>\n      <td>0.028155</td>\n      <td>-0.016220</td>\n      <td>0.077121</td>\n      <td>0.023986</td>\n      <td>0.032958</td>\n      <td>-0.011463</td>\n      <td>0.025321</td>\n      <td>0.052303</td>\n      <td>0.016749</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>-0.011166</td>\n      <td>-0.068614</td>\n      <td>-0.010872</td>\n      <td>0.001316</td>\n      <td>0.003212</td>\n      <td>0.014187</td>\n      <td>-0.003547</td>\n      <td>0.011849</td>\n      <td>-0.000954</td>\n      <td>-0.000287</td>\n      <td>...</td>\n      <td>0.000597</td>\n      <td>0.025200</td>\n      <td>0.048501</td>\n      <td>-0.030074</td>\n      <td>0.013636</td>\n      <td>0.005569</td>\n      <td>0.017931</td>\n      <td>-0.007934</td>\n      <td>-0.008001</td>\n      <td>0.028786</td>\n    </tr>\n  </tbody>\n</table>\n<p>108 rows × 36 columns</p>\n</div>"
      },
-     "execution_count": 37,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,8 +735,9 @@
     "df.head()\n",
     "ranks = df['rank']\n",
     "df = df.drop(['rank', 'name'], axis=1)\n",
-    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['# Authors'], sub['Type']])\n",
-    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'], columns=['target','tweetLen', '# Authors', 'Score'], values='result')\n",
+    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['No. of Authors'], sub['Type']])\n",
+    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'],\n",
+    "               columns=['target','tweetLen', 'No. of Authors', 'Score'], values='result')\n",
     "piv"
    ],
    "metadata": {
@@ -438,13 +749,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 87,
    "outputs": [
     {
      "data": {
       "text/plain": "['dist-char-word-lemma', 'ordered-full', 'singular']"
      },
-     "execution_count": 38,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 88,
    "outputs": [],
    "source": [
     "def rep(x):\n",
@@ -505,19 +816,21 @@
     "                        mod = 'feature-wise'\n",
     "                        l1 = 'baseline'\n",
     "                    if 'ordered-full' in st:\n",
-    "                        featureset = 'ordered, full Featureset'\n",
+    "                        featureset = 'ordered, full feature set'\n",
     "                        l2 = 'full'\n",
     "                    elif 'singular' in st:\n",
-    "                        featureset = 'individual  Featuretypes'\n",
+    "                        featureset = 'individual feature types'\n",
     "                        l2 = 'individual'\n",
     "                        tmp = tmp.drop(labels=('polarity'), level=0, axis=0)\n",
     "                    elif 'dist-char' in st:\n",
-    "                        featureset = r'reduced Featureset (DIST, CHAR, LEMMA, WORD)'\n",
+    "                        featureset = r'reduced feature set (DIST, CHAR, LEMMA, WORD)'\n",
     "                        l2='dwald'\n",
     "                        if key == 'acc':\n",
     "                            sc = 'Accuracy and F1-scores'\n",
+    "                        elif key == 'f1_dist':\n",
+    "                            sc = 'F1-scores & stability of feature importance'\n",
     "                        else:\n",
-    "                            sc = 'Average Distortion of Features'\n",
+    "                            sc = 'Stability of feature importance'\n",
     "                    tmp.index = tmp.index.set_levels([el.upper() for el in tmp.index.levels[0]], level=0)\n",
     "                    os.makedirs(os.path.join(savedir, target, 'latex'), exist_ok=True)\n",
     "                    os.makedirs(os.path.join(savedir, target, 'html'), exist_ok=True)\n",
@@ -529,20 +842,32 @@
     "                        tmp.to_html(w, sparsify=True)\n",
     "                    with open(file_t, 'w') as w:\n",
     "                        table = tmp.to_latex(sparsify=True, multirow=True, multicolumn=True,\n",
-    "                                        #caption='''{} for the prediction of {} on a minimal tweet length of {} chars using a {}\n",
-    "                                     #model on the {}'''.format(sc, target, tweetLen, mod, featureset),\n",
-    "                                     label='tab:{}_{}_{}_{}_{}'.format(target, key, tweetLen, l1,l2),\n",
+    "                                        caption='''{} for the prediction of {} on a minimal input instance\n",
+    "                                        length of {} characters using a {} model on the {}'''.format(sc, target, tweetLen, mod, featureset),\n",
+    "                                     label='tab:{}_{}_{}_{}_{}'.format(target[0].upper()+target[1:], key, tweetLen, l1,l2),\n",
     "                                     float_format=\"%.4f\")\n",
     "                        #table = re.sub(r'{table}', '{sidewaystable}', table)\n",
-    "\n",
+    "                        table = re.sub(r'begin{table}', 'begin{table}[!ht]', table)\n",
+    "                        #table = re.sub(r'\\\\begin\\{table}', '', table)\n",
+    "                        #table = re.sub(r'\\\\end\\{table}', '', table)\n",
     "                        table = re.sub(\"Average Spearman's Rho \\(ext.\\)\",\n",
-    "                                       r\"\\\\thead{Average Spearman's \\\\\\\\ Rho (ext.)}\", table)\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's $\\\\rho$ (ext.)}\", table)\n",
     "                        table = re.sub(\"Average Spearman's Rho \\(red.\\)\",\n",
-    "                                       r\"\\\\thead{Average Spearman's \\\\\\\\ Rho (red.)}\", table)\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's  $\\\\rho$ (red.)}\", table)\n",
+    "                        table = re.sub('& target &', '& Target &', table)\n",
+    "                        table = re.sub('feature types &', 'Feature types &', table)\n",
+    "                        table = re.sub('featuretypes &', 'Feature types &', table)\n",
+    "                        table = re.sub('& subgrams &', '& N-gram ranges &', table)\n",
+    "                        table = re.sub('& tweetLen &', r'& Min. No. of Characters &', table)\n",
+    "                        table = re.sub('& f1-score', '& F1-score', table)\n",
+    "                        table = re.sub('& accuracy', '& Accuracy', table)\n",
+    "                        table = re.sub(r'\\{age}', '{Age}', table)\n",
+    "                        table = re.sub(r'\\{gender}', '{Gender}', table)\n",
     "                        table = re.sub(pat,rep, table)\n",
     "                        table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
     "                        table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
-    "                        #print(table)\n",
+    "                        table = re.sub(r\"0\\.0000\", '--', table)\n",
+    "                            #print(table)\n",
     "                        w.write(table)"
    ],
    "metadata": {
@@ -554,14 +879,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 89,
    "outputs": [
     {
      "data": {
-      "text/plain": "target                                                       age            \\\ntweetLen                                                     250       500   \n# Authors                                                   50        50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.677175  0.726180   \n                                              2-3       0.762708  0.819234   \n                                              2-3-4     0.753421  0.832146   \n                                              2-3-4-5   0.738025  0.823241   \n                               dist_char_asis 2         0.774927  0.850846   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.345308  0.408281   \n                                              1-2       0.507087  0.547640   \n                                              1-2-3     0.522483  0.587711   \n                               word           1         0.499756  0.628228   \n                                              1-2       0.509286  0.643811   \n\ntarget                                                                      \\\ntweetLen                                                     250       100   \n# Authors                                                   150       50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.577151  0.598279   \n                                              2-3       0.668444  0.690742   \n                                              2-3-4     0.670665  0.703102   \n                                              2-3-4-5   0.684733  0.680441   \n                               dist_char_asis 2         0.699319  0.716069   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.286687  0.263936   \n                                              1-2       0.371761  0.419535   \n                                              1-2-3     0.394713  0.448255   \n                               word           1         0.408485  0.419292   \n                                              1-2       0.423590  0.427412   \n\ntarget                                                                      \\\ntweetLen                                                               500   \n# Authors                                                   150       1000   \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.488672  0.446504   \n                                              2-3       0.559526  0.519917   \n                                              2-3-4     0.574910  0.543726   \n                                              2-3-4-5   0.537784  0.545106   \n                               dist_char_asis 2         0.582840  0.556557   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.215523  0.229119   \n                                              1-2       0.297047  0.299792   \n                                              1-2-3     0.342469  0.340174   \n                               word           1         0.300994  0.352343   \n                                              1-2       0.316853  0.355102   \n\ntarget                                                                      \\\ntweetLen                                                     250       500   \n# Authors                                                   500       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.454316  0.513337   \n                                              2-3       0.523891  0.593752   \n                                              2-3-4     0.547140  0.615124   \n                                              2-3-4-5   0.545427  0.618594   \n                               dist_char_asis 2         0.560691  0.620310   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.219258  0.230052   \n                                              1-2       0.293479  0.331799   \n                                              1-2-3     0.338564  0.387528   \n                               word           1         0.301100  0.399306   \n                                              1-2       0.341047  0.416777   \n\ntarget                                                                      \\\ntweetLen                                                               100   \n# Authors                                                   150       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.635018  0.373908   \n                                              2-3       0.734175  0.442379   \n                                              2-3-4     0.762293  0.452307   \n                                              2-3-4-5   0.759033  0.462428   \n                               dist_char_asis 2         0.772480  0.467655   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.331568  0.234941   \n                                              1-2       0.449199  0.274312   \n                                              1-2-3     0.485602  0.253952   \n                               word           1         0.543331  0.244099   \n                                              1-2       0.552431  0.256887   \n\ntarget                                                  ...    gender  \\\ntweetLen                                                ...       250   \n# Authors                                               ...      50     \nScore                                                   ...  f1-score   \nset                  modeltype featuretypes   subgrams  ...             \ndist-char-word-lemma cumulated dist_char      2         ...  0.784084   \n                                              2-3       ...  0.824736   \n                                              2-3-4     ...  0.830936   \n                                              2-3-4-5   ...  0.831464   \n                               dist_char_asis 2         ...  0.843374   \n...                                                     ...       ...   \nsingular             baseline  tag            1         ...  0.626742   \n                                              1-2       ...  0.661869   \n                                              1-2-3     ...  0.676937   \n                               word           1         ...  0.696058   \n                                              1-2       ...  0.700032   \n\ntarget                                                                      \\\ntweetLen                                                     500       250   \n# Authors                                                   150       150    \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.786840  0.749260   \n                                              2-3       0.839235  0.773833   \n                                              2-3-4     0.849448  0.785765   \n                                              2-3-4-5   0.858279  0.792721   \n                               dist_char_asis 2         0.862321  0.800185   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.569969  0.585281   \n                                              1-2       0.670376  0.630576   \n                                              1-2-3     0.682818  0.654571   \n                               word           1         0.740779  0.686946   \n                                              1-2       0.753451  0.691202   \n\ntarget                                                                      \\\ntweetLen                                                     100       250   \n# Authors                                                   150       500    \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.694655  0.673562   \n                                              2-3       0.732961  0.721850   \n                                              2-3-4     0.736983  0.730540   \n                                              2-3-4-5   0.734179  0.727924   \n                               dist_char_asis 2         0.745258  0.741822   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.573962  0.564753   \n                                              1-2       0.611240  0.603915   \n                                              1-2-3     0.621303  0.613924   \n                               word           1         0.626975  0.635154   \n                                              1-2       0.629456  0.639614   \n\ntarget                                                                      \\\ntweetLen                                                     500             \n# Authors                                                   500       1000   \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.705783  0.676926   \n                                              2-3       0.769621  0.731095   \n                                              2-3-4     0.785189  0.739204   \n                                              2-3-4-5   0.764951  0.742888   \n                               dist_char_asis 2         0.744117  0.749447   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.589206  0.577137   \n                                              1-2       0.626504  0.613985   \n                                              1-2-3     0.623024  0.627792   \n                               word           1         0.694029  0.678234   \n                                              1-2       0.698088  0.683637   \n\ntarget                                                                      \\\ntweetLen                                                     100       250   \n# Authors                                                   500       1000   \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.627626  0.651106   \n                                              2-3       0.664470  0.691603   \n                                              2-3-4     0.671122  0.700129   \n                                              2-3-4-5   0.671008  0.699478   \n                               dist_char_asis 2         0.677461  0.708362   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.549581  0.564286   \n                                              1-2       0.574319  0.582854   \n                                              1-2-3     0.581983  0.601986   \n                               word           1         0.583415  0.629157   \n                                              1-2       0.590147  0.629844   \n\ntarget                                                            \ntweetLen                                                     100  \n# Authors                                                   1000  \nScore                                                   f1-score  \nset                  modeltype featuretypes   subgrams            \ndist-char-word-lemma cumulated dist_char      2         0.596627  \n                                              2-3       0.630147  \n                                              2-3-4     0.631162  \n                                              2-3-4-5   0.630545  \n                               dist_char_asis 2         0.633999  \n...                                                          ...  \nsingular             baseline  tag            1         0.549799  \n                                              1-2       0.569978  \n                                              1-2-3     0.577939  \n                               word           1         0.575988  \n                                              1-2       0.580928  \n\n[100 rows x 48 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"10\" halign=\"left\">age</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">100</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>...</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th># Authors</th>\n      <th>50</th>\n      <th>50</th>\n      <th>150</th>\n      <th>50</th>\n      <th>150</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>500</th>\n      <th>150</th>\n      <th>500</th>\n      <th>...</th>\n      <th>50</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>...</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist_char</th>\n      <th>2</th>\n      <td>0.677175</td>\n      <td>0.726180</td>\n      <td>0.577151</td>\n      <td>0.598279</td>\n      <td>0.488672</td>\n      <td>0.446504</td>\n      <td>0.454316</td>\n      <td>0.513337</td>\n      <td>0.635018</td>\n      <td>0.373908</td>\n      <td>...</td>\n      <td>0.784084</td>\n      <td>0.786840</td>\n      <td>0.749260</td>\n      <td>0.694655</td>\n      <td>0.673562</td>\n      <td>0.705783</td>\n      <td>0.676926</td>\n      <td>0.627626</td>\n      <td>0.651106</td>\n      <td>0.596627</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.762708</td>\n      <td>0.819234</td>\n      <td>0.668444</td>\n      <td>0.690742</td>\n      <td>0.559526</td>\n      <td>0.519917</td>\n      <td>0.523891</td>\n      <td>0.593752</td>\n      <td>0.734175</td>\n      <td>0.442379</td>\n      <td>...</td>\n      <td>0.824736</td>\n      <td>0.839235</td>\n      <td>0.773833</td>\n      <td>0.732961</td>\n      <td>0.721850</td>\n      <td>0.769621</td>\n      <td>0.731095</td>\n      <td>0.664470</td>\n      <td>0.691603</td>\n      <td>0.630147</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.753421</td>\n      <td>0.832146</td>\n      <td>0.670665</td>\n      <td>0.703102</td>\n      <td>0.574910</td>\n      <td>0.543726</td>\n      <td>0.547140</td>\n      <td>0.615124</td>\n      <td>0.762293</td>\n      <td>0.452307</td>\n      <td>...</td>\n      <td>0.830936</td>\n      <td>0.849448</td>\n      <td>0.785765</td>\n      <td>0.736983</td>\n      <td>0.730540</td>\n      <td>0.785189</td>\n      <td>0.739204</td>\n      <td>0.671122</td>\n      <td>0.700129</td>\n      <td>0.631162</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.738025</td>\n      <td>0.823241</td>\n      <td>0.684733</td>\n      <td>0.680441</td>\n      <td>0.537784</td>\n      <td>0.545106</td>\n      <td>0.545427</td>\n      <td>0.618594</td>\n      <td>0.759033</td>\n      <td>0.462428</td>\n      <td>...</td>\n      <td>0.831464</td>\n      <td>0.858279</td>\n      <td>0.792721</td>\n      <td>0.734179</td>\n      <td>0.727924</td>\n      <td>0.764951</td>\n      <td>0.742888</td>\n      <td>0.671008</td>\n      <td>0.699478</td>\n      <td>0.630545</td>\n    </tr>\n    <tr>\n      <th>dist_char_asis</th>\n      <th>2</th>\n      <td>0.774927</td>\n      <td>0.850846</td>\n      <td>0.699319</td>\n      <td>0.716069</td>\n      <td>0.582840</td>\n      <td>0.556557</td>\n      <td>0.560691</td>\n      <td>0.620310</td>\n      <td>0.772480</td>\n      <td>0.467655</td>\n      <td>...</td>\n      <td>0.843374</td>\n      <td>0.862321</td>\n      <td>0.800185</td>\n      <td>0.745258</td>\n      <td>0.741822</td>\n      <td>0.744117</td>\n      <td>0.749447</td>\n      <td>0.677461</td>\n      <td>0.708362</td>\n      <td>0.633999</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.345308</td>\n      <td>0.408281</td>\n      <td>0.286687</td>\n      <td>0.263936</td>\n      <td>0.215523</td>\n      <td>0.229119</td>\n      <td>0.219258</td>\n      <td>0.230052</td>\n      <td>0.331568</td>\n      <td>0.234941</td>\n      <td>...</td>\n      <td>0.626742</td>\n      <td>0.569969</td>\n      <td>0.585281</td>\n      <td>0.573962</td>\n      <td>0.564753</td>\n      <td>0.589206</td>\n      <td>0.577137</td>\n      <td>0.549581</td>\n      <td>0.564286</td>\n      <td>0.549799</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.507087</td>\n      <td>0.547640</td>\n      <td>0.371761</td>\n      <td>0.419535</td>\n      <td>0.297047</td>\n      <td>0.299792</td>\n      <td>0.293479</td>\n      <td>0.331799</td>\n      <td>0.449199</td>\n      <td>0.274312</td>\n      <td>...</td>\n      <td>0.661869</td>\n      <td>0.670376</td>\n      <td>0.630576</td>\n      <td>0.611240</td>\n      <td>0.603915</td>\n      <td>0.626504</td>\n      <td>0.613985</td>\n      <td>0.574319</td>\n      <td>0.582854</td>\n      <td>0.569978</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.522483</td>\n      <td>0.587711</td>\n      <td>0.394713</td>\n      <td>0.448255</td>\n      <td>0.342469</td>\n      <td>0.340174</td>\n      <td>0.338564</td>\n      <td>0.387528</td>\n      <td>0.485602</td>\n      <td>0.253952</td>\n      <td>...</td>\n      <td>0.676937</td>\n      <td>0.682818</td>\n      <td>0.654571</td>\n      <td>0.621303</td>\n      <td>0.613924</td>\n      <td>0.623024</td>\n      <td>0.627792</td>\n      <td>0.581983</td>\n      <td>0.601986</td>\n      <td>0.577939</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>0.499756</td>\n      <td>0.628228</td>\n      <td>0.408485</td>\n      <td>0.419292</td>\n      <td>0.300994</td>\n      <td>0.352343</td>\n      <td>0.301100</td>\n      <td>0.399306</td>\n      <td>0.543331</td>\n      <td>0.244099</td>\n      <td>...</td>\n      <td>0.696058</td>\n      <td>0.740779</td>\n      <td>0.686946</td>\n      <td>0.626975</td>\n      <td>0.635154</td>\n      <td>0.694029</td>\n      <td>0.678234</td>\n      <td>0.583415</td>\n      <td>0.629157</td>\n      <td>0.575988</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.509286</td>\n      <td>0.643811</td>\n      <td>0.423590</td>\n      <td>0.427412</td>\n      <td>0.316853</td>\n      <td>0.355102</td>\n      <td>0.341047</td>\n      <td>0.416777</td>\n      <td>0.552431</td>\n      <td>0.256887</td>\n      <td>...</td>\n      <td>0.700032</td>\n      <td>0.753451</td>\n      <td>0.691202</td>\n      <td>0.629456</td>\n      <td>0.639614</td>\n      <td>0.698088</td>\n      <td>0.683637</td>\n      <td>0.590147</td>\n      <td>0.629844</td>\n      <td>0.580928</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 48 columns</p>\n</div>"
+      "text/plain": "target                                                       age            \\\ntweetLen                                                     250       500   \nNo. of Authors                                              50        50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.677175  0.726180   \n                                              2-3       0.762708  0.819234   \n                                              2-3-4     0.753421  0.832146   \n                                              2-3-4-5   0.738025  0.823241   \n                               dist_char_asis 2         0.774927  0.850846   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.345308  0.408281   \n                                              1-2       0.507087  0.547640   \n                                              1-2-3     0.522483  0.587711   \n                               word           1         0.499756  0.628228   \n                                              1-2       0.509286  0.643811   \n\ntarget                                                                      \\\ntweetLen                                                     250       100   \nNo. of Authors                                              150       50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.577151  0.598279   \n                                              2-3       0.668444  0.690742   \n                                              2-3-4     0.670665  0.703102   \n                                              2-3-4-5   0.684733  0.680441   \n                               dist_char_asis 2         0.699319  0.716069   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.286687  0.263936   \n                                              1-2       0.371761  0.419535   \n                                              1-2-3     0.394713  0.448255   \n                               word           1         0.408485  0.419292   \n                                              1-2       0.423590  0.427412   \n\ntarget                                                                      \\\ntweetLen                                                               500   \nNo. of Authors                                              150       1000   \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.488672  0.446504   \n                                              2-3       0.559526  0.519917   \n                                              2-3-4     0.574910  0.543726   \n                                              2-3-4-5   0.537784  0.545106   \n                               dist_char_asis 2         0.582840  0.556557   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.215523  0.229119   \n                                              1-2       0.297047  0.299792   \n                                              1-2-3     0.342469  0.340174   \n                               word           1         0.300994  0.352343   \n                                              1-2       0.316853  0.355102   \n\ntarget                                                                      \\\ntweetLen                                                     250       500   \nNo. of Authors                                              500       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.454316  0.513337   \n                                              2-3       0.523891  0.593752   \n                                              2-3-4     0.547140  0.615124   \n                                              2-3-4-5   0.545427  0.618594   \n                               dist_char_asis 2         0.560691  0.620310   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.219258  0.230052   \n                                              1-2       0.293479  0.331799   \n                                              1-2-3     0.338564  0.387528   \n                               word           1         0.301100  0.399306   \n                                              1-2       0.341047  0.416777   \n\ntarget                                                                      \\\ntweetLen                                                               100   \nNo. of Authors                                              150       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.635018  0.373908   \n                                              2-3       0.734175  0.442379   \n                                              2-3-4     0.762293  0.452307   \n                                              2-3-4-5   0.759033  0.462428   \n                               dist_char_asis 2         0.772480  0.467655   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.331568  0.234941   \n                                              1-2       0.449199  0.274312   \n                                              1-2-3     0.485602  0.253952   \n                               word           1         0.543331  0.244099   \n                                              1-2       0.552431  0.256887   \n\ntarget                                                  ...    gender  \\\ntweetLen                                                ...       250   \nNo. of Authors                                          ...      50     \nScore                                                   ...  f1-score   \nset                  modeltype featuretypes   subgrams  ...             \ndist-char-word-lemma cumulated dist_char      2         ...  0.784084   \n                                              2-3       ...  0.824736   \n                                              2-3-4     ...  0.830936   \n                                              2-3-4-5   ...  0.831464   \n                               dist_char_asis 2         ...  0.843374   \n...                                                     ...       ...   \nsingular             baseline  tag            1         ...  0.626742   \n                                              1-2       ...  0.661869   \n                                              1-2-3     ...  0.676937   \n                               word           1         ...  0.696058   \n                                              1-2       ...  0.700032   \n\ntarget                                                                      \\\ntweetLen                                                     500       250   \nNo. of Authors                                              150       150    \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.786840  0.749260   \n                                              2-3       0.839235  0.773833   \n                                              2-3-4     0.849448  0.785765   \n                                              2-3-4-5   0.858279  0.792721   \n                               dist_char_asis 2         0.862321  0.800185   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.569969  0.585281   \n                                              1-2       0.670376  0.630576   \n                                              1-2-3     0.682818  0.654571   \n                               word           1         0.740779  0.686946   \n                                              1-2       0.753451  0.691202   \n\ntarget                                                                      \\\ntweetLen                                                     100       250   \nNo. of Authors                                              150       500    \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.694655  0.673562   \n                                              2-3       0.732961  0.721850   \n                                              2-3-4     0.736983  0.730540   \n                                              2-3-4-5   0.734179  0.727924   \n                               dist_char_asis 2         0.745258  0.741822   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.573962  0.564753   \n                                              1-2       0.611240  0.603915   \n                                              1-2-3     0.621303  0.613924   \n                               word           1         0.626975  0.635154   \n                                              1-2       0.629456  0.639614   \n\ntarget                                                                      \\\ntweetLen                                                     500             \nNo. of Authors                                              500       1000   \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.705783  0.676926   \n                                              2-3       0.769621  0.731095   \n                                              2-3-4     0.785189  0.739204   \n                                              2-3-4-5   0.764951  0.742888   \n                               dist_char_asis 2         0.744117  0.749447   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.589206  0.577137   \n                                              1-2       0.626504  0.613985   \n                                              1-2-3     0.623024  0.627792   \n                               word           1         0.694029  0.678234   \n                                              1-2       0.698088  0.683637   \n\ntarget                                                                      \\\ntweetLen                                                     100       250   \nNo. of Authors                                              500       1000   \nScore                                                   f1-score  f1-score   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.627626  0.651106   \n                                              2-3       0.664470  0.691603   \n                                              2-3-4     0.671122  0.700129   \n                                              2-3-4-5   0.671008  0.699478   \n                               dist_char_asis 2         0.677461  0.708362   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.549581  0.564286   \n                                              1-2       0.574319  0.582854   \n                                              1-2-3     0.581983  0.601986   \n                               word           1         0.583415  0.629157   \n                                              1-2       0.590147  0.629844   \n\ntarget                                                            \ntweetLen                                                     100  \nNo. of Authors                                              1000  \nScore                                                   f1-score  \nset                  modeltype featuretypes   subgrams            \ndist-char-word-lemma cumulated dist_char      2         0.596627  \n                                              2-3       0.630147  \n                                              2-3-4     0.631162  \n                                              2-3-4-5   0.630545  \n                               dist_char_asis 2         0.633999  \n...                                                          ...  \nsingular             baseline  tag            1         0.549799  \n                                              1-2       0.569978  \n                                              1-2-3     0.577939  \n                               word           1         0.575988  \n                                              1-2       0.580928  \n\n[100 rows x 48 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"10\" halign=\"left\">age</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">100</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>...</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>No. of Authors</th>\n      <th>50</th>\n      <th>50</th>\n      <th>150</th>\n      <th>50</th>\n      <th>150</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>500</th>\n      <th>150</th>\n      <th>500</th>\n      <th>...</th>\n      <th>50</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>...</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n      <th>f1-score</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist_char</th>\n      <th>2</th>\n      <td>0.677175</td>\n      <td>0.726180</td>\n      <td>0.577151</td>\n      <td>0.598279</td>\n      <td>0.488672</td>\n      <td>0.446504</td>\n      <td>0.454316</td>\n      <td>0.513337</td>\n      <td>0.635018</td>\n      <td>0.373908</td>\n      <td>...</td>\n      <td>0.784084</td>\n      <td>0.786840</td>\n      <td>0.749260</td>\n      <td>0.694655</td>\n      <td>0.673562</td>\n      <td>0.705783</td>\n      <td>0.676926</td>\n      <td>0.627626</td>\n      <td>0.651106</td>\n      <td>0.596627</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.762708</td>\n      <td>0.819234</td>\n      <td>0.668444</td>\n      <td>0.690742</td>\n      <td>0.559526</td>\n      <td>0.519917</td>\n      <td>0.523891</td>\n      <td>0.593752</td>\n      <td>0.734175</td>\n      <td>0.442379</td>\n      <td>...</td>\n      <td>0.824736</td>\n      <td>0.839235</td>\n      <td>0.773833</td>\n      <td>0.732961</td>\n      <td>0.721850</td>\n      <td>0.769621</td>\n      <td>0.731095</td>\n      <td>0.664470</td>\n      <td>0.691603</td>\n      <td>0.630147</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.753421</td>\n      <td>0.832146</td>\n      <td>0.670665</td>\n      <td>0.703102</td>\n      <td>0.574910</td>\n      <td>0.543726</td>\n      <td>0.547140</td>\n      <td>0.615124</td>\n      <td>0.762293</td>\n      <td>0.452307</td>\n      <td>...</td>\n      <td>0.830936</td>\n      <td>0.849448</td>\n      <td>0.785765</td>\n      <td>0.736983</td>\n      <td>0.730540</td>\n      <td>0.785189</td>\n      <td>0.739204</td>\n      <td>0.671122</td>\n      <td>0.700129</td>\n      <td>0.631162</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.738025</td>\n      <td>0.823241</td>\n      <td>0.684733</td>\n      <td>0.680441</td>\n      <td>0.537784</td>\n      <td>0.545106</td>\n      <td>0.545427</td>\n      <td>0.618594</td>\n      <td>0.759033</td>\n      <td>0.462428</td>\n      <td>...</td>\n      <td>0.831464</td>\n      <td>0.858279</td>\n      <td>0.792721</td>\n      <td>0.734179</td>\n      <td>0.727924</td>\n      <td>0.764951</td>\n      <td>0.742888</td>\n      <td>0.671008</td>\n      <td>0.699478</td>\n      <td>0.630545</td>\n    </tr>\n    <tr>\n      <th>dist_char_asis</th>\n      <th>2</th>\n      <td>0.774927</td>\n      <td>0.850846</td>\n      <td>0.699319</td>\n      <td>0.716069</td>\n      <td>0.582840</td>\n      <td>0.556557</td>\n      <td>0.560691</td>\n      <td>0.620310</td>\n      <td>0.772480</td>\n      <td>0.467655</td>\n      <td>...</td>\n      <td>0.843374</td>\n      <td>0.862321</td>\n      <td>0.800185</td>\n      <td>0.745258</td>\n      <td>0.741822</td>\n      <td>0.744117</td>\n      <td>0.749447</td>\n      <td>0.677461</td>\n      <td>0.708362</td>\n      <td>0.633999</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.345308</td>\n      <td>0.408281</td>\n      <td>0.286687</td>\n      <td>0.263936</td>\n      <td>0.215523</td>\n      <td>0.229119</td>\n      <td>0.219258</td>\n      <td>0.230052</td>\n      <td>0.331568</td>\n      <td>0.234941</td>\n      <td>...</td>\n      <td>0.626742</td>\n      <td>0.569969</td>\n      <td>0.585281</td>\n      <td>0.573962</td>\n      <td>0.564753</td>\n      <td>0.589206</td>\n      <td>0.577137</td>\n      <td>0.549581</td>\n      <td>0.564286</td>\n      <td>0.549799</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.507087</td>\n      <td>0.547640</td>\n      <td>0.371761</td>\n      <td>0.419535</td>\n      <td>0.297047</td>\n      <td>0.299792</td>\n      <td>0.293479</td>\n      <td>0.331799</td>\n      <td>0.449199</td>\n      <td>0.274312</td>\n      <td>...</td>\n      <td>0.661869</td>\n      <td>0.670376</td>\n      <td>0.630576</td>\n      <td>0.611240</td>\n      <td>0.603915</td>\n      <td>0.626504</td>\n      <td>0.613985</td>\n      <td>0.574319</td>\n      <td>0.582854</td>\n      <td>0.569978</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.522483</td>\n      <td>0.587711</td>\n      <td>0.394713</td>\n      <td>0.448255</td>\n      <td>0.342469</td>\n      <td>0.340174</td>\n      <td>0.338564</td>\n      <td>0.387528</td>\n      <td>0.485602</td>\n      <td>0.253952</td>\n      <td>...</td>\n      <td>0.676937</td>\n      <td>0.682818</td>\n      <td>0.654571</td>\n      <td>0.621303</td>\n      <td>0.613924</td>\n      <td>0.623024</td>\n      <td>0.627792</td>\n      <td>0.581983</td>\n      <td>0.601986</td>\n      <td>0.577939</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>0.499756</td>\n      <td>0.628228</td>\n      <td>0.408485</td>\n      <td>0.419292</td>\n      <td>0.300994</td>\n      <td>0.352343</td>\n      <td>0.301100</td>\n      <td>0.399306</td>\n      <td>0.543331</td>\n      <td>0.244099</td>\n      <td>...</td>\n      <td>0.696058</td>\n      <td>0.740779</td>\n      <td>0.686946</td>\n      <td>0.626975</td>\n      <td>0.635154</td>\n      <td>0.694029</td>\n      <td>0.678234</td>\n      <td>0.583415</td>\n      <td>0.629157</td>\n      <td>0.575988</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.509286</td>\n      <td>0.643811</td>\n      <td>0.423590</td>\n      <td>0.427412</td>\n      <td>0.316853</td>\n      <td>0.355102</td>\n      <td>0.341047</td>\n      <td>0.416777</td>\n      <td>0.552431</td>\n      <td>0.256887</td>\n      <td>...</td>\n      <td>0.700032</td>\n      <td>0.753451</td>\n      <td>0.691202</td>\n      <td>0.629456</td>\n      <td>0.639614</td>\n      <td>0.698088</td>\n      <td>0.683637</td>\n      <td>0.590147</td>\n      <td>0.629844</td>\n      <td>0.580928</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 48 columns</p>\n</div>"
      },
-     "execution_count": 40,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -575,8 +900,8 @@
     "df.head()\n",
     "ranks = df['rank']\n",
     "df = df.drop(['rank', 'name'], axis=1)\n",
-    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['# Authors'], sub['Type']])\n",
-    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'], columns=['target','tweetLen', '# Authors', 'Score'], values='result')\n",
+    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['No. of Authors'], sub['Type']])\n",
+    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'], columns=['target','tweetLen', 'No. of Authors', 'Score'], values='result')\n",
     "piv"
    ],
    "metadata": {
@@ -588,13 +913,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 90,
    "outputs": [
     {
      "data": {
       "text/plain": "['dist-char-word-lemma', 'ordered-full', 'singular']"
      },
-     "execution_count": 41,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -620,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 91,
    "outputs": [],
    "source": [
     "savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/tables'))\n",
@@ -651,19 +976,21 @@
     "                        mod = 'feature-wise'\n",
     "                        l1 = 'baseline'\n",
     "                    if 'ordered-full' in st:\n",
-    "                        featureset = 'ordered, full Featureset'\n",
+    "                        featureset = 'ordered, full feature set'\n",
     "                        l2 = 'full'\n",
     "                    elif 'singular' in st:\n",
-    "                        featureset = 'individual  Featuretypes'\n",
+    "                        featureset = 'individual feature types'\n",
     "                        l2 = 'individual'\n",
     "                        tmp = tmp.drop(labels=('polarity'), level=0, axis=0)\n",
     "                    elif 'dist-char' in st:\n",
-    "                        featureset = r'reduced Featureset (DIST, CHAR, LEMMA, WORD)'\n",
+    "                        featureset = r'reduced feature set (DIST, CHAR, LEMMA, WORD)'\n",
     "                        l2='dwald'\n",
     "                        if key == 'acc':\n",
     "                            sc = 'Accuracy and F1-scores'\n",
+    "                        elif key == 'f1_dist':\n",
+    "                            sc = 'F1-scores & stability of feature importance'\n",
     "                        else:\n",
-    "                            sc = 'Average Distortion of Features'\n",
+    "                            sc = 'Stability of feature importance'\n",
     "                    tmp.index = tmp.index.set_levels([el.upper() for el in tmp.index.levels[0]], level=0)\n",
     "                    os.makedirs(os.path.join(savedir, target, 'latex'), exist_ok=True)\n",
     "                    os.makedirs(os.path.join(savedir, target, 'html'), exist_ok=True)\n",
@@ -675,18 +1002,32 @@
     "                        tmp.to_html(w, sparsify=True)\n",
     "                    with open(file_t, 'w') as w:\n",
     "                        table = tmp.to_latex(sparsify=True, multirow=True, multicolumn=True,\n",
-    "                                     caption='''{} for the prediction of {} on a minimal tweet length of {} chars using a {}\n",
-    "                                     model on the {}'''.format(sc, target, tweetLen, mod, featureset),\n",
-    "                                     label='tab:{}_{}_{}_{}_{}'.format(target, key, tweetLen, l1,l2),\n",
+    "                                        caption=''''{} for the prediction of {} on a minimal input instance\n",
+    "                                        length of {} characters using a {} model on the {}'''.format(sc, target, tweetLen, mod, featureset),\n",
+    "                                     label='tab:{}_{}_{}_{}_{}'.format(target[0].upper()+target[1:], key, tweetLen, l1,l2),\n",
     "                                     float_format=\"%.4f\")\n",
     "\n",
     "                        #table = re.sub(r'{table}', '{sidewaystable}', table)\n",
-    "                        #table = re.sub(r\"Average Spearman's Rho (ext.)\",\n",
-    "                        #               r\"\\\\thread{Average Spearman's \\\\\\\\ Rho (ext.)}\", table)\n",
-    "                        #table = re.sub(r\"Average Spearman's Rho (ext.)\",\n",
-    "                        #               r\"\\\\thread{Average Spearman's \\\\\\\\ Rho (red.)}\", table)\n",
+    "                        table = re.sub(r'begin{table}', 'begin{table}[!ht]', table)\n",
+    "                        #table = re.sub(r'\\\\begin\\{table}', '', table)\n",
+    "                        #table = re.sub(r'\\\\end\\{table}', '', table)\n",
+    "                        table = re.sub(\"Average Spearman's Rho \\(ext.\\)\",\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's $\\\\rho$ (ext.)}\", table)\n",
+    "                        table = re.sub(\"Average Spearman's Rho \\(red.\\)\",\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's  $\\\\rho$ (red.)}\", table)\n",
+    "                        table = re.sub('& target &', '& Target &', table)\n",
+    "                        table = re.sub('feature types &', 'Feature types &', table)\n",
+    "                        table = re.sub('featuretypes &', 'Feature types &', table)\n",
+    "                        table = re.sub('& subgrams &', '& N-gram ranges &', table)\n",
+    "                        table = re.sub('& tweetLen &', r'& Min. No. of Characters &', table)\n",
+    "                        table = re.sub('& f1-score', '& F1-score', table)\n",
+    "                        table = re.sub('& accuracy', '& Accuracy', table)\n",
+    "                        table = re.sub(r'\\{age}', '{Age}', table)\n",
+    "                        table = re.sub(r'\\{gender}', '{Gender}', table)\n",
+    "                        table = re.sub(pat,rep, table)\n",
     "                        table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
     "                        table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
+    "                        table = re.sub(r\"0\\.0000\", '--', table)\n",
     "                        w.write(table)"
    ],
    "metadata": {
@@ -698,23 +1039,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 92,
    "outputs": [
     {
      "data": {
-      "text/plain": "                                           featuretypes  # Authors subgrams  \\\n2395     dist_char_asis_pos_tag_dep_lemma_word_emoticon       1000        1   \n2396  dist_char_asis_pos_tag_dep_lemma_word_emoticon...       1000        1   \n2397  dist_char_asis_pos_tag_dep_lemma_word_emoticon...       1000        1   \n2398  dist_char_asis_pos_tag_dep_lemma_word_emoticon...       1000        1   \n2399  dist_char_asis_pos_tag_dep_lemma_word_emoticon...       1000        1   \n\n      modeltype        name  tweetLen  target             rank  accuracy  \\\n2395  cumulated  emoticon 1       100  gender      01234567901  0.668297   \n2396    stacked  polarity 1       100  gender    0123456791001  0.606557   \n2397  cumulated  polarity 1       100  gender    0123456791001  0.667819   \n2398    stacked       num 1       100  gender  012345679101101  0.603349   \n2399  cumulated       num 1       100  gender  012345679101101  0.652245   \n\n      f1-score           set  set_enum set_feat  \\\n2395  0.667832  ordered-full         3      all   \n2396  0.572559  ordered-full         3      all   \n2397  0.667251  ordered-full         3      all   \n2398  0.566807  ordered-full         3      all   \n2399  0.648281  ordered-full         3      all   \n\n      Average Spearman's Rho (ext.)  Average Spearman's Rho (red.)  \n2395                       0.044578                       0.034959  \n2396                      -0.011659                      -0.005499  \n2397                      -0.031285                      -0.032979  \n2398                      -0.028504                      -0.022564  \n2399                       0.000620                      -0.000666  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>featuretypes</th>\n      <th># Authors</th>\n      <th>subgrams</th>\n      <th>modeltype</th>\n      <th>name</th>\n      <th>tweetLen</th>\n      <th>target</th>\n      <th>rank</th>\n      <th>accuracy</th>\n      <th>f1-score</th>\n      <th>set</th>\n      <th>set_enum</th>\n      <th>set_feat</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2395</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>emoticon 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>01234567901</td>\n      <td>0.668297</td>\n      <td>0.667832</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>0.044578</td>\n      <td>0.034959</td>\n    </tr>\n    <tr>\n      <th>2396</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>stacked</td>\n      <td>polarity 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>0123456791001</td>\n      <td>0.606557</td>\n      <td>0.572559</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.011659</td>\n      <td>-0.005499</td>\n    </tr>\n    <tr>\n      <th>2397</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>polarity 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>0123456791001</td>\n      <td>0.667819</td>\n      <td>0.667251</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.031285</td>\n      <td>-0.032979</td>\n    </tr>\n    <tr>\n      <th>2398</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>stacked</td>\n      <td>num 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>012345679101101</td>\n      <td>0.603349</td>\n      <td>0.566807</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.028504</td>\n      <td>-0.022564</td>\n    </tr>\n    <tr>\n      <th>2399</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>num 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>012345679101101</td>\n      <td>0.652245</td>\n      <td>0.648281</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>0.000620</td>\n      <td>-0.000666</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                                           featuretypes  No. of Authors  \\\n2395     dist_char_asis_pos_tag_dep_lemma_word_emoticon            1000   \n2396  dist_char_asis_pos_tag_dep_lemma_word_emoticon...            1000   \n2397  dist_char_asis_pos_tag_dep_lemma_word_emoticon...            1000   \n2398  dist_char_asis_pos_tag_dep_lemma_word_emoticon...            1000   \n2399  dist_char_asis_pos_tag_dep_lemma_word_emoticon...            1000   \n\n     subgrams  modeltype        name  tweetLen  target             rank  \\\n2395        1  cumulated  emoticon 1       100  gender      01234567901   \n2396        1    stacked  polarity 1       100  gender    0123456791001   \n2397        1  cumulated  polarity 1       100  gender    0123456791001   \n2398        1    stacked       num 1       100  gender  012345679101101   \n2399        1  cumulated       num 1       100  gender  012345679101101   \n\n      accuracy  f1-score           set  set_enum set_feat  \\\n2395  0.668297  0.667832  ordered-full         3      all   \n2396  0.606557  0.572559  ordered-full         3      all   \n2397  0.667819  0.667251  ordered-full         3      all   \n2398  0.603349  0.566807  ordered-full         3      all   \n2399  0.652245  0.648281  ordered-full         3      all   \n\n      Average Spearman's Rho (ext.)  Average Spearman's Rho (red.)  \n2395                       0.044578                       0.034959  \n2396                      -0.011659                      -0.005499  \n2397                      -0.031285                      -0.032979  \n2398                      -0.028504                      -0.022564  \n2399                       0.000620                      -0.000666  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>featuretypes</th>\n      <th>No. of Authors</th>\n      <th>subgrams</th>\n      <th>modeltype</th>\n      <th>name</th>\n      <th>tweetLen</th>\n      <th>target</th>\n      <th>rank</th>\n      <th>accuracy</th>\n      <th>f1-score</th>\n      <th>set</th>\n      <th>set_enum</th>\n      <th>set_feat</th>\n      <th>Average Spearman's Rho (ext.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2395</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>emoticon 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>01234567901</td>\n      <td>0.668297</td>\n      <td>0.667832</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>0.044578</td>\n      <td>0.034959</td>\n    </tr>\n    <tr>\n      <th>2396</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>stacked</td>\n      <td>polarity 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>0123456791001</td>\n      <td>0.606557</td>\n      <td>0.572559</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.011659</td>\n      <td>-0.005499</td>\n    </tr>\n    <tr>\n      <th>2397</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>polarity 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>0123456791001</td>\n      <td>0.667819</td>\n      <td>0.667251</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.031285</td>\n      <td>-0.032979</td>\n    </tr>\n    <tr>\n      <th>2398</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>stacked</td>\n      <td>num 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>012345679101101</td>\n      <td>0.603349</td>\n      <td>0.566807</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>-0.028504</td>\n      <td>-0.022564</td>\n    </tr>\n    <tr>\n      <th>2399</th>\n      <td>dist_char_asis_pos_tag_dep_lemma_word_emoticon...</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>cumulated</td>\n      <td>num 1</td>\n      <td>100</td>\n      <td>gender</td>\n      <td>012345679101101</td>\n      <td>0.652245</td>\n      <td>0.648281</td>\n      <td>ordered-full</td>\n      <td>3</td>\n      <td>all</td>\n      <td>0.000620</td>\n      <td>-0.000666</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 43,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#make the combination table in which we only show f1 score and spearman extended\n",
-    "merger = ['tweetLen','target', 'set','set_enum',  'modeltype', 'featuretypes', 'subgrams', '# Authors']\n",
+    "merger = ['tweetLen','target', 'set','set_enum',  'modeltype', 'featuretypes', 'subgrams', 'No. of Authors']\n",
     "df = acc.merge(dist.loc[:, ['tweetLen','target', 'set','set_enum',  'modeltype',\n",
-    "                      'featuretypes', 'subgrams', '# Authors',\"Average Spearman's Rho (ext.)\",\n",
+    "                      'featuretypes', 'subgrams', 'No. of Authors',\"Average Spearman's Rho (ext.)\",\n",
     "                      \"Average Spearman's Rho (red.)\" ]], on=merger, how='left')\n",
     "df.loc[df[\"Average Spearman's Rho (ext.)\"].isna(),[\"Average Spearman's Rho (ext.)\",\"Average Spearman's Rho (red.)\"]] = 0\n",
     "df.tail()"
@@ -728,14 +1069,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 93,
    "outputs": [
     {
      "data": {
-      "text/plain": "target                                                       age            \\\ntweetLen                                                     250       500   \n# Authors                                                   50        50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.677175  0.726180   \n                                              2-3       0.762708  0.819234   \n                                              2-3-4     0.753421  0.832146   \n                                              2-3-4-5   0.738025  0.823241   \n                               dist_char_asis 2         0.774927  0.850846   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.345308  0.408281   \n                                              1-2       0.507087  0.547640   \n                                              1-2-3     0.522483  0.587711   \n                               word           1         0.499756  0.628228   \n                                              1-2       0.509286  0.643811   \n\ntarget                                                                      \\\ntweetLen                                                     250       100   \n# Authors                                                   150       50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.577151  0.598279   \n                                              2-3       0.668444  0.690742   \n                                              2-3-4     0.670665  0.703102   \n                                              2-3-4-5   0.684733  0.680441   \n                               dist_char_asis 2         0.699319  0.716069   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.286687  0.263936   \n                                              1-2       0.371761  0.419535   \n                                              1-2-3     0.394713  0.448255   \n                               word           1         0.408485  0.419292   \n                                              1-2       0.423590  0.427412   \n\ntarget                                                                      \\\ntweetLen                                                               500   \n# Authors                                                   150       1000   \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.488672  0.446504   \n                                              2-3       0.559526  0.519917   \n                                              2-3-4     0.574910  0.543726   \n                                              2-3-4-5   0.537784  0.545106   \n                               dist_char_asis 2         0.582840  0.556557   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.215523  0.229119   \n                                              1-2       0.297047  0.299792   \n                                              1-2-3     0.342469  0.340174   \n                               word           1         0.300994  0.352343   \n                                              1-2       0.316853  0.355102   \n\ntarget                                                                      \\\ntweetLen                                                     250       500   \n# Authors                                                   500       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.454316  0.513337   \n                                              2-3       0.523891  0.593752   \n                                              2-3-4     0.547140  0.615124   \n                                              2-3-4-5   0.545427  0.618594   \n                               dist_char_asis 2         0.560691  0.620310   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.219258  0.230052   \n                                              1-2       0.293479  0.331799   \n                                              1-2-3     0.338564  0.387528   \n                               word           1         0.301100  0.399306   \n                                              1-2       0.341047  0.416777   \n\ntarget                                                                      \\\ntweetLen                                                               100   \n# Authors                                                   150       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.635018  0.373908   \n                                              2-3       0.734175  0.442379   \n                                              2-3-4     0.762293  0.452307   \n                                              2-3-4-5   0.759033  0.462428   \n                               dist_char_asis 2         0.772480  0.467655   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.331568  0.234941   \n                                              1-2       0.449199  0.274312   \n                                              1-2-3     0.485602  0.253952   \n                               word           1         0.543331  0.244099   \n                                              1-2       0.552431  0.256887   \n\ntarget                                                  ...  \\\ntweetLen                                                ...   \n# Authors                                               ...   \nScore                                                   ...   \nset                  modeltype featuretypes   subgrams  ...   \ndist-char-word-lemma cumulated dist_char      2         ...   \n                                              2-3       ...   \n                                              2-3-4     ...   \n                                              2-3-4-5   ...   \n                               dist_char_asis 2         ...   \n...                                                     ...   \nsingular             baseline  tag            1         ...   \n                                              1-2       ...   \n                                              1-2-3     ...   \n                               word           1         ...   \n                                              1-2       ...   \n\ntarget                                                                        gender  \\\ntweetLen                                                                         250   \n# Authors                                                                       50     \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                                  0.0   \n                                              2-3                                0.0   \n                                              2-3-4                              0.0   \n                                              2-3-4-5                            0.0   \n                               dist_char_asis 2                                  0.0   \n...                                                                              ...   \nsingular             baseline  tag            1                                  0.0   \n                                              1-2                                0.0   \n                                              1-2-3                              0.0   \n                               word           1                                  0.0   \n                                              1-2                                0.0   \n\ntarget                                                                                \\\ntweetLen                                                                         500   \n# Authors                                                                       150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.026470   \n                                              2-3                          -0.036546   \n                                              2-3-4                         0.031449   \n                                              2-3-4-5                      -0.012079   \n                               dist_char_asis 2                            -0.021672   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.008557   \n                                              1-2                           0.132701   \n                                              1-2-3                         0.057714   \n                               word           1                             0.028155   \n                                              1-2                           0.025200   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \n# Authors                                                                       150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.025105   \n                                              2-3                           0.014135   \n                                              2-3-4                         0.034523   \n                                              2-3-4-5                       0.009622   \n                               dist_char_asis 2                             0.045824   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.027320   \n                                              1-2                           0.079936   \n                                              1-2-3                        -0.000031   \n                               word           1                            -0.016220   \n                                              1-2                           0.048501   \n\ntarget                                                                                \\\ntweetLen                                                                         100   \n# Authors                                                                       150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                            -0.028486   \n                                              2-3                           0.002831   \n                                              2-3-4                         0.025852   \n                                              2-3-4-5                       0.015653   \n                               dist_char_asis 2                             0.013339   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.029294   \n                                              1-2                           0.173908   \n                                              1-2-3                        -0.019950   \n                               word           1                             0.077121   \n                                              1-2                          -0.030074   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \n# Authors                                                                       500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.085429   \n                                              2-3                           0.035659   \n                                              2-3-4                         0.015212   \n                                              2-3-4-5                       0.028261   \n                               dist_char_asis 2                            -0.017280   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.203978   \n                                              1-2                          -0.104392   \n                                              1-2-3                         0.003267   \n                               word           1                             0.023986   \n                                              1-2                           0.013636   \n\ntarget                                                                                \\\ntweetLen                                                                         500   \n# Authors                                                                       500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.046563   \n                                              2-3                           0.027787   \n                                              2-3-4                         0.026084   \n                                              2-3-4-5                       0.036459   \n                               dist_char_asis 2                             0.007673   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.146045   \n                                              1-2                          -0.027975   \n                                              1-2-3                        -0.014083   \n                               word           1                            -0.011463   \n                                              1-2                           0.017931   \n\ntarget                                                                                \\\ntweetLen                                                                               \n# Authors                                                                       1000   \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.088929   \n                                              2-3                           0.041862   \n                                              2-3-4                         0.036172   \n                                              2-3-4-5                       0.023196   \n                               dist_char_asis 2                             0.014127   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.004342   \n                                              1-2                           0.109083   \n                                              1-2-3                         0.121241   \n                               word           1                             0.052303   \n                                              1-2                          -0.008001   \n\ntarget                                                                                \\\ntweetLen                                                                         100   \n# Authors                                                                       500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                            -0.062807   \n                                              2-3                           0.046394   \n                                              2-3-4                         0.005221   \n                                              2-3-4-5                       0.013836   \n                               dist_char_asis 2                             0.005983   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.051619   \n                                              1-2                          -0.123443   \n                                              1-2-3                         0.019267   \n                               word           1                             0.032958   \n                                              1-2                           0.005569   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \n# Authors                                                                       1000   \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.060031   \n                                              2-3                           0.011727   \n                                              2-3-4                         0.034497   \n                                              2-3-4-5                       0.036985   \n                               dist_char_asis 2                            -0.014717   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.043825   \n                                              1-2                          -0.008411   \n                                              1-2-3                        -0.028449   \n                               word           1                             0.025321   \n                                              1-2                          -0.007934   \n\ntarget                                                                                \ntweetLen                                                                         100  \n# Authors                                                                       1000  \nScore                                                  Average Spearman's Rho (red.)  \nset                  modeltype featuretypes   subgrams                                \ndist-char-word-lemma cumulated dist_char      2                             0.049725  \n                                              2-3                           0.055324  \n                                              2-3-4                         0.023822  \n                                              2-3-4-5                       0.002119  \n                               dist_char_asis 2                             0.023177  \n...                                                                              ...  \nsingular             baseline  tag            1                            -0.096762  \n                                              1-2                           0.040120  \n                                              1-2-3                         0.040217  \n                               word           1                             0.016749  \n                                              1-2                           0.028786  \n\n[100 rows x 96 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"10\" halign=\"left\">age</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">100</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>...</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th># Authors</th>\n      <th>50</th>\n      <th>50</th>\n      <th>150</th>\n      <th>50</th>\n      <th>150</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>500</th>\n      <th>150</th>\n      <th>500</th>\n      <th>...</th>\n      <th>50</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>...</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist_char</th>\n      <th>2</th>\n      <td>0.677175</td>\n      <td>0.726180</td>\n      <td>0.577151</td>\n      <td>0.598279</td>\n      <td>0.488672</td>\n      <td>0.446504</td>\n      <td>0.454316</td>\n      <td>0.513337</td>\n      <td>0.635018</td>\n      <td>0.373908</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.026470</td>\n      <td>0.025105</td>\n      <td>-0.028486</td>\n      <td>0.085429</td>\n      <td>0.046563</td>\n      <td>0.088929</td>\n      <td>-0.062807</td>\n      <td>0.060031</td>\n      <td>0.049725</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.762708</td>\n      <td>0.819234</td>\n      <td>0.668444</td>\n      <td>0.690742</td>\n      <td>0.559526</td>\n      <td>0.519917</td>\n      <td>0.523891</td>\n      <td>0.593752</td>\n      <td>0.734175</td>\n      <td>0.442379</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.036546</td>\n      <td>0.014135</td>\n      <td>0.002831</td>\n      <td>0.035659</td>\n      <td>0.027787</td>\n      <td>0.041862</td>\n      <td>0.046394</td>\n      <td>0.011727</td>\n      <td>0.055324</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.753421</td>\n      <td>0.832146</td>\n      <td>0.670665</td>\n      <td>0.703102</td>\n      <td>0.574910</td>\n      <td>0.543726</td>\n      <td>0.547140</td>\n      <td>0.615124</td>\n      <td>0.762293</td>\n      <td>0.452307</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.031449</td>\n      <td>0.034523</td>\n      <td>0.025852</td>\n      <td>0.015212</td>\n      <td>0.026084</td>\n      <td>0.036172</td>\n      <td>0.005221</td>\n      <td>0.034497</td>\n      <td>0.023822</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.738025</td>\n      <td>0.823241</td>\n      <td>0.684733</td>\n      <td>0.680441</td>\n      <td>0.537784</td>\n      <td>0.545106</td>\n      <td>0.545427</td>\n      <td>0.618594</td>\n      <td>0.759033</td>\n      <td>0.462428</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.012079</td>\n      <td>0.009622</td>\n      <td>0.015653</td>\n      <td>0.028261</td>\n      <td>0.036459</td>\n      <td>0.023196</td>\n      <td>0.013836</td>\n      <td>0.036985</td>\n      <td>0.002119</td>\n    </tr>\n    <tr>\n      <th>dist_char_asis</th>\n      <th>2</th>\n      <td>0.774927</td>\n      <td>0.850846</td>\n      <td>0.699319</td>\n      <td>0.716069</td>\n      <td>0.582840</td>\n      <td>0.556557</td>\n      <td>0.560691</td>\n      <td>0.620310</td>\n      <td>0.772480</td>\n      <td>0.467655</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.021672</td>\n      <td>0.045824</td>\n      <td>0.013339</td>\n      <td>-0.017280</td>\n      <td>0.007673</td>\n      <td>0.014127</td>\n      <td>0.005983</td>\n      <td>-0.014717</td>\n      <td>0.023177</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.345308</td>\n      <td>0.408281</td>\n      <td>0.286687</td>\n      <td>0.263936</td>\n      <td>0.215523</td>\n      <td>0.229119</td>\n      <td>0.219258</td>\n      <td>0.230052</td>\n      <td>0.331568</td>\n      <td>0.234941</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.008557</td>\n      <td>0.027320</td>\n      <td>-0.029294</td>\n      <td>0.203978</td>\n      <td>-0.146045</td>\n      <td>0.004342</td>\n      <td>0.051619</td>\n      <td>0.043825</td>\n      <td>-0.096762</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.507087</td>\n      <td>0.547640</td>\n      <td>0.371761</td>\n      <td>0.419535</td>\n      <td>0.297047</td>\n      <td>0.299792</td>\n      <td>0.293479</td>\n      <td>0.331799</td>\n      <td>0.449199</td>\n      <td>0.274312</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.132701</td>\n      <td>0.079936</td>\n      <td>0.173908</td>\n      <td>-0.104392</td>\n      <td>-0.027975</td>\n      <td>0.109083</td>\n      <td>-0.123443</td>\n      <td>-0.008411</td>\n      <td>0.040120</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.522483</td>\n      <td>0.587711</td>\n      <td>0.394713</td>\n      <td>0.448255</td>\n      <td>0.342469</td>\n      <td>0.340174</td>\n      <td>0.338564</td>\n      <td>0.387528</td>\n      <td>0.485602</td>\n      <td>0.253952</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.057714</td>\n      <td>-0.000031</td>\n      <td>-0.019950</td>\n      <td>0.003267</td>\n      <td>-0.014083</td>\n      <td>0.121241</td>\n      <td>0.019267</td>\n      <td>-0.028449</td>\n      <td>0.040217</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>0.499756</td>\n      <td>0.628228</td>\n      <td>0.408485</td>\n      <td>0.419292</td>\n      <td>0.300994</td>\n      <td>0.352343</td>\n      <td>0.301100</td>\n      <td>0.399306</td>\n      <td>0.543331</td>\n      <td>0.244099</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.028155</td>\n      <td>-0.016220</td>\n      <td>0.077121</td>\n      <td>0.023986</td>\n      <td>-0.011463</td>\n      <td>0.052303</td>\n      <td>0.032958</td>\n      <td>0.025321</td>\n      <td>0.016749</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.509286</td>\n      <td>0.643811</td>\n      <td>0.423590</td>\n      <td>0.427412</td>\n      <td>0.316853</td>\n      <td>0.355102</td>\n      <td>0.341047</td>\n      <td>0.416777</td>\n      <td>0.552431</td>\n      <td>0.256887</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.025200</td>\n      <td>0.048501</td>\n      <td>-0.030074</td>\n      <td>0.013636</td>\n      <td>0.017931</td>\n      <td>-0.008001</td>\n      <td>0.005569</td>\n      <td>-0.007934</td>\n      <td>0.028786</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 96 columns</p>\n</div>"
+      "text/plain": "target                                                       age            \\\ntweetLen                                                     250       500   \nNo. of Authors                                              50        50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.677175  0.726180   \n                                              2-3       0.762708  0.819234   \n                                              2-3-4     0.753421  0.832146   \n                                              2-3-4-5   0.738025  0.823241   \n                               dist_char_asis 2         0.774927  0.850846   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.345308  0.408281   \n                                              1-2       0.507087  0.547640   \n                                              1-2-3     0.522483  0.587711   \n                               word           1         0.499756  0.628228   \n                                              1-2       0.509286  0.643811   \n\ntarget                                                                      \\\ntweetLen                                                     250       100   \nNo. of Authors                                              150       50     \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.577151  0.598279   \n                                              2-3       0.668444  0.690742   \n                                              2-3-4     0.670665  0.703102   \n                                              2-3-4-5   0.684733  0.680441   \n                               dist_char_asis 2         0.699319  0.716069   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.286687  0.263936   \n                                              1-2       0.371761  0.419535   \n                                              1-2-3     0.394713  0.448255   \n                               word           1         0.408485  0.419292   \n                                              1-2       0.423590  0.427412   \n\ntarget                                                                      \\\ntweetLen                                                               500   \nNo. of Authors                                              150       1000   \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.488672  0.446504   \n                                              2-3       0.559526  0.519917   \n                                              2-3-4     0.574910  0.543726   \n                                              2-3-4-5   0.537784  0.545106   \n                               dist_char_asis 2         0.582840  0.556557   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.215523  0.229119   \n                                              1-2       0.297047  0.299792   \n                                              1-2-3     0.342469  0.340174   \n                               word           1         0.300994  0.352343   \n                                              1-2       0.316853  0.355102   \n\ntarget                                                                      \\\ntweetLen                                                     250       500   \nNo. of Authors                                              500       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.454316  0.513337   \n                                              2-3       0.523891  0.593752   \n                                              2-3-4     0.547140  0.615124   \n                                              2-3-4-5   0.545427  0.618594   \n                               dist_char_asis 2         0.560691  0.620310   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.219258  0.230052   \n                                              1-2       0.293479  0.331799   \n                                              1-2-3     0.338564  0.387528   \n                               word           1         0.301100  0.399306   \n                                              1-2       0.341047  0.416777   \n\ntarget                                                                      \\\ntweetLen                                                               100   \nNo. of Authors                                              150       500    \nScore                                                   accuracy  accuracy   \nset                  modeltype featuretypes   subgrams                       \ndist-char-word-lemma cumulated dist_char      2         0.635018  0.373908   \n                                              2-3       0.734175  0.442379   \n                                              2-3-4     0.762293  0.452307   \n                                              2-3-4-5   0.759033  0.462428   \n                               dist_char_asis 2         0.772480  0.467655   \n...                                                          ...       ...   \nsingular             baseline  tag            1         0.331568  0.234941   \n                                              1-2       0.449199  0.274312   \n                                              1-2-3     0.485602  0.253952   \n                               word           1         0.543331  0.244099   \n                                              1-2       0.552431  0.256887   \n\ntarget                                                  ...  \\\ntweetLen                                                ...   \nNo. of Authors                                          ...   \nScore                                                   ...   \nset                  modeltype featuretypes   subgrams  ...   \ndist-char-word-lemma cumulated dist_char      2         ...   \n                                              2-3       ...   \n                                              2-3-4     ...   \n                                              2-3-4-5   ...   \n                               dist_char_asis 2         ...   \n...                                                     ...   \nsingular             baseline  tag            1         ...   \n                                              1-2       ...   \n                                              1-2-3     ...   \n                               word           1         ...   \n                                              1-2       ...   \n\ntarget                                                                        gender  \\\ntweetLen                                                                         250   \nNo. of Authors                                                                  50     \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                                  0.0   \n                                              2-3                                0.0   \n                                              2-3-4                              0.0   \n                                              2-3-4-5                            0.0   \n                               dist_char_asis 2                                  0.0   \n...                                                                              ...   \nsingular             baseline  tag            1                                  0.0   \n                                              1-2                                0.0   \n                                              1-2-3                              0.0   \n                               word           1                                  0.0   \n                                              1-2                                0.0   \n\ntarget                                                                                \\\ntweetLen                                                                         500   \nNo. of Authors                                                                  150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.026470   \n                                              2-3                          -0.036546   \n                                              2-3-4                         0.031449   \n                                              2-3-4-5                      -0.012079   \n                               dist_char_asis 2                            -0.021672   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.008557   \n                                              1-2                           0.132701   \n                                              1-2-3                         0.057714   \n                               word           1                             0.028155   \n                                              1-2                           0.025200   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \nNo. of Authors                                                                  150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.025105   \n                                              2-3                           0.014135   \n                                              2-3-4                         0.034523   \n                                              2-3-4-5                       0.009622   \n                               dist_char_asis 2                             0.045824   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.027320   \n                                              1-2                           0.079936   \n                                              1-2-3                        -0.000031   \n                               word           1                            -0.016220   \n                                              1-2                           0.048501   \n\ntarget                                                                                \\\ntweetLen                                                                         100   \nNo. of Authors                                                                  150    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                            -0.028486   \n                                              2-3                           0.002831   \n                                              2-3-4                         0.025852   \n                                              2-3-4-5                       0.015653   \n                               dist_char_asis 2                             0.013339   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.029294   \n                                              1-2                           0.173908   \n                                              1-2-3                        -0.019950   \n                               word           1                             0.077121   \n                                              1-2                          -0.030074   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \nNo. of Authors                                                                  500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.085429   \n                                              2-3                           0.035659   \n                                              2-3-4                         0.015212   \n                                              2-3-4-5                       0.028261   \n                               dist_char_asis 2                            -0.017280   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.203978   \n                                              1-2                          -0.104392   \n                                              1-2-3                         0.003267   \n                               word           1                             0.023986   \n                                              1-2                           0.013636   \n\ntarget                                                                                \\\ntweetLen                                                                         500   \nNo. of Authors                                                                  500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.046563   \n                                              2-3                           0.027787   \n                                              2-3-4                         0.026084   \n                                              2-3-4-5                       0.036459   \n                               dist_char_asis 2                             0.007673   \n...                                                                              ...   \nsingular             baseline  tag            1                            -0.146045   \n                                              1-2                          -0.027975   \n                                              1-2-3                        -0.014083   \n                               word           1                            -0.011463   \n                                              1-2                           0.017931   \n\ntarget                                                                                \\\ntweetLen                                                                               \nNo. of Authors                                                                  1000   \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.088929   \n                                              2-3                           0.041862   \n                                              2-3-4                         0.036172   \n                                              2-3-4-5                       0.023196   \n                               dist_char_asis 2                             0.014127   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.004342   \n                                              1-2                           0.109083   \n                                              1-2-3                         0.121241   \n                               word           1                             0.052303   \n                                              1-2                          -0.008001   \n\ntarget                                                                                \\\ntweetLen                                                                         100   \nNo. of Authors                                                                  500    \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                            -0.062807   \n                                              2-3                           0.046394   \n                                              2-3-4                         0.005221   \n                                              2-3-4-5                       0.013836   \n                               dist_char_asis 2                             0.005983   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.051619   \n                                              1-2                          -0.123443   \n                                              1-2-3                         0.019267   \n                               word           1                             0.032958   \n                                              1-2                           0.005569   \n\ntarget                                                                                \\\ntweetLen                                                                         250   \nNo. of Authors                                                                  1000   \nScore                                                  Average Spearman's Rho (red.)   \nset                  modeltype featuretypes   subgrams                                 \ndist-char-word-lemma cumulated dist_char      2                             0.060031   \n                                              2-3                           0.011727   \n                                              2-3-4                         0.034497   \n                                              2-3-4-5                       0.036985   \n                               dist_char_asis 2                            -0.014717   \n...                                                                              ...   \nsingular             baseline  tag            1                             0.043825   \n                                              1-2                          -0.008411   \n                                              1-2-3                        -0.028449   \n                               word           1                             0.025321   \n                                              1-2                          -0.007934   \n\ntarget                                                                                \ntweetLen                                                                         100  \nNo. of Authors                                                                  1000  \nScore                                                  Average Spearman's Rho (red.)  \nset                  modeltype featuretypes   subgrams                                \ndist-char-word-lemma cumulated dist_char      2                             0.049725  \n                                              2-3                           0.055324  \n                                              2-3-4                         0.023822  \n                                              2-3-4-5                       0.002119  \n                               dist_char_asis 2                             0.023177  \n...                                                                              ...  \nsingular             baseline  tag            1                            -0.096762  \n                                              1-2                           0.040120  \n                                              1-2-3                         0.040217  \n                               word           1                             0.016749  \n                                              1-2                           0.028786  \n\n[100 rows x 96 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>target</th>\n      <th colspan=\"10\" halign=\"left\">age</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">gender</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>tweetLen</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">100</th>\n      <th>500</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>...</th>\n      <th>250</th>\n      <th>500</th>\n      <th>250</th>\n      <th>100</th>\n      <th>250</th>\n      <th colspan=\"2\" halign=\"left\">500</th>\n      <th>100</th>\n      <th>250</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>No. of Authors</th>\n      <th>50</th>\n      <th>50</th>\n      <th>150</th>\n      <th>50</th>\n      <th>150</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>500</th>\n      <th>150</th>\n      <th>500</th>\n      <th>...</th>\n      <th>50</th>\n      <th>150</th>\n      <th>150</th>\n      <th>150</th>\n      <th>500</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>500</th>\n      <th>1000</th>\n      <th>1000</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>Score</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>accuracy</th>\n      <th>...</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n      <th>Average Spearman's Rho (red.)</th>\n    </tr>\n    <tr>\n      <th>set</th>\n      <th>modeltype</th>\n      <th>featuretypes</th>\n      <th>subgrams</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">dist-char-word-lemma</th>\n      <th rowspan=\"5\" valign=\"top\">cumulated</th>\n      <th rowspan=\"4\" valign=\"top\">dist_char</th>\n      <th>2</th>\n      <td>0.677175</td>\n      <td>0.726180</td>\n      <td>0.577151</td>\n      <td>0.598279</td>\n      <td>0.488672</td>\n      <td>0.446504</td>\n      <td>0.454316</td>\n      <td>0.513337</td>\n      <td>0.635018</td>\n      <td>0.373908</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.026470</td>\n      <td>0.025105</td>\n      <td>-0.028486</td>\n      <td>0.085429</td>\n      <td>0.046563</td>\n      <td>0.088929</td>\n      <td>-0.062807</td>\n      <td>0.060031</td>\n      <td>0.049725</td>\n    </tr>\n    <tr>\n      <th>2-3</th>\n      <td>0.762708</td>\n      <td>0.819234</td>\n      <td>0.668444</td>\n      <td>0.690742</td>\n      <td>0.559526</td>\n      <td>0.519917</td>\n      <td>0.523891</td>\n      <td>0.593752</td>\n      <td>0.734175</td>\n      <td>0.442379</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.036546</td>\n      <td>0.014135</td>\n      <td>0.002831</td>\n      <td>0.035659</td>\n      <td>0.027787</td>\n      <td>0.041862</td>\n      <td>0.046394</td>\n      <td>0.011727</td>\n      <td>0.055324</td>\n    </tr>\n    <tr>\n      <th>2-3-4</th>\n      <td>0.753421</td>\n      <td>0.832146</td>\n      <td>0.670665</td>\n      <td>0.703102</td>\n      <td>0.574910</td>\n      <td>0.543726</td>\n      <td>0.547140</td>\n      <td>0.615124</td>\n      <td>0.762293</td>\n      <td>0.452307</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.031449</td>\n      <td>0.034523</td>\n      <td>0.025852</td>\n      <td>0.015212</td>\n      <td>0.026084</td>\n      <td>0.036172</td>\n      <td>0.005221</td>\n      <td>0.034497</td>\n      <td>0.023822</td>\n    </tr>\n    <tr>\n      <th>2-3-4-5</th>\n      <td>0.738025</td>\n      <td>0.823241</td>\n      <td>0.684733</td>\n      <td>0.680441</td>\n      <td>0.537784</td>\n      <td>0.545106</td>\n      <td>0.545427</td>\n      <td>0.618594</td>\n      <td>0.759033</td>\n      <td>0.462428</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.012079</td>\n      <td>0.009622</td>\n      <td>0.015653</td>\n      <td>0.028261</td>\n      <td>0.036459</td>\n      <td>0.023196</td>\n      <td>0.013836</td>\n      <td>0.036985</td>\n      <td>0.002119</td>\n    </tr>\n    <tr>\n      <th>dist_char_asis</th>\n      <th>2</th>\n      <td>0.774927</td>\n      <td>0.850846</td>\n      <td>0.699319</td>\n      <td>0.716069</td>\n      <td>0.582840</td>\n      <td>0.556557</td>\n      <td>0.560691</td>\n      <td>0.620310</td>\n      <td>0.772480</td>\n      <td>0.467655</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.021672</td>\n      <td>0.045824</td>\n      <td>0.013339</td>\n      <td>-0.017280</td>\n      <td>0.007673</td>\n      <td>0.014127</td>\n      <td>0.005983</td>\n      <td>-0.014717</td>\n      <td>0.023177</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">singular</th>\n      <th rowspan=\"5\" valign=\"top\">baseline</th>\n      <th rowspan=\"3\" valign=\"top\">tag</th>\n      <th>1</th>\n      <td>0.345308</td>\n      <td>0.408281</td>\n      <td>0.286687</td>\n      <td>0.263936</td>\n      <td>0.215523</td>\n      <td>0.229119</td>\n      <td>0.219258</td>\n      <td>0.230052</td>\n      <td>0.331568</td>\n      <td>0.234941</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>-0.008557</td>\n      <td>0.027320</td>\n      <td>-0.029294</td>\n      <td>0.203978</td>\n      <td>-0.146045</td>\n      <td>0.004342</td>\n      <td>0.051619</td>\n      <td>0.043825</td>\n      <td>-0.096762</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.507087</td>\n      <td>0.547640</td>\n      <td>0.371761</td>\n      <td>0.419535</td>\n      <td>0.297047</td>\n      <td>0.299792</td>\n      <td>0.293479</td>\n      <td>0.331799</td>\n      <td>0.449199</td>\n      <td>0.274312</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.132701</td>\n      <td>0.079936</td>\n      <td>0.173908</td>\n      <td>-0.104392</td>\n      <td>-0.027975</td>\n      <td>0.109083</td>\n      <td>-0.123443</td>\n      <td>-0.008411</td>\n      <td>0.040120</td>\n    </tr>\n    <tr>\n      <th>1-2-3</th>\n      <td>0.522483</td>\n      <td>0.587711</td>\n      <td>0.394713</td>\n      <td>0.448255</td>\n      <td>0.342469</td>\n      <td>0.340174</td>\n      <td>0.338564</td>\n      <td>0.387528</td>\n      <td>0.485602</td>\n      <td>0.253952</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.057714</td>\n      <td>-0.000031</td>\n      <td>-0.019950</td>\n      <td>0.003267</td>\n      <td>-0.014083</td>\n      <td>0.121241</td>\n      <td>0.019267</td>\n      <td>-0.028449</td>\n      <td>0.040217</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">word</th>\n      <th>1</th>\n      <td>0.499756</td>\n      <td>0.628228</td>\n      <td>0.408485</td>\n      <td>0.419292</td>\n      <td>0.300994</td>\n      <td>0.352343</td>\n      <td>0.301100</td>\n      <td>0.399306</td>\n      <td>0.543331</td>\n      <td>0.244099</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.028155</td>\n      <td>-0.016220</td>\n      <td>0.077121</td>\n      <td>0.023986</td>\n      <td>-0.011463</td>\n      <td>0.052303</td>\n      <td>0.032958</td>\n      <td>0.025321</td>\n      <td>0.016749</td>\n    </tr>\n    <tr>\n      <th>1-2</th>\n      <td>0.509286</td>\n      <td>0.643811</td>\n      <td>0.423590</td>\n      <td>0.427412</td>\n      <td>0.316853</td>\n      <td>0.355102</td>\n      <td>0.341047</td>\n      <td>0.416777</td>\n      <td>0.552431</td>\n      <td>0.256887</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.025200</td>\n      <td>0.048501</td>\n      <td>-0.030074</td>\n      <td>0.013636</td>\n      <td>0.017931</td>\n      <td>-0.008001</td>\n      <td>0.005569</td>\n      <td>-0.007934</td>\n      <td>0.028786</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 96 columns</p>\n</div>"
      },
-     "execution_count": 44,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,8 +1089,8 @@
     "df.head()\n",
     "ranks = df['rank']\n",
     "df = df.drop(['rank', 'name'], axis=1)\n",
-    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['# Authors'], sub['Type']])\n",
-    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'], columns=['target','tweetLen', '# Authors', 'Score'], values='result')\n",
+    "#sub.columns = pd.MultiIndex.from_product([sub['tweetLen'], sub['No. of Authors'], sub['Type']])\n",
+    "piv = df.pivot(index=['set','modeltype','featuretypes', 'subgrams'], columns=['target','tweetLen', 'No. of Authors', 'Score'], values='result')\n",
     "piv"
    ],
    "metadata": {
@@ -761,13 +1102,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 94,
    "outputs": [
     {
      "data": {
       "text/plain": "['dist-char-word-lemma', 'ordered-full', 'singular']"
      },
-     "execution_count": 45,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -791,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 95,
    "outputs": [],
    "source": [
     "savedir = os.path.join(*ml_utils.split_path_unix_win('../../tex/tables'))\n",
@@ -811,60 +1152,69 @@
     "                    tmp = tmp.reindex(order2, level=0)\n",
     "                #print(tmp.head())\n",
     "                #make text parts for captions modeltype\n",
-    "                if 'stacked' in modeltype:\n",
-    "                    mod = 'stacked'\n",
-    "                    l1 = 'dynAA'\n",
-    "                elif 'cumulated' in modeltype:\n",
-    "                    mod = 'cumulated'\n",
-    "                    l1 = 'direct'\n",
-    "                elif 'baseline' in modeltype:\n",
-    "                    mod = 'feature-wise'\n",
-    "                    l1 = 'baseline'\n",
-    "                if 'ordered-full' in st:\n",
-    "                    featureset = 'ordered, full Featureset'\n",
-    "                    l2 = 'full'\n",
-    "                elif 'singular' in st:\n",
-    "                    featureset = 'individual  Featuretypes'\n",
-    "                    l2 = 'individual'\n",
-    "                    tmp = tmp.drop(labels=('polarity'), level=0, axis=0)\n",
-    "                elif 'dist-char' in st:\n",
-    "                    featureset = r'reduced Featureset (DIST, CHAR, LEMMA, WORD)'\n",
-    "                    l2='dwald'\n",
-    "                    if key == 'acc':\n",
-    "                        sc = 'Accuracy and F1-scores'\n",
-    "                    elif key == 'f1_dist':\n",
-    "                        sc = 'F1-Score and Average Distortion of Features'\n",
-    "                    else:\n",
-    "                        sc = 'Average Distortion of Features'\n",
-    "                tmp.index = tmp.index.set_levels([el.upper() for el in tmp.index.levels[0]], level=0)\n",
-    "                os.makedirs(os.path.join(savedir, target, 'latex'), exist_ok=True)\n",
-    "                os.makedirs(os.path.join(savedir, target, 'html'), exist_ok=True)\n",
-    "                file_h = os.path.join(savedir, target, 'html', 'results_{}_{}_{}_{}_{}.html'.format(target, l2, l1,\n",
-    "                                                                                        tweetLen, key))\n",
-    "                file_t = os.path.join(savedir, target, 'latex', 'results_{}_{}_{}_{}_{}.tex'.format(target, l2, l1,\n",
-    "                                                                                        tweetLen, key))\n",
-    "                tmp.index = tmp.index.set_levels([el.upper() for el in tmp.index.levels[0]], level=0)\n",
-    "                #print(tmp.head())\n",
-    "                with open(file_h, 'w') as w:\n",
-    "                    tmp.to_html(w, sparsify=True)\n",
-    "                with open(file_t, 'w') as w:\n",
-    "                    table = tmp.to_latex(sparsify=True, multirow=True, multicolumn=True,\n",
-    "                                 caption='''{} for the Prediction of {} on a Minimal Tweet Length of {} characters using a {}\n",
-    "                                 model on the {}'''.format(sc, target[0].upper()+target[1:], tweetLen, mod, featureset),\n",
-    "                                 label='tab:{}_{}_{}_{}_{}'.format(target, key, tweetLen, l1,l2),\n",
-    "                                 float_format=\"%.4f\")\n",
-    "\n",
-    "                    #table = re.sub(r'{table}', '{sidewaystable}', table)\n",
-    "                    table = re.sub(\"Average Spearman's Rho \\(ext.\\)\",\n",
-    "                                   r\"\\\\thead{Average Spearman's \\\\\\\\ Rho (ext.)}\", table)\n",
-    "                    table = re.sub(\"Average Spearman's Rho \\(red.\\)\",\n",
-    "                                   r\"\\\\thead{Average Spearman's \\\\\\\\ Rho (red.)}\", table)\n",
+    "                    if 'stacked' in modeltype:\n",
+    "                        mod = 'stacked'\n",
+    "                        l1 = 'dynAA'\n",
+    "                    elif 'cumulated' in modeltype:\n",
+    "                        mod = 'cumulated'\n",
+    "                        l1 = 'direct'\n",
+    "                    elif 'baseline' in modeltype:\n",
+    "                        mod = 'feature-wise'\n",
+    "                        l1 = 'baseline'\n",
+    "                    if 'ordered-full' in st:\n",
+    "                        featureset = 'ordered, full feature set'\n",
+    "                        l2 = 'full'\n",
+    "                    elif 'singular' in st:\n",
+    "                        featureset = 'individual feature types'\n",
+    "                        l2 = 'individual'\n",
+    "                        tmp = tmp.drop(labels=('polarity'), level=0, axis=0)\n",
+    "                    elif 'dist-char' in st:\n",
+    "                        featureset = r'reduced feature set (DIST, CHAR, LEMMA, WORD)'\n",
+    "                        l2='dwald'\n",
+    "                        if key == 'acc':\n",
+    "                            sc = 'Accuracy and F1-scores'\n",
+    "                        elif key == 'f1_dist':\n",
+    "                            sc = 'F1-scores & stability of feature importance'\n",
+    "                        else:\n",
+    "                            sc = 'Stability of feature importance'\n",
+    "                    tmp.index = tmp.index.set_levels([el.upper() for el in tmp.index.levels[0]], level=0)\n",
+    "                    os.makedirs(os.path.join(savedir, target, 'latex'), exist_ok=True)\n",
+    "                    os.makedirs(os.path.join(savedir, target, 'html'), exist_ok=True)\n",
+    "                    file_h = os.path.join(savedir, target, 'html', 'results_{}_{}_{}_{}_{}.html'.format(target, l2, l1,\n",
+    "                                                                                            tweetLen, key))\n",
+    "                    file_t = os.path.join(savedir, target, 'latex', 'results_{}_{}_{}_{}_{}.tex'.format(target, l2, l1,\n",
+    "                                                                                            tweetLen, key))\n",
+    "                    with open(file_h, 'w') as w:\n",
+    "                        tmp.to_html(w, sparsify=True)\n",
+    "                    with open(file_t, 'w') as w:\n",
+    "                        table = tmp.to_latex(sparsify=True, multirow=True, multicolumn=True,\n",
+    "                                        caption='''{} for the prediction of {} on a minimal input instance\n",
+    "                                        length of {} characters using a {} model on the {}'''.format(sc, target, tweetLen, mod, featureset),\n",
+    "                                     label='tab:{}_{}_{}_{}_{}'.format(target[0].upper()+target[1:], key, tweetLen, l1,l2),\n",
+    "                                     float_format=\"%.4f\")\n",
     "\n",
-    "                    table = re.sub(pat,rep, table)\n",
-    "                    table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
-    "                    table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
-    "                    table = re.sub(r\"0\\.0000\", '--', table)\n",
-    "                    w.write(table)\n"
+    "                        #table = re.sub(r'{table}', '{sidewaystable}', table)\n",
+    "                        table = re.sub(r'begin{table}', 'begin{table}[!ht]', table)\n",
+    "                        #table = re.sub(r'\\\\begin\\{table}', '', table)\n",
+    "                        #table = re.sub(r'\\\\end\\{table}', '', table)\n",
+    "                        table = re.sub(\"Average Spearman's Rho \\(ext.\\)\",\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's $\\\\rho$ (ext.)}\", table)\n",
+    "                        table = re.sub(\"Average Spearman's Rho \\(red.\\)\",\n",
+    "                                        r\"\\\\thead{Avg. \\\\\\\\ Spearman's  $\\\\rho$ (red.)}\", table)\n",
+    "                        table = re.sub('& target &', '& Target &', table)\n",
+    "                        table = re.sub('feature types &', 'Feature types &', table)\n",
+    "                        table = re.sub('featuretypes &', 'Feature types &', table)\n",
+    "                        table = re.sub('& subgrams &', '& N-gram ranges &', table)\n",
+    "                        table = re.sub('& tweetLen &', r'& Min. No. of Characters &', table)\n",
+    "                        table = re.sub('& f1-score', '& F1-score', table)\n",
+    "                        table = re.sub('& accuracy', '& Accuracy', table)\n",
+    "                        table = re.sub(r'\\{age}', '{Age}', table)\n",
+    "                        table = re.sub(r'\\{gender}', '{Gender}', table)\n",
+    "                        table = re.sub(pat,rep, table)\n",
+    "                        table = re.sub(r\"\\\\begin\\{tabular\", r\"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\\\\begin{tabular\", table)\n",
+    "                        table = re.sub(r\"\\\\end\\{tabular}\", r\"\\n\\\\end{tabular}\\\\end{adjustbox}\", table)\n",
+    "                        table = re.sub(r\"0\\.0000\", '--', table)\n",
+    "                        w.write(table)\n"
    ],
    "metadata": {
     "collapsed": false,
@@ -875,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 96,
    "outputs": [],
    "source": [
     "grouped = dist.copy(deep=True).dropna().groupby(by=['tweetLen', 'target'])\n",
@@ -894,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 97,
    "outputs": [
     {
      "name": "stderr",
@@ -906,7 +1256,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -914,7 +1264,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -922,7 +1272,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -930,7 +1280,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -938,7 +1288,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -946,14 +1296,14 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-58-c74abe33f89a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-97-56bc438b4e7a>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n"
      ]
     },
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -963,7 +1313,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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UAKmpqQwZMoSff/4ZCwsL6tWrx7Zt27CwsCA5OZl//vOfbN++HXt7eyZMmMC4ceNM2j5z5gzVqlXj1q1blCpVCj8/P3x9ffnpp584dOgQLVq0YPny5bi4uAAQExPDxIkTOXr0KJ6ensyfPx8/Pz+9Pj8/PyIjI/H29n7o6yLEoyTJvBBCCPGYZGSkM3/++GKrb/z4+fcsc/jwYWrVqqWv5+TkMHToUFauXEl2djbDhg1j7NixrFu37qHj2b17Ny+//DKpqamsXbsWf39/fv/9d5ydnSlfvjwbN26kevXqbN++nS5dutCkSRMaNWrE3LlzcXd3JyUlBchNtJVS5OTk8OKLL9KjRw9WrFhBYmIi7du3p1atWnTq1Ome8Sxfvpzo6Gg8PDzo0qULc+bM4V//+hdJSUl069aNZcuW0blzZ3788Ud69erFsWPHcHV1BaBOnTr8/PPPD31NhHjUZJiNEEII8RRLT0/HYDDo6+XKlaNXr16UKVMGg8FAcHAw27ZtK5a2ypcvT2BgIFZWVvTt25datWoRGRkJQLdu3ahRowZKKdq0aUPHjh3ZsWMHAFZWVpw/f56zZ89iZWWFr68vSin27t1LSkoKU6dOxdramurVqzNy5Ei+/vrrIsUzdOhQatasia2tLX369CE2NhaAiIgIunbtSteuXbGwsKBDhw54e3sTFRWlH2swGEhPL75vUYR4VCSZF0IIIZ5iTk5OZGZm6utXr15l1KhReHp64uDgQOvWrUlPTy+WWWAqV66MUkpf9/T0JDk5Gcgd7tO8eXOcnZ1xdHQkKiqK1NRUAN566y28vLzo2LEj1atX51//+hcAZ8+eJTk5GUdHR32ZOXMmf/75Z5HicXNz0/+/TJky+gOyZ8+eZdWqVUb1/vzzz5w/f14vn5mZiaOj48NdECEeAxlmI4QQQjzFGjRowIkTJ2jSpAkAc+fO5fjx4+zevRs3NzdiY2P5xz/+gaZpD91WUlISmqbpCf25c+fo3r07N27coFevXoSHh9OjRw+srKzo2bOn3qbBYGDu3LnMnTuXI0eO8Pzzz9OkSRM8PDyoVq0a8fHxDx3b3Tw8PBg0aBCLFi0qsExcXBwNGzYs1naFeBSkZ14IIYR4inXt2tVoGE1mZia2trY4OjqSlpbG9OnTi1yXn58fISEhBe6/cOECCxYs4NatW6xatYq4uDi6du3KzZs3uXHjBq6urpQqVYro6Gh++OEH/biNGzdy8uRJNE3DwcEBS0tLLC0tadq0KQ4ODnz00Udcu3aN7OxsfvvtN/bu3ftA1yLPwIED2bBhA99//z3Z2dlcv36drVu3kpiYqJfZtm0bXbp0eah2hHgcJJkXQgghnmIBAQFERUVx7do1AAIDA7l27RouLi40b978vuZST0hIwMfHp8D9zZo1Iz4+HhcXF4KDg1m9ejXlypXDYDCwYMEC+vTpg5OTE8uXL6d79+76cfHx8bRv3x57e3tatGjBmDFj8PPzw9LSkg0bNhAbG0u1atVwcXFhxIgRXL58+cEvCLk98//73/+YOXMmrq6ueHh4MHv2bHJycgDYu3cvdnZ2NG3a9KHaEeJxUMXxtdrflbe3t/ZXnYN2zJgxAHzxxRdPOBIhhPj7iouLo06dOvr6k5iaEiAoKEh/OPVBJSYm0rt3b3bt2vXAdZQUvXr1Yvjw4XTt2vVJhyL+pvL/7QBQSu3XNM1krlQZMy+EEEI8JkVJvB+FmTNnPnQd7u7uf4tEHmDNmjVPOgQhikyG2QghhBBCCFFCSTIvhBBCCCFECSXJvBBCCCGEECVUiUjmlVJ1lVI/KqWuKqWSlVIzlFKW9zimiVJqqVLq5J3jjiulpimlSpsp20MpdVgpdV0pdVQp1ffRnY0QQgghhBDF4y//AKxSygnYDBwFegA1gLnk3ohMKeTQvnfKfgTEAw2A9+78t9dd9bcC1gBfAOOArsAKpdQlTdN+yF+pEEIIIYQQfxV/+WQeGA3YAv6apmUAm5RSDkCIUmrWnW3mfKRpWspd61uVUteBL5VSnpqmnb2z/V1gu6Zp4+6sb1FK1QOmApLMCyGEEEKIv6ySMMymC/B9vqT9a3IT/DYFHZQvkc/z653/lgdQStkAzwMr85X7GmihlCr7oEELIYQQfxWTJ0/mk08+KVJZPz8/Fi9eDEBoaCitWrV6lKEVu6pVq7J58+bH0taQIUOYMiV3kMChQ4do2bLlY2lXiLuVhGS+NnDs7g2app0Drt7Zdz9aAjnA8TvrNQCr/PUDceRem5r3G6wQQgjxV5KSkkJ4eDijRo160qH85SilOHnyZLHU1aBBAxwdHdmwYUOx1CdEUZWEZN4JMPe6vEt39hWJUsoNCAaW3dXLn3d8/vov5dsvhBBClEihoaF07doVW1vbJx3KU2/AgAF8+eWXTzoM8TdTEsbMA2hmtqkCtpsWVMqa3KE0WcCEItSvCmpXKfUq8CpAlSpVitK8EEIIAcCENyeSln7p3gWLyNnRiY/nzCu0THR0NMOGDdPXL126xKBBg9i9eze3b9/Gx8eHhQsX4u7u/tDxLFq0iHnz5pGYmIiHhwcRERE0atQIpRTx8fF4eXkBucNT3N3def/999m6dSsDBw5k3LhxzJkzB0tLS/79739jbW1NYGAgqampvPnmmwQFBZkcC+jHJyYmmsSzZ88exo8fT1xcHLa2tvTq1Yt58+ZhbW1N69atAWjYsCFKKZYsWULfvn3ZuHEjU6ZM4cyZM9StW5eFCxfSoEEDAH799VeGDx9OfHw8Xbt2RSll1J6fnx8jRozgxo0b2NjYPPT1FKIoSkIyfwlwNLO9LOZ77I2o3N+0cKAe4KNp2t1/RfP+P3/9eesm9Wua9h/gPwDe3t5FupkQQgghANLSL9E1sE+x1Rf1Sf5HvkwdPnyYWrVq6es5OTkMHTqUlStXkp2dzbBhwxg7dizr1q17qFhWrVpFSEgI69atw9vbm1OnTmFlZVWkY//44w+uX79OUlISoaGhjBw5kg4dOrB//37OnTtH48aNeeWVV6hevfp9xWRpacnHH3+Mt7c3iYmJdOnShS+++ILAwEC2b9+OUoqDBw/qNxkHDhxg2LBhbNiwAW9vbyIiIujevTvHjx9HKUXPnj0JDAxk7Nix/O9//6Nfv3688847enuVK1fGysqK48eP6zcAQjxqJWGYzTHyjY1XSnkAdpiOdTfnY3KntOyhaVr+8qeAW/nrv7OeA5x4kICFEEKIv4r09HQMBoO+Xq5cOXr16kWZMmUwGAwEBwezbdu2h25n8eLFvP322zRp0gSlFF5eXnh6ehbpWCsrK4KDg7GysuKVV14hNTWV8ePHYzAYqFevHvXq1ePQoUP3HVPjxo1p3rw5pUqVomrVqowaNarQc120aBGjRo2iWbNmWFpaMnjwYGxsbIiJiSEmJoZbt24RGBiIlZUVL7/8Mk2aNDGpw2AwkJ5+z75GIYpNSUjmo4FOSinDXdv6AteAQv/6KKUmA/8EBmqa9nP+/Zqm3QC2AL3z7eoL7NI07fLDBC6EEEI8aU5OTmRmZurrV69eZdSoUXh6euLg4EDr1q1JT08nOzv7odpJSEigRo0aD3RsuXLlsLTMfRdk3tj+ChUq6PttbW3Jysq673pPnDjBCy+8gJubGw4ODgQFBZGamlpg+bNnzzJ37lwcHR31JSEhgeTkZJKTk6lcubLR0BpzNyuZmZk4OpobUCDEo1ESkvmFwA1grVKq/Z0x6yHAvLunq7zzptcld633B2aSO8QmSSnV/K7F9a763wP8lFKfKKX8lFKzyH1x1IxHf2pCCCHEo9WgQQNOnPj/L5rnzp3L8ePH2b17NxkZGWzfvh0ATXu4kaMeHh6cOnXK7L4yZcpw9epVff2PP/544Hbs7OyKXNdrr71G7dq1iY+PJyMjg5kzZxZ6nh4eHgQHB5Oenq4vV69epV+/flSsWJGkpCSj48+dO2d0fHJyMjdv3jQa1iTEo/aXT+bvjHFvB1gCG4Dp5A6dmZavaKk7ZfJ0vPPfIcCufEu3u+r/GXgZaA98D3QH+svbX4UQQjwNunbtajS0JDMzE1tbWxwdHUlLS2P69OlFrsvPz4+QkBCz+0aMGMGcOXPYv38/mqZx8uRJzp7NfT/jc889x/Lly8nOzua77757qGE9zz33HFFRUaSlpfHHH38UOn9+ZmYmDg4O2Nvbc+zYMf79738b7a9QoQKnT5/W10eOHMnChQvZvXs3mqZx5coVIiMjyczMpEWLFpQqVYoFCxZw+/Zt1q5dy549e4zq27p1K23btpWHX8Vj9ZdP5gE0TTuqaVpbTdNsNU2rqGnau5qmZecrU1XTtCF3rQ/RNE0VsITmO3adpmn1NU2z0TSttqZpXz+eMxNCCCEerYCAAKKiorh27RoAgYGBXLt2DRcXF5o3b07nzp2LXFdCQgI+Pj5m9/Xu3Zvg4GD69++PwWCgZ8+epKWlATB//nw2bNiAo6Mj//3vf+nZs+cDn8+gQYNo2LAhVatWpWPHjvTt27fAsnPmzGH58uUYDAZGjhxpUjYkJITBgwfj6OjIypUr8fb2ZtGiRYwdOxYnJye8vLwIDQ0FwNramrVr1xIaGoqTkxPffPMN/v7+RvX997//ZfTo0Q98bkI8CPWwX6v9nXl7e2v79u170mGYNWbMGAC++OKLJxyJEEL8fcXFxVGnTh19/UlMTQkQFBRE+fLlCQwMfOC2EhMT6d27N7t27XrgOp5mhw8f5tVXX5XrI4pF/r8dAEqp/ZqmeecvWxKmphRCCCGeCkVJvB+FmTNnPnQd7u7ukqgW4tlnn5XrI56IEjHMRgghhBBCCGFKknkhhBBCCCFKKEnmhRBCCCGEKKEkmRdCCCGEEKKEkmReCCGEEEKIEkqSeSGEEEIIIUooSeaFEEKIp9zkyZMLfVPq3fz8/Fi8eDEAoaGhtGrV6lGGdl+2bt2Ku7t7sdSllOLkyZMATJw4kYULFxZLvUI8bjLPvBBCCPEUS0lJITw8XE9cnwSlFPHx8Xh5eT2xGArz1ltv0bRpU4YNG4a1tfWTDkeI+yI980IIIcRTLDQ0lK5du2Jra/ukQ/nLqlixIrVr12b9+vVPOhQh7pv0zAshhBCPyZgJE7mQdqnY6ivv7MQXHxf+Vtno6GiGDRumr1+6dIlBgwaxe/dubt++jY+PDwsXLnzo4SsnT55k+PDhxMbGYmVlRbt27fjmm29o3bo1AA0bNkQpxZIlS+jYsWOhMaSlpfHGG2/w/fffc+3aNdq0acO6detM2lywYAELFy7khx9+wNXVleDgYFauXMmNGzd46aWX+Pjjj/WbmNmzZzNv3jyUUrz//vsmdfn5+REZGcnLL7/8UNdBiMdNknkhhBDiMbmQdomcbsWXLF6IXH3PMocPH6ZWrVr6ek5ODkOHDmXlypVkZ2czbNgwxo4dazZZvh/vvvsuHTt2ZMuWLdy8eZN9+/YBsH37dpRSHDx4UB9mc/HixUJjGDRoEPb29hw5cgR7e3t++eUXk/bee+89vv32W7Zt24arqyuBgYGcPn1av5no378/M2bM4MMPP+S7775jzpw5/Pjjj1SrVo2RI0ea1FenTh3WrFnzUNdAiCdBknkhhBDiKZaeno7BYNDXy5UrR69evfT14OBgnn/++Ydux8rKirNnz5KcnIy7u3uhD84WFsP58+eJjo7m4sWLODk5AdCmTRu9rKZpTJw4kT179rBlyxbKli2LpmksWrSIQ4cO4ezsDEBQUBD9+/fnww8/ZOXKlQwdOpT69esDEBISwooVK4xiMhgMpKenP/R1EOJxkzHzQgghxFPMycmJzMxMff3q1auMGjUKT09PHBwcaN26Nenp6WRnZz9UO7NmzULTNJo2bUq9evX46quvCixbWAwJCQk4OzvriXx+6enp/Oc//2Hy5MmULVsWyH3I9+rVqzRu3BhHR0ccHR3p3LkzKSkpACQnJ+Ph4aHX4enpaVJvZmYmjo6OD3MJhHgiJJkXQgghnmINGjTgxIkT+vrcuXM5fvw4u3fvJiMjg+3btwO5Pd4Pw83NjUWLFpGcnMyXX37JmDFjCpxBp7AYPDw8SEtLK7CX3MnJiY0bNzJ06FB27twJgIuLC7a2trpkWMoAACAASURBVBw5coT09HTS09O5fPkyWVlZQO4DrgkJCXod586dM6k3Li6Ohg0bPtQ1EOJJkGReCCGEeIp17dqVbdu26euZmZnY2tri6OhIWloa06dPL3Jdfn5+hISEmN23atUqEhMTgdyEWymFpaUlABUqVOD06dNFiqFixYp06dKFMWPGcOnSJW7duqUn+3fH8d///peXXnqJ3bt3Y2FhwciRI5kwYQIXLlwAICkpie+//x6APn36EBoaytGjR7l69arZc962bRtdunQp8rUQ4q9CknkhhBDiKRYQEEBUVBTXrl0DIDAwkGvXruHi4kLz5s3p3LlzketKSEjAx8fH7L69e/fSrFkz7O3t6d69O/Pnz6datWpA7hj1wYMH4+joyMqVK+8Zw7Jly7CysqJ27dqUL1/e7AuvOnTowNKlS+nevTv79+/no48+wsvLi+bNm+Pg4ED79u05fvw4AF26dCEwMJC2bdvi5eVF27Ztjeo6f/48R48epWfPnkW+FkL8VaiH/Vrt78zb21vLe1r/r2bMmDEAfPHFF084EiGE+PuKi4ujTp06+vqTmJoSch8GLV++PIGBgQ/cVmJiIr1792bXrl0PXMdf1RtvvEGNGjX0fzuFeNLy/+0AUErt1zTNO39Zmc1GCCGEeEyKkng/CjNnznzoOtzd3Z/KRB5yx/ALUVLJMBshhBBCCCFKKEnmhRBCCCGEKKEkmRdCCCGEEKKEkmReCCGEEEKIEkqSeSGEEEIIIUooSeaFEEIIIYQooSSZF0IIIZ5ykydPNvviJSH+7pRSnDx58rG05efnx+LFiwFYv349r7zySrHUK8m8EEII8RRLSUkhPDycUaNGAbB161YsLCywt7fH3t4ed3d3+vTpw969e42OuzvJSU9PZ9iwYbi5uWEwGKhZsyYfffQR586d0+uxt7dHKYWdnZ2+vmPHDpN4zp8/z/Dhw6lYsSIGg4HatWszbdo0rly5YtJunpCQEAYOHGi0TdM0qlevTt26dU3a8PPzo3Tp0tjb2+Pi4oK/vz/nz583e306derErFmz9PWkpCSUUma3/fHHH/r1eO2113Bzc6NMmTI8++yzLF261KjeqlWrYmtri729PW5ubgwZMoSsrCx9/5AhQ7C2tsZgMGAwGKhfvz6TJ0/m8uXLZuPMU1DyGRoaiqWlpdHnYW9vT3Jysh6PtbU1qampRsc999xzKKU4c+aMHpdSivXr1xuVCwwMRClFaGio0fatW7eaXK+n0ZkzZ1BKcfv27WKpr3v37vz2228cOnTooeuSZF4IIYR4ioWGhtK1a1dsbW31bZUqVSIrK4vMzExiYmKoXbs2vr6+/Pjjj2brmDBhAllZWcTFxXH58mXWr19PjRo1qFKlCllZWfoCcPDgQX3d19fXqJ60tDRatGjBtWvX2LVrF5mZmWzatIn09HROnTp1X+e1fft2Lly4wOnTp01uRAA+++wzsrKyOHnyJFlZWbz55ptm62ndujXbtm0zqrd27dom25555hnc3Ny4efMm7du35+zZs+zatYvLly8ze/ZsJk2axLx5xi8F27BhA1lZWcTGxvLrr7/y4YcfGu1/++23yczMJCUlhaVLlxITE4OPj49+Y3O/WrRoYfR5ZGVlUalSJX1/tWrVWLFihb5++PBhrl27ZlJPzZo1CQsL09dv377NqlWrqFGjhknZsLAwnJ2djcqLounXrx//+c9/HroeSeaFEEKIx2T0uAn49x9cbMvocRPu2WZ0dDRt2rQxu08phbu7OzNmzGDEiBG88847Zsvt3buX/v374+TkhIWFBbVr1+bll1++7/OfN28eBoOBiIgIqlatCoCHhwfz58+nQYMG91VXWFgYPXr0oGvXroUmko6OjvTs2ZPY2Fiz+1u3bs3OnTvJyckBYMeOHQQGBrJv3z6jba1btwZg2bJlnDt3jlWrVlGtWjWsrKzo3LkzCxYsYOrUqWRkZJi04ebmRqdOnQqMoXTp0jRp0oT169dz8eJFk17+4jJo0CDCw8P19bCwMAICAkzKvfjii+zcuZNLly4B8N1339GgQQPc3NyMyl29epXVq1fz+eefEx8fz759+4ocS3Z2NjNnzqRGjRoYDAYaN25MQkKC2R7wu4enhIaG4uPjw4QJE3B0dKR69er88ssvhIaG4uHhQfny5Y1+Hu4+Nu/4Vq1amY0pMjKSf/zjHzg4OODh4UFISIi+L+/zd3R0xN7eXn8b8ldffUWdOnVwcnKiU6dOnD17Vj9m06ZN1K5dm7JlyzJ27Fg0TTNqz8/Pj8jIyCJfs4JIMi+EEEI8JhdS00it0aXYlgupafds8/Dhw9SqVeue5fz9/Tlw4IDZXuHmzZsTHBzM0qVLiY+Pf6BzB9i8eTP+/v5YWDxc+pGXRA4YMIABAwbw9ddfc/PmTbNlL168yNq1a/Hy8jK7v2nTpty4cYODBw8Cub3wHTp0wMvLy2hbXjK3adMmunTpgp2dnVE9vXr14vr163qSd7fExESio6MLjCGPwWCgQ4cOZocnFYfmzZuTkZFBXFwc2dnZfPPNNybDlyD35qJ79+58/fXXAISHh5tN+tesWYO9vT29e/emU6dORjcK9zJv3jxWrFhBVFQUGRkZfPXVV5QpU6ZIx+7evZsGDRpw8eJF+vfvzyuvvMLevXs5efIkERERjB071mhIU1HZ2dkRHh5Oeno6kZGR/Pvf/2bdunVA7s8A5A6xysrKokWLFqxbt46ZM2eydu1aUlJS8PX1pV+/fgCkpqbSq1cv3n//fVJTU6lRowY7d+40aq9OnTqcOXPG7A3g/ZBkXgghhHiKpaenYzAY7lmuUqVKaJpGenq6yb5PP/2UAQMG8Nlnn1G3bl28vLyIjo6+71guXrxIxYoV71muUaNGODo66su//vUvo/1r167FxsaGjh078sILL3D79m2THs5x48ZRtmxZXFxcSE1N5dNPPzXblo2NDc2aNWP79u2kpaWRnp5O9erV8fX11bcdPXpU/3YjNTXV7DmUKlVKbytPz549MRgMeo/x9OnT73nulSpVIi3t3jdp5sTExBhdN3PDYvJ65/N6jStXrmy2roCAAMLDw7l8+TLbtm2jZ8+eJmXCwsLo27cvlpaW9O/fnxUrVnDr1q0ixbp48WLef/99atWqhVKKhg0bUq5cuSIdW61aNYYOHYqlpSV9+/YlISGBqVOn6j8T1tbWD/RQq5+fH88++ywWFhY0aNCAfv36GQ23yu/LL79k8uTJ1KlTh1KlShEUFERsbCxnz54lKiqKunXr8vLLL2NlZUVgYKDJNxt5v5fmfufuhyTzQgghxFPMycmJzMzMe5bLe8jT0dHRZJ+trS1BQUHs37+fixcv0qdPH3r37l1o0rljxw79Icx69eoBUK5cuQIfRL3bgQMHSE9P15dJkyYZ7Q8LC6NPnz6UKlUKGxsb/P39TYbaLFiwgMuXL3Po0CEuXbpEYmIiADNnztTjGj16NJA7hGL79u3s2LFDH4LRqlUrfZuHhweenp4AuLi4mD2H27dvk5qaiouLi75t3bp1ZGZmsnXrVo4dO2by8Kk5SUlJODs7A1CvXr1CHybOr3nz5kbXzdxzCIMGDWL58uWEhoaa7W3P06pVK1JSUnj//fd54YUXjJ65AEhISGDLli0MGDAAgB49enD9+vUiDxtJSEgwe7NRFBUqVND/Py+u/NsepGd+9+7dPP/887i6ulK2bFkWLlxY6Gd29uxZxo8fr988OTs7o2kaSUlJJCcn4+HhoZdVShmtA/rvpbnfufshybwQQgjxFGvQoAEnTpy4Z7lvv/2WRo0amQwfyc/BwYGgoCCuXLnC77//XmA5X19f/SHMI0eOANC+fXu+/fZbfSz6g0hMTOSnn34iIiICNzc33NzcWL16NVFRUWYTr2effZYpU6bw+uuvo2kaQUFBelwLFy4EcpP5HTt2sH37dv2hXR8fH3bu3Gk0xCbvHKKjo02GI61ZswYbGxuaN29uEkObNm0YMmRIgQ/h5snKymLz5s16DEeOHCnwYeIH5enpSbVq1YiKisLf37/QsgMHDmTu3Llmk/5ly5aRk5PDiy++iJubG9WrV+f69etFHmrj4eFh9mYj7+fv6tWr+ra8WYQehJ2dXZHr6t+/P927dychIYHLly8zevRofZy7UsrsOXz55ZdGN1DXrl2jZcuWVKxYkYSEBL2spmlG6wBxcXFUrVoVBweHBz4/kGReCCGEeKp17dq1wKECeb2I06dPZ/HixcycOdNsuffee4+9e/dy8+ZNrl+/zvz583F0dCzSWPy7TZw4kYyMDAYPHqw/KJiUlMTEiROLPEXfsmXLqFmzJsePHyc2NpbY2FhOnDiBu7u70Uwtdxs8eDAXLlwwmW4xT8uWLUlPTyciIkJPmp2cnHB1dSUiIsIomR80aBDu7u707t2bM2fOcOvWLb7//nvGjRtHSEgIZcuWNdtGYGAgmzZtMvsQ7I0bN9i/fz89e/bEycmJoUOHFnoN8j6HvCU7O7vQ8vktWbKEn3766Z43buPGjWPTpk1G558nPDycadOm6Z9BbGwsa9asITIykosXLwK502Hmn8oyz4gRI3j33XeJj49H0zQOHTrExYsXcXV1pXLlykRERJCdnc1XX3113zMd3e25555j7dq1XL16lZMnT7JkyZICy2ZmZuLs7Ezp0qXZs2cPy5cv1/e5urpiYWHB6dOn9W2jR4/mww8/1G9WL1++zKpVqwDo1q0bR44cYe3atdy+fZsFCxaY3Ehs27aNLl26PPC55ZFkXgghhHiKBQQEEBUVZTQFYXJysj58o0mTJhw+fJitW7fSsWNHs3UopRg6dCguLi5UqlSJTZs2ERkZib29/X3F4uzszC+//IKVlRXNmjXDYDDQrl07ypYte8+HQ/OEhYUxZswYvVc+bxk9enSBs9pYW1szbtw43nvvPbP7y5QpQ+PGjblx4wb169fXt/v6+nLhwgWjZNbGxobNmzfj4eFBs2bNcHBwYOLEiXzwwQe89dZbBcbt6upKQECAUQyzZs3CYDDg7OxMQEAAjRs35pdffrlnkl2vXj1sbW31JW/2m127dpnMM29u2s4aNWrg7e1daBuQ+3m1a9fOpFc6JiaGM2fO8Prrrxt9Bt27d8fLy4sVK1Zw8+ZNLl68aPabCsi9sevTpw8dO3bEwcGB4cOH6z+jixYtYvbs2ZQrV44jR47QsmXLe8ZakAkTJmBtbU2FChUYPHiwPizInC+++IKpU6diMBiYMWMGffr00feVKVOG4OBgfHx8cHR0JCYmhpdeeol33nmHV155BQcHB+rXr68/S+Li4sKqVauYNGkS5cqVIz4+Hh8fH6P2VqxYob//4WGo/NPkiKLz9vbW7mcapsdpzJgxQO4PphBCiCcjLi6OOnXq6Oujx00o0gw0RVXexZmFCz6+Z7mgoCDKly9PYGBgsbUtRGF+/vlnPv/88wK/Lfm727BhA8uWLWPlypVm9+f/2wGglNqvaZrJXVipRxOiEEIIIfIrSuL9KBQ0fEaIR6VVq1YFzucucufyf/HFF4ulriIn80opa6ARUAmwBVKB45qmnSmWSIQQQgghhBD3pdBkXillCbwEjADaANbA3QOnNKVUErACWKRp2v1P6imEEEIIIYR4IAU+AKuU6gUcAyKAG8AUoAPQEKgJNAf6A6uBnkCcUmqRUqqC+RqFEEIIIYQQxamwnvlPgVlAqKZpBb2aag/wDTBRKdUMeAcYCbxfrFEKIYQQQgghTBQ2NWV1TdM+KSSRN6Jp2m5N0/yB2cUT2v9TStVVSv2olLqqlEpWSs24MwSosGOslVKzlVI7lFLXlFJmp+1RSoUqpTQzS+3iPg8hhBBCCCGKU4E985qmXX+QCjVNu/Hg4ZhSSjkBm4GjQA+gBjCX3BuRKYUcWobcsf57gF+AtoWUPQbkf0PDmQeLWAghhBBCiMejJLw0ajS5s+f4a5q2SdO0hcB0cof2FPj+2zvfKDhrmtYJ+PYebVzRNC0m3/JANzNCCCHEX83kyZP55JNPnnQYQjxWoaGhxTI95pkzZ1BKcfv2bQD8/f357rvvHrre4vLQybxSapNS6sfiCKYAXYDvNU3LuGvb1+Qm+G0KO1CTN2IJIYT4m0tJSSE8PFx/0+TWrVuxsLDQ3xDq7u5Onz59TN4UqpTi5MncSerS09MZNmwYbm5uGAwGatasyUcffcS5c+eM3jaqlMLOzk5f37Fjh0k858+fZ/jw4VSsWBGDwUDt2rWZNm0aV65cMWk3T0hICAMHDjTapmka1atXp27duiZt+Pn5Ubp0aezt7XFxccHf35/z58+bvT6dOnVi1qxZ+npSUhJKKbPb/vjjD/16vPbaa7i5uVGmTBmeffZZ/S2seapWrYqtrS329va4ubkxZMgQsrKy9P1DhgzB2toag8GAwWCgfv36TJ48mcuXL5uNM4+56wO5iaulpaXJG2CTk5P1eKytrUlNTTU67rnnnkMpxZkzZ/S4lFKsX7/eqFxgYCBKKUJDQ422b9261eR6PYz8ifNf0aRJkwgODn7SYeiKo2e+2p3lUalN7jAYnaZp54Crd/YVh7pKqQyl1A2l1M9KqUJvEoQQQoiSIjQ0lK5du2Jra6tvq1SpEllZWWRmZhITE0Pt2rXx9fXlxx/N981NmDCBrKws4uLiuHz5MuvXr6dGjRpUqVKFrKwsfQE4ePCgvu7r62tUT1paGi1atODatWvs2rWLzMxMNm3aRHp6OqdOnbqv89q+fTsXLlzg9OnTJjciAJ999hlZWVmcPHmSrKws3nzzTbP1tG7dmm3bthnVW7t2bZNtzzzzDG5ubty8eZP27dtz9uxZdu3axeXLl5k9ezaTJk1i3rx5RnVv2LCBrKwsYmNj+fXXX/nwww+N9r/99ttkZmaSkpLC0qVLiYmJwcfHR7+xuV8tWrQw+jyysrKoVKmSvr9atWpGb2Q9fPgw165dM6mnZs2ahIWF6eu3b99m1apV1KhRw6RsWFgYzs7ORuWfdk2bNiUjI4N9+/Y96VCAYngDrKZpXsURSCGcAHMP4V66s+9h/QrsJndMvivwBrBJKdVK07Q9xVC/EEIIAcCo0eP488LFYquvQvlyfLlwQaFloqOjGTZsmNl9Sinc3d2ZMWMGaWlpvPPOO2YTlL179/L+++/j5JT7z27t2rWpXfv++9PmzZuHwWAgIiICC4vc/kQPDw/mz59/33WFhYXRo0cPrl27RlhYGE2aNDFbztHRkZ49e/L555+b3d+6dWtmz55NTk4OFhYW7Nixg8DAQKZOnWq0rXXr1gAsW7aMc+fOsW3bNuzs7ADo3LkzCxYsYPjw4YwYMQIHB+NRwG5ubnTq1InY2FizMZQuXZomTZqwfv16atasydKlSxk7dux9X5N7GTRoEOHh4fzzn/8Ecq9hQEAAU6YYP4L44osvEhERwaVLl3BycuK7776jQYMGZGZmGpW7evUqq1evZtGiRQQEBLBv3z68vb2LFMuePXsYM2YMJ06cwNbWlgEDBjBv3jz9Ojs6OgKwadMmypcvz8iRIzl48CBKKTp16sTnn3+ul0lISGD8+PHs2LGDnJwc+vXrx2effWbS5ltvvcWuXbuIjIwEYOLEiURFRWFhYcHQoUOZPn06lpaWZGdn88477xAaGoqDgwNvvPGGSV1+fn5ERkYW+XwfpZIwZh7A3HAZVcD2+6tY0+ZrmvZvTdO2aZq2mtwHZZOAIHPllVKvKqX2KaX2paSkPGzzQggh/kb+vHCRxIv1i20pyo3B4cOHqVWr1j3L+fv7c+DAAbO9ws2bNyc4OJilS5cSHx//QOcOsHnzZvz9/fVE/kHlJZEDBgxgwIABfP3119y8edNs2YsXL7J27Vq8vMz3PTZt2pQbN25w8OBBILcXvkOHDnh5eRlty0syN23aRJcuXfREPk+vXr24fv06u3btMmkjMTGR6OjoAmPIYzAY6NChg9nhScWhefPmZGRkEBcXR3Z2Nt98843J8CXIvbno3r07X3/9NQDh4eEEBASYlFuzZg329vb07t2bTp06ER4eXuRYxo8fz/jx48nIyODUqVP06dMHyL3WkDuUKSsrixYtWqBpGpMnTyY5OZm4uDgSEhIICQkBIDs7mxdeeAFPT0/OnDlDUlISr7zyilFbOTk5jBw5kkOHDvHDDz9QtmxZBg8eTKlSpTh58iS//vorP/zwA4sXLwZg0aJFbNy4kV9//ZV9+/axevVqk/jr1Kmj/3w8aUX6bVJKZSulmhawr7FSKrt4wzJyCXA0s70s5nvsH4qmadeAKKBRAfv/o2mat6Zp3q6ursXdvBBCCFGs0tPTMRgM9yxXqVIlNE0jPd30n9ZPP/2UAQMG8Nlnn1G3bl28vLyIjo6+71guXrxIxYoV71muUaNGODo66su//vUvo/1r167FxsaGjh078sILL3D79m29tzXPuHHjKFu2LC4uLqSmpvLpp5+abcvGxoZmzZqxfft20tLSSE9Pp3r16vj6+urbjh49Sps2uSNwU1NTzZ5DqVKl9Lby9OzZE4PBgIeHB+XLl2f69On3PPdKlSqRlpZ2z3LmxMTEGF03c8Ni8nrnN23aRO3atalcubLZugICAggPD+fy5cts27aNnj17mpQJCwujb9++WFpa0r9/f1asWMGtW7eKFKuVlRUnT54kNTUVe3t7mjdvXmBZLy8vOnTogI2NDa6urkycOFEfBrVnzx6Sk5OZPXs2dnZ2lC5d2uih11u3btGvXz/S0tLYsGEDZcqU4c8//yQ6OppPPvkEOzs7ypcvz4QJE/Sbl5UrVxIYGIiHhwfOzs5MnjzZJCaDwWD2d+VJKOqtsSpknyXF0ENeiGPkGxuvlPIA7Mg3lr6YycOzQgghSjwnJyeT4RHm5D3kmTd04W62trYEBQWxf/9+Ll68SJ8+fejdu3ehSeeOHTv0hzDr1asHQLly5Qp8EPVuBw4cID09XV8mTZpktD8sLIw+ffpQqlQpbGxs8Pf3NxmzvWDBAi5fvsyhQ4e4dOkSiYmJAMycOVOPa/To0UDuUJvt27ezY8cOPRFs1aqVvs3DwwNPT08AXFxczJ7D7du3SU1NxcXFRd+2bt06MjMz2bp1K8eOHTN5+NScpKQknJ2dAahXr16hDxPn17x5c6PrZu45hEGDBrF8+XJCQ0PN9rbnadWqFSkpKbz//vu88MILRs9cQO7Qli1btjBgwAAAevTowfXr101uqgqyZMkSTpw4Qe3atWnSpAkbN24ssOyFCxd45ZVXqFy5Mg4ODgwcOFC/lgkJCXh6elKqlPmR4ydPnuR///sf06ZNw9raGoCzZ89y69YtKlasqN/4jBo1igsXLgCQnJyMh4eHXkfeZ3+3zMxMs78rT0KhybxSyuKulzNZ3Fm/e7Ejd7aZe/90PrhooJNS6u5uhb7ANWCb+UMenFLKltxz2l/cdQshhBCPW4MGDThx4sQ9y3377bc0atTIZPhIfg4ODgQFBXHlyhV+//33Asv5+vrqD2EeOXIEgPbt2/Ptt9+Sk5Nzfydxl8TERH766SciIiJwc3PDzc2N1atXExUVZTZZfvbZZ5kyZQqvv/46mqYRFBSkx7Vw4UIgN5nfsWMH27dv1x/a9fHxYefOnUZDbPLOITo62mQ40po1a7CxsTHbw9ymTRuGDBlS4EO4ebKysti8ebMew5EjRwp8mPhBeXp6Uq1aNaKiovD39y+07MCBA5k7d67ZpH/ZsmXk5OTw4osv4ubmRvXq1bl+/XqRh9o888wzrFixggsXLvDOO+/w8ssvc+XKFZQy7T+ePHkySikOHTpERkYGERER5E1Y6OHhwblz5wqc/aZOnTosXbqULl26cPz4cf0YGxsbUlNT9RufjIwM/ee0YsWKJCQk6HWcO3fOpN64uDgaNmxYpHN91ApM5pVS04BbwE1ye6l33lm/e8kApgKrHmGMC4EbwFqlVHul1KtACDDv7ukqlVInlVJL8p1DF6XUy8Bzd9ZfvrN43lkve+cNsaOUUu2UUn2BLUBlYOYjPCchhBDisejatavRzCx30zSNpKQkpk+fzuLFi5k50/w/fe+99x579+7l5s2bXL9+nfnz5+Po6Fiksfh3mzhxIhkZGQwePJizZ88CuT3REydO5NChQ0WqY9myZdSsWZPjx48TGxtLbGwsJ06cwN3d3WimlrsNHjyYCxcumEy3mKdly5akp6cTERGhJ81OTk64uroSERFhlMwPGjQId3d3evfuzZkzZ7h16xbff/8948aNIyQkhLJly5ptIzAwkE2bNpl9CPbGjRvs37+fnj174uTkxNCh+d9jaSzvc8hbsrPvb7TzkiVL+Omnn+554zZu3Dg2bdpkdP55wsPDmTZtmv4ZxMbGsmbNGiIjI7l4MfdZjqpVq5pMZZknIiKClJQULCws9B5uS0tLXF1dsbCw4PTp03rZzMxM7O3tcXR0JCkpidmzZ+v7mjZtSsWKFZk0aRJXrlzh+vXr7Ny506itfv36MXPmTNq3b8+pU6eoWLEiHTt25I033iAjI4OcnBxOnTql/5706dOHBQsWkJiYyKVLl0yGeQFs27aNLl26FHr9HpfCeua3AjOA98gdZvPVnfW7l2DgJWD8owpQ07RLQDtyh/NsIPeFUR8D0/IVLXWnzN3+Te6NxvA766vuLM/fWb8BpJD7Jtko4D/kjsNvo2naX2O+ISGEEOIhBAQEEBUVZTQFYXJysj58o0mTJhw+fJitW7fSsWNHs3UopRg6dCguLi5UqlSJTZs2ERkZib29/X3F4uzszC+//IKVlRXNmjXDYDDQrl07ypYte8+HQ/OEhYUxZswYvVc+bxk9enSB0yNaW1szbtw43nvvPbP7y5QpQ+PGjblx4wb169fXt/v6+nLhwgWjZNbGxobNmzfj4eFBs2bNcHBwYOLEiXzwwQe89dZbC8gPDQAAIABJREFUBcbt6upKQECAUQyzZs3CYDDg7OxMQEAAjRs35pdffrlnkl2vXj1sbW31JW+O+127dpnMM29u2s4aNWoUaRYWZ2dn2rVrZ9JbHhMTw5kzZ3j99deNPoPu3bvj5eXFihUruHnzJhcvXixwLPx3332nDyMaP348X3/9NaVLl6ZMmTIEBwfj4+ODo6MjMTExTJs2jQMHDlC2bFn+r717j5OrLg8//nkIkAsgCRhE3ERu0QhUK41afqKo2CJia6tQrFVBUaqp5VK1KoqCl3pDA15SRaqAVkWQahEQwQuIigpeikBwo1yyXENIAuQe8vz+OGdhmMzunt2Z2ZnZfN6v17x253vO+c4zm5w9z3z3e57vYYcd9pi/KEyaNImLLrqIxYsXM3v2bPr6+jjvvPM2e72jjjqK973vfbzoRS/i1ltv5dxzz2X9+vXss88+zJgxg8MPP/yR6VNvetObOOSQQ3jGM57B/vvvv9lfMH71q1+x3Xbb8exnN7yddNxFlXWVylH6L2bmne0PqXfMmzcvu6XGaL358+cDsHDhwg5HIklbrptuuomnPe1pjzzvRGlKgJNOOolddtmFE044oWWvLQ3n6quv5nOf+9yQfy3pZa985Ss55phjeOlLX9q216j/3QEQEddl5mafwqrWmT9ruEQ+Ig7IzM1rMUmSpEdUSbzbYajpM1K7HHjggY+pKjORfOtb3+p0CI9RtZrN7yJis5pEUXgfbbgRVZIkSdLwqibzlwHfioiFETEFHikP+WOK+eYjF06VJEmS1FKVptlk5j9FxOXAp4HnR8SZFBVlVgDPz8xr2heiJEmSpEYqr6ecmWcDzwP2oqgmsxh4uom8JElDq1JoQpIGjfZ3RuVkPiKeDnyNor785cA84D8iYttRvaIkSVuIKVOmsGzZMhN6SZVkJsuWLWPKlCmVj6k0zSYi/hX4OHA98MzM/GNEvAE4AzgoIl6VmTeNJWhJkiaqvr4+BgYGWLp0aadDkdQjpkyZQl9fX+X9q5amPB04DXhPZm4EyMwvRcTVFKP11wLDr3AgSdIWZptttmGPPfbodBiSJrCqyfwhmXlFfWNm/iEiDgAsYCtJkiSNs6rVbDZL5Gu2bQCGXr9YkiRJUluM5gbYZ0bEhRFxX0RsjIj9y/b/iIiXtC9ESZIkSY1USuYj4kDg58BcijnytcdtAt7c+tAkSZIkDafqyPxHKVaB3Rf4t7ptvwb2b2VQkiRJkkZW9QbY/YFXZGZGRH2x3PuAma0NS5IkSdJIqo7MrwWmDbHticDK1oQjSZIkqaqqyfzVwAkRMammbXCE/hjghy2NSpIkSdKIqk6zORn4KfA74AKKRP6oiPgU8BfAs9oTniRJkqShVBqZz8zfAc8H7gHeAwTw1nLzQZl5c3vCkyRJkjSUqiPzZOavgYMjYgqwE7AiM1e3LTJJkiRJw6qczA/KzLXAnW2IRZIkSdIoDDnNJiJOLEfhK4uI/V0NVpIkSRofw82Zfx1wa0R8NCKeMdROETEjIl4bEd+nqHrzuFYHKUmSJGlzw02z2R94LfA24N8j4gHgemApsA6YAewJ7FU+Pw/YJzNvbWfAkiRJkgpDJvOZmcC5wLkR8RzgJcBzKBL4KcAy4CfAh4HvZOaK9ocrSZIkaVClG2Az8xfAL9ociyRJkqRRqLoCrCRJkqQuYzIvSZIk9SiTeUmSJKlHmcxLkiRJPcpkXpIkSepRJvOSJElSj6pUmhIgIqYBbwAOAnaiqDP/Y+DszFzdlugkSZIkDanSyHxE7Ar8Gvg0MA+YBjwL+CxwXUQ8oW0RSpIkSWqo6jSbjwMzgOdl5h6ZeUBm7gEcCEwHPtauACVJkiQ1VjWZPxR4d2b+tLYxM38GvBc4rNWBSZIkSRpe1WR+e+DOIbYNlNslSZIkjaOqyfzNwGuH2PYaYFFrwpEkSZJUVdVqNqcB55Y3un4NuAvYFXgV8GKGTvQlSZIktUmlZD4zv1qWpvwAcFbNpnuAN2fm19oRnCRJkqShVa4zn5lnRsRZwFMp6szfD9ycmZvaFZwkSZKkoVVO5gHKxP2mNsUiSZIkaRRGswLs44CXArOBKXWbMzM/2MrAJEmSJA2vUjIfEc8FLqJYIKqRBNqWzEfEPsBngAOAFRTz9k/NzIeHOWZb4MPAX1KsWjslM2OIfV8OfAiYA/yp7Pu8lr4JSZIkqcWqlqY8HbgVeBZFUrxV3WNSuwKMiBnAFRQfGF5OcRPu24BTRzh0GvBGYDXws2H6PxD4FvAjisWxLga+HhF/3XTwkiRJUhtVnWbzNOAfMvO6dgYzhDcDU4FXZOYDwOXllJ9TIuLjZdtmMnNFROyUmRkRbwVeNET/JwNXZeZx5fMfRcS+wPuA77f2rUiSJEmtU3Vk/nZgcjsDGcahwGV1Sfs3KBL8g4Y7MDNzuO0RMRl4IfDNuk3fAA6IiB1HH64kSZI0Pqom86cC7ypHxMfbXOpWmM3M2ymmz8xtsu+9gG3q+6eo2LMV8JQm+5ckSZLaZshpNhFxbl3TE4BbIuLnFDXma2VmHtXq4EozKG56rbe83NZs3zTof3nddkmS1EELFiygv79/2H0GBgYA6OvrG3KfOXPmcOKJJ7Y0Nj1Wq/6twH+vKoabM/98iptOByXwALBvg32Hnc7SAo36jxa+bn0/MUQ7EXEscCzA7NmzW/TykiSpWWvWrOl0CKrIf6vWGTKZz8zdxzGO4SyncUnMHWk8Yj/avmnQ/+DzzfrPzDOBMwHmzZvX7g8xkiQJKo3Ozp8/H4CFCxe2OxwNw3+r8VV1zvywyhtJ22URdXPjI2IWsB2bz3UfrT8CG+r7L59vAv7QZP+SJElS21ReAbaRiJgCzKeo+/6klkS0uUuBd0TEDpn5YNl2JLAGuLKZjjNzXUT8CDgC+ELNpiOBn2fmymb6H6sqc81GMnj84CffZjhfTZIkqTsNm8xHxGzgVcBsYDHw5cxcWa6u+q/AO4BdGGZRphb4PHAccGFEfAzYEzgF+FRtucqIWAxcmZnH1LQdSjGC/+fl88PLTb/KzNvK7z8I/DgiTge+Dby0fLykje9pWP39/fT3L2LvvWeNuY8pU4p/2sxVTcWyePGSpo6XJElS+wxXzea5FKuh1paj/OeI+FvgQoobYX8NvCEzL2lXgJm5PCIOBj4LXEQxj30BRUJfa2ugfiXa/wSeXPP8/PLr64Gzy/6vLpP8DwFvAW4BXp2ZHV0wau+9Z3HGGW/vZAgAHH/8aZ0OQZIkSUMYbmT+/cAyilHqXwN7UCTHPwOmAa/PzHPaHiGQmTcy9Aqug/vsXqVtiGO/TTEqL0mSJPWM4ZL5ZwHHZebgFJqbImI+8Hvg+PFK5CVJkiQ1Nlw1mx2B+rswB5//sj3hSJIkSapqpNKUD9c931R+Xd+GWCRJkiSNwkilKU+NiPtqng+ujPrBiLi/pj0z86jWhiZJkiRpOMMl87cDT2vQfhtFJZtaroQqSZIkjbMhk/mqlWAkSZIkdcZIc+YlSZIkdSmTeUmSJKlHmcxLkiRJPcpkXpIkSepRI5WmlCRJUp0FCxbQ31+/tuajBgYGAOjr6xu2nzlz5nDiiSe2NDZtWUzmJUmSWmzNmjWdDkFbiErJfEQ8HpiWmbfXtP0zsB9wWWZ+t03xSZIkdZ2RRtPnz58PwMKFC8cjHG3Bqs6Z/xLwrsEnEXEy8J/Aq4HvRMSRbYhNkiRJ0jCqJvPzgB/UPH8z8B+ZuTPwOeDfWh2YJEmSpOFVTeZ3Au4BiIj9gF2Bc8pt3wae2vrQJEmSJA2najK/DBi8HftFwJ2ZOXgL9zaj6EeSJElSi1StZnMFcEp5I+zbKEbjB80Fbmt1YJIkSZKGV3VE/d+BJcBHgD8Cp9Zs+yfg6hbHJUmSJGkElUbmM/Me4K+G2PxiYG3LIpIkSZJUSdOLRmXmA60IRJIkSdLoDJnMR8SXap9n5hvaH44kSZKkqoYbmd9j3KKQJEmSNGpDJvOZ+cLxDESSJEnS6FgfXpIkSepRlW+AjYitgGcDs4Ep9dsz89wWxiVJkiRpBJWS+YjYh2KhqL2AaLBLAibzkiRJ0jiqOjK/sNz3H4DrgXVti0iSJElSJVWT+f2BozPzwnYGI0mSJKm6qjfA3gesb2cgkiRJkkanajK/APiXiJjUzmAkSZIkVVd1ms1M4KnAjRFxOXB/3fbMzPe3NDJJkiRJw6qazL+35vs5DbYnYDIvSZIkjaNKyXxmuriUJEmS1GVM0iVJkqQeZTIvSZIk9ajKyXxEHBsRv4mI1RHxcP2jnUFKkiRJ2lylZD4iXgd8BvgVMAX4MvBV4AHgj8AH2hWgJEmSpMaqjsyfAHwEeEv5fGFmHgXsCawBlrUhNkmSJEnDqJrMzwGuAjaVj20BMnM58GHg+LZEJ0mSJGlIVZP5NcBWmZnA3RQj8oMeAnZrdWCSJEmShld10ajrgb2BK4CfACdFxC3ARuAUYFFbopMkSZI0pKrJ/Jk8Ohp/MkVSf3X5/EHg71oc12NExD4UN+AeAKwAzgJOzcxhq+hExI7A6WV8WwHfBY7LzGU1+5wNHNXg8Kdlph9SJHWtBQsW0N/fP+w+AwMDAPT19Q2735w5czjxxBNbFpskaXxUXQH2vJrvF0fEvhSJ9TTgZ5l5X5viIyJmUHx4uBF4ObAX8EmK5Py9Ixx+HvBU4I0Uc/0/BnwbeF7dfouA19e13dpM3JLUDdasWdPpECRJbVR1ZP4xMnMVRYI9Ht4MTAVekZkPAJdHxOOAUyLi42XbZiLiAOAQ4KDMvKpsuwP4RUS8ODNr41+Vmde0921ImghGGg0fz5HwKsfPnz8fgIULFzb1WpKk7jSqZD4iZgGzKGrNP0Zm/rBVQdU5FLisLmn/BsUo+0HARcMcd89gIl/G+Mtyrv+hjN+HEUlbEEfCJUnjqVIyHxF7Av8NPHuwqfya5fcJTGp5dIW5wGM+KGTm7RGxutw2VDI/l8Y35t5Ubqu1T0Q8AEymWBjrPZl5ZVNRS5qQRhoNdyRckjSeqo7MnwXMplg8ahGwvm0RbW4GxU2v9ZaX28ZyXG1pzd8Av6CYkz8TeBvFVJ4DM/OXY4pYkiRJGgdVk/lnAUdn5rfaGcwwskFbDNE+quMy84zHbIy4mCKxP4kGVXoi4ljgWIDZs2eP8PKSJElS+1RdNGqA8R2Nr7UcmN6gfUcaj7yPdNz04Y7LzDXAJcD+Q2w/MzPnZea8mTNnDvPykiRJUntVTeb/A3hnRGzXzmCGsIi6Oe7ljbjbMfxiVZsdVxpqLn29kUb9JUmSpI6qWmf+KxExF7g1Iq6hGPWu2yUbLbzUCpcC74iIHTLzwbLtSGANMNxNqpcCJ5dz368GiIh5FPPlLx3qoIiYSlHt5rpWBC9JkiS1S9VqNkcD7wYepph+Uj/lpp2j2J8HjgMujIiPUSTjpwCfqi1XGRGLgSsz8xiAzPx5RFwGnBsRb+fRRaOuHqwxX64Q+13gq8Bi4PHAicCTgH9o43uSJEmSmlb1BthTgf8BjsnM4eapt1xmLo+Ig4HPUpShXAEsoEjoa23N5uUxX1Xu+yWKKUXfpfhgMGgdsJRiJdldgLXAzykWmrq2pW9EW6xuWmRIkqRmjHRNq2qwj8FyvmNV5do40a/DVZP5nYGF453ID8rMG4EXjbDP7g3aVgCvLx+NjlkLvKIFIUpj5iJDkqRe0d/fz2+uv4HYvrkiILm+mNTx21vuHXsfDy1tKoZBvX4drprMXw08DfhBG2ORJiQXGZIkTSSx/Uy2nnd4p8Ng47UXVNpvol+HqybzxwPfjIjlwPfY/AZYMnNTKwOTJEnqhFZMJWnVNBLo3ukd6g5Vk/mbyq/nDrE9R9GXJElS1+rv7+eGRTcyc9auY+4jtw4A7l11f1OxLF1yd1PHa+KrmoB/AOuuS5KkNui2myoHBgaYOWtXjnhHu6puV3f+J87pdAjqciMm8xExiaKSzZ2Z2Zo7DSRJkkr9/f309y9i771nNdXPlClFWpO5asx9LF68BJjEtjOmNRXLeKryYajXK7ZoaFVG5hO4FjgM+H57w5EkSVuivfeexRlnvL3TYXD88aexePGdnQ6j5Xq9YouGNmIyn5mbImIJsN04xCNJkqRRqDKS3usVWzS0rSru9wXghIjYtp3BSJIkSaqu6g2wOwB7AX+KiO8Bd/HYG2IzM9/f6uAkSZIkDa1qMn9SzfdvaLA9AZN5SZIkaRxVSuYzs+p0HEmSJEnjxIWeJEmS1JMGBgaaXleg11frNZmXJElST1qzZg2//s31ZOw45j4iNwJw3W9vbyqWyJVNHT9WlZP5iDgWeAvwVGBy/fbMnNTCuCRJkqQRZezIw9s8t9NhMGnDTzvyupXmwkfE64DPAL8CpgBfBr4KPAD8EfhAuwKUJEmS1FjVG1tPAD5CMTIPsDAzjwL2BNYAy9oQmyRJkqRhVE3m5wBXAZvKx7YAmbkc+DBwfFuikyRJkjSkqnPm1wBbZWZGxN0UI/LXlNseAnZrR3CSJI3VggULHqlSMZSBgQEA+vr6htynE9UpJKmqqsn89cDewBXAT4CTIuIWYCNwCrCoLdFJknpGq5JnGL8Ees2aNW1/DalZ3VR+sb+/H7Yee+UYtV7VZP5MitF4gJMpkvqry+cPAn/X4rgkSRPQeCbPVT4MDCY2CxcubHc40pitWbOG3954I9vMfMKY+9hIAHDD0uZuc1y/ahWxo8l8N6m6Aux5Nd8vjoh9gQOAacDPMvO+NsUnSeoRJs9S+2wz8wnsfMTrOh0Gd3/uE50OQXXGtGhUZq6iGJ2XJEmS1CFVq9kQEdtFxHERcUFE/Cgi5pTtr4qIue0LUZIkSVIjlUbmI2IW8GOgj+Jm1/2AHcrNLwReDLyxDfFtkQYGBli9+iGOP/60TodCf/8Spk3bvtNhSJK0xVq3bh0b772HZeef2+lQyA3rYfWKTofxiHXr1hG5tmOrr9aKXPnITf7jqeo0m08C6yjqzd8JrK/ZdiVFRRtJUgtVqQ4zklZVsABLNEpSN6qazP8VcGxm3h4Rk+q23QE8qbVhbdn6+vrIXMUZZ7y906Fw/PGnEbFdp8MYFWtLa6Lo7+/nhkU3MnPWrmPuI7cuKljcu+r+pmJZuuTupo6XNHaTJ08mpu/UFTfA3nXGR+DhDWy89oJOh0I+uBS2gowdeXib53Y6HCZt+OmIZXfboWoyvy1FCcpGdgQ2tCYcaXxYW1q9YuasXTniHUd1OgzO/8Q5nQ5BGjfr1q1j6ZK7u+L//dIld5MbN5WFJaXNVU3m/w94JfC9BtsOBa5rWURSkyyPJ0lSe8TWW8O0x7P1vMM7HQobr72AyRtXsm5jpyPprKrJ/CeACyIC4Gtl2z4R8XLgGOBv2xCbJEnSuJs8eTI77rZz1/xVbOWdyx5zs6IeK3JlUzfARq4CIJucVhy5sqnjx6rqolEXRsR84KPAG8rmcymm3rw1MxuN2GsL0YtLuEuS1Es2LG2ums3GFcsB2Hr6jKbiyA3ru2rKz9SpU5kzZ05TfQzmMHPmzG46nmZjGYvKi0Zl5ucj4ivA/wNmAssoVn8dai699AjnqEuSNDYtSVhXFDfBz5m5c9P9rGqqh9bq6+sbccpsKyqDQfcOOI5qBdhy5dfL2xSLepRz1CWNt1ZdnFtVurNbL/KaGKokrCNp1XV4/vz5/PaWe5vqo9tMnTq10yE0pXIyHxHTgROBAyhKUd4B/Aw4PTO7Z/UASRqjbqvrPjAwwLYzpjXdz0TU39/Pb66/gdh+ZlP95PoEaCo5yYeWNhWDpPaa6B+0q64A+wzgCooylNcANwJPAE4C5kfEwZl5fduilLpUtyV/jg42p7+/n/7+Rey996wx9zFlSvFrNbO5P0QvXrwEmGQyP4zYfmbXVNSQpE6pOjL/aYo58vMy87bBxojYnaJc5WeAF7Q4NqnrtWJ0sBUjg+DoYKvsvfesrlmwbfHiOzsdhqQeUGVgqerAkYNCvadqMv8s4KjaRB4gM2+NiPcDX255ZFKPcHRQktTten1euIZWNZlfBqwbYtvacrskSZLGmSPpW7atKu73n8A7ImJKbWNETAXeDnyu1YFJkiRJGl7VkflpwJOB2yPiEuAeihtgXwqsAbaLiA+U+2Zmvr/lkUqSJEl6jKrJ/Ek137+uwfb31HyfgMm8JKltBgYGyAdXdsW9IvngUgYG1nc6jJ42MDDA6tUPcfzxp3U6FPr7l7B+/cZOhyFVVimZz8yq03EkSZJ63tIld3P+J84Z8/Er7i1WXJ2+y05Nx7HL3Ob60MQ2qhVgJUnqBn19fdy3YduuqSTV17dLp8PoaX19fWSu6pqysHfcsZy+vr6m+lm5sagNsst2zSXiu8zdiTlz5jTVhya2USfzETEZOAbYB7gL+HJmtrUYckTsQ1HL/gBgBXAWcGpmPjzCcTsCpwN/R3Gz73eB4zJzWd1+Lwc+BMwB/lT2fV6r34ckjca6deuaHh1slaVL7mb99qs7HYY0Lvr6+li4cGFTfQzWc2+2H2kkQybz5Q2tr8zMfWvaJgO/AP4MiLL5uIj4y8y8pR0BRsQMitVnbwReDuwFfJIiOX/vCIefBzwVeCOwCfgY8G3geTX9Hwh8C1gIHEdxU+/XI2J5Zn6/pW9mFBYvXtLU3MGBgWIBomZHixYvXsKcOXOb6kOSJEntMdzI/IuBS+ra/gV4OvBx4MPA04ALKZLqY9oRIPBmYCrwisx8ALg8Ih4HnBIRHy/bNhMRBwCHAAdl5lVl2x3ALyLixZl5RbnrycBVmXlc+fxHEbEv8D6gI8l8K/6ctnZtcfNOxHZN9TN16vYMDAyMuGLcSKquPDcSV6bTlmTy5MnsuNvOHPGOozodCud/4pympwtIklpvuGR+L2BBXdvfUUyteXdmJvDLiPgE0M7s6lDgsrqk/RsUo+wHARcNc9w9g4k8QGb+MiJuKbddUf6l4YUUI/K1vgF8OSJ2zMyVLXoflbUiWW3Vn/fmz5/PDYtuZNtV9zfVT25d/CHn3ib6Wbrk7qZikCRJmmiGS+Z3pKgnD0BEbAs8G7igTOQH/Q54YnvCA2Au8MPahsy8PSJWl9uGSubnAosatN9UboPiA8s2Dfa7iWIaz1OAX40t7Ilj5qxdu2ZkUJIkdVY+tLTpsrC5egUAMW16U3GAN58Pl8zfAewODI5sPwfYFvhZ3X7bAKtaHtmjZlDc9FpvebltLMftWbMPDfZbXrddkrZoK+69n5Ubl3XNlLuBgQGKS5Kk8dSqyjr9/cXEhzl7NJOM72KlH4ZP5n8CnBAR/wuspJiKsgm4uG6/ZwID7QnvEdmgLYZoH8tx9c9jiHYi4ljgWIDZs2eP8PKSNDFsWLeeDZvghqXLRt55GBvLX6/N9LNh6T1st802sLXJvDTeWnXfmtV+Wme4ZP5U4DqKqTZrgR2Az2fmbXX7HQ38uB3BlZYDjf4GsyONR95rj5vZoH16zXHLa9rq96FR/5l5JnAmwLx580b6MCFJE8Y2M5/Azkc0WgR8fC07/1xY0dx9PJ2wYMGCR/4y0Ujx1wZGrG9uIQBJtYZM5jPzloj4c4qyjjOAX2bmV2r3iYjdgB8A57YxxkU8Osd98HVnAdvReE587XHPa9A+l6I8JcAfgQ1l25V1+2wC/jC2kKXuNFIyASYU0lhUqfo1MDDAmjVrhty+enVRx3+4fQb7Gek89vyUthzDLhqVmbdTlGgcavudwL+2Oqg6lwLviIgdMvPBsu1IYA2PTcAbHXdyRByYmVcDRMQ8ivnylwJk5rqI+BFwBPCFmmOPBH7eiUo2Gl4rymSC83aHM1IiMZENDAywevVDTa3x0Cr9/UtYv35j1ywpv2HderZpqofW66ab8FZEcu/SpTS3sHrxx94HH1o77F4PPrSWe5cuH2aPjQwMDJjMS1uIZn7rjJfPU8zXvzAiPkaRjJ8CfKq2XGVELAauzMxjADLz5xFxGXBuRLydRxeNurqmxjzAB4EfR8TpFCP2Ly0fL2n7O9OorVmzht/eeCPbzHxCU/1sqfN2q1zcncfYPbbeemvmzm1u0bZWLSm/ctoy1jfVQ2tNnTq1JTe+teomvEWLFrF+Q5KxY9MxNSsch5K2KF2fzGfm8og4GPgsRRnKFRT170+p23VrYFJd26vKfb9EUWryu9TVlM/MqyPicOBDwFuAW4BXd3L1Vw3Pebtql76+PjJXccYZb+90KBx//GlEbNc1S8rPnz+/6ZtfW6mvr68lHzhb+fP59W+ub6qPyKIwXDa52B+MPE1O0sTR9ck8QGbeCLxohH12b9C2Anh9+Rju2G/z6Dx61RgYGGDlQw90RY33pUvuJjdueqTUkCR1i9b8laC/7Kv5SmmW65O2HD2RzEvdat26deSa5ufttkI+uJRFi+7vmjrg4E142nJ008rdkrYsJvMaVl9fH9uuur9rVoBdeWd3zdvtNhs3buTXv7m+qXm7kRsBuO63tzcVi/N2pccaqZpU1Q/SfkiWVGtUyXxEPB74S2Bn4KLMvD8ipgDrM3NTOwKUutnkyZPZuPWObD3v8E6HwsZrL2DyxpWs2ziZh7d5bqfDYdKGn3Y6BKmnTJ06tdMhSOpBlZL5iAjg4xRlKLelqJ/1LOB+4DvA1RRVYSRJE9DAwAAbVj5Q3PjdYRvuvYeBdb1XQtXRdEntsFXF/d4NvBX4APAceMw9iBcBL2txXJIkSZJGUHWazRuBD2TmRyKivvzjYmCv1oYlSeomfX19rJy8rGvKwvbN3HnE/aqseFxlnrpz1CV1s6rJ/JOAa4bYth5oviiuJEnjzHnqknpd1WT+DmA/4EcNtj2DYqElSR22bt06Itd2xc2nkSsZGBjodBjagjma3lsWL17C8cef1lQfAwP3AtDXN/YVfRcvXsKcOc2tvCyNp6rJ/PnA+yLi1zw6Qp8R8RTgbcCZ7QhOkiRNfK1dpS1YAAAa6UlEQVRa5Grt2qK0bjSxiu6cOXNddEs9pWoyfwrw/4CrgNvKtvOBWcDPgI+2PDJJozZ58uSuKk3pkvKSqmjVX1FceEtbokrJfGauiYgXAK8GDqG46XUZRTnK/84sV5mRJEmSNG4qLxqVmQ8DXykfUkesW7eOjffe0zW1rjM3uY6yJEnqmKp15iVJkiR1maorwN5CseprI5uAlcB1wKcz8/ctik3azOTJk4npO3VNrettV9yPc8wmlmYrarSimsZgHFbUkCSNpOoEgSuBFwK7UNzweg/wBOC5wN0UN8X+DfDaiDg4M3/Whlglqa1aUcGiFdU0ilisqCFJGlnVZP4nwP7AczLz7sHGiHgicBlwKfBa4AfAqcBftThOSWq7VlTUsJqGJGk8VZ0z/07gA7WJPEBm3gV8CHhnZq4CzgCe3doQJUmSJDVSdWR+FrBuiG1rgSeV398BbNtsUOouS5fczfmfOKepPlbcez8A03fZqak4tp20TVNxSJIkTSRVk/mbgLdFxPcz85GkPiKmAG8vtwPsRjGfXhNEq+bsrty4DIBdtht7Mr/L3J0YGBhgfUsikiRJ6n1Vk/l/B74L3B4RlwD3UtwM+1JgevkVilViv9/qINU53bYq3/z581m5dFkrQpqwIlcyacNPmzh+FQDZ5A2ckSubOl6SJI2s6gqwV0TE/sB7gecDTwTuAq4APpSZN5X7HdeuQKVBG5Y2v2jUxhXLAdh6+oym4th2m+6a9jN16tSm/5rS398PwJw5s5uOx2oskiS112hWgL0ReHUbY5FG1KrksH9FMYd/zsydx97JzJ0ZGBjgoeVL2XjtBWPuJlevACCmTR97LEA+tJS+P9u3JX/9AKuxSJLUC1yIXj2l26b9LFiw4JGR7LHq7y+mo8zZo7lFhmAXR8IlSdrCVE7mI2IX4B+BpwJT6jZnZh7TysCkXmBdckmS1EmVkvmIeCpwDTAJ2A64D9ipfL4c8E43SZIkaZxVXTTqE8AvgScAARwKTAXeCKwG/r4t0UmSJEkaUtVpNs8C3syjC0dtlZkbgS9FxOOB04EXtiE+SVKX6KZKUjRz87p6UpV7lAa3D05fbGTOnDktmSI5UjxVYmllPN2kVf9WMDF/Pq1WNZnfHrg/MzdFxErg8TXbrgXe1/LIJEldo9sqSXmztxqZOnVqp0N4RDfF0o38+bRO1WT+VmDX8vubgSOA75XPXwasaG1YkqRu0m2VpLTl6bbR2W6Lp5v4sxlfVefMXw78Vfn9p4DXR8TNEXEDcDzwpXYEJ0mSJGloVUfm3w1MBsjMb0bEGuBIYBpwBvDF9oQnSd2lm+bJOi9VkjRiMh8Rk4C5wJ2DbZl5EXBRG+OSpJ7UbfNAuy0eSVJrVRmZT4qbXA8Dvt/ecCSpu3XT6HU3xSJJ6owR58xn5iZgCcViUZIkSZK6RNU5818AToiIizNzfTsDkprVbbWIu4lzrCVJmliqJvM7AHsBf4qI7wF3UUy/GZSZ+f5WBye1i/OIh+bPRpKk3lE1mT+p5vs3NNiegMm8uoKjxUPzZyNJ0sRSKZnPzKr16CVJkiSNE5N0SZIkqUdVTuaj8LcRcVpEfDkinly2HxQRu7UvREmSJEmNVJpmExEzgEuA5wAPUNwQ+xngNuBNwP3AcW2KUZIkSVIDVUfmPwHMAp4LPB6Imm1XAAe3OC5JkiRJI6iazL8ceE9m/pzHlqQEuJ0i0W+biHhTRPRHxNqIuC4iKn14iIjnRsQvImJNRNwSEZv99SAissHjmta/C0mSJKm1qibz2wN3DLFtCo8dqW+piHgV8HngXOBQ4AbguxGx3wjH7Q1cBtwCHEax8NWnIuKNDXb/JHBAzeOYlr0BSZIkqU2q1pm/Gfhriik19Q4Crm9ZRJs7FTgnMz8IEBFXAs8E3gW8Zpjj3gHcCbwmMzcCP4yI2cD7I+K/MrP2Lwy3Zqaj8ZLUBFcYlqTxVzWZ/xzwuYhYCXytbJseEa8H3goc247gImJP4CnA8YNtmbkpIs6vbRvCocDXykR+0DeAtwD70d4PIFsUL+DDG+nnsyX/bLTlcYVhSWqtqotGfTEi9qIYJf9A2Xw5sAn4eGb+d5vim1t+XVTXfhOwU0TMzMyl9QdFxHYU8/gbHTfYb20yf0pEnA6sAP4XeHtm3t9s8HqUF/Ch+bPRROGHTUkaf1VH5snMd0XEfwJ/BewCLAMuz8w/tSs4YEb5dUVd+/Ka7Zsl88D0CscNOge4qOxnHnAy8IyIeHZmPjyWoLc0XsCH589HkiS1S9U685My8+HMvA04q5kXjIgdgSeOtF9m1o6q11fQiSHaN+tmpPbMPLqm/aqIuImipv7fAN+uPzAijqWcVjR79uwRXl6SJElqn6oj83dGxNeBr2TmdU2+5hHAFyvsFzw6kj4dWFmzbaiRd+rap9e1DzXSX+t7wEPA/jRI5jPzTOBMgHnz5o30YUKSJElqm6qlKS+kqBzzy4i4MSLeVVaGGbXMPCszY6RHufvg6Pzcum7mAvc3mi9fvsYqYMkQx9X22+jYwQTdRF2SJEldrVIyn5lvoZga80qKm0jfD/wpIn4YEUdHxA7tCK6cj/8HitF8ACJiq/L5pSMcfinw9xExqabtSIok//dDHRQRL6Goq9/sXyAkSZKkthrNDbAbKKadfLuc934kxWj9WcBnKRLgdjgF+GpE3Ar8FDgKmAO8enCHiDgI+AFwcGZeWTZ/Avgn4CsR8UXgWcA/A28ZHH0v57/Po6iffx/F1Jr3Ar8ELm7T+5EkSZJaonIyXyszV0bEpcDOwJ5UuKF1rDLz6xGxPfBOikozNwAvy8za0fUAJlGzEm1mLi5H2T9FMUp/N/C2zKy9gfePFB8OXgk8rtznXOBkK9lIkiSp240qmS+n0xxBMSL/fGAtRVnHr7Q+tEdl5hcZ5qbZzPwxNYl8TfvVwLOHOe4HFCP6kiRJUs+pWpryMOC1FOUapwBXUZRnPD8zH2xfeJIkSZKGUnVk/iLgZuDDwFcz8/b2hSRJkiSpiqrJ/HMy81eNNpQ3nx6VmW9oXViSJEmSRlK1NOVjEvmI2DsiPhARtwA/Av6hHcFJkiRJGlrVRaOIiB0j4tiIuJpiys17KFZofQuwW5vikyRJkjSEYafZlAs0vQR4HfC3FDe/3gl8DvgX4ITMvKrdQUqSJEna3JDJfEScRrHo0i4UJSj/BziHYoGlxwFvHY8AJUmSJDU23Mj8vwEJXAIcnZnLBjdERLY7MEmSJEnDG27O/JeAB4HDgJsj4rMRMeQCTJIkSZLG15DJfGa+EdiVYrXX64A3Az+PiJuAd1KM2kuSJEnqkGGr2WTm2sz8WmYeAswCTgIeBt4FBPDRiHhNRExpf6iSJEmSalUuTZmZd2XmxzJzP+A5wEJgDnAucFeb4pMkSZI0hMrJfK3M/FVmvpWivvzhwJUtjUqSJEnSiIatMz+SzNwAXFg+JEmSJI2jMY3MS5IkSeo8k3lJkiSpR5nMS5IkST3KZF6SJEnqUSbzkiRJUo8ymZckSZJ6lMm8JEmS1KNM5iVJkqQeZTIvSZIk9SiTeUmSJKlHmcxLkiRJPcpkXpIkSepRJvOSJElSjzKZlyRJknqUybwkSZLUo0zmJUmSpB5lMi9JkiT1KJN5SZIkqUeZzEuSJEk9ymRekiRJ6lEm85IkSVKPMpmXJEmSepTJvCRJktSjTOYlSZKkHmUyL0mSJPUok3lJkiSpR5nMS5IkST3KZF6SJEnqUT2RzEfEmyKiPyLWRsR1EXFwhWPmRcTZEXFzRGyKiLOH2G9yRHwyIu6NiFURcXFE7N7ityBJkiS1XNcn8xHxKuDzwLnAocANwHcjYr8RDn0ucCDwK+DuYfb7NHA08HbgcODxwOURMaW5yCVJkqT22rrTAVRwKnBOZn4QICKuBJ4JvAt4zTDHfSYzzyiPubbRDhHRBxwDvCEzzy3b/g+4pez7rFa9CUmSJKnVunpkPiL2BJ4CfHOwLTM3AedTjNIPqdxvJH9dfr2w5rg7gKtH6l+SJEnqtK5O5oG55ddFde03ATtFxMwW9D+QmQ816H9ug/0lSZKkrtHtyfyM8uuKuvblddub6b++78H+G/YdEcdGxLURce3SpUubfHlJkiRp7MZ9znxE7Ag8caT9MrN2ND7ruxmifSwa9RFD9Z2ZZwJnAsybN68Vry9JkiSNSSdugD0C+GKF/YJHR+CnAytrtk0vvzYaVR+N5TV91Zregr4lSZKkthr3aTaZeVZmxkiPcvfB0fn6+etzgfszs9l5LouAWRGxXYP+6+fpS5IkSV2lq+fMZ+afgD9QjOYDEBFblc8vbcFLfL/8+vc1/e8GPK9F/UuSJElt0wt15k8BvhoRtwI/BY4C5gCvHtwhIg4CfgAcnJlXlm0zgYPKXWYAT46IwwEy84Ly60BE/BdwekQEsLR8vduAr7b7jUmSJEnN6PpkPjO/HhHbA+8ETqZYAfZlmfn7mt0CmMSjN8YC7EtRj37QnsALavYfdBywCvgUMA24EvjHzFzbwrchSZIktVzXJ/MAmflFhrlpNjN/zGMT9IZtQxy7Dvi38iFJkiT1jK6eMy9JkiRpaCbzkiRJUo8ymZckSZJ6lMm8JEmS1KNM5iVJkqQeZTIvSZIk9SiTeUmSJKlHmcxLkiRJPcpkXpIkSepRJvOSJElSjzKZlyRJknqUybwkSZLUo0zmJUmSpB5lMi9JkiT1KJN5SZIkqUeZzEuSJEk9ymRekiRJ6lEm85IkSVKPMpmXJEmSepTJvCRJktSjTOYlSZKkHmUyL0mSJPUok3lJkiSpR5nMS5IkST3KZF6SJEnqUSbzkiRJUo8ymZckSZJ6lMm8JEmS1KO27nQAGpsFCxbQ398/5PbBbfPnzx+2nzlz5nDiiSe2NDZJkiSND5P5CWrq1KmdDkGSJEltZjLfoxxNlyRJknPmJUmSpB5lMi9JkiT1KJN5SZIkqUeZzEuSJEk9ymRekiRJ6lEm85IkSVKPMpmXJEmSepTJvCRJktSjTOYlSZKkHmUyL0mSJPUok3lJkiSpR/VEMh8Rb4qI/ohYGxHXRcTBFY6ZFxFnR8TNEbEpIs4eYr9s8Lim5W9CkiRJarGtOx3ASCLiVcDngVOAq4HXA9+NiGdl5u+HOfS5wIHANcAOI7zMJ4ELap4/OOaAJUmSpHHS9ck8cCpwTmZ+ECAirgSeCbwLeM0wx30mM88oj7l2hNe4NTMdjZckSVJP6eppNhGxJ/AU4JuDbZm5CTgfOHS4Y8v9JEmSpAmrq5N5YG75dVFd+03AThExs0Wvc0pEbIyI+yLiSxGxU4v6lSRJktqm26fZzCi/rqhrX16zfWmTr3EOcFHZzzzgZOAZEfHszHy4fueIOBY4FmD27NlNvrQkSZI0duOezEfEjsATR9ovM2tH47O+myHaRy0zj655elVE3ARcAvwN8O0G+58JnAkwb968pl9fkiRJGqtOjMwfAXyxwn7BoyPw04GVNduml1/rR+xb4XvAQ8D+NEjma1133XX3RcRtbYhB4+PxwH2dDkLaAnnuSZ3hudfbntyocdyT+cw8Czir4u6Do/NzgdqkeS5wf2Y2O8VmM5mZEQEVRv0zs1Vz9tUBEXFtZs7rdBzSlsZzT+oMz72JqatvgM3MPwF/oBjNByAitiqfX9qO14yIlwDbA9e1o39JkiSpVbr9BlgoFov6akTcCvwUOAqYA7x6cIeIOAj4AXBwZl5Zts0EDip3mQE8OSIOB8jMC8p9jqW46fUKij877Q+8F/glcHGb35ckSZLUlK5P5jPz6xGxPfBOikozNwAvq1v9NYBJPHpjLMC+FPXoB+0JvKBmf4A/Unw4eCXwOOBu4Fzg5EaVbDThnNnpAKQtlOee1BmeexNQZFqQRZIkSepFXT1nXpIkSdLQTObVlSLi6IjIBo831+wTEXFSRCyJiDURcVVE/HldP7uXx+1e07Z3RHwhIn4XEQ9HxI+HiOHWBq9/d4P99omIH0TE6oi4MyI+EBGTWvbDkNqoyvnQynMhIs6OiLPb2P+Ivxek8daqa1q531jPs/kRcXFELCtf+wVDxNqy88zr4/jo+jnz2uK9CFhT8/xPNd+/i+I+indQlDH9N+CKiNgvMzdLBGrsC7wUuAbYdoTX/xrwmZrn62s3RsQMihuobwReDuwFfJLig/J7R+hb6gZVz4d2nwut6n+svxek8dDUNa3J8+x1FGW3LwP+sdEOrTzPvD6Oo8z04aPrHsDRFL90th9i+xSKhcTeV9O2HbAU+FBN2+5lP7vXtG1V8/0FwI+HeI1bgdNGiPPdFIubPa6m7d+B1bVtPnx066PK+dDKcwE4Gzi7Hf1X/b3gw8d4P1p4TWvmPNuq/LpfGcsLGsTRsvPM6+P4PZxmo171/ygqEH1zsCEzVwEXAYcOd2BmbmphHIcCl2XmAzVt3wCm8mhpVKlrtfB8aPe5UKX/Mf9ekDqs6v/dMZ9nFc/1Vp5nXh/Hicm8ut0fI2JjRNwcEf9c0z4XeBjor9v/pnJbq7whItZHxMqIuCAi6pdSnsujKxUDkJm3U4w8tDIOqdPafS60ov/x+r0gjVWz17R2X3NaeZ55fRwnzplXt7qLYj7eLynWEPhH4PMRMS0zF1AsBPZQbr4ewHJgWkRsm5nrac53KOYRDwBPA94P/CQi/iwzV5b7zABWNDh2eblNmgjafS60qv/x+L0gjUWrrmntvua08jzz+jhOTObVlTLzMoqbdAZdGhGTgfdGxBmDuzU4NIbZNtoYjq95+pOI+BnwW+D1wOm1uw4Rh4s4aEJo97nQ4v7b+ntBGosWX9Pafc1p5Xnm9XEcOM1GveQCYCeKm1qXAzs0KHE1HVidmRta/eJZrDp8M7B/TfPy8jXr7UjjEQmp57X7XGii/3H/vSA1YSzXtHZfc1p5nnl9HCcm8+pFSTEPbxKwd922zeboten1By2ibu5fRMyiuLO/3XFIndbuc2G0/Xfy94I0VqO5prX7mtPK88zr4zgxmVcveSVwH3Ab8DPgAeCIwY0RMQ34G+DSdrx4ROwHPBW4rqb5UuCQiNihpu1IijrCV7YjDqnT2n0uNNH/uP9ekJowlmtau685rTzPvD6OE+fMqytFxLcobhT6P4oRgCPLx3Flea21EfFR4OSIWM6ji1ZsxWMXnmnU9zSKRXIAngQ8LiIOL59fkpmrI+Iw4DXAd4E7KUYX3gvcTlG/d9DngeOACyPiY8CewCnAp+rKcUldaaTzAXghbTwXWnmuZeaYfy9I7dTCa9qYrzkRMY9iSs+ssumgiHg8cGtmXlu1/1GcZ14fx0unC9378NHoAfwHxZzZ1RSf4q8DXlu3TwDvoaiAsQb4CfDMun12Z/NFowbbGj12L/d5OvADikUwNgB3UyQWuzWIdR/gh2UMdwEfBCZ1+mfow0eVx0jnQ6vPBeoWs2lD/yP+XvDhY7wfrbqmlfuN+jyraWt0ntfv17LzzOvj+Dyi/GFLE1JE7A7cAuyRmbd2NBhJRMTZAJl5dGcjkSYuz7Mti3PmJUmSpB5lMi9JkiT1KJN5SZIkqUc5Z16SJEnqUY7MS5IkST3KZF6SJEnqUSbzkiRJUo8ymZekCS4iXhcRt9U8vyki3jLKPmZFxMMRsb5cNbKZeE6IiFc0aD8lIjIiXJ1ckioymZekie8vKFacJCK2B54y+HwUXkdxzdgG+Mcm4zkB2CyZlySNnsm8JE18jyTz5febgP8bZR+vA34P3A4c1brQxldETO50DJLUSibzkjSBRcRWwJ8Dvy6b/gK4MTPXjqKPAyhG888FvgL8RUTsW7fP7uUUmaPr2l9Qtr+gfH4r8GTgn8r2HFx6vsYeEXFxRDwUEbdFxPvK91Hb71Mj4n8iYkVErImIayLiJXX7DE7b2S8iLouIh4BvltsOiYifRsTK8nVujoj3Vf2ZSFK3MJmXpAkoIm6NiAQeBrYDLimffxJ4ek0ivXuF7o6iGM3/b4qEHoqR+rH4e+Bu4DLggPLxwbp9/gf4IfB3wLeBU6n5a0BE7AZcDTwDeCvwD8AK4OKIOLTBa34HuBL4W2BBROwJ/C9wK3Bk2f4pip+TJPUUbzKSpInppcC2FEn3IcA/le1XAe8HflQ+v3O4TsppKUcCV2TmnWXbNcBrIuLdmblpNEFl5m8iYh1wX2ZeM8Run8zML5ffXxERL6KYpz/Y9m/ADOCAzFxcxnQJcCPwYeDSuv4+nZln1Lynwyl+Nm/JzAfK5h+O5n1IUrdwZF6SJqDMvDEzfwvMAn5cfr8K2AE4PzN/Wz7Wj9DVy4HpPDoiD3AOsBvw4jaEDnBx3fPfA7Nrnj8fuGYwkQfIzIeBrwN/HhGPqzv+f+qe/xbYAHwjIg6PiF1aE7YkjT+TeUmaYCJiUkRsXZZ4fC7w8/L75wF3AHeX26NCd0cBq4EfRcT0iJhOMUVmA+27Efb+uufrgCk1z3cC7mpw3N1AUIza13rMvuWHgEMoroFfofh5/CIiDmomaEnqBJN5SZp4fkCRbG8AnkiRsG4A/gt4Us22YZPXiHgC8NfANIoPAcvLx58oSlT+fUTsUO4+eEPttnXd7Nzke2nkfmDXBu27AsnmHwayfsfM/FFmvoTirw4vpvh5XNxsDX1JGm/OmZekieefKabTHElxE+lgXfhLgDMoRtYBbh6hn9dQXCfeAiyq2/YM4HTgCOBLwD0UI+j71e13WIN+1wFTR3oTw7gSOCEids/MW6H4awTF+/1NZj5YtaPMXAf8sKy//x1gD+C+JmKTpHFlMi9JE0xm3gwQEScDF2fmtRHxVODxwH9l5t0Vu3odcAvwhcx8zOh2RPwE+HeKqTZfysyMiPOAYyLiDxQfFA4DXtCg3xuB50XEyyimxtw3mJRXtAA4Grg8It4PPADMpyif2ejDw2NExJsp5t1fAiyh+Lm8m+Jm4N+PIg5J6jin2UjSBBQR2wIHA98rmw6lGLWulMhHxDOBpwNfrk/k4ZEbTs+mSMr3KJuPBy4ETgHOo5jn/q8Nun83RbL/TeBX5f6VlVV1DgRuAP4TuIBiHv1hmfm94Y4t/Y6iDOVHgO8Dn6X40PKizFwzmlgkqdOiwe9oSZIkST3AkXlJkiSpR5nMS5IkST3KZF6SJEnqUSbzkiRJUo8ymZckSZJ6lMm8JEmS1KNM5iVJkqQeZTIvSZIk9SiTeUmSJKlH/X/pp36G51MbdQAAAABJRU5ErkJggg==\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -973,7 +1323,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -983,7 +1333,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -993,7 +1343,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
+      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1003,7 +1353,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1013,17 +1363,17 @@
    ],
    "source": [
     "for group in grouped.groups.keys():\n",
-    "    #gr2= grouped.get_group(group).groupby(by=['set', 'modeltype', '# Authors'], as_index=False).mean()\n",
+    "    #gr2= grouped.get_group(group).groupby(by=['set', 'modeltype', 'No. of Authors'], as_index=False).mean()\n",
     "    gr2 = grouped.get_group(group)\n",
-    "    gr2.loc[:, ['Featuretypes & Model']] = gr2.apply(lambda x: '({}, {})'.format(str(x['set_feat']), x['modeltype']), axis=1)\n",
-    "    #print(np.unique(gr2['Featureset & Model']))\n",
+    "    gr2.loc[:, ['Feature Types & Model']] = gr2.apply(lambda x: '({}, {})'.format(str(x['set_feat']), x['modeltype']), axis=1)\n",
+    "    #print(np.unique(gr2['Feature Set & Model']))\n",
     "    #print(gr2.head())\n",
     "    fig, ax = plt.subplots(figsize=(12,8))\n",
     "    #print(gr2.head())\n",
     "    #sel = (gr2[\"Average Spearman's Rho (ext.)\"]>= -0.15) & (gr2[\"Average Spearman's Rho (ext.)\"]<=0.15)\n",
     "    sel = [True for _ in range(gr2.shape[0])]\n",
     "    #print(gr2.loc[sel, : ].head())\n",
-    "    sns.boxplot(x='# Authors', y=\"Average Spearman's Rho (ext.)\", hue='Featuretypes & Model',\n",
+    "    sns.boxplot(x='No. of Authors', y=\"Average Spearman's Rho (ext.)\", hue='Feature Types & Model',\n",
     "                   data=gr2.loc[sel, : ], #exclude outliers\n",
     "                  palette=colors, dodge=True, ax=ax, showfliers=False,\n",
     "                hue_order=list(colors.keys()))\n",
@@ -1034,7 +1384,7 @@
     "    ax.tick_params(axis='both', which='major', labelsize=15)\n",
     "    ax.tick_params(axis='both', which='minor', labelsize=13)\n",
     "    ax.set_ylabel(\"Average Spearman's Rho (ext.)\", fontsize=16)\n",
-    "    ax.set_xlabel(\"# Authors\", fontsize=16)\n",
+    "    ax.set_xlabel(\"No. of Authors\", fontsize=16)\n",
     "    plt.setp(ax.get_legend().get_texts(), fontsize='12')\n",
     "    plt.setp(ax.get_legend().get_title(), fontsize='14')\n",
     "    os.makedirs(os.path.join(savedir, '..', 'figures', str(group[1])), exist_ok=True)\n",
@@ -1051,7 +1401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 98,
    "outputs": [
     {
      "name": "stderr",
@@ -1063,7 +1413,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -1071,7 +1421,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -1079,7 +1429,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -1087,7 +1437,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -1095,7 +1445,7 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n",
       "C:\\Users\\schubert\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -1103,14 +1453,14 @@
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  self._setitem_single_column(ilocs[0], value, pi)\n",
-      "<ipython-input-60-8ff6affd3a9e>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "<ipython-input-98-c07dc11c6ad2>:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
       "  fig.show()\n"
      ]
     },
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1120,7 +1470,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1130,7 +1480,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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jx483aPvixYtUr16drKwsypQpg6enJ23btuWnn37ixIkTtGrVinXr1uHg4ADAwYMHmTRpEmfOnMHNzY3Fixfj6emp1efp6UloaCju7gZP3BbiiSNBtxBCCHGX69fTWLx4wmOrb8KExQ8sc/LkSZ577jltOzc3l6FDh7Jx40ZycnIYNmwY48aNY9u2bY/cn0OHDvHqq6+SnJzM1q1b6d27N3/++Sf29vY4OTmxY8cOatSoQUREBN26daNZs2Y0adKEBQsW4OLiQlJSEpAXECuKQm5uLj169KBXr16sX7+euLg4OnXqxHPPPUfXrl0f2J9169YRFhaGq6sr3bp149NPP+WTTz7hypUrdO/enbVr1/Lyyy/z448/0qdPH/744w8cHR0BqFevHvv373/keyJESZD0EiGEEKKUpaWlodPptO0KFSrQp08fypUrh06nY/r06YSHhz+WtpycnPD398fMzIx+/frx3HPPERoaCkD37t2pWbMmiqLQvn17unTpwr59+wAwMzMjISGBS5cuYWZmRtu2bVEUhcOHD5OUlMTMmTMxNzenRo0ajBw5kg0bNhSqP0OHDqVOnTpYWlri4+NDZGQkACEhIXh5eeHl5YWJiQmdO3fG3d2dnTt3aufqdDrS0h7ftxJCFCcJuoUQQohSZmdnR3p6urZ98+ZNRo0ahZubGzY2NrRr1460tLTHsupHlSpVUBRF23ZzcyM+Ph7IS3Np2bIl9vb22NrasnPnTpKTkwGYMmUKtWrVokuXLtSoUYNPPvkEgEuXLhEfH4+tra32mjNnDn///Xeh+uPs7Kz9u1y5ctpEy0uXLrFp0ya9evfv309CQoJWPj09HVtb20e7IUKUEEkvEUIIIUpZo0aNOHfuHM2aNQNgwYIFnD17lkOHDuHs7ExkZCQvvvgiqqo+cltXrlxBVVUt8L58+TI9e/bkzp079OnTh+DgYHr16oWZmRne3t5amzqdjgULFrBgwQJOnz7NSy+9RLNmzXB1daV69epER0c/ct/u5urqiq+vLytWrCiwTFRUFI0bN36s7QpRXGSkWwghhChlXl5eeukj6enpWFpaYmtrS2pqKrNnzy50XZ6engQEBBR4PDExkSVLlpCVlcWmTZuIiorCy8uLzMxM7ty5g6OjI2XKlCEsLIwffvhBO2/Hjh3ExMSgqio2NjaYmppiampK8+bNsbGxYe7cudy6dYucnBxOnTrF4cOHH+pe5Bs0aBDbt2/n+++/Jycnh9u3b7N3717i4uK0MuHh4XTr1u2R2hGipJR40K0oSn1FUX5UFOWmoijxiqK8ryiKaSHOc1cU5QdFUVIURUlVFGWPoigtSqLPQgghRHHy8/Nj586d3Lp1CwB/f39u3bqFg4MDLVu2LNJa1LGxsXh4eBR4vEWLFkRHR+Pg4MD06dPZvHkzFSpUQKfTsWTJEnx8fLCzs2PdunX07NlTOy86OppOnTphbW1Nq1atGDNmDJ6enpiamrJ9+3YiIyOpXr06Dg4OjBgxgmvXrj38DSFvpPt///sfc+bMwdHREVdXV+bPn09ubi4Ahw8fxsrKiubNmz9SO0KUFOVxfFVV6MYUxQ44DZwB5gI1gQXAIlVVZ9znPFfgJHDsn/IAU4AXgUaqql66X7vu7u6qrOMphBDCmKioKOrVq6dtl8aSgQDTpk3TJjk+rLi4OPr27cuBAwceuo5/iz59+jB8+HC8vLxKuyviGXbv7w8ARVGOqqpqsI5lSQfdU4G3ATdVVa//s+9tIABwzt9n5LzRwBdABVVV0/7ZZwckA+NUVf3v/dqVoLtoFi1aVGBuXv7XesbWiq1duzYTJ04s1r4JIcTjZuyPphBCFEZRgu6STi/pBnx/T3C9AbAE2t/nPDMgG7j72bEZ/+xTjJ4hisWtW7e0rz+FEEIIIUThlPTqJXWBn+7eoarqZUVRbv5zbHsB520B3gcWKIry0T/7ZgJXgU3F1Ndn1v1Gq8eMGQPAsmXLSqo7QgghhBD/eiUddNsBxhLlrv5zzChVVeMVRXkJ2AHkP1c2AeiqqmrSY+/lE+hpTfko6Lrud03w5F+XEEIIIcTdSmOdbmNJ5EoB+/MOKkolYDNwFBjxz+6xQKiiKK1VVb1s5JzXgdcBqlat+qh9fqI9bLrH/QL5guSXzx/xLqyiBsmSwiKEEEKIp0lJB91XAWOPjiqP8RHwfFPI6+urqqpmASiK8hMQDUzm/0e/NaqqfgV8BXkTKR+t26WvOFI+oqOjiTxzBjPHioU+J/ufFPrTSSmFPicrqeCnkhV0XcWRxiKj6kIIIYQoLSUddP9BXu625p/lAK3+OVaQusDp/IAbQFXVTEVRTpO37KB4SGaOFanQ169Y20jZFFys9T8qGVUXQgghRHEr6aA7DJiiKIpOVdX0f/b1A24B4QWfxiXAS1EUc1VVMwEURbEAGlLw5MsSIaOn/x4lOaouhBBFNXXqVCpWrFiodbo9PT0ZNGgQI0aMIDAwkJUrV7J///4S6OXjUa1aNVauXEmnTp2Kva0hQ4bg4uLChx9+yIkTJxg9ejS//vprsbcrxL1KOuheTl4qyFZFUeYCNchbo3vh3csIKooSA4Srqjr8n10rycvl/lZRlGXk5YCPBSrxTwrJk+bfMHoaFxdH1rXrxT4SnZX4N3F3SuZ+PMl56kIIUZCkpCSCg4OJiYkp7a48cRRFITo6mlq1aj1yXY0aNcLW1pbt27fTo0ePx9A7IQqvRINuVVWvKorSEVhK3gh1GrCIvMD73n6Z3nXeUUVRXgZmAWv/2X0S6Kyq6vHi7vf9yOjpkyU6Oppjv59EVcoX+hxFzQbgaKTBfNz7nPNojzcWQoi7BQYG4uXlhaWlZWl35ak3cOBAvvzySwm6RYkr8dVLVFU9A3R4QJlqRvb9CPxYTN16Jrm4uHDNIqVEcrpdHCsUaxt3U5Xy5Jh5FGsbplm/FGv9QojSM3HyJFLTrj62+uxt7Vj06cL7lgkLC2PYsGHa9tWrV/H19eXQoUNkZ2fj4eHB8uXLC0xZLIoVK1awcOFC4uLicHV1JSQkhCZNmhiMKN+dlrF3714GDRrE+PHj+fTTTzE1NeW///0v5ubm+Pv7k5yczOTJk5k2bZrBuYB2fn7q5d1+++03JkyYQFRUFJaWlvTp04eFCxdibm5Ou3btAGjcuDGKorBq1Sr69evHjh07mDFjBhcvXqR+/fosX76cRo0aAfD7778zfPhwoqOj8fLyQlH0n6Hn6enJiBEjuHPnDhYWFo98P4UorNJYMlCIYhMXF4eiXiv2oFhRrxn94yGE+PdLTbuKl7/PY6tv52cbH1jm5MmTPPfcc9p2bm4uQ4cOZePGjeTk5DBs2DDGjRvHtm3bHqkvmzZtIiAggG3btuHu7s758+cxMzMr1Ll//fUXt2/f5sqVKwQGBjJy5Eg6d+7M0aNHuXz5Mk2bNuW1116jRo0aReqTqakpixYtwt3dnbi4OLp168ayZcvw9/cnIiICRVE4fvy49mHg2LFjDBs2jO3bt+Pu7k5ISAg9e/bk7NmzKIqCt7c3/v7+jBs3jv/973/079+fd955R2uvSpUqmJmZcfbsWS1QF6IkSNAtil122lWi01KLlDP9sHnWaWn3W3lSCCGeTGlpaeh0Om27QoUK9OnTR9uePn06L7300iO3s3LlSt5++22aNWsGUKQ8aTMzM6ZPn46pqSmvvfYar7/+OhMmTECn09GgQQMaNGjAiRMnihx0N23aVPt3tWrVGDVqFOHh4QVOKF2xYgWjRo2iRYsWAAwePJg5c+Zw8OBBFEUhKysLf39/FEXh1VdfZeFCw28ZdDqd/L0QJU6CblHs1MxMMnJVIv9MLMI5eUurF+UcADUrG1WxL5H0ksfxNa8QQgDY2dmRnp6ubd+8eZOJEyeya9curl7NS3VJT08nJycHU1PTgqp5oNjYWGrWfLiVditUqKC1nZ97XrHi/z/nwdLSkoyMjCLXe+7cOSZNmsSRI0e4efMm2dnZeoH4vS5dukRQUBCff/65ti8zM5P4+HgURaFKlSp6KSVubm4GdaSnp2Nra+yxIUIUHwm6RYlQrB0p4/5qsbeT9fN/IbfYmxFCiMeqUaNGnDt3ThuBXrBgAWfPnuXQoUM4OzsTGRnJiy++iKo+2rPeXF1dOX/+vNFj5cqV4+bNm9r2X3/99dCDC1ZWVgZ1FeSNN97gxRdfZP369eh0Oj777DM2b95cYHlXV1emT5/O9OnTDY6Fh4dz5coVVFXVAu/Lly/rfdCIj48nMzNTL51HiJIgQfczLivp7yItGZj9z+SiMrZ2hT5HzcpEeXAxIYR4Znl5eREeHs7AgQOBvJFYS0tLbG1tSU1NZfbs2YWuy9PTE09PTwICAgyOjRgxgkmTJtGmTRuaNGmi5XS7ubnxwgsvsG7dOho0aMDu3bsJDw/H3d39oa7nhRdeYMGCBcyYMYPMzEw+++yzAsump6djY2ODtbU1f/zxB//9739xdHTUjlesWJELFy5oqTAjR47klVdeoVOnTjRv3pybN2+yd+9e2rVrR6tWrShTpgxLlixh7NixfPfdd/z22296qTl79+6lQ4cOMolSlDiT0u6AKD21a9fmhfr1aeBYodCvcqiUQy3SOdZWVqV9qUII8UTz8/Nj586d2jMe/P39uXXrFg4ODrRs2ZKXX3650HXFxsbi4WE8xa5v375Mnz6dAQMGoNPp8Pb2JjU1FYDFixezfft2bG1t+frrr/H29n7o6/H19aVx48ZUq1aNLl260K9fvwLLfvrpp6xbtw6dTsfIkSMNygYEBDB48GBsbW3ZuHEj7u7urFixgnHjxmFnZ0etWrUIDAwEwNzcnK1btxIYGIidnR3ffPMNvXv31qvv66+/ZvTo0Q99bUI8LOVRv6r6N3B3d1ePHDlSom2W5DrdT3pbY8aMIfLPxBJLL8nN1ZVITnfTF6rKOuxCPAWioqKoV6+etl0aSwYCTJs2DScnp0I9kbIgcXFx9O3blwMHDjx0HU+zkydP8vrrr8v9EY/Nvb8/ABRFOaqqqsHXRJJeIoQQQtylMAFycZgzZ84j1+Hi4iIB5X08//zzcn9EqZH0EiGEEEIIIYqZBN1CCCGEEEIUM0kvecIsWrRIezBMYT3sg2Rq167NxIkTi3SOEEIIIYQoOgm6nzDR0dGc/uMMjq7OhT5HLZO3IF/ijdRCn5MUW/CaqUIIIYQQ4vGSoPsJ5OjqTN8pg4u1jU3zg4q1fiGEEEII8f8kp1sIIYQQQohiJkG3EEII8QSYOnXqfZ/ceDdPT09WrlwJQGBgIG3atCnOrhXJ3r17H/rx8fdSFIWYmBgAJk2axPLlyx9LvUKUBkkvEUIIIUpZUlISwcHBWoBZGhRFITo6Wnvc+pNmypQpNG/enGHDhmFubl7a3RGiyGSkWwghhChlgYGBeHl5YWlpWdpdeWJVqlSJunXr8t1335V2V4R4KDLSLYQQQtxlzMRJJKY+vsfAO9nbsWzR/Z9yGRYWxrBhw7Ttq1ev4uvry6FDh8jOzsbDw4Ply5c/ctpGTEwMw4cPJzIyEjMzMzp27Mg333xDu3btAGjcuDGKorBq1Sq6dOly3z6kpqby1ltv8f3333Pr1i3at2/Ptm3bDNpcsmQJy5cv54cffsDR0ZHp06ezceNG7ty5wyuvvMKiRYu0Dxvz589n4cKFKIrChx9+aFCXp6cnoaGhvPrqq490H4QoDRJ0CwP3Wyv8fmuCF7Tud1xcHGr6NbKPbH68HTUmJwuFG8XfjhDiqZWYepXc7o8vqEsMffDvvpMnT/Lcc89p27m5uQwdOpSNGzeSk5PDsGHDGDdunNGgtijee+89unTpws8//0xmZiZHjhwBICIiAkVROH78uJZekpKSct8++Pr6Ym1tzenTp7G2tubXX381aO+DDz7g22+/JTw8HEdHR/z9/blw4YIW9A8YMID333+fjz/+mF27dvHpp5/y448/Ur16dUaOHGlQX7169diyZcsj3QMhSosE3aJI5KtPIYR4/NLS0tDpdNp2hQoV6NOnj7Y9ffp0XnrppUdux8zMjEuXLhEfH4+Li8t9J2Derw8JCQmEhYWRkpKCnZ0dAO3bt9fKqqrKpEmT+O2339DyPKcAACAASURBVPj5558pX748qqqyYsUKTpw4gb29PQDTpk1jwIABfPzxx2zcuJGhQ4fSsGFDAAICAli/fr1en3Q6HWlpaY98H4QoDRJ0CwOP+ymVLi4uJGeZU8a9+L8OzPr5v6i5VsXejhBCPE52dnakp6dr2zdv3mTixIns2rWLq1fzUl3S09PJycnB1NT0oduZN28e7733Hs2bN8fOzo633npLL63lbvfrQ2xsLPb29lrAfa+0tDS++uorvvnmG8qXLw/kTRa9efMmTZs21cqpqkpOTg4A8fHxesfc3NwM6k1PT8fW1vbhLl6IUiYTKYUQQohS1qhRI86dO6dtL1iwgLNnz3Lo0CGuX79OREQEkBekPgpnZ2dWrFhBfHw8X375JWPGjClwxZT79cHV1ZXU1NQCR53t7OzYsWMHQ4cO5ZdffgHAwcEBS0tLTp8+TVpaGmlpaVy7do2MjAwgb6JkbGysVsfly5cN6o2KiqJx48aPdA+EKC0SdAshhBClzMvLi/DwcG07PT0dS0tLbG1tSU1NZfbs2YWuy9PTk4CAAKPHNm3aRFxcHJAXGCuKoo2cV6xYkQsXLhSqD5UqVaJbt26MGTOGq1evkpWVpQXld/fj66+/5pVXXuHQoUOYmJgwcuRIJk6cSGJiIgBXrlzh+++/B8DHx4fAwEDOnDnDzZs3jV5zeHg43bp1K/S9EOJJIkG3EEIIUcr8/PzYuXMnt27dAsDf359bt27h4OBAy5YtefnllwtdV2xsLB4eHkaPHT58mBYtWmBtbU3Pnj1ZvHgx1atXB/JyqAcPHoytrS0bN258YB/Wrl2LmZkZdevWxcnJyeiDfTp37syaNWvo2bMnR48eZe7cudSqVYuWLVtiY2NDp06dOHv2LADdunXD39+fDh06UKtWLTp06KBXV0JCAmfOnMHb27vQ90KIJ4nyqF9V/Ru4u7ur+TO0S0r+6h7Lli0r0nk9e/bkWsZ1HF2di6NbmqTYvyhvbVMi652OGTOGyD8TSyynOzdXR46Z8T84j4tp1i80faFqkd9fIcSTJyoqinr16mnbpbFkIORNKnRycsLf3/+h24qLi6Nv374cOHDgoet4Ur311lvUrFnT6OpZQpSWe39/ACiKclRVVfd7y8pESiGEEOIuhQmQi8OcOXMeuQ4XF5enMuCGvBxzIf7NJOh+wri4uGB+I5W+UwYXazub5gfhZGVfrG0IIYQQQog8ktMthBBCCCFEMZOgWwghhBBCiGImQbcQQgghhBDFTIJuIYQQQgghipkE3UIIIYQQQhQzCbqFEEKIJ8DUqVONPmBGiGedoijExMSUSFuenp6sXLkSgO+++47XXnvtsdUtQbd46ijqNUyzfin0q0zmD5TJ/KFI5yjqtdK+TCHEUyQpKYng4GBGjRoFwN69ezExMcHa2hpra2tcXFzw8fHh8OHDeufdHYykpaUxbNgwnJ2d0el01KlTh7lz53L58mWtHmtraxRFwcrKStvet2+fQX8SEhIYPnw4lSpVQqfTUbduXWbNmsWNGzcM2s0XEBDAoEGD9PapqkqNGjWoX7++QRuenp6ULVsWa2trHBwc6N27NwkJCUbvT9euXZk3b562feXKFRRFMbrvr7/+0u7HG2+8gbOzM+XKleP5559nzZo1evVWq1YNS0tLrK2tcXZ2ZsiQIWRkZGjHhwwZgrm5OTqdDp1OR8OGDZk6dSrXrt3/b0BBQWJgYCCmpqZ674e1tTXx8fFaf8zNzUlOTtY774UXXkBRFC5evKj1S1EUgwfc+fv7oygKgYGBevv37t1rcL+eRhcvXkRRFLKzsx9LfT179uTUqVOcOHHisdQn63SLEqFmJJF9ZHPhy99MA0ApZ1u0hnKz0VmXo3btqoU+JTo6GqBI5+SVr12k8kIIUZDAwEC8vLywtLTU9lWuXJm4uDhUVeXKlSt89dVXtG3bltDQUDp27GhQx8SJE7lx4wZRUVGUL1+ec+fOcerUKapWraoXSCqKwvHjx6lVq5bRvqSmptKqVStat27NgQMHqFatGrGxsXz66aecP3+eRo0aFfq6IiIiSExMJDs7m8OHD9OsWTO940uXLmXEiBGkpaXh4+PD5MmT+frrrw3qadeuHeHh4bz99ttavXXr1jXYV7t2bZydncnMzKRTp044OTlx4MABXFxc+PHHHxk8eDBXr15l0qRJWt3bt2+nU6dO/PXXX3Tt2pWPP/6Yjz76SDv+9ttv8+GHH3L79m1OnjzJ22+/jYeHB4cOHcLKyqrQ9yJfq1at2L9/f4HHq1evzvr163nzzTcBOHnyJLdu3TIoV6dOHYKCgujZsycA2dnZbNq0iZo1axqUDQoKwt7enqCgIO1+icLp378/X331FUuXLn3kuiTofgIlxf7FpvlBhS6flpgKgK1T4R92kxT7F051S+bhOA8TnEZH540i1K7uVMQznahduzYTJ04s9Bn5jxSWR7oLIQBGj59IYnLqY6vPycGe5UsW3bdMWFgYw4YNM3pMURRcXFx4//33SU1N5Z133uHIkSMG5Q4fPsyHH36InZ0dAHXr1qVu3bpF7u/ChQvR6XSEhIRgYpL3hbirqyuLFy8ucl1BQUH06tWLW7duERQUZBB057O1tcXb25svvvjC6PF27doxf/58cnNzMTExYd++ffj7+zNz5ky9fe3atQNg7dq1XL58mfDwcC0wfvnll1myZAnDhw9nxIgR2NjY6LXh7OxM165diYyMNNqHsmXL0qxZM7777jvq1KnDmjVrGDduXJHvyYP4+voSHBysBd1BQUH4+fkxY8YMvXI9evQgJCSEq1evYmdnx65du2jUqBHp6el65W7evMnmzZtZsWIFfn5+HDlyBHd3gyeUG5WTk8PcuXNZtWoViYmJ1KlTh23btpGTk0P16tXJysqiTJm8UNLT05NBgwYxYsQIAgMDWbFiBc2bN2fNmjXY29sTEhLCuXPneO+997hz5w7z589n8ODBBudC3ofQlStXGv1wEhoayowZMzh//jzly5dn+PDhBAQEAGjvv61t3oDd7t27adWqFatXr2b+/Pn89ddfNG/enK+++go3NzetzJtvvklCQgK+vr6oqqrXXn7fJOh+Cj1MgHotOwWgSE+YdKprX2IjtUUJgPNJICyEKC2Jyakk1+z2+Co8H/bAIidPnuS55557YLnevXuzbNkybty4YTDK2rJlS6ZPn87Vq1dp06bNQ/+O37NnD71799YC7oeVH+xt2LCBW7duMWrUKBYuXIi5ublB2ZSUFLZu3Vrg6Hvz5s25c+cOx48f58UXXyQiIoLJkycTHBysty9/FHf37t1069bN4B716dOHgQMHcuDAAbp27ap3LC4ujrCwMDp06HDf69LpdHTu3Jl9+/YVS9DdsmVL1q5dS1RUFHXq1OGbb75h//79BkF32bJl6dmzJxs2bOCNN94gODgYPz8/gw8uW7Zswdramr59+/L1118THBxc6KB74cKFrF+/np07d1KnTh1OnDhBuXLlDAJ7Yw4dOsSIESNISUlh1qxZvPbaa/To0YOYmBjCw8Pp06cPffr0wdrauvA3B7CysiI4OJgGDRpw6tQpOnfuzAsvvIC3tzcRERFUr16dtLQ07cPAtm3bmDNnDtu3b6d27dp88skn9O/fn19//ZXk5GT69OnD6tWr6dWrF0uXLmX58uX4+vpq7dWrV4+LFy9y/fp1gw9qRSVB9xNGAlQhhHj2pKWlodPpHliucuXKqKpKWlqaQUD5+eefs2jRIpYuXcrrr7+Om5sbn3/+Od26Fe0DREpKCpUqVXpguSZNmugF5rdv3+bVV1/Vtrdu3YqFhQVdunQhJyeH7OxsQkNDeeWVV7Qy48eP56233uL69es0btyY1atXG23LwsKCFi1aEBERgZubG2lpadSoUYO2bdtq+86cOUP79u0BSE5ONhpYlilTBgcHB72caW9vbxRFISMjgw4dOjB79uwHXnvlypU5evToA8sZc/DgQW0kFqBChQqcP39er0z+aHf79u2pW7cuVapUMVqXn58fU6ZMYcCAAYSHhxMUFGQQdAcFBdGvXz9MTU0ZMGAA48ePZ8GCBZiZmT2wrytXrmTevHnaB8LGjRsDFCrorl69OkOHDgWgX79+fPTRR8ycOVP7mTA3NycmJoYXXnjhgXXdzdPTU/t3o0aN6N+/P+Hh4Xh7exst/+WXXzJ16lTq1asHwLRp05gzZw6XLl0iPDyc+vXraz+3/v7+LFiwQO/8/P+XaWlpjxx0y0RKIYQQopTZ2dkVKpDJnyx4d9CWz9LSkmnTpnH06FFSUlLw8fGhb9++pKYWnCqzb98+bTJfgwYNgLwgsKAJjXc7duwYaWlp2uvdd9/VOx4UFISPjw9lypTBwsKC3r17ExSknzq5ZMkSrl27xokTJ7h69SpxcXEAzJkzR+vX6NGjgbzUgYiICPbt20ebNm0AaNOmjbbP1dVVSxlwcHAweg3Z2dkkJyfj4OCg7du2bRvp6ens3buXP/74w2ASozFXrlzB3j7v2+UGDRrcd1LqvVq2bKl33+4NuCEv6F63bh2BgYH4+fkVWFebNm1ISkriww8/5D//+Y/enACA2NhYfv75ZwYOHAhAr169uH37NqGhoQ/sZ/75xnLEC6NixYrav/P7de++u+caFNahQ4d46aWXcHR0pHz58ixfvvy+79mlS5eYMGECtra22NraYm9vr82TiI+Px9XVVSurKIreNvz/Bwxj/+eKSoJuIYQQopQ1atSIc+fOPbDct99+S5MmTR44gc/GxoZp06Zx48YN/vzzzwLLtW3bloyMDDIyMjh9+jQAnTp14ttvvyU3N7doF3GXuLg4fvrpJ0JCQnB2dsbZ2ZnNmzezc+dOowHS888/z4wZMxg7diyqqjJt2jStX8uXLwfygu59+/YRERFB27ZtAfDw8OCXX34hIiJCy+fNv4awsDBttZV8W7ZswcLCgpYtWxr0oX379gwZMoTJkyff99oyMjLYs2eP1ofTp09rfc3f96jc3NyoXr06O3fupHfv3vctO2jQIBYsWGA0OF+7di25ubn06NEDZ2dnatSowe3btwkODi5UP1xdXY1+KMj/+bt586a2L3/VmIdhZWVV6LoGDBhAz549iY2N5dq1a4wePVrLw1YUxeg1fPnll3ofdG7dukXr1q2pVKkSsbGxWllVVfW2AaKioqhWrdojj3KDBN1CCCFEqfPy8iI8PNzosfxRudmzZ7Ny5UrmzJljtNwHH3zA4cOHyczM5Pbt2yxevBhbW9tC5YrfbdKkSVy/fp3Bgwdz6dIlIG9kd9KkSYVeOm3t2rXUqVOHs2fPEhkZSWRkJOfOncPFxYX169cbPWfw4MEkJiYaLIOXr3Xr1qSlpRESEqIFt3Z2djg6OhISEqIXdPv6+uLi4kLfvn25ePEiWVlZfP/994wfP56AgADKly9vtA1/f392795tdDLlnTt3OHr0KN7e3tjZ2WmpEwXJfx/yXzk5Ofctf69Vq1bx008/PfAD1vjx49m9e7fe9ecLDg5m1qxZ2nsQGRnJli1bCA0NJSUlbz5YtWrVDJYYzDdixAjee+89oqOjUVWVEydOkJKSgqOjI1WqVCEkJIScnBxWr15tNDgvrBdeeIGtW7dy8+ZNYmJiWLVqVYFl09PTsbe3p2zZsvz222+sW7dOO+bo6IiJiQkXLlzQ9o0ePZqPP/5Y+1B57do1Nm3aBED37t05ffo0W7duJTs7myVLlhgE/OHh4UVO0SqIBN1CCCFEKfPz82Pnzp16S8PFx8draQvNmjXj5MmT7N27ly5duhitQ1EUhg4dioODA5UrV2b37t2EhoYWeaKavb09v/76K2ZmZrRo0QKdTkfHjh0pX758gRMd7xUUFMSYMWO0Ue781+jRow1STPKZm5szfvx4PvjgA6PHy5UrR9OmTblz5w4NGzbU9rdt25bExES9oNPCwoI9e/bg6upKixYtsLGxYdKkSXz00UdMmTKlwH47Ojri5+en14d58+ah0+mwt7fHz8+Ppk2b8uuvvz4wGG7QoAGWlpbaK3+N8AMHDhis033v+usANWvWLNSER3t7ezp27Ggwynvw4EEuXrzI2LFj9d6Dnj17UqtWLdavX09mZiYpKSlGR/4h7wOYj48PXbp0wcbGhuHDh2s/oytWrGD+/PlUqFCB06dP07p16wf2tSATJ07E3NycihUrMnjwYC0dxphly5Yxc+ZMdDod77//Pj4+PtqxcuXKMX36dDw8PLC1teXgwYO88sorvPPOO7z22mvY2NjQsGFDwsLyJjc7ODiwadMm3n33XSpUqEB0dDQeHh567a1fv15bP/9RKfcujfI0cnd3V40tr1ScSnJy49M4kVLunxCipERFRWmTrKB0lgyEvAleTk5O+Pv7P7a2hbif/fv388UXXxT47cOzbvv27axdu5aNGzcWWObe3x8AiqIcVVXV4BOTrF4ihBBC3KUwAXJxKChtRIji0qZNG21SqjDUo0cPevTo8djqk/QSIYQQQgghipkE3UIIIYQQQhQzSS8Rz4xFixYRHR1tsD9/X35u972K+lh5IYQQQoh7SdAtnnn3PkxACCGEEOJxk6BbPDNktFoIIYQQpUVyuoUQQgghhChmEnQLIYQQT4CpU6fy2WeflXY3hChRgYGBj2XZwosXL6IoCtnZ2QD07t2bXbt2PXK9j5ME3UIIIUQpS0pKIjg4WHvy3d69ezExMdGeWOji4oKPj4/BkwsVRSEmJgaAtLQ0hg0bhrOzMzqdjjp16jB37lwuX76s9/RDRVGwsrLStvft22fQn4SEBIYPH06lSpXQ6XTUrVuXWbNmcePGDYN28wUEBDBo0CC9faqqUqNGDerXr2/QhqenJ2XLlsXa2hoHBwd69+5NQkKC0fvTtWtX5s2bp21fuXIFRVGM7st/jHdaWhpvvPEGzs7OlCtXjueff157KmS+atWqYWlpibW1Nc7OzgwZMoSMjAzt+JAhQzA3N0en06HT6WjYsCFTp07l2rVrRvuZz9j9gbwA09TU1OCJlPHx8Vp/zM3NSU5O1jvvhRdeQFEULl68qPVLURS+++47vXL+/v4oimLwWPe9e/ca3K9HcW+A+yR69913mT59eml3Q48E3UIIIUQpCwwMxMvLS29id+XKlcnIyCA9PZ2DBw9St25d2rZty48//mi0jokTJ5KRkUFUVBTXrl3ju+++o2bNmlStWpWMjAztBXD8+HFtu23btnr1pKam0qpVK27dusWBAwdIT09n9+7dpKWlcf78+SJdV0REBImJiVy4cMHoo86XLl1KRkYGMTExZGRkMHnyZKP1tGvXjvDwcL1669ata7Cvdu3aODs7k5mZSadOnbh06RIHDhzg2rVrzJ8/n3fffZeFCxfq1b19+3YyMjKIjIzk999/5+OPP9Y7/vbbb5Oenk5SUhJr1qzh4MGDeHh4aB9AiqpVq1Z670dGRgaVK1fWjlevXl3vCZEnT57UHr1+tzp16hAUFKRtZ2dns2nTJmrWrGlQNigoCHt7e73yT7vmzZtz/fp1SvqJ5PcjEylFqZJl/IQQT5pRo8fzd2LKY6uvolMFvly+5L5lwsLCGDZsmNFjiqLg4uLC+++/T2pqKu+8847RQOLw4cN8+OGH2NnZAVC3bl3q1q1b5P4uXLgQnU5HSEgIJiZ5Y3Ourq4sXry4yHUFBQXRq1cvbt26RVBQEM2aNTNaztbWFm9vb7744gujx9u1a8f8+fPJzc3FxMSEffv24e/vz8yZM/X2tWvXDoC1a9dy+fJlwsPDsbKyAuDll19myZIlDB8+nBEjRmBjY6PXhrOzM127diUyMtJoH8qWLUuzZs347rvvqFOnDmvWrGHcuHFFvicP4uvrS3BwMG+++SaQdw/9/PyYMWOGXrkePXoQEhLC1atXsbOzY9euXTRq1Ij09HS9cjdv3mTz5s2sWLECPz8/jhw5gru7wRPKjfrtt98YM2YM586dw9LSkoEDB7Jw4ULtPtva2gKwe/dunJycGDlyJMePH0dRFLp27coXX3yhlYmNjWXChAns27eP3Nxc+vfvz9KlSw3anDJlCgcOHCA0NBSASZMmsXPnTkxMTBg6dCizZ8/G1NSUnJwc3nnnHQIDA7GxseGtt94yqMvT05PQ0NBCX29xk5Fu8USytLSUpfyEEKXi78QU4lIaPrZXYQL4kydP8txzzz2wXO/evTl27JjRUdaWLVsyffp01qxZY3Qwo7D27NlD7969tYD7YeUHewMHDmTgwIFs2LCBzMxMo2VTUlLYunUrtWrVMnq8efPm3Llzh+PHjwN5o9qdO3emVq1aevvyg8Hdu3fTrVs3LeDO16dPH27fvs2BAwcM2oiLiyMsLKzAPuTT6XR07tzZaFrO49CyZUuuX79OVFQUOTk5fPPNNwZpO5D3IaBnz55s2LABgODgYPz8/AzKbdmyBWtra/r27UvXrl0JDg4udF8mTJjAhAkTuH79OufPn8fHxwfIu9eQl8KTkZFBq1atUFWVqVOnEh8fT1RUFLGxsQQEBACQk5PDf/7zH9zc3Lh48SJXrlzhtdde02srNzeXkSNHcuLECX744QfKly/P4MGDKVOmDDExMfz+++/88MMPrFy5EoAVK1awY8cOfv/9d44cOcLmzZsN+l+vXj3t5+NJICPdolTJaLUQQuQFLzqd7oHlKleujKqqpKWlGQSUn3/+OYsWLWLp0qW8/vrruLm58fnnn9OtW7ci9SUlJYVKlSo9sFyTJk30AvPbt2/z6quvattbt27FwsKCLl26kJOTQ3Z2NqGhobzyyitamfHjx/PWW29x/fp1GjduzOrVq422ZWFhQYsWLYiIiMDNzY20tDRq1KhB27ZttX1nzpyhffv2ACQnJxsd3SxTpgwODg56OdPe3t4oikJGRgYdOnRg9uzZD7z2ypUrc/To0QeWM+bgwYPa6C9AhQoVDNJ28ke727dvT926dalSpYrRuvz8/JgyZQoDBgwgPDycoKAgg28LgoKC6NevH6ampgwYMIDx48ezYMECzMzMHthXMzMzYmJiSE5OxsHBgZYtWxZYtlatWtoHFkdHRyZNmqTdy99++434+Hjmz59PmTJ5oefdkyezsrLo378/2dnZbN++HXNzc/7++2/CwsJIS0vD0tISKysrJk6cyFdffcWoUaPYuHEj/v7+uLq6AnkTkffu3avXJ51OR1pa2gOvs6TISLcQQghRyuzs7AzSAozJnyx4d9CWz9LSkmnTpnH06FFSUlLw8fGhb9++pKamFljfvn37tMl8DRo0APKCwIImNN7t2LFjpKWlaa93331X73hQUBA+Pj6UKVMGCwsLevfubZBTvGTJEq5du8aJEye4evUqcXFxAMyZM0fr1+jRo4G8FJOIiAj27dunBWxt2rTR9rm6uuLm5gaAg4OD0WvIzs7WAsh827ZtIz09nb179/LHH38YTGI05sqVK9jb2wPQoEGD+05KvVfLli317puxPHlfX1/WrVtHYGCg0dHrfG3atCEpKYkPP/yQ//znPwbfEMfGxvLzzz8zcOBAAHr16sXt27e11I0HWbVqFefOnaNu3bo0a9aMHTt2FFg2MTGR1157jSpVqmBjY8OgQYO0exkbG4ubm5sWcN8rJiaG//3vf8yaNQtzc3MALl26RFZWFpUqVcLW1hZbW1tGjRpFYmIiAPHx8VrADWjv/d3S09ON/l8pLRJ0CyGEEKWsUaNGnDt37oHlvv32W5o0aWIwyn0vGxsbpk2bxo0bN/jzzz8LLNe2bVttMt/p06cB6NSpE99++y25ublFu4i7xMXF8dNPPxESEoKzszPOzs5s3ryZnTt3Gg1qn3/+eWbMmMHYsWNRVZVp06Zp/Vq+fDmQF3Tv27ePiIgIbfKnh4cHv/zyi15qSf41hIWFGaThbNmyBQsLC6Mjtu3bt2fIkCEFTubMl5GRwZ49e7Q+nD59usBJqQ/Lzc2N6tWrs3PnTnr37n3fsoMGDWLBggVGg/O1a9eSm5tLjx49cHZ2pkaNGty+fbvQKSa1a9dm/fr1JCYm8s477/Dqq69y48YNFEUxKDt16lQUReHEiRNcv36dkJAQVFUF8uYEXL58ucDVTurVq8eaNWvo1q0bZ8+e1c6xsLAgOTlZ+4By/fp17ee0UqVKxMbGanVcvnzZoN6oqCgaN25cqGstCRJ0CyGEEKXMy8tLbyWOu6mqypUrV5g9ezYrV65kzpw5Rst98MEHHD58mMzMTG7fvs3ixYuxtbUtVK743SZNmsT169cZPHgwly5dAvJGdidNmsSJEycKVcfatWupU6cOZ8+eJTIyksjISM6dO4eLi4veyhx3Gzx4MImJiQbL4OVr3bo1aWlphISEaMGtnZ0djo6OhISE6AXdvr6+uLi40LdvXy5evEhWVhbff/8948ePJyAggPLlyxttw9/fn927dxudTHnnzh2OHj2Kt7c3dnZ2DB069L73IP99yH/l5OTct/y9Vq1axU8//fTAD1jjx49n9+7detefLzg4mFmzZmnvQWRkJFu2bCE0NJSUlLy5BtWqVTNYYjBfSEgISUlJmJiYaCPGpqamODo6YmJiwoULF7Sy6enpWFtbY2try5UrV5g/f752rHnz5lSqVIl3332XGzducPv2bX755Re9tvr378+cOXPo1KkT58+fp1KlSnTp0kVLP8rNzeX8+fPa/xMfHx+WLFlCXFwcV69e5ZNPPjHof3h4eJHTq4qTBN1CCCFEKfPz82Pnzp16S8PFx8draQvNmjXj5MmT7N27ly5duhitQ1EUhg4dioODA5UrV2b37t2EhoZibW1dpL7Y29vz66+/YmZmRosWLdDpdHTs2JHy5cs/cJJhvqCgIMaMGaONcue/Ro8eXeCydebm5owfP54PPvjA6PFy5crRtGlT7ty5Q8OGDbX9bdu2JTExN+mEBgAAIABJREFUUS/otLCwYM+ePbi6utKiRQtsbGyYNGkSH330EVOmTCmw346Ojvj5+en1Yd68eeh0Ouzt7fHz86Np06b8+uuvDwyGGzRooC0KYGlpqa0RfuDAAYN1uo0tp1izZs1Crbphb29Px44dDUafDx48yMWLFxk7dqzee9CzZ09q1arF+vXryczMJCUlpcBc7V27dmnpMxMmTGDDhg2ULVuWcuXKMX36dDw8PLC1teXgwYPMmjWLY8eOUb58ebp37643Qm9qasr27duJiYmhatWquLi48M033xi0N3jwYGbOnEmHDh24ePEiwcHBZGZmUr9+fezs7Hj11Ve1tKGRI0fStWtXGjduTJMmTQy+ETh8+DBWVlY0b978gfewpCj5Q/9PM3d3d7Wk12nMX+pu2bJlT1VbQgjxtImKiqJevXradmksGQgwbdo0nJyc8Pf3f2xtC3E/+/fv54svvijw24d/sz59+jB8+HC8vLyKtZ17f38AKIpyVFVVg09MsnqJEEIIcZfCBMjFoaC0ESGKS5s2bR7LI9ifRFu2bCntLhiQ9BIhhBBCCCGKmYx0/0sU9ORGuP/TG+XJjUIIIYQQpU+C7qeAPLlRCCGEEOLJJkH3v4SMVgshRPFRVdXo2sNCCFGQoi5GIjndQgghnmlly5YlJSWlyH9AhRDPLlVVSUlJoWzZ/2vv3uPkLOuD/3++JMQs4YxJsd0gaPYhpdb61PhUayuKtZaqpaUqtp7FH4/mscZY8FQq0aoVQdJUTRGt9VwqPLR9UA6CVGq1HoJi1RicVDQZOSRADuQAOX1/f9yzuExmdu/ZnXt2d/bzfr3mtTvXfV/39Z11HL5z5Xtf19zSfZzpliTNaIODg9TrdTZv3jzZoUiaRubOncvg4GDp8026JUkz2qGHHspJJ5002WFI6nOWl0iSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFVs9mQHIGn6W7lyJbVa7aD2er0OwODgYMt+Q0NDLF++vNLYxqvda4LRX9dUfk2SpMlj0i2pMrt3757sECrRr69LklQdk25JE9ZuZnfp0qUArF69upfhdMVos9XT+XVJkiaHNd2SJElSxUy6JUmSpIpZXiJpRhvthsl2hs8fLjMpy5ssJWnmMumWNKPVajW+870fEIfPL90n9yQAt96+qXyfHZs7jk2S1D96nnRHxCnAB4CnAFuBjwLvyMz9o/RZAVzQ5vDbMvOvux2npJkjDp/P7CXPr3SMfWuurPT6kqSpradJd0QcA9wIrAXOAB4LvJ+itvz8Ubp+FLiuqe0PgTcD13Y/UkkzRb1eJ+/fVnlSnPdvpl7fU+kYkqSpq9cz3a8BBoAzM3M7cENEHAmsiIj3NdoOkpl1oD6yLSL+EliXmbdWHbQka58lSZqIXifdpwPXNyXXlwMXAqcCV5e5SEQcCzwLeFfXI5TUUq1W4wfr1jJ/4fGl++TsAGDTzvtK99m88a6OY5uIwcFB7tk7pyflJYODCyodQ5I0dfU66V4M3DSyITM3RMSuxrFSSTfwfOBQioRdUo/MX3g8Lzjv5ZWOccVFn6j0+pIkTYZer9N9DMXNk822NI6V9SLg25n5o65EJUmSJFVoMpYMzBZt0ab94BMjHkVRivLmMc47BzgH4IQTTugwRElT0Wh15fV6cdvH4OBgy+NTuU58vK9rKr8mSdLD9Trp3gIc3aL9KFrPgLfyQook/Z9GOykzLwMuA1iyZEmphF7S1FCv11vefFmv19m9e3fLPrt27QJoe7xer7dMbIukds74g61Yu9cjSZpeep10r6Oo3X5IRCwE5jWOlfEi4D8yc2OXY5M0inq9zrYd2yuvud688S72PbiXzffcQxxaPhlOips2d+7Z2/L4zj17uWfb2of32buHQ2fPhnmTe4PjaLPVw18+Vq9e3atwJEkV6HXSfS1wXkQckZn3N9rOAnYDN4/VOSJOBJ4MdLb+mCRNsty1lVptW8fLJ7rsoiT1h14n3ZcCrweuiogLgccAK4BLRi4jGBHrgZsz8+ym/i8C9gFu7SaNoV2d8HhrnwcHB5mz876erF6yZ8uutvG1M/xah4aGOupXr9e5p/XkeHft38v9Ow5wy60bOuoWuQ+go36R2zoaQ5JUvY6S7oh4PPA04Djgw5l5V0QsAu4eMXPdVmZuiYhnAh+kWB5wK7CSIvFujmtWi0u8CPhSZm7uJG5JPzcdaoQHBwc7LqcYbxnG0qVLuef2TR31GZcD+xjPglEZ88Y13PCXK0nS1FAq6Y6IRwCfBs7k5yuNXA3cBbwP+BHwljLXysy1wGljnHNim/YnlBlDUvs64elcIzzaKh9jlWFYbiFJmkxlZ7rfDfwO8FLgBuDuEceupaixLpV0S1IVBgYGJjuE0R0ymzxwBPsPfWrlQ83a+9WOy3MkSdUqm3T/CXB+Zn42IprLPm4HTuxqVJLUgjPVkqTpqmyB4XHAD0e5xiO6E44kSZLUf8om3bcDT2lz7H8Bt3UnHEmSJKn/lE26Pwm8JSJezM+3bsuIeAawHPhYFcFJkiRJ/aBs0v0+4AvAp4D7Gm3/AdwIXJeZH6ggNkmSJKkvlLqRMjP3Ay+KiA8BzwYWAPdSJNxj7iQpSZIkzWRjJt0RMQf4OvCWzPwi8JXKo5I0JW3eeBdXXPSJ0udv3VT8w9jRC47taIwFi8uf3w25YzP71pTf6DZ3bQUgDju6/CD79xa7HEiSZqQxk+7M3BMRJ1Fsvy5phup0e3WAbfvuBWDBvPJJ9ILFx45rrPEaz1i1WrHN+tBJCzrqs31nx0NJkvpE2XW6bwB+F7ipwlgkTWHjWSN7Oux+2avXtXTpUm65dUPHY0mS+kPZpPsDwKcjYjbwL8CdFFvBPyQzf9zl2CRJkqS+UDbpHr5Z8o0USwS20rxTpSRJkiTKJ92vrDQKSZIkqY+VXTKw/HIFkiRJkh6m7Ew3ABERwCnAsRTrdP8wM3P0XpIkSdLMVjrpjohXA+8C5o9o3hQR52fm33c9MknqM5HbmLX3qx32KdYZzJjX0TiSpKmlVNIdES8GLgO+BHwauAs4HngxcFlE7MrMf6wsSkma5sa79nitVmv0P6En40mSqlF2pvtNwGcy86VN7Z+IiE8BbwZMuiWpjfGsBw7TY61zSdLYyibdJ1Mk3q18mmLtbknqGytXrnxolrnZcPtwQjzS0NDQuBNsSVL/Kpt03w8Mtjk22DguSTPCwMDAZIcgSZpmyibd1wLviYgfZeZXhhsj4ikUN1deW0VwkjRZnK2WJHVTJzXdTwa+HBE/o9gG/niKWe71tC89kSRJkma8spvj3BURTwBeBfw2xTrdP6HYHv7jmbmrsgglSZKkaa70Ot2NxPqDjYckSZKkkg4pc1JEPDkiXtjm2Asi4je6G5YkSZLUP0ol3cBfA7/S5tgvN45LkiRJaqFs0v1rwNfbHPsm8PjuhCNJkiT1n7JJ99xRzp0FzOtOOJIkSVL/KZt0/xD4gzbH/gC4rTvhSJIkSf2n7OollwIfjojtwEeAOvBLwDnA2cDBeyFLkiRJAsqv0/2RiDgZWA68ceQhYGVmXlZFcJIkSVI/6GSd7nMj4u+A3wGOA+4BbszMH1cVnCRJktQPSifdAJn538B/VxTLlLVy5UpqtVpHfYbPX7q0s8qboaEhli9f3lEfSZIkTW1tk+6IOBQYyMztTe3zgfOAU4A7gNWZeWulUU6yWq1GrbaORYsWlu4zd27xp83cWbrP+vUbO45NmgrafTEd68unXzIlSTPFaDPdFwHPBRYNN0TEUcC3KW6i3AIcBbw4Ip7a74n3okULWbXq3ErHWLbs4kqvL/XawMDAZIcwLYz2r2mjfXHxS4skTR+jJd2/BXymqe0NFAn36zJzdUQ8CrgJeCtwVjUhSprqTPyq4xcXSeoPoyXdjwaaZ6+fC9yemasBMvPOiLgEWFFNeJLU//zSIkn9b7TNceYB9w0/iYjDgCcAX2467zbgkV2PTJIkSeoToyXdG4GTRzz/LYot3/+j6bzDgO1IkiRJamm0pPt64E0R8bjGiiVvAfYAX2g678nAhorikyRJkqa90ZLud1HUfH8XuAt4OvCuzNw0fEJEzAJeycElJ5IkSZIa2t5ImZmbIuJXgRcAxwDfzMyvNJ32SOBvgc9XF6IkSZI0vY26I2Vm3g98bJTjdwPv73ZQkiRJUj8ZrbykpYg4JCJuioihKgKSJEmS+k3HSTcQFPXdR3Q3FEmSJKk/jSfpliRJktQBk25JkiSpYuNJug8A7wDu6HIskiRJUl8adfWSVjIzKZJuSZIkSSVMuLwkIk6IiJd1IxhJkiSpH3WjpvtJwD904TqSJElSX/JGSkmSJKlibWu6I+KmkteY36VYJEmSpL402o2UpwIbG4/RzOpeOJIkSVL/GS3pXg/8Z2a+YrQLRMTzgX/qZlCSJElSPxmtpnsNsKTENZJia3hJkiRJLYw20/2PwO4S1/gW8MruhCNJkiT1n7ZJd2Z+Hvj8WBfIzA3AJ7oZlCRJktRPXDJQkiRJqljbpDsiPhkRJzW1PSYiDq0+LEmSJKl/jDbT/RJGrMEdEbOAGvCrVQclSZIk9ZNOy0tcpUSSJEnqkDXdkiRJUsVMuiVJkqSKjbZON8A5EfHcxu9BsRHOayPizqbzMjMv6Hp0kiRJUh8YK+l+VYu2s1u0JWDSLUmSJLXQtrwkMw/p4DGr7IARcUpEfCkidkXEHRHxzsbKKGX6nhkR34qI3RFxb0RcFxHzyo4tSZIkTYae1nRHxDHAjRQz42cA7wT+HHhHib6vBj4LXAucDryaYgnDsWbrJUmSpEnV64T1NcAAcGZmbgduiIgjgRUR8b5G20Ei4pHASuDPMvMjIw79c+URS5IkSRPU69VLTgeub0quL6dIxE8dpd8LGz8/UVVgkiRJUlV6nXQvBtaNbMjMDcCuxrF2fgO4DTg7IuoRsTcivhERv1ldqJIkSVJ39DrpPgbY2qJ9S+NYO8cDJwPnA28GngfsBK6LiF/odpCSJElSN03G5jjZoi3atA87BDgcODszP5OZ1wF/COwHXteqQ0ScExFrImLN5s2bJxqzJEmSNG4dJd0R8ciIeG5EvDwijm20zY2IstfZAhzdov0oWs+AD7uv8fPLww2NuvBbgFNadcjMyzJzSWYumT9/fsnwJEmSpO4rlSxH4SKgDvw/4GPAiY3D/wr8Rcnx1tFUux0RC4F5NNV6N/khxUx4NIcGHCg5tiRJkjQpys5Qv5WijOOdFDc1jkx+rwae26pTC9cCz46II0a0nQXsBm4epd/nG2M+Y7ghIo4Cngh8t+TYkiRJ0qQom3S/GnhnZr4H+HbTsfXAY0te51LgQeCqiPidiDgHWAFcMnIZwYhYHxF/P/w8M9dQzKj/faO05TkUM+57gQ+VHFuSJEmaFGWT7l8Cvt7m2B6K8pAxZeYW4JnALIoZ8ndQbHpzQdOpsxvnjPQS4F+AS4ArKRLu0xrXlCRJkqassjtS/gx4HPBvLY79GnB72QEzcy1w2hjnnNiibQfw2sZDkiRJmjbKznRfAbw9Ip46oi0j4n8Af06xq6QkSZKkFsom3SsoVhf5d6DWaLsC+F7j+Xu7HpkkSZLUJ0qVl2Tm7oh4OvCnwLMpbp68F/gr4DOZua+yCCVJkqRprmxNN5m5H/hU4yFJ0pS3cuVKarVay2P1eh2AwcHBg44NDQ2xfPnySmObiHava7TXBFP/dUn9rHTSLUlSP9m9e/dkh9B1/fiapH5RKumOiNspdoRs5QCwjWJL9r/NzO93KTZJkkoZbUZ7PGq1GkuXLm15bCrMFrcbfzjm1atX9zIcSSWUnem+mWI3yAXA14C7gV8AngrcBfwUeB7w0oh4ZmZ+rYJYJUlqqVar8YN1a5m/8PjSfXJ2sbnypp33le6zeeNdHccmSVA+6f4K8OvAb2TmQ584EfEo4HqK7d1fCnyJYsObZ3U5TkmSRjV/4fG84LyXVzrGFRd9otLrS+pfZZPuNwNvG5lwA2TmnRHxLuA9mfmRiFhFsdW7JEmagPGUzAyf3640pp2pUDIj9buySfdC4ME2xx6g2CYeip0r50w0KEmSZrparcZ3vvcD4vD5pfvknuL2q1tv31S+z47NHccmqXNlk+4fAn8eEV/MzIeS74iYC5zbOA7wixT13pIkaYLi8PnMXvL8SsfYt+bKSq8vqVA26X4T8HlgQ0RcA2yiuKny94GjGz8BfhP4YreDlCRppqnX6+T92ypPivP+zdTreyodQ1L5HSlvjIhfB84HngY8CrgTuBF4V2b+sHHe66sKVJKkdur1Ott2bK/8RsfNG+9iz+G7Kh1DUn/qZEfKtRTbwEuSpIoNDg5yz945PSkvGRxcUOkYktyRUpLUBwYHB5mz876eLBm4YN6xlY4hqT+VTrojYgHwJ8DJwNymw5mZZ3czMEmSppN2S/zV63Wg+GLQisv1STND2W3gTwa+DswC5gH3AMc2nm+h2AZekiQ12b1792SHIGkKKDvTfRHwTeAPgZ3A6cB/AS+j2IHyjyqJTpKkaaLdbPXwRjWrV6/uZThdMdoGPc7gS50pm3Q/CXgNP98g55DM3Ad8LCIeCfwN8IwK4pMkSVOQM/hSZ8om3YcD92XmgYjYBjxyxLE1wNu7HpmkMfVym2hw5kqaaUb7//t0nsGXJkPZpPsnwPGN328DXgBc13j+XGBrd8OSVEatVqNWW8eiRQtL95k7t/i/febOjsZav35jR+dL/WjrpvvYtu/ejr60jveLbq1Wg9lHddRH0tRVNum+AXgWcAVwCXB5RPwWsA9YDLy7mvAkjWXRooWsWnVu5eMsW3Zx5WNIU93eB/ew9wD8YPO9pfvsI4DO+gDs2bmTOKo3SXe9Xh/flwI6/zLhv5hppiqbdL8VeARAZn4uInYDZwGHAauAj1QTniRJU8uh83+B417wssrHuetDF1U+xrDdu3fz7e98j4zySX7kPgBuuXVDB31c7Ewz15hJd0TMopjNvmO4LTOvBq6uMC5JUp8a74oYU2GGdN+efeSmu7n3ik9WPlbu3QO7elO9+eCDD459UpOMeeMaa/h/Y2mmKTPTnRQ3Sz4H+GK14UiSZrLpsCJG7t3D3k13H9y+bx/kgc4vGIcQs1v85zhzHNFJmqrGTLobK5ZspNgUR5KkCZnOK2IMHHEYsS8ZGho66Fi9Xh/Xl4aBgYGWM/u1Wo2dPbqR8hGPeAQP7nsE+w99aqXjzNr71bbrekv9rmxN94eBN0TEFzJzT5UBSZI0VR294FgWzDu2J18Kli5dyq23b6p8HEm9UTbpPgJ4LPDjiLgOuJOi7GRYZuYF3Q5OkiRJ6gdlk+63jfj9VS2OJ2DSLUmaNJs33sUVF32i9PlbN90HFLPXnYyxYHH58yVpWKmkOzMPqToQSZLGq1WN9Vi27SvWzV4wr3wSvWDxseMaS5LKznRLkjRljWcpwal+0yZA7tjMvjVXlj+/scRgHHZ0R2Mwd07HsUnqTOmkOyICeB7wNOA4YEVm/jQiTgVqmXnHqBeQJEmljWdGvVYrNp8ZOmlBB70WUK/X2b5zHMsdSiqtVNIdEccA1wC/AWynuLHyA8BPgf8PuA94fUUxSpI04/Ry9n7p0qXcfU/5nSUlda5srfZFwELgqcAjgRhx7EbgmV2OS5IkSeobZctLzgDOzcz/bGwLP9IGioRckiRJUgtlZ7oPB37W5thcHj7zLUmSJGmEskn3bcDvtjl2KvC97oQjSZIk9Z+y5SUfAj4UEduAzzbajo6IVwKvA86pIjhJktQbkduYtferHZy/E4CMeR2NIc1UZTfH+UhEPBZ4B/DORvMNwAHgfZn5mYrikyRJFRvf8oS1Rt8TKh9L6gel1+nOzLdExN8BzwIWAPcCN2Tmj6sKTpIkVa9fNxeSppKy63TPysz9mflT4KMVxyRJkiT1lbI3Ut4REX8TEU+sNBpJkiSpD5VNuq8CXgJ8MyLWRsRbIqKzIi5JkiRphiqVdGfma4FHAX8M/BC4APhxRNwUEa+IiCMqjFGSJEma1srOdJOZezPzXzLzj4HjgaUUNeEfBe6sKD5JkiRp2iu9eslImbktIq4FjgMeQzELLkmS+sjKlSsfWhqw2XD78ComzYaGhsa1KorUrzpKuhtlJC+gqO9+GvAAcDXwqe6HJkmSpqqBgYHJDkGaVsouGfgc4KXA84C5wL9T7EJ5RWbeX114kiRpsjhTLXVP2Znuq4HbgHcDn87MDdWFJElSd4y3PGKql0a0e12WfEhTV9mk+zcy81utDkTEqcDLM/NV3QtLkqRq9WN5RD++JqlflEq6mxPuiFgEvIyi5OTRwC6gVNIdEacAHwCeAmylWP3kHZm5f5Q+JwK3tzj0T5n5ojLjSpJmnn6d1e3X1yX1s9I3UkbEUcBZFMn2UxrN3wXeC/xjyWscA9wIrAXOAB4LvJ9i6cLzS1ziXOCrI57fU2ZcSZIkaTKNmnRHxCHA71Ek2n9AcRPlHcCHgP8DvCEz/72D8V4DDABnZuZ24IaIOBJYERHva7SN5rbM/HoH40mSJE1J7Wrz6/U6AIODgy37WZs/PbXdHCciLgZ+RnET5fOAf6ZIwE8A3g7EOMY7Hbi+Kbm+nCIRP3Uc15MkSeoru3fvZvfu3ZMdhrpstJnuNwIJXAO8IjPvHT4QETnO8RYDN41syMwNEbGrcezqMfr/Q0QcC2yiKGn5i8z0XSlJkqaddrPVw6vPrF69upfhdMVoKwaNNoM/E2bvR0u6PwY8H3gOcFtEXA58MjO/OYHxjqG4ebLZlsaxdh6kKGn5IrAdeDrwZoqa8DMmEI8kSVJlRktC2xlr6cd2epm4jlYa026WfteuXQAtj9fr9bZ/p35JyNsm3Zn56oh4HXAm8HKKeuzXRsSPKEpNxjvb3apfjHa9zLwTeN2Ipi9HxN3A6oh4QmbeetAFI86h2MCHE044YZyhSpIkjV+tVuMH69Yyf+Hxpfvk7KKCd9PO+0r32bzxrrbHqph9rtVqfOd7PyAOn990ZA7MntOyT84uUr2ds4866NjOvXDP7ZsO7rPtTmq1WscJ/mgGBgYmpV5+1BspM/MB4LPAZyPiUfx8mcC3NE55b0SsBq5snDuWLcDRLdqPovUM+GiuBFYDvw4clHRn5mXAZQBLliwZ7xcEoPgfddeuHSxbdvFELjOmWm0jhx12eKVjSJKk3pq/8HhecN7LKx3jios+wbp163jWs5510LEHH3yQ/ftbr8x84MABAO69996Djn3/+9/nmmuuadlv//79xOHzmb3k+ROIemx7/+3vuH/HA9xy68H7MkbuBPZ1fM37d+zj7ntaXW/beEIsrfSSgY3Z5guBCyPiSRSz32cBn6RYd3u08pBh6yhqtx8SEQuBeY1jncimn5IkSTPWvn372P3AA8ShTTPNcQjMbrN2xr4iac3ZB6eE+4B9e/Ye1J5793Do7NnwiIlGXE7GUew/9KmVjzNr71fHPmkCSifdIzU2y/lWRCynWNnkZSW7XgucFxFHZOb9jbazgN3AzR2GMfzV6pYO+3VscHCQzJ2sWnVupeMsW3YxEfMqHUOSJPVOvV5n247tXHHRJyodZ7i8JObM4dD5v1C6376tWwCYfXSZudPC3s13w4ED5P2b2bfmys4C7dT+PQT3VZ4QQzHTPVxuU4VxJd3DMnMvcFXjUcalwOuBqyLiQuAxwArgkpHLCEbEeuDmzDy78XwFcATFxjjbgacB5wFXZeZ/TeQ1SJIk9YPZs2fzuMWLxz5xhNrWom58aP5x5TvNP45169axd0/bzcTVwoSS7k5l5paIeCbwQYrlAbcCKykS7+a4Zo14vo5iN8pXU6zpvQG4CHh3xSFLkiSN2+DgIHN23teTmu4F845tuczgeFZQgdFvKly6dCm33r6pJzXdeeCInpWXtLvBsht6mnQDZOZa4LQxzjmx6fnlFJvoSJIkVWam7RI5MDAw2SHMGD1PuiVJkqab6bxD5HT8MtCPTLolSZIa+nGXSE0NbdaPkSRJktQtJt2SJElSxSwvkSRJqtDmjXd1tE731k3FMn5HLzi2ozEWLC5/fjfkjs7W6c5dxebjcVirzcnb2L+XYFtH63QXO1VCdrj3yZTZkVKSJEmdGRoa6rjPtn3FluwL5pVPohcsPnZcY43XeMaq1YqkduikBaX71Ot7gParxrQep1h9ZmjohA6io9Gnur+hSbckSVJFxrNyyHS4aXMqv66p+vezpluSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIqZdEuSJEkVM+mWJEmSKuY63ZIkSZNg5cqVD23kMtJw2/B6082GhobGtU52L7R7TTD665rKr6lbTLolSZKmkIGBgckOoRL9+rrKMumWJEmaBP04s9uPr6lbrOmWJEmSKuZMtyRJkqad6VYTb9ItSZKkvjFVa8dNuiVJkjTtTLf6cWu6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxdwcR5JUiXZbNI9mrO2b25msbZ0lqSyTbklSJWq1GrXaOhYtWli6z9y5xX+WMneW7rN+/caOY5OkXjPpliRVZtGihaxadW6lYyxbdnGl15ekbrCmW5IkSaqYSbckSZJUMZNuSZIkqWIm3ZIkSVLFTLolSZKkivU86Y6IUyLiSxGxKyLuiIh3RsSsDvofEhG3RERGxHOrjFWSJEnqhp4uGRgRxwA3AmuBM4DHAu+nSP7PL3mZVwO/VEmAkiRJUgV6PdP9GmAAODMzb8jMS4F3AG+MiCPH6txI2t8N/EW1YUqSJEnd0+uk+3Tg+szcPqLtcopE/NQS/f8K+CrwpQpikyRSA7plAAATs0lEQVRJkirR66R7MbBuZENmbgB2NY61FRGPB14JVLu1mSRJktRlvU66jwG2tmjf0jg2mg8AH8rM9V2PSpIkSapQT2+kbMgWbdGmvTgY8SLgZOB5ZQeJiHOAcwBOOOGEDkOUJEmSuqfXM91bgKNbtB9F6xlwIuJQ4CLgQuCQiDgaGL7pcl5EHNGqX2ZelplLMnPJ/PnzJx65JEmSNE69TrrX0VS7HRELgXk01XqPMA8YBC6hSNq3AN9tHLsc+E4lkUqSJEld0uvykmuB8yLiiMy8v9F2FrAbuLlNnx3AM5rajgf+EXgbcFMVgUqSJEnd0uuk+1Lg9cBVEXEh8BhgBXDJyGUEI2I9cHNmnp2Z+4Avj7xIRJzY+PV7mfmN6sOWJEmSxq+nSXdmbomIZwIfBK6mqONeSZF4N8dVemt4SZIkaSrr+eolmbkWOG2Mc04c4/hPKFY8kWa0er3Orl07WLbs4srHqtU2cthhh1c+jiRJ/ajXN1JKkiRJM85krNMtqUsGBwfJ3MmqVdVv1Lps2cVEzKt8HEmS+pEz3ZIkSVLFTLolSZKkilleUtL69Rs7ulmtXt8EwODggo7GGBpaPPaJkiRJmlZMuksYGhrquM8DD+wD6KgGdmho8bjGkiRJ0tRm0l3C8uXLO+6zdOlSAFavXt3tcCRJkjTNWNMtSZIkVcykW5IkSaqYSbckSZJUMZNuSZIkqWLeSClJkmaUlStXUqvVOuozfP7wQgllDQ0NjWtBBvUfk25JkjSj1Go1arV1LFq0sHSfuXOLlClzZ+k+69dv7Dg29S+TbkmSNOMsWrSQVavOrXSMTjbVU/+zpluSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIr1POmOiFMi4ksRsSsi7oiId0bErDH6/EpEXNc4/8GI2BARH42IR/UqbkmSJGm8ZvdysIg4BrgRWAucATwWeD9F8n/+KF2PAm4HPgncAZwEXAA8MSKelJn7qoxbkiRJmoieJt3Aa4AB4MzM3A7cEBFHAisi4n2NtoNk5teAr41o+nJE1IEvAo8Hvl1x3JKkDtXrdXbt2sGyZRdXOk6ttpHDDju80jEkaaJ6XV5yOnB9U3J9OUUifmqH17q38XNONwKTJEmSqtLrme7FwE0jGzJzQ0Tsahy7erTOEXEIRcwnAe8FvgV8s5pQJUkTMTg4SOZOVq06t9Jxli27mIh5lY4hSRPV65nuY4CtLdq3NI6N5RrgQWAdcCzw3Mw80L3wJEmSpO6bjCUDs0VbtGlv9mfAk4GXAocD10bE3FYnRsQ5EbEmItZs3rx53MFKkiRJE9XrpHsLcHSL9qNoPQP+MJlZy8xvZOangWcD/xP40zbnXpaZSzJzyfz58ycSsyRJkjQhvU6611HUbj8kIhYC8xrHSsvMnwL3AY/pWnSSJElSBXqddF8LPDsijhjRdhawG7i5kwtFxMnAcRTrd0uSJElTVq9XL7kUeD1wVURcSDFLvQK4ZOQyghGxHrg5M89uPL8Y2Ad8g6IM5ZeBNwH/TbHkoCRJkjRl9TTpzswtEfFM4IMUywNuBVZSJN7NcY3cGn4NxU2U5wBzgQ3A/wX+OjN3Vhy2JEmSNCG9nukmM9cCp41xzolNzy/HGW1JkiRNU5OxZKAkSZI0o5h0S5IkSRXreXmJJEnSZKrX6+zatYNlyy6udJxabSOHHXZ4pWNo+nCmW5IkSaqYM92SJGlGGRwcJHMnq1adW+k4y5ZdTMS8SsfQ9OFMtyRJklQxk25JkiSpYibdkiRJUsWs6ZamufXrN3Z0B369vgmAwcEFHY8zNLS4oz6SJKlg0i1NY0NDQx33eeCBfQAd39wzNLR4XONJkiSTbmlaW758ecd9li5dCsDq1au7HY4kSWrDmm5JkiSpYibdkiRJUsVMuiVJkqSKmXRLkiRJFTPpliRJkipm0i1JkiRVzKRbkiRJqljPk+6IOCUivhQRuyLijoh4Z0TMGqPPkyLiHyJifaPfbRFxQUTM7VXckiRJ0nj1dHOciDgGuBFYC5wBPBZ4P0Xyf/4oXc9qnHshUAMeD/xV4+cfVxiyJGkC1q/fyLJlF5c+v17fBMDg4IKOxhgaWtxxbJLUS73ekfI1wABwZmZuB26IiCOBFRHxvkZbKxdm5uYRz78cEQ8AH46IR2fmTyuOW5LUoaGhoY77PPDAPgAi5nUwzuJxjSVJvdTrpPt04Pqm5PpyihnsU4GrW3VqSriHfafxcwFg0i1JU8zy5cs77rN06VIAVq9e3e1wJGlS9bqmezGwbmRDZm4AdjWOdeI3gQPAbd0JTZIkSapGr5PuY4CtLdq3NI6VEhHHA38BfGqUkhRJkiRpSpiMJQOzRVu0aT/4xIg5wOeAHUDbf7uMiHMiYk1ErNm8uVV1iiRJktQbvU66twBHt2g/itYz4A8TEQF8EvgV4Pczc0u7czPzssxckplL5s+fP954JUmSpAnr9Y2U62iq3Y6IhcA8mmq921hJsdTgszKzzPmSJEkHcTlL9Vqvk+5rgfMi4ojMvL/RdhawG7h5tI4R8Vbgz4AXZuZ/VBumJEnqVy5nqcnQ66T7UuD1wFURcSHwGGAFcMnIGyIjYj1wc2ae3Xj+p8B7gI8DP4uIJ4+45n+3WVJQkiTpIC5nqcnQ06Q7M7dExDOBD1Ksyb2VomRkRYu4Rm4N/7uNn69oPEZ6JUUyLkmSJE1JvZ7pJjPXAqeNcc6JTc9fwcHJtiRJkjQtTMaSgZIkSdKMYtItSZIkVazn5SX9ZuXKldRqtYPah9uGb7xoNjQ0NK4bOSRJkjT9mHRXZGBgYLJD0AznF0JJkqYOk+4JMjnRdOMXQkmSes+kW+pTfiGUJGnq8EZKSZIkqWIm3ZIkSVLFTLolSZKkipl0S5IkSRUz6ZYkSZIq5uolkqSeareGPIy+jrxryEuazky6JUlThuvIS+pXkZm9HTDiFOADwFOArcBHgXdk5v5R+swB3g08GVgCzM3MKDvmkiVLcs2aNROKW5Ik9b+xdvMdGhpq2c9/idGwiLglM5c0t/e0pjsijgFuBBI4A3gn8OfAO8boehjwamAX8LUqY5QkSWo2MDDgv8RoQnpdXvIaYAA4MzO3AzdExJHAioh4X6PtIJm5NSKOzcyMiNcBp/UwZkmSNEM4W62q9Hr1ktOB65uS68spEvFTR+uYva6DkSRJkrqk10n3YmDdyIbM3EBRNrK4x7FIkiRJPdHrpPsYipsnm21pHOuaiDgnItZExJrNmzd389KSJElSRyZjc5xWZSLRpn38g2RelplLMnPJ/Pnzu3lpSZIkqSO9Trq3AEe3aD+K1jPgkiRJ0rTX66R7HU212xGxEJhHU623JEmS1C96vWTgtcB5EXFEZt7faDsL2A3cXNWgt9xyyz0R8dOqrj8DPRK4Z7KDkFrwvampyvempjLfn9316FaNvU66LwVeD1wVERcCjwFWAJeMXEYwItYDN2fm2SPaTqeYEX9C4/nzG4e+lZmjJtSZaVF3F0XEmlY7LUmTzfempirfm5rKfH/2Rk+T7szcEhHPBD4IXE1Rx72SIvFujmtWU9vf8fBvDlc0fr4S+Hi3Y5UkSZK6pdcz3WTmWsbYUTIzTyzTJkmSJE0Hk7FkoKa/yyY7AKkN35uaqnxvairz/dkD4e7qkiRJUrWc6ZYkSZIqZtKth4mIV0REtni8ZsQ5ERFvi4iNEbE7Iv49Ip4wmXGr/0TEooj4cER8NyL2R8SXW5zzkxbv1btanHdKRHwpInZFxB0R8c6IaL5ZWyqlm5+Tvjc1USU/K7v2fjQHGL+e30ipaeM0ivXTh/14xO9vAf4SOI9iU6M3AjdGxOMy86CERxqnXwF+H/g6MGeU8z4LfGDE8z0jD0bEMcCNwFrgDOCxwPspJh3O72K8mnkm9Dnpe1NdUuazspvvR3OA8cpMHz4eegCvABI4vM3xucA24O0j2uYBm4F3TXb8PvrnARwy4vcrgS+3OOcnwMVjXOetwBbgyBFtbwJ2jWzz4aPso1ufk743fXTjMdZnZTffj+YAE3tYXqJO/SZwJPC54YbM3Emx7vrpkxWU+k9mHujSpU4Hrs8RG3ABlwMDwKldGkMaqeznpO9NTViJz8puvh/NASbApFvt/HdE7IuI2yLif49oXwzsB2pN5/+wcUzqtVdFxJ6I2BYRV0ZE8/a7iyn+CfQhmbmBYvbG96wmYqKfk7431QvdfD+aA0yANd1qdidFrdY3KXYF/RPg0og4LDNXAscAOzJzf1O/LcBhETEnM/cg9ca/UtQx1oFfBi4AvhIRv5qZ2xrnHEOx+22zLY1jUqe69Tnpe1O90M33oznABJh062Ey83rg+hFN10bEI4DzI2LV8GktusYox6RKZOayEU+/EhFfA24FXgn8zchTW3SPNu3SqLr8Oel7U73QzfejOcA4WV6iMq4EjgVOpPg2e0SLJa2OBnZl5t4exyY9JDO/D9wG/PqI5i0U789mR9F6Vkcaj/F8TvreVC908/1oDjABJt3qRFLUe80CFjUdO6gWTJpEI2db1tFUaxgRCynuuPc9q27r5HPS96Z6oZvvR3OACTDpVhl/DNwD/BT4GrAdeMHwwYg4DHgecO2kRCc1RMTjgJOBW0Y0Xws8OyKOGNF2FsX6yjf3MDz1t/F8TvreVC908/1oDjAB1nTrYSLi/1LcHPRfFN9mz2o8Xt9YluiBiHgv8JcRsYWfL4x/CA/foESakMYH+e83nv4ScGREPL/x/BrgGcBLgM8Dd1DMtJwPbAA+PuJSlwKvB66KiAuBxwArgEualsaSSuni56TvTU3YWJ+VmbmrW+/HzDQHmIBoLGwuARAR76GYsVlIcWPEWuBvMvNTI84J4G3Aa4HjgDUU/7H5Tu8jVr+KiBOB29scPolirdiVwOMp6gnvBa4D3paZdzRd6xTgg8BTKGoTPwqsaHEHvjSmbn5O+t7URI31WZmZP+nm+9EcYPxMuiVJkqSKWdMtSZIkVcykW5IkSaqYSbckSZJUMZNuSZIkqWIm3ZIkSVLFTLolSZKkipl0S9IUEREvi4ifjnj+w4h4bYfXWBgR+yNiT0Q8coLxvCEizmzRviIiMiLcYE2SSjLplqSp44k0trCPiMOB/8HDt7Qv42UUn+2HAn8ywXjeAByUdEuSOmfSLUlTx0NJd+P3AxRbjXfiZcD3gQ3Ay7sXWm9FxCMmOwZJ6iaTbkmaAiLiEOAJwLcbTU8E1mbmAx1c4ykUs+OfBD4FPDEifqXpnBMbpSGvaGp/eqP96Y3nPwEeDby40Z4R8fGmIU+KiC9ExI6I+GlEvL3xOkZe9+SI+OeI2BoRuyPi6xHxe03nDJerPC4iro+IHcDnGseeHRFfjYhtjXFui4i3l/2bSNJUYdItSZMoIn4SEQnsB+YB1zSevx94/IiE98QSl3s5xez4ZygSbyhmvsfjj4C7gOuBpzQef9V0zj8DNwF/CPwL8A5GzK5HxC8C/wH8GvA64IXAVuALEXF6izH/FbgZ+ANgZUQ8Bvh/wE+Asxrtl1D8nSRpWvEmGEmaXL8PzKFIjp8NvLjR/u/ABcC/NZ7fMdpFGuUYZwE3ZuYdjbavAy+JiLdm5oFOgsrM70TEg8A9mfn1Nqe9PzP/ofH7jRFxGkUd+XDbG4FjgKdk5vpGTNcAa4F3A9c2Xe9vM3PViNf0fIq/zWszc3uj+aZOXockTRXOdEvSJMrMtZl5K7AQ+HLj953AEcAVmXlr47FnjEudARzNz2e4AT4B/CLwOxWEDvCFpuffB04Y8fxpwNeHE26AzNwP/CPwhIg4sqn/Pzc9vxXYC1weEc+PiAXdCVuSes+kW5ImSUTMiojZjaX3ngr8Z+P33wZ+BtzVOB4lLvdyYBfwbxFxdEQcTVEaspfqbqi8r+n5g8DcEc+PBe5s0e8uIChmwUd62LmNZP3ZFP+t+hTF3+MbEXHqRIKWpMlg0i1Jk+dLFEnxXuBRFInlXuDvgV8acWzUJDMifgH4XeAwimR9S+PxY4qlA/8oIo5onD58Y+acpsscN8HX0sp9wPEt2o8HkoOT9mw+MTP/LTN/j2IW/3co/h5fmOga5JLUa9Z0S9Lk+d8UZSRnUdyMOLyu9jXAKoqZaoDbxrjOSyg+z18LrGs69mvA3wAvAD4G3E0xI/24pvOe0+K6DwIDY72IUdwMvCEiTszMn0Axu0/xer+TmfeXvVBmPgjc1Fi//F+Bk4B7JhCbJPWUSbckTZLMvA0gIv4S+EJmromIk4FHAn+fmXeVvNTLgNuBD2fmw2aLI+IrwJsoSkw+lpkZEf8EnB0RP6JI6J8DPL3FddcCvx0Rz6UoCblnOHkuaSXwCuCGiLgA2A4spVjWsFWS/zAR8RqKuvBrgI0Uf5e3UtxU+v0O4pCkSWd5iSRNooiYAzwTuK7RdDrFLHCphDsi/ifweOAfmhNueOjGxY9TJM8nNZqXAVcBK4B/oqjD/rMWl38rRVL+OeBbjfNLa6yi8lvAD4C/A66kqPN+TmZeN1rfhu9SLA/418AXgQ9SfLk4LTN3dxKLJE22aPEZLUmSJKmLnOmWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIqZdEuSJEkVM+mWJEmSKvb/A2a/vgpweiLgAAAAAElFTkSuQmCC\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1140,7 +1490,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
+      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1150,7 +1500,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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7TdOMd+zrBVwHwu9XWSl1Qyl1VCn1N9CP7PjXFUikQgghhBBCPCKFnXQvAW4CGzVNa6Np2qtkr9E9/85lBDVNO61p2vI7th00TftI07ROmqa11zTtQ7KfcI9WSiUX8jkIIYQQj9zEiRP55JNP8lTW19eXZcuyv+hduXIlzZo1y1O9/JR9VKZPn06/fv0AuHDhAgaDweIr2/OrW7dubNu27aHbEaKwFOrwEqXUZU3TWgOLgM1kj+P+mOzE++647nw1fCbwHDCU7DW9jwE9lFKbCjpmIYQQoqAlJCQQHBzM6dOnizqUAvXUU0/pr4V/WG+//TYjRozghRdeeCTtCVHQCntMN0qpE0Cr+5Qpf9d2OtCuAMMSQgghiszKlSvp2LEjdnZ2RR3KP0bDhg25evUqBw8epH59s5f/CfHYKfSkWwghhHicvfXWeK5ezXVBrXxzcHDko4/m3rNMWFgYgwYN0rcvX76Mv78/+/fv5/bt2/j4+LBkyRI8PT0fOh6lFK+//jrBwcGUKVOGzz77jNatWwOwYsUKZs+eTWxsLG5ubrz11lsMGzYMgMTERAYMGMCePXuwsrKiVq1ahIeHY2VlRVxcHK+//joREREYDAbGjh3L6NGjzfo+d+4cFSpUICMjg2LFiuHr60vz5s356aefOHLkCE2aNGH16tW4uroCsG/fPsaNG8eJEyfw9vZmwYIF+Pr66u35+voSGhoqSbf4R5CkWwghhLjD1aspLFgw5pG1N2bMgvuWOXr0KNWqVdO3s7KyGDhwIOvWrSMzM5NBgwYxatQoNm16+FGV+/fv5+WXXyYxMZGNGzfSrVs3/vzzT5ydnXF3d2fLli1UrFiRiIgIOnToQIMGDahbty7z5s3D09OTnHdf7Nu3D03TyMrKonPnznTt2pU1a9YQGxtLmzZtqFatGu3bt79vPKtXryYsLAwvLy86dOjA3Llz+fDDD7l48SKdOnVi1apVvPDCC/z44490796dP/74g5ylgGvUqMGePXse+poIURiK4uU4QgghhLhDSkoKRuP/L+zl4uJC9+7dKVmyJEajkcmTJxMeft9FvvLE3d2dwMBAbGxs6NWrF9WqVSM0NBSATp06UalSJTRNo2XLlrRr147du3cDYGNjw6VLlzh//jw2NjY0b94cTdM4cOAACQkJTJ06leLFi1OxYkWGDh3K2rVr8xTPwIEDqVq1KnZ2dvTs2ZPIyEgAQkJC6NixIx07dsTKyoq2bdtSv359tm7dqtc1Go2kpDy6byWEKEiSdAshhBBFzMnJidTUVH372rVrDBs2DG9vbxwcHGjRogUpKSmPZNWPcuXKoWmavu3t7U1cXByQPcylcePGODs74+joyNatW0lMTATgzTffpHLlyrRr146KFSvy4YcfAnD+/Hni4uJwdHTUP7NmzeLvvy2+u86Mh4eH/t8lS5bUJ1qeP3+e9evXm7S7Z88eLl26pJdPTU3F0dHSO/eEePzI8BIhhBCiiNWuXZtTp07RoEEDAObNm8fJkyfZv38/Hh4eREZG8txzz6GUeui+Ll68iFJKT7wvXLhAly5duHnzJt27dyc4OJiuXbtiY2ODn5+f3qfRaGTevHnMmzeP48eP8/zzz9OgQQO8vLyoUKEC0dHRDx3bnby8vPD392fp0qW5lomKiqJOnTqPtF8hCoo86RZCCCGKWMeOHU2Gj6SmpmJnZ4ejoyPJycnMmDEjz235+voyffr0XI/Hx8ezcOFCMjIyWL9+PVFRUXTs2JFbt25x8+ZN3NzcKFasGGFhYfzwww96vS1btnD69GmUUjg4OGBtbY21tTUNGzbEwcGBjz76iOvXr5OZmcmxY8c4cODAA12LHP369WPz5s18//33ZGZmcuPGDXbt2kVsbKxeJjw8nA4dOjxUP0IUFkm6hRBCiCIWEBDA1q1buX79OgCBgYFcv34dV1dXGjdunK+1qGNiYvDx8cn1eKNGjYiOjsbV1ZXJkyezYcMGXFxcMBqNLFy4kJ49e+Lk5MTq1avp0qWLXi86Opo2bdpgMBho0qQJI0eOxNfXF2trazZv3kxkZCQVKlTA1dWVIUOGcOXKlQe/IGQ/6f7vf//LrFmzcHNzw8vLizlz5pCVlQXAgQMHsLe3p2HDhg/VjxCFRXsUX1U97urXr68OHjxY1GEIIYR4DEVFRVGjRg19uyiWDASYNGmSPsnxQcXGxtKjRw/27t37wG38U3Tv3p3BgwfTsWPHog5F/Ivd/fcDQNO0Q0ops3UsJekWQgjxr2bpf5pCCJEX+Um6ZXiJEEIIIYQQBUySbiGEEEIIIQqYLBkozHz88ce5Lv2UM2vc0quIq1SpwtixYws0NiGEEEKIfyJJukW+5MysF0IIIYQQeSdJtzBzr6fVI0eOBGDx4sWFFY4QQgghxD+ejOkWQgghhBCigEnSLYQQQjwGJk6cyCeffJKnsr6+vixbtgyAlStX0qxZs4IM7ZErX748O3bsKJS+BgwYwJQpUwA4cuQITZs2LZR+hbibJN1CCCFEEUtISCA4OJhhw4YVdSiPHU3TOH369CNpq3bt2jg6OrJ58+ZH0p4Q+SFjuv/F7rVKSW5yyueM7c4rWdlECCFyt3LlSjp27IidnV1Rh/LE69u3L59//jmdO3cu6lDEv4wk3f9i0dHRRJ44gY1b6TzXuY0GwPGEpDzXyUj4O9+xCSFEURk7fhzJKZcfWXvOjk58PHf+PcuEhYUxaNAgffvy5cv4+/uzf/9+bt++jY+PD0uWLLG4XGt+LV26lPnz5xMbG4uXlxchISHUrVsXTdOIjo6mcuXKQPawDE9PT2bOnMmuXbvo168fo0ePZu7cuVhbW/Of//yH4sWLExgYSGJiIuPHj2fSpElmdQG9fs6ys3f69ddfGTNmDFFRUdjZ2dG9e3fmz59P8eLFadGiBQB16tRB0zSWL19Or1692LJlC1OmTOHcuXPUrFmTJUuWULt2bQB+//13Bg8eTHR0NB07dkTTNJP+fH19GTJkCDdv3sTW1vahr6cQeSVJ9z9EQaydHRsbCyp/cRRzdMpfBQCFxT+0kPt53euc4MGenBdmX0KIf67klMt0DOz5yNrb+sm6+5Y5evQo1apV07ezsrIYOHAg69atIzMzk0GDBjFq1Cg2bdr0ULGsX7+e6dOns2nTJurXr8+ZM2ewsbHJU92//vqLGzducPHiRVauXMnQoUNp27Ythw4d4sKFC9SrV49XXnmFihUr5isma2trPv74Y+rXr09sbCwdOnRg8eLFBAYGEhERgaZpHD58WL8Z+O233xg0aBCbN2+mfv36hISE0KVLF06ePImmafj5+REYGMioUaP473//S+/evXnrrbf0/sqVK4eNjQ0nT57UE3UhCoOM6X4CXL9+/YlbP7swz+lJvH5CiH+WlJQUjEajvu3i4kL37t0pWbIkRqORyZMnEx4e/tD9LFu2jAkTJtCgQQM0TaNy5cp4e3vnqa6NjQ2TJ0/GxsaGV155hcTERMaMGYPRaKRWrVrUqlWLI0eO5DumevXq0bhxY4oVK0b58uUZNmzYPc916dKlDBs2jEaNGmFtbU3//v2xtbVl37597Nu3j4yMDAIDA7GxseHll1+mQYMGZm0YjUZSUlLyHasQD0OedP9DFMTa2Z6enlyxTcKlR8BDxXY/SeuD8XRzsXgst/MqiPXAC7MvIYTIDycnJ1JTU/Xta9euMXbsWLZt28bly9lDXVJTU8nMzMTa2vqB+4mJiaFSpUoPVNfFxUXvO2fseenS/z880c7OjrS0tHy3e+rUKcaNG8fBgwe5du0at2/fpl69ermWP3/+PEFBQXz66af6vlu3bhEXF4emaZQrV85kSImlm4rU1FQcHR3zHasQD0OedAshhBBFrHbt2pw6dUrfnjdvHidPnmT//v1cvXqViIgIAJTK55jAu3h5eXHmzBmLx0qWLMm1a9f07b/++uuB+7G3t89zWyNGjKB69epER0dz9epVZs2adc/z9PLyYvLkyaSkpOifa9eu0bt3b8qUKcPFixdN6l+4cMGkflxcHLdu3TIZziNEYZCkWwghhChiHTt2NBlSkZqaip2dHY6OjiQnJzNjxow8t+Xr68v06dMtHhsyZAhz587l0KFDKKU4ffo058+fB+DZZ59l9erVZGZmsm3btocazvLss8+ydetWkpOT+euvv+65/nhqaioODg4YDAb++OMP/vOf/5gcL126NGfPntW3hw4dypIlS9i/fz9KKdLT0wkNDSU1NZUmTZpQrFgxFi5cyO3bt9m4cSO//vqrSXu7du2iVatWMolSFDoZXiKeKLIMohDinyggIIBnn32W69evY2dnR2BgIH369MHV1ZWyZcvyxhtv5HkSZUxMDD4+PhaP9ejRg6SkJPr06cPFixcpX748q1atwtvbmwULFtC/f38+++wz/Pz88PPze+Dz8ff3Z8eOHZQvX57y5cszcOBA5s2bZ7Hs3LlzefXVV5k9ezbPPfccvXr14qefftKPT58+nf79+3P9+nW++OILevbsydKlSxk1ahTR0dHY2dnRrFkzWrRoQfHixdm4cSNDhw5lypQpdOzYkW7dupn099VXXzF8+PAHPjchHpT2sF9V/RPUr19fHTx4sEDafhxWxHjQMckjR47M/5KB/1tGKz+rmGQk/M2zNWvmK76HOafffj+K0krluY6m0gFQmn0+6lyh7nPPyDhwIZ4AUVFR1KhRQ98uiiUDASZNmoS7uzuBgYEP3FdsbCw9evRg7969D9zGk+zo0aO8+uqrcn3EI3P33w8ATdMOKaXq311WnnQXkH/CahhVqlTJd53olOTsurlMjLTIzeWB+npQSitFpo3lpzyPinXGzwXavhCi6OQlQS4Is2bNeug2PD09JaG8h2eeeUaujygyknQ/pH/yihgP8qT9cT+v2NhYNHWlwJNiTV3Jde1xIYQQQoi7yURKIYQQQgghCpg86RZm7jUZ8V6TDh+HiYWenp78nZhVKMNLHsXrmIUQQgjx7yBJt8iXnBciCCGEEEKIvJOkW5h51E+rC3MZv+x6svaqEEIIIR4vknSLAhcdHc3vR4+jGdzyXEfdyl7KMvLP+Hz1pdLSQZOkWwghhBCPF0m6RaHQDG4Uq/9ygfeTsfM/kFXg3QghxCM3ceJESpcunad1un19fenXrx9Dhgxh5cqVLFu2jD179hRClPe3a9cu+vXr90hWeNI0jejoaCpXrsy4ceOoWrWqvNhG/GNJ0i2EEEIUsYSEBIKDgzl9+nSRxXBngvs4evPNN2nYsCGDBg2iePHiRR2OEPkmSwYKIYQQRWzlypV07NhRJqvfQ5kyZahevTrfffddUYcixAORJ91CCCHEHUaOHUd88qN7Dby7sxOLP773Wy7DwsIYNGiQvn358mX8/f3Zv38/t2/fxsfHhyVLljz0UqWnT59m8ODBREZGYmNjQ+vWrfn6669p0aIFAHXq1EHTNJYvX067du3uGUNycjJvvPEG33//PdevX6dly5Zs2rTJrM+FCxeyZMkSfvjhB9zc3Jg8eTLr1q3j5s2bvPTSS3z88cf6zcacOXOYP38+mqYxc+ZMs7Z8fX0JDQ3l5ZcLfriiEI+aJN2PmcJc6eNxWFdbCCEeN/HJl8nq9OiSuvjQDfctc/ToUapVq6ZvZ2VlMXDgQNatW0dmZiaDBg1i1KhRFpPa/HjnnXdo164dO3fu5NatWxw8eBCAiIgINE3j8OHD+vCSpKSke8bg7++PwWDg+PHjGAwGfvnlF7P+3nvvPb799lvCw8Nxc3MjMDCQs2fP6kl/nz59ePfdd/nggw/Ytm0bc+fO5ccff6RChQoMHTrUrL0aNWrwzTffPNQ1EKKoSNL9mImOjub4Hydw8/LIcx1VTAMgPj05z3USYv7Kd2xCCCEKRkpKCkajUd92cXGhe/fu+vbkyZN5/vnnH7ofGxsbzp8/T1xcHJ6enjRr1izXsveK4dKlS4SFhZGUlISTkxMALVu21MsqpRg3bhy//vorO3fupFSpUiilWLp0KUeOHMHZ2RmASZMm0adPHz744APWrVvHwIEDefrppwGYPn06a9asMYnJaDSSkpLy0NdBiKIgSfdjyM3Lgx5v9i/QPtbPCSrQ9kD2n28AACAASURBVIUQQuSdk5MTqamp+va1a9cYO3Ys27Zt4/Ll7KEuqampZGZmYm1t/cD9zJ49m3feeYeGDRvi5OTEG2+8YTKs5U73iiEmJgZnZ2c94b5bSkoKX3zxBV9//TWlSpUCsieLXrt2jXr16unllFJkZmYCEBcXZ3LM29vbrN3U1FQcHR0f7OSFKGIykVIIIYQoYrVr1+bUqVP69rx58zh58iT79+/n6tWrREREANlJ6sPw8PBg6dKlxMXF8fnnnzNy5MhcV0y5VwxeXl4kJyfn+tTZycmJLVu2MHDgQH7++WcAXF1dsbOz4/jx46SkpJCSksKVK1dIS0sDsidKxsTE6G1cuHDBrN2oqCjq1KnzUNdAiKIiSbcQQghRxDp27Eh4eLi+nZqaip2dHY6OjiQnJzNjxow8t+Xr68v06dMtHlu/fr2+fraTkxOapulPzkuXLs3Zs2fzFEOZMmXo0KEDI0eO5PLly2RkZOhJ+Z1xfPXVV7z00kvs378fKysrhg4dytixY4mPz37x2cWLF/n+++8B6NmzJytXruTEiRNcu3bN4jmHh4fToUOHPF8LIR4nknQLIYQQRSwgIICtW7dy/fp1AAIDA7l+/Tqurq40btyYF154Ic9txcTE4OPjY/HYgQMHaNSoEQaDgS5durBgwQIqVKgAZI+h7t+/P46Ojqxbt+6+MaxatQobGxuqV6+Ou7s7n3zyiVl/bdu2ZcWKFXTp0oVDhw7x0UcfUblyZRo3boyDgwNt2rTh5MmTAHTo0IHAwEBatWpF5cqVadWqlUlbly5d4sSJE/j5+eX5WgjxONEe9quqf4L69eurnBnahSVnJZHFixfnu158enKhjOl2t3fOd3wPokuXLiQkX0Ez5v018A9KpcShsOV28XYF2o91xs/Ue/apQrl+QoiCFRUVRY0aNfTtolgyELInFbq7u+fpjZS5iY2NpUePHuzdu/eB23hcvfHGG1SqVCnfK3UJUZDu/vsBoGnaIaVU/bvLykRKIYQQ4g55SZALwqxZsx66DU9Pzycy4YbsMeZC/JNJ0i0KnKenJ4kZxSlWv+BfZpCx8z+oLPsC70cIIYQQIj9kTLcQQgghhBAFTJ50P2ZiY2O5kna1wNfRToj5i1uGawXahxBCCCGEyCZPuoUQQgghhChg8qT7MePp6UnxQly9RAghhBBCFDx50i2EEEIIIUQBk6RbCCGEeAxMnDjR4gtmhPi30zSN06dPF0pfvr6+LFu2DIDvvvuOV1555ZG1LUm3EEIIUcQSEhIIDg5m2LBhAOzatQsrKysMBgMGgwFPT0969uzJgQMHTOrdmYykpKQwaNAgPDw8MBqNVK1alY8++ogLFy7o7RgMBjRNw97eXt/evXu3WTyXLl1i8ODBlClTBqPRSPXq1Zk2bRrp6elm/eaYPn06/fr1M9mnlKJixYrUrFnTrA9fX19KlCiBwWDA1dWVbt26cenSJYvXp3379syePVvfvnjxIpqmWdz3119/6ddjxIgReHh4ULJkSZ555hlWrFhh0m758uWxs7PDYDDg4eHBgAEDSEtL048PGDCA4sWLYzQaMRqNPP3000ycOJErV65YjDNHbkniypUrsba2Nvl5GAwG4uLi9HiKFy9OYmKiSb1nn30WTdM4d+6cHpemaXz33Xcm5QIDA9E0jZUrV5rs37Vrl9n1ehKdO3cOTdO4ffv2I2mvS5cuHDt2jCNHjjyS9iTpFk8cTV3BOuPnPH+K3fqBYrd+yFcdTd37D64QQuTHypUr6dixI3Z2dvq+smXLkpaWRmpqKvv27aN69eo0b96cH3/80WIbY8eOJS0tjaioKK5cucJ3331HpUqVeOqpp0hLS9M/AIcPH9a3mzdvbtJOcnIyTZo04fr16+zdu5fU1FS2b99OSkoKZ86cydd5RUREEB8fz9mzZ81uGAAWLVpEWloap0+fJi0tjfHjx1tsp0WLFoSHh5u0W716dbN9VapUwcPDg1u3btGmTRvOnz/P3r17uXLlCnPmzOHtt99m/nzTlx9t3ryZtLQ0IiMj+f333/nggw9Mjk+YMIHU1FQSEhJYsWIF+/btw8fHR78Bya8mTZqY/DzS0tIoW7asfrxChQqsWbNG3z569CjXr183a6dq1aoEBf3/Sme3b99m/fr1VKpUyaxsUFAQzs7OJuVF3vTu3ZsvvvjikbQlEynFk8XaBqOdRpUqT+W5SnR0NEC+6mSXr5Kv8kKIf4bho8cSn5j8yNpzd3VmycKP71kmLCyMQYMGWTymaRqenp68++67JCcn89Zbb3Hw4EGzcgcOHGDmzJk4OTkBUL16dapXr57veOfPn4/RaCQkJAQrq+xnc15eXixYsCDfbQUFBdG1a1euX79OUFAQDRo0sFjO0dERPz8/PvvsM4vHW7RowZw5c8jKysLKyordu3cTGBjI1KlTTfa1aNECgFWrVnHhwgXCw8Oxt89+YdoLL7zAwoULGTx4MEOGDMHBwcGkDw8PD9q3b09kZKTFGEqUKEGDBg347rvvqFq1KitWrGDUqFH5vib34+/vT3BwMK+//jqQfQ0DAgKYMmWKSbnOnTsTEhLC5cuXcXJyYtu2bdSuXZvU1FSTcteuXWPDhg0sXbqUgIAADh48SP36Zm8otygzM5OPPvqI5cuXEx8fT9WqVdm0aROZmZlUqFCBjIwMihXLTiV9fX3p168fQ4YMYeXKlSxdupSGDRuyYsUKnJ2dCQkJ4dSpU7zzzjvcvHmTOXPm0L9/f7O6kH0TumzZMvbs2WMWU2hoKFOmTOHMmTOUKlWKwYMHM336dAD95+/o6AjA9u3badKkCV9++SVz5szhr7/+omHDhnzxxRd4e3vrZV5//XUuXbqEv78/SimT/nJiW7RoUZ6u2b1I0i2eKFpJR6pUcGfx4sV5rjNy5EiAfNURQjy54hOTSazU4dE1eCbsvkWOHj1KtWrV7luuW7duLF68mPT0dD2ZzNG4cWMmT57M5cuXadas2QM/GNixYwfdunXTE+4HlZPsrV27luvXrzNs2DDmz59P8eLFzcomJSWxceNGKleubLGthg0bcvPmTQ4fPsxzzz1HREQE48ePJzg42GTfhAkTgOxEqkOHDmbXqHv37vTt25e9e/fSvn17k2OxsbGEhYXRqlWre56X0Wikbdu27N69u0CS7saNG7Nq1SqioqKoWrUqX3/9NXv27DFLukuUKEGXLl1Yu3YtI0aMIDg4mICAALMbl2+++QaDwUCPHj346quvCA4OznPSPX/+fNasWcPWrVupWrUqR44coWTJkmaJvSX79+9nyJAhJCUlMW3aNF555RU6d+7M6dOnCQ8Pp3v37nTv3h2DwZD3iwPY29sTHBxMrVq1OHbsGG3btuXZZ5/Fz8+PiIgIKlSoQEpKin4zsGnTJmbNmsXmzZupUqUKH374Ib179+aXX34hMTGR7t278+WXX9K1a1cWLVrEkiVL8Pf31/urUaMG586d4+rVq2Y3avklw0uEEEKIIpaSkoLRaLxvubJly6KUIiUlxezYp59+St++fVm0aBE1a9akcuXKhIXdP+G/W1JSEmXKlLlvubp16+Lo6Kh/PvzwQ5PjGzduxNbWlnbt2vHiiy9y+/ZtQkNDTcqMHj2aUqVK4erqSmJiIp9++qnFvmxtbWnUqBEREREkJyeTkpJCxYoVad68ub7vxIkTtGzZEoDExESL51CsWDG9rxx+fn4YjUa8vLxwd3dnxowZ9z33smXLkpz8YN+G7Nu3z+S6WRoOkvO0e/v27VSvXp1y5cpZbCsgIIDg4GCuXLlCeHg4fn5+ZmWCgoLo1asX1tbW9OnThzVr1pCRkZGnWJctW8bMmTOpVq0amqZRp04dXFxc8lS3QoUKDBw4EGtra3r16kVMTAxTp07V/00UL178gSZH+vr68swzz2BlZUXt2rXp3bu3yTCju33++edMnDiRGjVqUKxYMSZNmkRkZCTnz59n69at1KxZk5dffhkbGxsCAwPx8PAwqZ/ze2npdy6/JOkWQgghipiTk1Oenh7mTBbM+fr8TnZ2dkyaNIlDhw6RlJREz5496dGjxz2Tw927d+uT+WrVqgWAi4tLrhMa7/Tbb7+RkpKif95++22T40FBQfTs2ZNixYpha2tLt27dzMYUL1y4kCtXrnDkyBEuX75MbGwsALNmzdLjGj58OJA9dCAiIoLdu3fTrFkzAJo1a6bv8/Ly0ocMuLq6WjyH27dvk5iYiKurq75v06ZNpKamsmvXLv744w+zSYyWXLx4EWfn7Hdd1KpV656TUu/WuHFjk+tmaZy8v78/q1evZuXKlQQEBOTaVrNmzUhISGDmzJm8+OKLJnMCAGJiYti5cyd9+/YFoGvXrty4ccPs5ic3MTExFm8K8qJ06dL6f+fEdfe+Oyet5tX+/ft5/vnncXNzo1SpUixZsuSeP7Pz588zZswY/SbH2dkZpRQXL14kLi4OLy8vvaymaSbbgP57ael3Lr8k6RZCCCGKWO3atTl16tR9y3377bfUrVvXbNjE3RwcHJg0aRLp6en8+eefuZZr3ry5Ppnv+PHjALRp04Zvv/2WrKys/J3EHWJjY/npp58ICQnBw8MDDw8PNmzYwNatWy0mSM888wxTpkzhtddeQynFpEmT9LiWLFkCZCfdu3fvJiIiQp/86ePjw88//0xERIQ+njfnHMLCwswmO37zzTfY2trSuHFjsxhatmzJgAEDcp3MmSMtLY0dO3boMRw/fjzXSakPytvbmwoVKrB161a6det2z7L9+vVj3rx5FpPzVatWkZWVRefOnfHw8KBixYrcuHGD4ODgPMXh5eVl8aYg59/ftWvX9H05q8Y8CHt7+zy31adPH7p06UJMTAxXrlxh+PDh+jhsTdMsnsPnn39ucqNz/fp1mjZtSpkyZYiJidHLKqVMtgGioqIoX778Qw8tAUm6hRBCiCLXsWPHXL8iz3kqN2PGDJYtW8asWbMslnvvvfc4cOAAt27d4saNGyxYsABHR8c8jRW/07hx47h69Sr9+/fn/PnzQPaT3XHjxuV56bRVq1ZRtWpVTp48SWRkJJGRkZw6dQpPT0+TlTnu1L9/f+Lj482WwcvRtGlTUlJSCAkJ0ZNbJycn3NzcCAkJMUm6/f398fT0pEePHpw7d46MjAy+//57Ro8ezfTp0ylVqpTFPgIDA9m+fbvFyZQ3b97k0KFD+Pn54eTkxMCBA+95DXJ+DjmfzMzMe5a/2/Lly/npp5/ue4M1evRotm/fbnL+OYKDg5k2bZr+M4iMjOSbb74hNDSUpKQkIHuZwruXGMwxZMgQ3nnnHaKjo1FKceTIEZKSknBzc6NcuXKEhISQmZnJl19+me+Vbe707LPPsnHjRq5du8bp06dZvnx5rmVTU1NxdnamRIkS/Prrr6xevVo/5ubmhpWVFWfPntX3DR8+nA8++EC/qbxy5Qrr168HoFOnThw/fpyNGzdy+/ZtFi5caJbwh4eH06HDo5njIUm3EEIIUcQCAgLYunWrydJwcXFx+rCFBg0acPToUXbt2kW7du0stqFpGgMHDsTV1ZWyZcuyfft2QkND8z1RzdnZmV9++QUbGxsaNWqE0WikdevWlCpVKteJjncLCgpi5MiR+lPunM/w4cNzXbauePHijB49mvfee8/i8ZIlS1KvXj1u3rzJ008/re9v3rw58fHxJkmnra0tO3bswMvLi0aNGuHg4MC4ceN4//33efPNN3ON283NjYCAAJMYZs+ejdFoxNnZmYCAAOrVq8cvv/xy32S4Vq1a2NnZ6Z+cNcL37t1rtk63peUUK1WqlKcJj87OzrRu3drsKe++ffs4d+4cr732msnPoEuXLlSuXJk1a9Zw69YtkpKSLD75h+wbsJ49e9KuXTscHBwYPHiw/m906dKlzJkzBxcXF44fP07Tpk3vG2tuxo4dS/HixSldujT9+/fXh8NYsnjxYqZOnYrRaOTdd9+lZ8+e+rGSJUsyefJkfHx8cHR0ZN++fbz00ku89dZbvPLKKzg4OPD000/rcx1cXV1Zv349b7/9Ni4uLkRHR+Pj42PS35o1a/T18x+WdvfSKE+i+vXrK0vLKxWkB10RY+TIkcSnJ9Pjzf4FEZZu/Zwg3O2dC2XFjpEjRxL5ZzzF6r9c4H3dPriBZ2X1EiFEPkRFRVGjRg19uyiWDASYNGkS7u7uBAYGPrK+hbiXPXv28Nlnn+X67cO/3ebNm1m1ahXr1q3Ltczdfz8ANE07pJQyu2OSJQMfQwkxf7F+Tt4XsE+Jz/6fg6O7c776cK+e9/JCCPFvkZcEuSDkNmxEiILSrFkzfVKqMNe5c2c6d+78yNqTpPsx8yDrql65nT0uy90+70m0e3VnebmLEEIIIUQhkaT7MTN27Nh815HhEUIIIYQQjzdJukWhUGkJ3D64Ie/lr2UvQq+VzN+6mCotAXDPVx0hhBBCiIImSbcocA8yjCU6+kp23Qr5TaDdZdiMEEIIIR47knSLAidDZoQQQgjxbydJt/jX+Pjjj4mOjjbbn7MvJ9G/W5UqVR7oxkEIIYQQIoe8HEf86+W8uEAIIYrSxIkT+eSTT4o6DCEK1cqVKx/JsoXnzp1D0zRu374NQLdu3di2bdtDt/tIKaWe+E+9evVUYRsxYoQaMWLEE9dXYXkSz0kI8Xg6ceJEUYeg4uPjVdmyZdW1a9eUUkrt3LlTaZqm7O3tlb29vSpXrpzq0aOH+vXXX03qASo6OloppdTly5fVwIEDVenSpZXBYFBVqlRRH374oTp//rzejr29vQJUyZIl9e2IiAizeOLi4tSgQYOUh4eHMhgMqlq1amrq1KkqLS3NrN8c06ZNU3379jXZl5WVpSpUqKBq1Khh1kfLli2Vra2tsre3Vy4uLuqll15ScXFxFq9Pu3bt1EcffaRvx8bGKsDivkuXLunXY/jw4ap06dLKzs5OPf300+rLL780adfb21uVKFFC2dvbq9KlS6v+/fur1NRU/Xj//v2VjY2NMhgMymAwqFq1aqm3335bpaSkWIwzh6Xro5RSK1asUFZWViY/D3t7e3Xx4kU9HhsbG5WQkGBSr06dOgpQf/75px4XoP773/+alBszZowC1IoVK0z279y50+x6PYw///xTASojI+Oh21qxYoXy8fF55DHt379f1a1b96HbvR9Lfz+Ag8pCPirDS/4hchsaAfceHvG4D42QIR9CCJH9tK9jx44m37qVLVuW2NhYlFJcvHiRL774gubNmxMaGkrr1q3N2hg7dizp6elERUVRqlQpTp06xbFjx3jqqadIS0vTy2maxuHDh3N9pXtycjJNmjShadOm7N27l/LlyxMTE8PcuXM5c+YMtWvXzvN5RUREEB8fz+3btzlw4AANGjQwOb5o0SKGDBlCSkoKPXv2ZPz48Xz11Vdm7bRo0YLw8HAmTJigt1u9enWzfVWqVMHDw4Nbt27Rpk0b3N3d2bt3L56envz444/079+fy5cvM27cOL3tzZs306ZNG/766y/at2/PBx98wPvvv68fnzBhAjNnzuTGjRscPXqUCRMm4OPjw/79++/7KnhLmjRpwp49e3I9XqFCBdasWcPrr78OwNGjR/VXr9+patWqBAUF0aVLFwBu377N+vXrqVSpklnZoKAgnJ2dCQoK0q/Xk65hw4ZcvXqVgwcPUr++2cshi4Qk3U+AJ3FoxJN4TkKIf4Zhw0fzd3zSI2uvtLsLny9ZeM8yYWFhDBo0yOIxTdPw9PTk3XffJTk5mbfeeouDBw+alTtw4AAzZ87EyckJgOrVq1O9evV8xzt//nyMRiMhISFYWWWPQvXy8mLBggX5bisoKIiuXbty/fp1goKCzJLuHI6Ojvj5+fHZZ59ZPN6iRQvmzJlDVlYWVlZW7N69m8DAQKZOnWqyr0WLFgCsWrWKCxcuEB4erifGL7zwAgsXLmTw4MEMGTIEBwcHkz48PDxo3749kZGRFmMoUaIEDRo04LvvvqNq1aqsWLGCUaNG5fua3I+/vz/BwcF60h0UFERAQABTpkwxKde5c2dCQkK4fPkyTk5ObNu2jdq1a5OammpS7tq1a2zYsIGlS5cSEBCQryT0119/ZeTIkZw6dQo7Ozv69u3L/Pnz9evs6Ji9rO/27dtxd3dn6NChHD58GE3TaN++PZ999pleJiYmhjFjxrB7926ysrLo3bs3ixYtMuvzzTffZO/evYSGhgIwbtw4tm7dipWVFQMHDmTGjBlYW1uTmZnJW2+9xcqVK3FwcOCNN94wa8vX15fQ0FBJukX+PKlPdZ/U8xJC/HP9HZ9EbNLTj7DFY/ctcfToUapVq3bfct26dWPx4sWkp6ebPWVt3LgxkydP5vLlyzRr1uyBl0/dsWMH3bp10xPuB5WT7K1du5br168zbNgw5s+fT/Hixc3KJiUlsXHjxlyfvjds2JCbN29y+PBhnnvuOSIiIhg/fjzBwcEm+3Ke4m7fvp0OHTqYXaPu3bvTt29f9u7dS/v27U2OxcbGEhYWRqtWre55XkajkbZt27J79+4CSbobN27MqlWriIqKomrVqnz99dfs2bPHLOkuUaIEXbp0Ye3atYwYMYLg4GACAgLMbly++eYbDAYDPXr04KuvviI4ODjPSeiYMWMYM2YM/v7+pKWlcexY9r/liIgIKlSoQEpKCsWKZaeSp0+fZuLEibRo0YKrV6/SvXt3pk+fzieffEJmZiYvvvgirVq1YtWqVVhbW5vdOGZlZTFs2DAuXLjADz/8QMmSJfHz86N06dKcPn2a9PR0XnzxRby8vBg2bBhLly5ly5Yt/P7779jb29O9e3ez+GvUqHHPbxUKm0ykFEIIIYpYSkoKRqPxvuXKli2LUoqUlBSzY59++il9+/Zl0aJF1KxZk8qVKxMWFpbvWJKSkihTpsx9y9WtWxdHR0f98+GHH5oc37hxI7a2trRr144XX3yR27dv608vc4wePZpSpUrh6upKYmIin376qcW+bG1tadSoERERESQnJ5OSkkLFihVp3ry5vu/EiRO0bNkSgMTERIvnUKxYMb2vHH5+fhiNRry8vHB3d2fGjBn3PfeyZcuSnJx833KW7Nu3z+S6WRoOkvO0e/v27VSvXp1y5cpZbCsgIIDg4GCuXLlCeHg4fn5+ZmWCgoLo1asX1tbW9OnThzVr1pCRkZGnWG1sbDh9+jSJiYkYDAYaN26ca9nKlSvTtm1bbG1tcXNzY9y4cYSHhwPZT8zj4uKYM2cO9vb2lChRwmTyZEZGBr179yY5OZnNmzdTsmRJ/v77b8LCwvjkk0+wt7fH3d2dsWPHsnbtWgDWrVtHYGAgXl5eODs7M3HiRLOYjEajxd+VoiJJtxBCCFHEnJyczIYFWHLx4kU0TdO/sr+TnZ0dkyZN4tChQyQlJdGzZ0969Ohxz+Rw9+7dGAwGDAYDtWrVAsDFxYVLly7dN5bffvuNlJQU/fP222+bHA8KCqJnz54UK1YMW1tbunXrRlBQkEmZhQsXcuXKFY4cOcLly5eJjY0FYNasWXpcw4cPB7KHmERERLB79249YWvWrJm+z8vLC29vbwBcXV0tnsPt27dJTEzE1dVV37dp0yZSU1PZtWsXf/zxh0lCnpuLFy/i7OwMQK1atfRYd+/efd+6jRs3NrluZ86cMSvj7+/P6tWrWblyJQEBAbm21axZMxISEpg5cyYvvvii2dDMmJgYdu7cSd++fQHo2rUrN27cMLv5yc3y5cs5deoU1atXp0GDBmzZsiXXsvHx8bzyyiuUK1cOBwcH+vXrp1/LmJgYvL299afidzt9+jT//e9/mTZtmv5NyPnz58nIyKBMmTL6DcqwYcOIj48HIC4uDi8vL72NnJ/9nVJTUy3+rhQVSbqFEEKIIla7dm1OnTp133LffvstdevWve8EPgcHByZNmkR6ejp//vlnruWaN29OWloaaWlpHD9+HIA2bdrw7bffkpWVlb+TuENsbCw//fQTISEheHh44OHhwYYNG9i6davFpPaZZ55hypQpvPbaayilmDRpkh7XkiVLgOyke/fu3URERNC8eXMAfHx8+Pnnn4mIiNDHGeecQ1hYGOnp6Sb9fPPNN9ja2lp8YtuyZUsGDBjA+PHj73luaWlp7NixQ4/h+PHjeqw5+x6Wt7c3FSpUYOvWrXTr1u2eZfv168e8efMsJuerVq0iKyuLzp074+HhQcWKFblx4wbBwcF5iqNKlSqsWbOG+Ph43nrrLV5++WXS09PRNM2s7MSJE9E0jSNHjnD16lVCQkLIXsgje07AhQsX9OX87lajRg1WrFhBhw4dOHnypF7H1taWxMRE/Qbl6tWr+r/TMmXKEBMTo7dx4cIFs3ajoqKoU6dOns61MEjSLYQQQhSxjh076l/F3y1n9ZIZM2awbNkyZs2aZbHce++9x4EDB7h16xY3btxgwYIFODo65mms+J3GjRvH1atX6d+/P+fPnweyn+yOGzeOI0eO5KmNVatWUbVqVU6ePElkZCSRkZGcOnUKT09P1qxZY7FO//79iY+P57vvvrN4vGnTpqSkpBASEqInt05OTri5uRESEmKSdPv7++Pp6UmPHj04d+4cGRkZfP/994wePZrp06dTqlQpi30EBgayfft2i5Mpb968yaFDh/Dz88PJyYmBAwfe8xrk/BxyPpmZmfcsf7fly5fz008/3fcGa/To0Wzfvt3k/HMEBwczbdo0/WcQGRnJN998Q2hoKElJ2ZOFy5cvz8qVKy22HRISQkJCAlZWVvoTY2tra9zc3LCysuLs2bN62dTUVAwGA46Ojly8eJE5c+boxxo2bEiZMmV4++23SU9P58aNG/z8888mffXu3ZtZs2bRpk0bzpw5Q5kyZWjXrh1vvPEGV69eJSsrizNnzui/Jz179mThwoXExsZy+fJls+FNAOHh4XTo0OGe168wSdIthBBC4OS0LQAAIABJREFUFLGAgAC2bt1qsjRcXFycPmyhQYMGHD16lF27dtGuXTuLbWiaxsCBA3F1daVs2bJs376d0NBQDAZDvmJxdnbml19+wcbGhkaNGmE0GmndujWlSpXKdaLj3YKCghg5cqT+lDvnM3z4cLMhJjmKFy/O6NGjee+99yweL1myJPXq1ePmzZs8/fT/T3Rt3rw58fHxJkmnra0tO3bswMvLi0aNGuHg4MC4ceN4//33efPNN3ON283NjYCAAJMYZs+ejdFoxNnZmYCAAOrVq8cvv/xy32S4Vq1a+svX7OzsWLFiBQB79+7Vf645nwMHDpjVr1SpUp4mPDo7O9O6dWuzp8/79u3j3LlzvPbaayY/gy5dulC5cmXWrFnDrVu3SEpKynWs9rZt2/ThM2PGjGHt2rWUKFGCkiVLMnnyZHx8fHB0dGTfvn1MmzaN3377jVKlStGpUyeTJ/TW1tZs3ryZ06dP89RTT+Hp6cnXX39t1l///v2ZOnUqrVq14ty5cwQHB3Pr1i1q1qyJk5MTL7/8sj5saOjQobRv3546depQt25ds28EDhw4gL29PQ0bNrzvNSwsWs6j/ydZ/fr1laXllQpSzvrSixcvLtR+hRBC5E9UVBQ1atTQt4tiyUCASZMm4e7uTmBg4CPrW4h72bNnz/+1d+9xdpXlwfd/FyEhQzgkYFJsJwiaeUjRWlvj+2i1oqCleKJFLLZaD8WXB/NYY1qwammNVNtykJSKEVFbj5UKYvuiHAQU6qEeQLFKDN2pQDKFSAg5kGQgp+v9Y+2hw86embVn9tozs+f3/Xz2Z2bfa93rvva43Vz7zrXumw9/+MPD/uvDVPbqV7+aM888k5e97GWVjtP4+QEQEXdk5n7fmFwyUJKkIcokyFUYrmxEqsoLXvCCtmzBPhl98YtfnOgQ9mN5iSRJklQxk25JkiSpYibdkiRJUsVMuiVJ0950WFRAUnu1+rlh0i1JmtZmz57Npk2bTLwllZaZbNq0idmzZ5fu0/HVSyLieOBDwPOALcDHgfdl5oirxkfEEuCvgWcDAfwA+PPM/G61EUuSullvby/9/f1s3LhxokORNIXMnj2b3t7e0ud3NOmOiHnAzcBq4FTgacAHKWbczxuh38J6vx8Ag/ucngt8NSKemZn3VRm3JKl7zZw5k2OPPXaiw5DU5To903020AOclpnbgJsi4jBgRURcWG9r5uXAofV+WwAi4tvAQ8DLgI9UH7okSZI0Np1Ouk8BbmxIrq8ELgBOAK4dpt9MYA+wfUjb9npbNO0hqWNWrlxJrVbbr72/vx9g2H9+6+vrY/ny5ZXGNlbDvSYY+XVN5tckSZo4nb6RcjGwZmhDZq4DdtaPDeeL9XM+GBELImIBsBLYDFxVUaySxmlgYICBgYGJDqPtuvV1SZKq0+mZ7nkUN0822lw/1lRm3h8RLwa+DLy93vwAcHJmeueLNMGGm9ldunQpAKtWrepkOG0x0mz1VH5dkqSJMRFLBjZbkymGaS8ORjwZuBq4g6JE5ZT671+JiKOH6XNWRNweEbd7R7okSZImUqeT7s3A3Cbth9N8BnzQuRSz8qdn5g2ZeQPwamAvcE6zDpl5RWYuycwl8+fPH2fYkiRJ0th1urxkDQ212/XlAOfQUOvdYDFwV2buHmzIzF0RcRfFsoOSNCYj3TA5nMHzB8tMyvImS0mavjqddF8PnBsRh2bmI/W2M4AB4LYR+t0HvCwiZmXmLoCIOAh4BsOveCKpy4x1RREYPuGt1Wr88Md3EYeU/xex3FVUw915z4Pl+2y3zE2SprNOJ92XU9wIeU1EXAA8FVgBXDJ0GcGIWAvclpln1ps+DrwF+FJErKKoAf+/wJOBKzoXvqTJajyricQh8zlwyeltjGZ/e26/utLrS5Imt44m3Zm5OSJOAi6jmKHeQrH034omcc0Y0u+OiPht4L3AZ+rNPwZempk/qjpuSZODK4pIkqaqTs90k5mrgRNHOeeYJm23ALdUFJYkTRg34pGk7tfxpFuSRtPJmxuLpHZWS306yU14JKk7mHRLXWoqb83+9a9/nY0PPUTMLJ8M5549ANx51+ryfXbvYuaBB8KcBS3H2E6WzUhS9zPplqaZqTJzGjNnMXPBL1Q6xu4Hfw65r9IxJEkCk26pa03lrdl7e3vZetAmjnzNGyodZ9NVn2bWlofZU+kokiRNzDbwkiRJ0rTiTLekUjp+c+NBPS31mexy5xZqta0t/y3c/VKSuoNJt6RSarUad61ZzfyFR5XukwcGAA/ueLh0n43rNzBrxsyuS7rZu5tHtu/jjjvXtdQtsih+aaVf5NaWxpAkVc+kW1Jp8xcexWvOfWOlY1x10afYev8mdm/8OZuu+nTpfnu2bAbgwLnzSvfZvfHnzJo5s+UYxyrjcPbOfH7l48zY/a3Kx5AktcakW9Kk09PTQ19fX0t9aluK2fS++UeW7zT/SNasWUMObKx+m/a9uwl2VDuGJGnSMumWNOn09va2vLrKWFdledWrXsXAw50ox0jgsY7MQkdufXw9dknS5GDSLWla6+3t5aHdszhwyemVjrP7lg9BumCUJE1XJt2S1AkHHEjuO7RjNd3D7TgqSZoYTrtIkiRJFTPpliRJkipmeYmkUvr7+9m6fRtXXfSpSsfZuH4Duw7ZWekYkiR1mjPdkiRJUsWc6ZZUSm9vL7N2PNyRzXEWzDmi0jEkSeo0k25JU8bKlSup1WpNjw22D67X3aivr4/ly5dXFpskSSNpKemOiGcCLwSOBD6amRsiYhHw88x8pIoAJamMnp6eiQ5BkqRhlUq6I+Ig4LPAaUBQbK12LbABuBD4T+BdFcUoSQDOVEuSpqyyN1J+AHgJ8IfAL1Ak3oOuB05uc1ySJElS1yhbXvL7wHmZ+U8RMaPh2D3AMW2NSpIkSeoiZWe6jwR+OsI1DmpPOJIkSVL3KZt03wM8b5hj/w9wd3vCkSRJkrpP2fKSTwPviYh7gWvqbRkRLwaWAyvaH5qkyWbj+g0t7Ui55cGHAZi7oPy62xvXb2DB4s6u053bN7Ln9qvLn79zCwBx8Nzyg+zdTbCVGbu/1VJskTuKMWNOC322tjSGJKl6ZZPuC4FfBT4DfLze9k1gNnBlZn6ogtgkTSJ9fX0t99m6ZxNAS5vdLFh8xJjGGquxjFWrFUlt37ELSvfp798FFJsMtTZWsf54X9/RLfXr5N9QkjS6Ukl3Zu4FXhsRH6ZYqWQBsAm4ITNvqzA+SZPEWJbrG9yoZtWqVe0Op20m++uaCn9DSdLoRk26I2IW8B3gXZn5VeAblUclSZIkdZFRb6TMzF3AscCe6sORJEmSuk/Z1UtuAn6rykAkSZKkblX2RsoPAZ+NiAOBfwEeoNgK/nGZ+bM2xyZJkiR1hbJJ9+DNkn9CsURgM407VUqSJEmifNL95kqjkCRJkrpY2SUDy++GIUmSJOkJys50AxARARwPHEGxTvdPMzNH7iVJkiRNb2VXLyEi3kJxA+V/ALcCPwbuj4gzqwlNkiRJ6g6lZroj4nXAFcAtwGeBDcBRwOuAKyJiZ2Z+vrIoJUmSpCmsbHnJO4HPZeYfNrR/KiI+A/wZYNItSZIkNVG2vOQ4ihnuZj5bPy5JkiSpibJJ9yNA7zDHeuvHJUmSJDVRNum+HvjriPjNoY0R8Tzg/fXjkiRJkppopab7ucCtEfHfFKuYHEUxy722frxrrVy5klqt1lKfwfOXLl3aUr++vj6WLx9u009JkiRNRWU3x9kQEc8C/gj4TYp1uu+l2B7+k5m5s7IIJ4FarUattoZFixaW7jN7dvGnzdxRus/atetbjk2SJEmTX+nNceqJ9WX1x7SzaNFCLr30nErHWLbs4kqvL0mSpIlRdp3u5wJHZ+YXmhx7DbAuM7/b7uAkaToYqYRtpFI1y9EkaeooeyPl3wBPH+bYL9ePS5LarKenh56enokOQ5I0TmXLS34VuHCYY98D3t6ecCRNRcPN1I52Q/Fknqnt5OzzZP0bSJLap2zSPZvhZ8VnAHPaE46kbtKtM7Td+rokSdUpm3T/FHgV8JUmx14F3N22iCRNOd04U9uNr0mSNHHKJt2XAx+NiG3Ax4B+4JeAs4AzgdYWo5YkSZKmkbLrdH8sIo4DlgN/MvQQsDIzr6giOEmSJKkbtLJO9zkR8RHgJcCRwEPAzZn5s6qCkyRJkrpB6aQbIDP/C/ivimKRJEmSutKwSXdEzAR6MnNbQ/t84FzgeOB+YFVm3llplJIkSdIUNtJM90XAK4BFgw0RcTjwA4qbKDcDhwOvi4jnm3hLkiRJzY20I+ULgM81tL2DIuF+W2YeCSwE1gHvriY8SZIkaeobKel+CtA4e/0K4J7MXAWQmQ8Al1Ak6JIkSZKaGCnpngM8PPgkIg4GngXc2nDe3cCT2h6ZJEmS1CVGSrrXA8cNef4Cii3fv9lw3sHANiRJkiQ1NVLSfSPwzoh4Rn3FkncBu9h/K/jnUtR1S5IkSWpipKT7/RSrm/wI2AC8CHh/Zj44eEJEzADezP4lJ5IkSZLqhl0yMDMfjIhfAV4DzAO+l5nfaDjtScDfA1+uLkRJkiRpahtxR8rMfAT4hxGO/xz4YLuDkiRJkrrJSOUlTUXEARHxtYjoqyIgSZIkqdu0nHQDQVHffWh7Q5EkSZK601iS7nGJiOMj4paI2BkR90fE+fUbMkfqsyIicpiHu2FKkiRpUhuxprvdImIecDOwGjgVeBpFTfgBwHkjdP04cEND2+8AfwZc3/5IJUmSpPYZS9K9D3gfcP8Y+p4N9ACnZeY24KaIOAxYEREX1tv2k5n9QP/Qtoj4C2BNZjZuVS9JkiRNKi2Xl2ThfZm5YQzjnQLc2JBcX0mRiJ9Q9iIRcQTwUuDzY4hBkiRJ6qhx13RHxNER8YaSpy8G1gxtyMx1wM76sbJOB2ZSJOySJEnSpNaOGymfA/xjyXPnAVuatG+uHyvrtcAPMvM/W+gjSZIkTYiOr14CZJO2GKZ9/xMjnkxRijJiaUlEnBURt0fE7Rs3bmw9SkmSJKlNhr2RMiK+VvIa81sYbzMwt0n74TSfAW/m9yiS9H8e6aTMvAK4AmDJkiWlEnpJkiSpCiOtXnICsL7+GMmIa2w3WEND7XZELATm0FDrPYLXAt/MzNHikiRJkiaFkZLutcC/Z+abRrpARJzOKLPOQ1wPnBsRh2bmI/W2M4AB4LbROkfEMcBzgaUlx5MkSZIm3Eg13bcDS0pcIynKPcq4HHgMuCYiXhIRZwErgEuGLiMYEWsj4hNN+r8W2ANcXXI8SZIkacKNNNP9eYoZ6NF8H3hzmcEyc3NEnARcBlxLUce9kiLxboyrWdnKa4FbMtM7IyVJkjRlDJt0Z+aXgS+PdoH6OtufKjtgZq4GThzlnGOGaX9W2XEkSZKkyWIilgyUJEmSppVhk+6I+HREHNvQ9tSImFl9WJIkSVL3GGmm+/UMWYM7ImYANeBXqg5KkiRJ6iatlpeUXaVEkiRJUp013ZIkSVLFTLolSZKkio20TjfAWRHxivrvQbERzlsj4oGG8zIz39v26CRJkqQuMFrS/UdN2s5s0paASbckSZLUxEib41h6IkmSJLWBibUkSZJUMZNuSZIkqWIm3ZIkSVLFTLolSZKkipl0S5IkSRUz6ZYkSZIqNto63U8QEU8CngscCVybmQ9HxGxgV2buqyJASZLGauXKldRqtabH+vv7Aejt7d3vWF9fH8uXL680tvEY7nWN9Jpg8r8uqZuVSrojIoALgT8GZlFshvMc4GHgX4FvAn9VUYySJLXdwMDARIfQdt34mqRuUXam+93A24DzgZuA7w45di3wh5h0S5ImmZFmdZcuXQrAqlWrOhVO2wz3uqbya5K6Xdmk+y3A+Zn5NxExo+HYWuBp7Q1LkqTpbaTSmOEMnj+YfJdl2YlUvbJJ9y8B3xnm2C5gTnvCkSSpdd2YoNZqNX7447uIQ+aX7pO7EoA773mwfJ/tG1uOTVLryibd/w08A/h6k2O/CtzTtogkSWpRrVbjrjWrmb/wqNJ98sAA4MEdD5fus3H9hpZjG484ZD4HLjm90jH23H51pdeXVCibdF8F/GVE/ID/mfHOiPhfwJ8CV1QRnCRJZc1feBSvOfeNlY5x1UWfqvT6krpX2XW6VwBrgH8DBv/97irgx/Xnf9v2yCRJkqQuUWqmOzMHIuJFwB8AJ1PcPLmJYsWSz2XmnsoilCRJkqa40pvjZOZe4DP1hyRJkqSS3AZekiRJqljZHSnvodiFspl9wFbgDuDvM/MnbYpNkiRJ6gplZ7pvA2YAT6ZYHvA79Z+/SJG43we8Evh+RPxGBXFKkiRJU1bZpPsbFLPZx2bmSZn5B5l5EnAssA24HlgE/Ah4XyWRSpIkSVNU2Rsp/wx4T2Y+YVeAzHwgIt4P/HVmfiwiLgUub3eQkiSNpL+/n63bt1W+jvbG9RvYdcjOSscY1N/fTz6ytfLNa/KRjfT376p0DEnlk+6FwGPDHHuUYpt4KHaunDXeoCRJmmqG24q+v78fgN7e3qb9OrWtvKSJVTbp/inwpxHx1cx8PPmOiNnAOfXjUNR4/7y9IUqSNLLe3l5m7Xi48h0pP3buSgYGBli6dOl+x/r7+xkYGNivfefOYma82bHBfs2S9YGBAeLQzmwD39u7oOmx4b5IgF8mpFaVTbrfCXwZWBcR1wEPAguAlwFz6z8BfgP4aruDlNTcSP9BHM7g+c2ShtH4H1FNd7sf28XufXDXxk37Hzyop3jsJwDYNXde02vuArY2ud6uHTuIww8fR7TVGu5LhKTmyu5IeXNE/DpwHvBCilVMHgBuBt6fmT+tn/f2qgKVtL9arUattoZFixaW7jN7dvF/+8wdLY21du36ls6XutGeXXtgRul95QA4cJhku4zcuWXMfVvR398/pi/iI6nVak2v6Zd3TVet7Ei5mmIbeEmTyKJFC7n00nMqH2fZsosrH0PSxBgYGOAHP/wxGeVn1iP3AHDHneta6LO15dikbtHa13VJkqaxA2cdyO59rfXZs2Vz0XcMM95x8NyW+4xVxuHsnfn8SseYsftblV5fmsxKJ90RsQD4feA4YHbD4czMM9sZmCRJk83Mg2Yxa0/SN//I0n1qWx4GaKnPYL/WisAkTWZlt4E/jmIXyhnAHOAh4Ij6880UG+dIktTV5i44ggVzjmDVqlWl+wzWNbfSZ7Dfnfc82FKfsXrssceIfLTymejIrY+veiJNN2V3pLwI+B7wCxS3YZ8C9ABvAXYCv1tJdJIkSVIXKFte8hzgbP5ng5wDMnMP8A8R8STg74AXVxCfJEmq2EEHHcRjew7qSE33cOt6S92ubNJ9CPBwZu6LiK3Ak4Ycux34y7ZHJknSFDLcuvmjrY3vEnrS9FC2vORe4Kj673cDrxly7BVAZxYSlSRpiunp6aGnp9mmOZKmk7Iz3TcBLwWuAi4BroyIFwB7gMXAB6oJT5KkqcHZakkjKZt0vxs4CCAzvxARA8AZwMHApcDHqglPkiRJmvpGTbojYgbFbPb9g22ZeS1wbYVxSZK61HC1z8Djy8k1u9nO2mdJU1mZmu6kuFny1yqORZI0zQ0MDDAwMDDRYUhS2406011fsWQ9xaY4kiSNy0iz1WPdSEbjF7l1v81xIndQ3L41FgeS8cTUIdK99DR9la3p/ijwjoj4SmbuqjIgSZLGYuP6DVx10adKn7/lwWJ79rkLjmhpjAWLy58/Xrl9I3tuv7r8+TuLxcTi4LktjdEz73D6+vr2O9bf3z/mf3no6ekZtkxImo7KJt2HAk8DfhYRNwAPUJSdDMrMfG+7g5MkqYyxJHJb92wCYMGc8kn0gsVHdCxpHMs4tVoxk9x37IIWei2wXl7qgLJJ93uG/P5HTY4nYNItSZoQY0kYJ3spSze+Jmk6K7U5TmYeMMpjRtkBI+L4iLglInZGxP0RcX59hZQyfU+LiO9HxEBEbIqIGyLCWnNJkiRNamV3pGyLiJgH3EwxM34qcD7wp8D7SvR9C/BPwPXAKcBbgBrlZ+slSZKkCVE6YY2IAF4JvBA4EliRmfdFxAlALTPvH/EChbOBHuC0zNwG3BQRhwErIuLCeluzsZ8ErAT+ODOHbsTzpbLxS5IkSROl1Ex3fYb628C/UMwwv4Ei8Qb4f4F3lRzvFODGhuT6SopE/IQR+v1e/Wf529IlSZKkSaJseclFwELg+cCTgBhy7GbgpJLXWQysGdqQmeuAnfVjw/nfwN3AmRHRHxG7I+K7EfEbJceVJEmSJkzZpPtU4M8z89954lKBAOsoEvIy5gFbmrRvrh8bzlHAccB5wJ9RlLnsAG6IiF9o1iEizoqI2yPi9o0bN5YMT5IkSWq/skn3IcB/D3NsNk+c+R5NY9JOvX+z9kEH1GM4MzM/l5k3AL8D7AXe1nSQzCsyc0lmLpk/f34L4UmSJEntVTbpvhv4rWGOnQD8uOR1NgPNtsk6nOYz4IMerv+8dbChXhd+B3B8ybElSZKkCVF29ZIPAx+OiK0Uy/YBzI2IN1PMNJ9V8jpraKjdjoiFwBwaar0b/JRiJrxxRj2AfSXHliRJkiZE2c1xPgZcQrGe9tp6803AFcDfZebnSo53PXByRBw6pO0MYAC4bYR+X6ZIsF882BARhwPPBn5UcmxJkiRpQpRepzsz3xURHwFeCiwANgE3ZebPWhjvcuDtwDURcQHwVGAFcMnQZQQjYi1wW2aeWR/79oj4V+ATEfEu4CHgncBuill4SZL2s3LlSmq1WtNjg+2DW6cP1dfXN6Zt2DtluNc10muCyf+6pG5WKumOiBmZuTcz7wM+PtbBMnNzRJwEXAZcS1HHvZIi8W6Mq3Fr+NdTLF14CXAw8C3gxMzcPNZ4JEnTV09Pz0SH0Hbd+JqkblF2pvv+iPg88JnMvGM8A2bmauDEUc45pknbduCt9YckSaPq1lndbn1dUjcru3rJNRQzzd+LiNUR8a6IOLrCuCRJkqSuUfZGyrcCTwZeTbGSyHuBn0XE1yLiTQ03RkqSJEkaopUbKXcD/wL8S33lkDMoZr8/TlGjfUglEUqSJHWh4W6I7e/vB6C3t7dpP2+InZpKJ91DZebWiLgeOJJiBZIntzUqSZKkaWpgYGCiQ1AFWkq662Ukr6GY4X4h8CjFKiSfaX9okiRJ3Wu42erBJR9XrVrVyXDaYqRlOkeawZ8Os/dllwx8OfCHwCuB2cC/UexCeVVmPlJdeJIkSeoG030Gv+xM97XA3cAHgM9m5rrqQpIkSeoOI838Dme0TY6G08nZ4rG8rpHUarWu39SpbNL9vzPz+80ORMQJwBsz84/aF5YkSdLUV6vVuGvNauYvPKp0nzwwAHhwx8Ol+2xcv6Hl2MajVqvxwx/fRRwyv3Sf3JUA3HnPg+X7bN/YcmyTVamkuzHhjohFwBsoSk6eAuwETLolSZIazF94FK85942VjnHVRZ+q9PrNxCHzOXDJ6ZWOsef2qyu9fieV3RyHiDg8Is6KiG9SlJr8ObCZYofIX6woPkmSJGnKG3GmOyIOAH6bYlb7VRQ3Ud4PfBj4v8A7MvPfqg5SkiRJY+OKIpPDsEl3RFwMvA5YQLE04JeATwE3A4cBb+tEgJIkSRrdmjVreOlLX7pf+2OPPcbevXub9tm3bx8AmzZt2u/YT37yE6677rqm/fbu3QsHHTGOaMvJnVuo1bY2vcmyv79/TCui9PT0TMjGQyPNdP8JkMB1wJsy8/H/NSIiK4lGkiRJY7Jnz54xL8s3mHw3tu3evbvp+TNnzoSDxjRUa/bu5pHt+7jjzv0XzovcAexp+ZKPbN/Dzx9qdr2tY4mwtJGS7n8ATgdeDtwdEVcCn87M71UakSRJUpfo7+9n6/Ztld/ouHH9Bg488ECe8YxnNI2h3TPC/f39PNQ8H2+vfXsY7hbEjDltH26w3KYKwybdmfmWiHgbcBrwRuBs4K0R8Z8UpSbOdkuSJE0ShxxySMd2sVy6dCkPtbD0n0a5kTIzHwX+CfiniHgy/7NM4Lvqp/xtRKwCrq6fK0mSpLre3l5m7Xi4I0sGLphTfY31oP7+fvKRrdUv6ZcJHMjemc+vdhxgxu5vDTuz3w5lN8chMx8ALgAuiIjnUMx+nwF8GvgQMK+SCCVJkjpkuJU+RlrlA1zpQ6MrnXQPVd8s5/sRsRx4JcUMuCRJUlca6w2K3aq3t5eHds+qfHOc3V//CLmv/bXbE2FMSfegzNwNXFN/SJIkTWnDzVYPLlnXqZppdZ/SO1JKkiRJGhuTbkmSJKli4yovkSRJkqoUuZUZu7/Vwvk7gNbX8Z7IzXFU19/fz86d21m27OJKx6nV1nPwwYdUOoYkSdKUMWMmh/YEfX1H73douE1/du7cBcDBBzdPc0fbBr4qJt2SJEkV2rh+Q0s7Um558GEA5i4ov+72xvUbWLC4c+t0A+T2jS2t0507twAQB88tP8i+3fT1Pb3pDaxTbXlHk+4Sent7ydzBpZeeU+k4y5ZdTFSwpakkSZoYY5k53bpnE0BLm90sWHxEpbO0jcYyVq1WlG/0HbughV4Lhh1rqq2LbtItSZJUkbEkhlNhecJufV1VcvUSSZIkqWIm3ZIkSVLFTLolSZKkipl0S5IkSRUz6ZYkSZIqZtItSZIkVcykW5IkSaqY63RLkiSpLYbbJRJ4vH1wve6hJmqXyE4y6ZYkSVLlenp6JjqECWXSLUmSpLbo9tnq8eh4TXdEHB8Rt0TEzoi4PyLOj4gZo/Q5JiKyyePKTsUtSZIkjVVHZ7ojYh5wM7AaOBV4GvBBiuT/vBKXOAf41pDnD7U7RkmSpE4Yrv55pNpnmB71z92o0+UlZwM9wGmZuQ24KSIOA1ZExIX1tpHcnZnfqTxKSZKkCTLda5+7VaeT7lOAGxuS6yuBC4ATgGs7HI8kSdKEcLZ6eul0TfdiYM3Qhsxq1kZNAAAVvUlEQVRcB+ysHxvNP0bE3oh4ICIuiQi/CkqSJGnS6/RM9zxgS5P2zfVjw3kM+DDwVWAb8CLgzyhqwk9tb4iSJElSe03EkoHZpC2GaS86ZD4AvG1I060R8XNgVUQ8KzPv3O+CEWcBZwEcffTR44tYkiRJGodOl5dsBuY2aT+c5jPgI7m6/vPXmx3MzCsyc0lmLpk/f36Ll5YkSZLap9NJ9xoaarcjYiEwh4Za7xKy4ackSZI0KXU66b4eODkiDh3SdgYwANzW4rVOr/+8ox2BSZIkSVXpdE335cDbgWsi4gLgqcAK4JKhywhGxFrgtsw8s/58BXAoxcY424AXAucC12Tmf3TyBUiSJEmt6mjSnZmbI+Ik4DKKNbm3ACspEu/GuIZuDb+GYjfKt1BsrrMOuAj4QMUhS5LGaLjd9kYy2k58w3GHPkmTXcdXL8nM1cCJo5xzTMPzKyk20ZEkTRG1Wo1abQ2LFi0s3Wf27OI/S5k7SvdZu3Z9y7FJUqdNxJKBU9LatetZtuzi0uf39z8IQG/vgpbG6Osrs0eQJE0NixYt5NJLz6l0jFY+myVpoph0l9DX19dyn0cf3QNAxJwWxlk8prEkSZI0uZl0lzCWOsHBesRVq1a1OxxJkiRNMZ1eMlCSJEmadky6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIq5Dbw0hfX397Nz53aWLbu48rFqtfUcfPAhlY8jSVI3cqZbkiRJqpgz3dIU1tvbS+YOLr30nMrHWrbsYiLmVD6OJEndyJluSZIkqWIm3ZIkSVLFTLolSZKkipl0S5IkSRUz6ZYkSZIqZtItSZIkVcykW5IkSaqYSbckSZJUMZNuSZIkqWIm3ZIkSVLFOr4NfEQcD3wIeB6wBfg48L7M3Fuy/wHA94FfB16ZmV+uKlZJktR9Vq5cSa1Wa6nP4PlLly5tqV9fXx/Lly9vqY+6U0eT7oiYB9wMrAZOBZ4GfJBixv28kpd5C/BLlQQoSZK6Xq1Wo1Zbw6JFC0v3mT27SJkyd5Tus3bt+pZjU/fq9Ez32UAPcFpmbgNuiojDgBURcWG9bVj1pP0DwLsoZsglSZJatmjRQi699JxKx1i27OJKr6+ppdM13acANzYk11dSJOInlOj/V8C3gFsqiE2SJEmqRKeT7sXAmqENmbkO2Fk/NqyIeCbwZqDar6WSJElSm3U66Z5HcfNko831YyP5EPDhzFzb9qgkSZKkCnV89RIgm7TFMO3FwYjXAscBryw7SEScBZwFcPTRR7cYoiRJktQ+nZ7p3gzMbdJ+OM1nwImImcBFwAXAARExFzisfnhORBzarF9mXpGZSzJzyfz588cfuSRJkjRGnU6619BQux0RC4E5NNR6DzEH6AUuoUjaNwM/qh+7EvhhJZFKkiRJbdLp8pLrgXMj4tDMfKTedgYwANw2TJ/twIsb2o4CPg+8B/haFYFKkiRJ7dLppPty4O3ANRFxAfBUYAVwydBlBCNiLXBbZp6ZmXuAW4deJCKOqf/648z8bvVhS5IkSWPX0aQ7MzdHxEnAZcC1FHXcKykS78a4ZnQyNkmSJKkqHV+9JDNXAyeOcs4xoxy/l2LFE0mSJGnS6/SNlJIkSdK0Y9ItSZIkVcykW5IkSaqYSbckSZJUMZNuSZIkqWIm3ZIkSVLFTLolSZKkipl0S5IkSRUz6ZYkSZIqZtItSZIkVazj28BLaq+1a9ezbNnFpc/v738QgN7eBS2P09e3uKU+kiSpYNItTWF9fX0t93n00T0ARMxpcazFYxpPkiSZdEtT2vLly1vus3TpUgBWrVrV7nAkSdIwrOmWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliHU+6I+L4iLglInZGxP0RcX5EzBilz9Mj4ob6+Y9FxLqI+HhEPLlTcUuSJElj1dElAyNiHnAzsBo4FXga8EGK5P+8EboeDtwDfBq4HzgWeC/w7Ih4TmbuqTJuSZIkaTw6vU732UAPcFpmbgNuiojDgBURcWG9bT+Z+W3g20Oabo2IfuCrwDOBH1QctyRJkjRmnS4vOQW4sSG5vpIiET+hxWttqv+c1Y7AJEmSpKp0eqZ7MfC1oQ2ZuS4idtaPXTtS54g4gCLmY4G/Bb4PfK+aUCVJ49Hf38/OndtZtuziSsep1dZz8MGHVDqGJI1Xp2e65wFbmrRvrh8bzXXAY8Aa4AjgFZm5r33hSZIkSe3X6ZlugGzSFsO0N/pjimS7j+LGy+sj4vmZ+eh+F4w4CzgL4Oijjx57tJKkMent7SVzB5deek6l4yxbdjERcyodQ5LGq9Mz3ZuBuU3aD6f5DPgTZGYtM7+bmZ8FTgZ+DfiDYc69IjOXZOaS+fPnjydmSZIkaVw6PdO9hqJ2+3ERsRCYUz9WWmbeFxEPA09tX3iSJKnbeb+BJkKnZ7qvB06OiEOHtJ0BDAC3tXKhiDgOOJJi/W5JkiRp0ur0TPflwNuBayLiAopZ6hXAJUOXEYyItcBtmXlm/fnFwB7guxRlKL8MvBP4L4olByVJkkrxfgNNhI4m3Zm5OSJOAi6jWB5wC7CSIvFujGvo1vC3U9xEeRYwG1gHfBH4m8zcUXHYkiRJ0rh0fPWSzFwNnDjKOcc0PL8SZ7QlSZI0RXW6pluSJEmadky6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliHd8cR5I0faxdu55lyy4ufX5//4MA9PYuaGmMvr7FLccmSZ1k0i1JqkRfX1/LfR59dA8AEXNaGGfxmMbS9OYXQnWaSbckqRLLly9vuc/SpUsBWLVqVbvDkR7nF0JNBJNuSZI0rfiFUBOh4zdSRsTxEXFLROyMiPsj4vyImDFKn+dExD9GxNp6v7sj4r0RMbtTcUuSJElj1dGZ7oiYB9wMrAZOBZ4GfJAi+T9vhK5n1M+9AKgBzwT+qv7z1RWGLEmSJI1bp8tLzgZ6gNMycxtwU0QcBqyIiAvrbc1ckJkbhzy/NSIeBT4aEU/JzPsqjluSJEkas06Xl5wC3NiQXF9JkYifMFynhoR70A/rP8vfRixJkiRNgE4n3YuBNUMbMnMdsLN+rBW/AewD7m5PaJIkSVI1Op10zwO2NGnfXD9WSkQcBfw58JkRSlIkSZKkSWEitoHPJm0xTPv+J0bMAr4AbAeGXfMnIs6KiNsj4vaNG5tVp0iSJEmd0emkezMwt0n74TSfAX+CiAjg08DTgZdl5ubhzs3MKzJzSWYumT9//ljjlSRJksat06uXrKGhdjsiFgJzaKj1HsZKiqUGX5qZZc6XJEmSJlynZ7qvB06OiEOHtJ0BDAC3jdQxIt4N/DHw+sz8ZnUhSpIkSe3V6aT7cuAx4JqIeElEnAWsAC4ZekNkfefJTwx5/gfAX1OUlvx3RDx3yMPaEUmSJE1qHS0vyczNEXEScBlwLUUd90qKxLsxrqFbw/9W/eeb6o+h3gx8sr2RSpIkSe3T6ZpuMnM1cOIo5xzT8PxN7J9sS5IkSVPCRCwZKEmSJE0rJt2SJElSxUy6JUmSpIqZdEuSJEkV6/iNlJI6Y+XKldRqtf3aB9uWLl3atF9fXx/Lly+vNDZJkqYbk25pmunp6ZnoECRJmnZMuqUu5Wy1JEmTR8druiPi+Ii4JSJ2RsT9EXF+RMwYpc+siLgoIr4REQMRkZ2KV5IkSRqvjibdETEPuBlI4FTgfOBPgfeN0vVg4C3ATuDbVcYoSZIktVuny0vOBnqA0zJzG3BTRBwGrIiIC+tt+8nMLRFxRGZmRLyNUXa07CRvVpMkSdJoOl1ecgpwY0NyfSVFIn7CSB0zc0qVlPT09HjDmiRJkoDOz3QvBr42tCEz10XEzvqxazscz7g5Wy1JUvfwX7BVlU4n3fOALU3aN9ePSZK63HBJDYyc2JjUaCL5r9car4lYMrBZmUgM0z5mEXEWcBbA0Ucf3c5LS5IqYmKjieYXO1Wl00n3ZmBuk/bDaT4DPmaZeQVwBcCSJUumVD24JHUzkxpJ01Gnb6RcQ1G7/biIWAjMqR+TJEmSuk6nk+7rgZMj4tAhbWcAA8BtHY5FkiRJ6ohOJ92XA48B10TES+p11yuAS4YuIxgRayPiE0M7RsQpEXE68Kz689Prj6d0LnxJkiSpdR2t6c7MzRFxEnAZxfKAW4CVFIl3Y1yNW8N/BBiaYF9V//lm4JPtjlWSJElql46vXpKZqxllR8nMPKZMmyRJkjQVxBTb6HFMImIjcN9Ex9FFngQ8NNFBSE343tRk5XtTk5nvz/Z6SmbOb2ycFkm32isibs/MJRMdh9TI96YmK9+bmsx8f3ZGp2+klCRJkqYdk25JkiSpYibdGosrJjoAaRi+NzVZ+d7UZOb7swOs6ZYkSZIq5ky3JEmSVDGTbj1BRLwpIrLJ4+wh50REvCci1kfEQET8W0Q8ayLjVveJiEUR8dGI+FFE7I2IW5ucc2+T9+qGJucdHxG3RMTOiLg/Is6PiMYNuKRS2vk56XtT41Xys7Jt70dzgLHr+OY4mjJOBAaGPP/ZkN/fBfwFcC6wBvgT4OaIeEZm7pfwSGP0dOBlwHeAWSOc90/Ah4Y83zX0YETMA24GVgOnAk8DPkgx6XBeG+PV9DOuz0nfm2qTMp+V7Xw/mgOMVWb68PH4A3gTkMAhwxyfDWwF/nJI2xxgI/D+iY7fR/c8gAOG/H41cGuTc+4FLh7lOu8GNgOHDWl7J7BzaJsPH2Uf7fqc9L3pox2P0T4r2/l+NAcY38PyErXqN4DDgC8MNmTmDuBa4JSJCkrdJzP3telSpwA3Zua2IW1XAj3ACW0aQxqq7Oek702NW4nPyna+H80BxsGkW8P5r4jYExF3R8T/GdK+GNgL1BrO/2n9mNRpfxQRuyJia0RcHRFPaTi+mOKfQB+XmesoZm98z2o8xvs56XtTndDO96M5wDhY061GD1DUan0PmAH8PnB5RBycmSuBecD2zNzb0G8zcHBEzMrMXUid8a8UdYz9wC8D7wW+ERG/kplb6+fMA7Y06bu5fkxqVbs+J31vqhPa+X40BxgHk249QWbeCNw4pOn6iDgIOC8iLh08rUnXGOGYVInMXDbk6Tci4tvAncCbgb8bemqT7jFMuzSiNn9O+t5UJ7Tz/WgOMEaWl6iMq4EjgGMovs0e2mRJq7nAzszc3eHYpMdl5k+Au4FfH9K8meL92ehwms/qSGMxls9J35vqhHa+H80BxsGkW61IinqvGcCihmP71YJJE2jobMsaGmoNI2IhxR33vmfVbq18TvreVCe08/1oDjAOJt0q49XAQ8B9wLeBbcBrBg9GxMHAK4HrJyQ6qS4ingEcB9wxpPl64OSIOHRI2xkU6yvf1sHw1N3G8jnpe1Od0M73oznAOFjTrSeIiC9S3Bz0HxTfZs+oP95eX5bo0Yj4W+AvImIz/7Mw/gE8cYMSaVzqH+Qvqz/9JeCwiDi9/vw64MXA64EvA/dTzLScB6wDPjnkUpcDbweuiYgLgKcCK4BLGpbGkkpp4+ek702N22iflZm5s13vx8w0BxiHqC9sLgEQEX9NMWOzkOLGiNXA32XmZ4acE8B7gLcCRwK3U/zH5oedj1jdKiKOAe4Z5vCxFGvFrgSeSVFPuAm4AXhPZt7fcK3jgcuA51HUJn4cWNHkDnxpVO38nPS9qfEa7bMyM+9t5/vRHGDsTLolSZKkilnTLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIqZdEvSJBERb4iI+4Y8/2lEvLXFayyMiL0RsSsinjTOeN4REac1aV8RERkRbrAmSSWZdEvS5PFs6lvYR8QhwP/iiVval/EGis/2mcDvjzOedwD7Jd2SpNaZdEvS5PF40l3/fR/FVuOteAPwE2Ad8Mb2hdZZEXHQRMcgSe1k0i1Jk0BEHAA8C/hBvenZwOrMfLSFazyPYnb808BngGdHxNMbzjmmXhrypob2F9XbX1R/fi/wFOB19faMiE82DHlsRHwlIrZHxH0R8Zf11zH0usdFxJciYktEDETEdyLitxvOGSxXeUZE3BgR24Ev1I+dHBHfioit9XHujoi/LPs3kaTJwqRbkiZQRNwbEQnsBeYA19WffxB45pCE95gSl3sjxez45ygSbyhmvsfid4ENwI3A8+qPv2o450vA14DfAf4FeB9DZtcj4heBbwK/CrwN+D1gC/CViDilyZj/CtwGvApYGRFPBf4/4F7gjHr7JRR/J0maUrwJRpIm1suAWRTJ8cnA6+rt/wa8F/h6/fn9I12kXo5xBnBzZt5fb/sO8PqIeHdm7mslqMz8YUQ8BjyUmd8Z5rQPZuY/1n+/OSJOpKgjH2z7E2Ae8LzMXFuP6TpgNfAB4PqG6/19Zl465DWdTvG3eWtmbqs3f62V1yFJk4Uz3ZI0gTJzdWbeCSwEbq3/vgM4FLgqM++sP3aNcqlTgbn8zww3wKeAXwReUkHoAF9peP4T4Oghz18IfGcw4QbIzL3A54FnRcRhDf2/1PD8TmA3cGVEnB4RC9oTtiR1nkm3JE2QiJgREQfWl957PvDv9d9/E/hvYEP9eJS43BuBncDXI2JuRMylKA3ZTXU3VD7c8PwxYPaQ50cADzTptwEIilnwoZ5wbj1ZP5niv1Wfofh7fDciThhP0JI0EUy6JWni3EKRFO8GnkyRWO4GPgH80pBjIyaZEfELwG8BB1Mk65vrj59RLB34uxFxaP30wRszZzVc5shxvpZmHgaOatJ+FJDsn7Rn44mZ+fXM/G2KWfyXUPw9vjLeNcglqdOs6ZakifN/KMpIzqC4GXFwXe3rgEspZqoB7h7lOq+n+Dx/K7Cm4divAn8HvAb4B+DnFDPSz2g47+VNrvsY0DPaixjBbcA7IuKYzLwXitl9itf7w8x8pOyFMvMx4Gv19cv/FTgWeGgcsUlSR5l0S9IEycy7ASLiL4CvZObtEXEc8CTgE5m5oeSl3gDcA3w0M58wWxwR3wDeSVFi8g+ZmRHxz8CZEfGfFAn9y4EXNbnuauA3I+IVFCUhDw0mzyWtBN4E3BQR7wW2AUspljVsluQ/QUScTVEXfh2wnuLv8m6Km0p/0kIckjThLC+RpAkUEbOAk4Ab6k2nUMwCl0q4I+LXgGcC/9iYcMPjNy5+kiJ5PrbevAy4BlgB/DNFHfYfN7n8uymS8i8A36+fX1p9FZUXAHcBHwGupqjzfnlm3jBS37ofUSwP+DfAV4HLKL5cnJiZA63EIkkTLZp8RkuSJElqI2e6JUmSpIqZdEuSJEkVM+mWJEmSKmbSLUmSJFXMpFuSJEmqmEm3JEmSVDGTbkmSJKliJt2SJElSxUy6JUmSpIr9/xmpyUYEJe58AAAAAElFTkSuQmCC\n"
+      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1160,7 +1510,7 @@
     {
      "data": {
       "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -1178,10 +1528,10 @@
     "exp = lambda x: 10**(x)\n",
     "log = lambda x: np.log10(x)\n",
     "for group in grouped.groups.keys():\n",
-    "    #gr2= grouped.get_group(group).groupby(by=['set', 'modeltype', '# Authors'], as_index=False).mean()\n",
+    "    #gr2= grouped.get_group(group).groupby(by=['set', 'modeltype', 'No. of Authors'], as_index=False).mean()\n",
     "    gr2 = grouped.get_group(group)\n",
-    "    gr2.loc[:, ['Featuretypes & Model']] = gr2.apply(lambda x: '({}, {})'.format(str(x['set_feat']), x['modeltype']), axis=1).tolist()\n",
-    "    #print(gr2['Featureset & Model'].unique())\n",
+    "    gr2.loc[:, ['Feature Types & Model']] = gr2.apply(lambda x: '({}, {})'.format(str(x['set_feat']), x['modeltype']), axis=1).tolist()\n",
+    "    #print(gr2['Feature Set & Model'].unique())\n",
     "\n",
     "    fig, ax = plt.subplots(figsize=(12,8))\n",
     "    #print(gr2.head())\n",
@@ -1189,7 +1539,7 @@
     "    sel = [True for _ in range(gr2.shape[0])]\n",
     "    gr2 =gr2.loc[sel, : ]\n",
     "    #print(gr2.loc[sel, : ].head())\n",
-    "    sns.boxplot(x='# Authors', y=\"f1-score\", hue='Featuretypes & Model',\n",
+    "    sns.boxplot(x='No. of Authors', y=\"f1-score\", hue='Feature Types & Model',\n",
     "                   data=gr2, #exclude outliers\n",
     "                  palette=colors, dodge=True, ax=ax, showfliers=False,\n",
     "                hue_order=list(colors.keys()))\n",
@@ -1199,37 +1549,15 @@
     "    ax.tick_params(axis='both', which='major', labelsize=15)\n",
     "    ax.tick_params(axis='both', which='minor', labelsize=13)\n",
     "    ax.set_ylabel(\"Average F1-Score\", fontsize=16)\n",
-    "    ax.set_xlabel(\"# Authors\", fontsize=16)\n",
+    "    ax.set_xlabel(\"No. of Authors\", fontsize=16)\n",
     "    plt.setp(ax.get_legend().get_texts(), fontsize='12')\n",
     "    plt.setp(ax.get_legend().get_title(), fontsize='14')\n",
     "    os.makedirs(os.path.join(savedir, '..', 'figures', str(group[1])), exist_ok=True)\n",
     "    fig.savefig(os.path.join(savedir, '../figures', str(group[1]), 'f1_scores_{}.pdf'.format(group[0])))\n",
     "    #ax.yaxis.set_ticks([np.exp(x) for p in range(0,1) for x in np.linspace(10**p, 10**(p+1), 10)], minor=True)\n",
-    "    fig.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array(['(1, baseline)', '(2, cumulated)', '(2, stacked)',\n       '(3, cumulated)', '(3, stacked)'], dtype=object)"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "colors = (\"#ffffcc\",\"#a1dab4\",\" #41b6c4\", \"#2c7fb8\",\"#253494\")\n",
-    "np.unique(gr2['Featureset & Model'])\n",
+    "    fig.show()\n",
+    "\n",
+    "\n",
     "\n"
    ],
    "metadata": {
diff --git a/Scripts/ML/__pycache__/evaluations.cpython-38.pyc b/Scripts/ML/__pycache__/evaluations.cpython-38.pyc
index 6c0fd6e96d2477fffbe7e6e5c170e55812205795..8e493c29786bd05605da8772f1d335a5b4d0250e 100644
Binary files a/Scripts/ML/__pycache__/evaluations.cpython-38.pyc and b/Scripts/ML/__pycache__/evaluations.cpython-38.pyc differ
diff --git a/Scripts/ML/__pycache__/plottings.cpython-38.pyc b/Scripts/ML/__pycache__/plottings.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..df5e26442d4968d32101025cb4aeb5c4094024fa
Binary files /dev/null and b/Scripts/ML/__pycache__/plottings.cpython-38.pyc differ
diff --git a/Scripts/ML/plottings.py b/Scripts/ML/plottings.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..a6861f2b505231adaa1c30d87043e2d58d307103 100644
--- a/Scripts/ML/plottings.py
+++ b/Scripts/ML/plottings.py
@@ -0,0 +1,341 @@
+import numpy as np
+import scipy as sci
+import pandas as pd
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+import matplotlib.patches as mpatches
+import seaborn as sns
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+
+def y_coord_cal(x:pd.DataFrame):
+
+    s = x.shape[0]
+    x['y'] = list(range(0, s))
+
+    return x
+
+def max_sorter(x:pd.DataFrame, to_plot:str):
+
+    x = x.reset_index(drop=False)
+    if to_plot != 'True_False':
+        aggs2 = x.loc[x['correct'] == True, :].groupby(['x']).agg({'y': 'max'}).sort_values(by=['y'],ascending=True).reset_index(drop=False)
+        aggs2['y'] = 1-aggs2['y']
+    else:
+        aggs2 = x.loc[x['correct'] == True, :].groupby(['x']).agg({'y': 'min'}).sort_values(by=['y'], ascending=True).reset_index(drop=False)
+    aggs = x.loc[x['correct']==False, :].groupby(['x']).agg({'y':'max'}).sort_values(by=['y'], ascending=False).reset_index(drop=False)
+
+    #import random
+    #random.seed(1234)
+    #ints = random.sample(range(0,1000), 500)
+    #aggs = aggs.loc[aggs.x.isin(ints), :]
+
+
+    selects = set(aggs['x'].to_list())
+    aggs2 = aggs2.loc[~aggs2.x.isin(selects),:]
+
+    aggs = pd.concat([aggs2, aggs], axis=0).drop_duplicates(subset=['x']).sort_values(by='y', ascending=False).reset_index(drop=True)
+
+    try:
+        assert set(aggs['x'].tolist()) == set(x['x'].to_list())
+    except:
+        print(x.head())
+        print(aggs['x'])
+        print(set(aggs['x'].tolist()).symmetric_difference(set(x['x'].to_list())))
+    minimum = min(aggs['x'])
+    relabel = {item: new+minimum for new, item in enumerate(aggs['x'].to_list())}
+    sums = [[],[]]
+    for key, item in relabel.items():
+        sums[0].append(key)
+        sums[1].append(item)
+
+
+
+    x['x'] = [relabel[el] for el in x['x'].to_list()]
+
+
+    return x
+
+def add_y_sec(x:pd.DataFrame):
+    adder = x.loc[x['correct'] == False, 'y'].max()+1
+    x.loc[x['correct'] == True, ['y']] = x.loc[x['correct'] == True, ['y']] + adder
+    return x
+
+def make_unique_x(df):
+    ids = df['ID'].unique().tolist()
+    xc = list(range(0, len(ids)))
+    xf = pd.DataFrame({'ID': ids, 'x': xc})
+    # print(df.columns.values)
+    df = pd.merge(df, xf, how='left', left_on='ID', right_on='ID')
+
+    return df
+
+def relative_x_y(x:pd.DataFrame):
+    assert sum(x['ID'].to_list()[0] != x['ID']) ==0
+
+    x['y'] = x['y']/(x.shape[0]-1)
+
+    return x
+
+def make_groupings(df):
+    df['groups'] = 'None'
+    #make groups of x - x max should be 999 but just to be sure it is dynamic
+    groups = [set(df.loc[df['# Authors'] == auth, 'x'].unique().tolist()) for auth in np.unique(df['# Authors'])]
+    #make unique parts
+    for i in range(len(groups)-1, 0, -1):
+        #print(groups[i-1].intersection(groups[i]))
+        groups[i] = groups[i] - groups[i-1]
+    for i, gr in enumerate(groups):
+        df.loc[df['x'].isin(gr), 'groups'] = i
+        #print(df['groups'].unique())
+
+    return df
+
+def props(x, sh):
+
+    return round(len(x)/sh,2)
+
+def plot_distrib_graph(df:pd.DataFrame, fig=None, ax=None, to_plot='True_False', color='correct', line=False, absolute=False):
+    colors_c = {True: "darkslategrey",
+              False: "lightslategrey",
+                'True': "darkslategrey",
+                'False': "lightslategrey"
+                }
+    colors_t = {'female': "lightsteelblue",
+                'male': "steelblue",
+                '1947': 'navy',
+                '1963': "darkolivegreen",
+                '1985': "saddlebrown",
+                '1975': 'black',
+                '1995': 'lightslategrey'}
+    relabel_c = {True: 'Correct',
+               False: 'Incorrect',
+               'True': 'Correct',
+               'False': 'Incorrect'}
+    relabel_t = {'female': 'Female',
+                 'male': 'Male',
+                 '1947': '1947',
+                 '1963': '1963',
+                 '1975': '1975',
+                 '1985': '1985',
+                 '1995': '1995'
+                 }
+    if type(ax) == type(None):
+        fig, ax = plt.subplots(1, figsize=(18,4))
+
+    df = df.copy(deep=True)
+    #make x coordinations (in order of appearances and from low num authors to high num auhtors
+    df = make_unique_x(df)
+    #make y coordinates
+    df = df.groupby(['ID', 'correct']).apply(y_coord_cal)
+
+
+
+    if to_plot=='True_False':
+        #add y_section of false which will be the lower ones
+        df = df.groupby(['ID']).apply(add_y_sec).reset_index(drop=True)
+    if not absolute:
+        df = df.groupby(['x']).apply(relative_x_y).reset_index(drop=True)
+
+    df = make_groupings(df)
+    df = df.groupby(['groups']).apply(max_sorter, to_plot=to_plot).reset_index(drop=True)
+
+
+    bool_sel = df['correct'] == False
+
+    if color == 'correct':
+        # only look at wrong ones as that is the other group
+        props = {}
+        props['True'] = round(df.correct.sum()/df.shape[0], 2)
+        props['False'] = 1 - props['True']
+        # append proportion correct ones
+    else:
+        res = df.groupby(color)[[color]].agg(lambda x: round(len(x) / df.shape[0], 2))
+        props = {str(ind): val for ind, val in  zip(list(res.index.values), res[color].to_list())}  # append proportion correct ones
+
+
+    if to_plot == 'True':
+        bool_sel = df['correct'] == True
+        df = df.loc[bool_sel, :]
+
+    elif to_plot =='False':
+        df = df.loc[df['correct'] == False, :]
+
+    #from collections import Counter
+    #print(df.loc[df['centered_age']==1975, 'x'])
+    #print(Counter(df.loc[df['centered_age']==1975, 'x']).most_common(100))
+    # ages_map = {}
+    # for row in df.itertuples():
+    #     id= getattr(row, 'ID')
+    #     if id not in ages_map.keys():
+    #         ages_map[id] = getattr(row, 'centered_age')
+    #     else:
+    #         try:
+    #             assert ages_map[id] == getattr(row, 'centered_age')
+    #         except:
+    #             print('ID: {}'.format(id))
+    #             print('First Age: {}'.format(ages_map[id]))
+    #             print('New Age: {}'.format(getattr(row, 'centered_age')))
+
+    if color == 'correct':
+        relabel = relabel_c
+        title = 'Prediction'
+        colors = colors_c
+        hue_order = [True, False]
+        y_lab = "Proportion of Test Instances"
+    else:
+        relabel = relabel_t
+        t = color.split('_')[-1]
+        t = t[0].upper()+t[1:]
+        title = t
+        colors = colors_t
+        df[color] = df[color].astype('str')
+        if 'age' in color:
+            hue_order = ['1947','1963', '1975', '1985','1995']
+        else:
+            hue_order = ['female', 'male']
+        y_lab = 'Proportion Misclassified'
+    if not line:
+        ax = sns.scatterplot(data=df, x='x', y='y', palette=colors, hue=color, hue_order=hue_order, ax=ax, s=1)
+        for t in ax.get_legend().get_texts(): t.set_text(relabel[t.get_text()])
+
+        ax.get_legend().get_title().set_text(title)
+    else:
+        #stack the bars for groupings of data
+        x = np.unique(df['x']).tolist()
+        #print(len(x))
+        labeller = []
+
+        if to_plot == 'True_False':
+            #make the bars ranging to 1
+            tmp = pd.DataFrame({'x': x, 'y': [1 for _ in range(len(x))]}).reset_index(drop=True)
+            #ax = sns.barplot(x='x', y='y', data=tmp, ax=ax, color=colors['True'])
+            ax.bar(x=tmp['x'].to_list(), height=tmp['y'].to_list(), color=colors['True'], width=1)
+            df = df.loc[bool_sel, :].sort_values(by='y', ascending=False).groupby(by=['x'], as_index=False).first().reset_index(drop=True)
+            labeller.append('True')
+            #print(len(df['x'].unique()))
+        else:
+            #we only have the subset we want inside our df
+            df = df.sort_values(by='y', ascending=False).groupby(by=['x'], as_index=False).first().reset_index(drop=True)
+            #print(len(df['x'].unique()))
+        cats = df[color].unique().tolist()
+        #iterate over it in correct order
+        for cat in relabel.keys():
+            #ckeck that they cat exists
+            #print(cats)
+            if cat in cats:
+                #print(cat)
+                tmp = df.loc[df[color] == cat, :]
+                #print(df.shape)
+                #print(tmp.shape)
+                #ax = sns.barplot(x='x', y='y', data=tmp, ax=ax, color=colors[str(cat)])
+                ax.bar(x=tmp['x'].to_list(), height=tmp['y'].tolist(), color=colors[str(cat)], width=1)
+                #print(ax.get_xlim())
+                #print(ax.get_xticklabels)
+
+                #ax.set_xticks([0, 50, 150, 500, 1000])
+                labeller.append(str(cat))
+        bars=[]
+        for cat in labeller:
+            prop = props[cat]
+            bars.append(mpatches.Patch(color=colors[cat], label = relabel[cat] +' ({}%)'.format(prop*100)))
+        ax.legend(handles=bars, loc='upper right', title=title)
+
+        ax.set_xticks([0, 50, 150, 500, 1000])
+    ax.margins(x=0)
+
+
+
+    ax.tick_params(axis='both', which='major', labelsize=15)
+    ax.tick_params(axis='both', which='minor', labelsize=13)
+    ax.set_ylabel(y_lab, fontsize=16)
+    ax.set_xlabel("Individual Authors", fontsize=16)
+    plt.setp(ax.get_legend().get_texts(), fontsize='12')
+    plt.setp(ax.get_legend().get_title(), fontsize='14')
+
+    return fig, ax
+
+def random_draw_select(x, cutoff):
+    #accuracy for this subset
+    ratio = x['correct'].sum()/x.shape[0]
+
+    #are we worse than random draw?
+    x['less_draw'] =  ratio <= cutoff
+
+    return x
+
+def calc_confusion_matrix(x:pd.DataFrame, target:str, preds:str, labs:list, normalize='true'):
+
+    cf= confusion_matrix(y_true=x[target].to_numpy(), y_pred=x[preds].to_numpy(), labels=labs, normalize=normalize)
+    return cf
+
+def plot_heatmap(df:pd.DataFrame, target:str, pred:str, normalize='true', major_wrong = False, ax=None):
+
+    df = df.copy(deep=True)
+    if type(ax) == type(None):
+        fig, ax = plt.subplots(1, figsize=(18,4))
+    cf_dic = {}
+    #for the x-axiis of the plot
+    authors = sorted([int(el) for el in df['# Authors'].unique()])
+    if 'age' in target:
+        labs = sorted([int(el) for el in df[target].unique()])
+    else:
+        labs = ['female', 'male']
+
+    #select only those for which we are only as good as random draw
+    if major_wrong == True:
+        draw = 1/(len(labs))
+        df = df.groupby(by=['# Authors', 'ID']).apply(func=random_draw_select, cutoff=draw)
+        df = df.loc[df.less_draw == True, :]
+    elif major_wrong == 'wrong':
+        df = df.loc[df.correct == False, :]
+
+    cf = None
+    for aut in authors:
+        cf_dic[aut] = calc_confusion_matrix(x=df.loc[df['# Authors']==aut, :], target=target, preds=pred, labs=labs, normalize=normalize)
+        if type(cf) == type(None):
+            cf = cf_dic[aut].copy()
+        else:
+            cf = np.hstack((cf, cf_dic[aut]))
+    if normalize:
+        vmin = 0
+        vmax = 1
+        fmt = '.2f'
+    else:
+        vmin = None
+        vmax = None
+        fmt = '.0f'
+    annot = []
+    for _ in range(0, len(authors)):
+        annot = annot + labs
+    #print(cf)
+    ax = sns.heatmap(data=cf, vmin=vmin, vmax=vmax, annot=True, fmt=fmt, cbar=True, yticklabels=labs, xticklabels=annot,
+                     ax=ax, cmap=sns.cubehelix_palette(start=2, rot=0, dark=0, light=.95, reverse=True, as_cmap=True))
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("top", size="11%", pad=0)
+    #set correct limits
+    cax.set_xlim(left=0, right=len(authors)*len(labs))
+    cax.get_xaxis().set_visible(False)
+    cax.get_yaxis().set_visible(False)
+    cax.set_facecolor('black')
+    for i in range(len(labs), len(labs)*(len(authors)), len(labs)):
+        ax.axvline(i, color='white', lw=4)
+        cax.axvline(i, color='white', lw=4)
+
+    #fix locs
+    locs = np.arange(len(labs))
+    plt.setp(ax.get_yticklabels(), rotation=45, ha="right",
+             rotation_mode="anchor")
+
+    #ax.yaxis.set(ticks=locs+0.3, ticklabels=labs)
+
+    #at = AnchoredText('50 Authors', loc=2,
+                     # prop=dict(backgroundcolor='black',
+                    #          size=12, color='white'))
+    #cax.add_artist(at)
+    for i, aut in enumerate(authors):
+
+        cax.text((1.8/5)*len(labs)+(len(labs)*i),0.4,s='{} Authors'.format(aut), bbox=dict(facecolor = 'black', boxstyle='square', ec='none'), c='white')
+
+    return fig
+
+
diff --git a/tex/app_figures.tex b/tex/app_figures.tex
index 5bf87d6f96abb7b1aa350fd2abd1e13764d7a69e..386ce5da638771c2d1171841cef4eb61e4b06353 100644
--- a/tex/app_figures.tex
+++ b/tex/app_figures.tex
@@ -1,19 +1,21 @@
 
+\FloatBarrier
+\subsection{Aggregate Overview}
 \begin{figure}[!htpb]
 	\centering
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/f1_scores_100.pdf}
-		\caption{Results for Target "Gender"}
+		\caption{Results for target \textit{gender}}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/f1_scores_100.pdf}
-		\caption{Results for Target "Age"}
+		\caption{Results for target \textit{age}}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of featuretypes used and way of input, i.e. baseline, cumulated, or stacked.}
-	\caption{F1-Score for all Featuretype-Sets for an Instancelength of 100 Characters}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{F1-Score for all feature type-Sets for an input instance length of 100 characters.}
 	\label{fig:f1_100}
 \end{figure}
 
@@ -22,16 +24,16 @@
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/spearman_ext_100.pdf}
-		\caption{Results for Target "Gender"}
+		\caption{Results for target \textit{gender}}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/spearman_ext_100.pdf}
-		\caption{Results for Target "Age"}
+		\caption{Results for target \textit{age}}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of featuretypes used and way of input, i.e. baseline, cumulated, or stacked.}
-	\caption{Extended Spearman Correlation for all Featuretype-Sets for an Instancelength of 100 Characters}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{Extended Spearman correlations for all feature type-Sets for an input instance length of 100 characters.}
 	\label{fig:ext_spearman_100}
 \end{figure}
 
@@ -40,15 +42,15 @@
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/f1_scores_250.pdf}
-		\caption{Results for Target "Gender"}
+		\caption{Results for target \textit{gender}}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/f1_scores_250.pdf}
-		\caption{Results for Target "Age"}
+		\caption{Results for target \textit{age}}
 	\end{subfigure}
-	\caption{F1-Score for all Featuretype-Sets for an Instancelength of 250 Characters}
+	\caption{F1-Score for all feature type-Sets for an input instance length of 250 characters}
 	\label{fig:f1_250}
 \end{figure}
 
@@ -57,19 +59,252 @@
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/spearman_ext_250.pdf}
-		\caption{Results for Target "Gender"}
+		\caption{Results for target \textit{gender}}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{.7\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/spearman_ext_250.pdf}
-		\caption{Results for Target "Age"}
+		\caption{Results for target \textit{age}}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of featuretypes used and way of input, i.e. baseline, cumulated, or stacked.}
-	\caption{Extended Spearman Correlation for all Featuretype-Sets for an Instancelength of 250 Characters}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{Extended Spearman correlation for all feature type sets for an input instance length of 250 characters.}
 	\label{fig:ext_spearman_250}
 \end{figure}
 
 
+\FloatBarrier
+\clearpage
+\subsection{Author-Level Analysis}
+
+%authorlevel all
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_color_gender.pdf}
+		\caption{Author-level errors for target \textit{gender}.}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_color_centered_age.pdf}
+		\caption{Author-level errors for target \textit{age}.}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full feature set as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000) and according to the proportion of errors within those subsets. The result per author shows the result over all subsets.}
+	\caption{Author-Level Results for the Full feature set with an input instance length of 100 characters.}
+	\label{fig:acc_auth_100}
+\end{figure}
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_color_gender.pdf}
+		\caption{Author-level errors for target \textit{gender}.}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_color_centered_age.pdf}
+		\caption{Author-level errors for target \textit{age}.}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full feature set as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000) and according to the proportion of errors within those subsets. The result per author shows the result over all subsets.}
+	\caption{Author-Level Results for the Full feature set in an input instance length of 100 characters.}
+	\label{fig:acc_auth_250}
+\end{figure}
+
+%authorlevel cwald
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/bars/proportions_cumulated_dist_char_asis_lemma_word_1_2_100_color_gender.pdf}
+		\caption{Author-level errors for target \textit{gender}.}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/bars/proportions_cumulated_dist_char_asis_lemma_word_1_2_100_color_centered_age.pdf}
+		\caption{Author-level errors for target \textit{age}.}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full feature set as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000) and according to the proportion of errors within those subsets. The result per author shows the result over all subsets.}
+	\caption{Author-Level Results for the full feature set with an input instance length of 100 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:acc_auth_100_cwald}
+\end{figure}
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/bars/proportions_cumulated_dist_char_asis_lemma_word_1_2_250_color_gender.pdf}
+		\caption{Author-level errors for target \textit{gender}.}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/bars/proportions_cumulated_dist_char_asis_lemma_word_1_2_250_color_centered_age.pdf}
+		\caption{Author-level errors for target \textit{age}.}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full feature set as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000) and according to the proportion of errors within those subsets. The result per author shows the result over all subsets.}
+	\caption{Author-Level Results for the full feature set in an input instance length of 100 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:acc_auth_250_cwald}
+\end{figure}
+
+
+%confusion all
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 100 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{gender} with an input instance length 100 characters - all feature types.}
+	\label{fig:gender_conf_auth_100}
+\end{figure}
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 250 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{gender} with an input instance length of 250 characters - all feature types.}
+	\label{fig:gender_conf_auth_250}
+\end{figure}
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_100_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 100 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{age} with an input instance length 100 characters - all feature types.}
+	\label{fig:age_conf_auth_100}
+\end{figure}
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_250_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 250 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{age} with an input instance length of 250 characters - all feature types.}
+	\label{fig:age_conf_auth_250}
+\end{figure}
+
+%confusion cwald
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_100_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/100/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_100_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using using the teh feature types ASIS, CHAR, LEMMA, WORD as cumulated input on an input instance length of 100 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{gender} with an input instance length of 100 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:gender_conf_auth_100_cwald}
+\end{figure}
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_250_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/250/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_250_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the teh feature types ASIS, CHAR, LEMMA, WORD as cumulated input on an input instance length of 250 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{gender} with an input instance length of 250 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:gender_conf_auth_250_cwald}
+\end{figure}
+
+
+
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_100_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/100/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_100_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using using the teh feature types ASIS, CHAR, LEMMA, WORD as cumulated input on an input instance length of 100 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{age} with an input instance length of 100 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:age_conf_auth_100_cwald}
+\end{figure}
+
+
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_250_mode_basic.pdf}
+		\caption{All authors (row-wise normalization).}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{0.8\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/250/confusion/confusion_cumulated_dist_char_asis_lemma_word_1_2_250_mode_major_false.pdf}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the teh feature types ASIS, CHAR, LEMMA, WORD as cumulated input on an input instance length of 250 characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{age} with an input instance length of 250 characters - ASIS-CHAR-LEMMA-WORD.}
+	\label{fig:age_conf_auth_250_cwald}
+\end{figure}
 
 
diff --git a/tex/app_figures2.tex b/tex/app_figures2.tex
new file mode 100644
index 0000000000000000000000000000000000000000..8085c1ce5f7b393a6e264bd59eee23afd6c401e8
--- /dev/null
+++ b/tex/app_figures2.tex
@@ -0,0 +1,70 @@
+\FloatBarrier
+\subsection{Aggregate Overview}
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/f1_scores_100.pdf}
+		\caption{Results for target \textit{gender}}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/f1_scores_100.pdf}
+		\caption{Results for target \textit{age}}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{F1-Score for all feature type-Sets for an input instance length of 100 characters.}
+	\label{fig:f1_100}
+\end{figure}
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/spearman_ext_100.pdf}
+		\caption{Results for target \textit{gender}}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/spearman_ext_100.pdf}
+		\caption{Results for target \textit{age}}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{Extended Spearman correlations for all feature type-Sets for an input instance length of 100 characters.}
+	\label{fig:ext_spearman_100}
+\end{figure}
+\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/f1_scores_250.pdf}
+		\caption{Results for target \textit{gender}}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/f1_scores_250.pdf}
+		\caption{Results for target \textit{age}}
+	\end{subfigure}
+	\caption{F1-Score for all feature type-Sets for an input instance length of 250 chcharacters
+		\label{fig:f1_250}
+	\end{figure}
+	
+	\begin{figure}[!htpb]
+	\centering
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/gender/spearman_ext_250.pdf}
+		\caption{Results for target \textit{gender}}
+	\end{subfigure}
+	\hfill
+	\begin{subfigure}[b]{.7\textwidth}
+		\centering
+		\includegraphics[width=\textwidth]{figures/age/spearman_ext_250.pdf}
+		\caption{Results for target \textit{age}}
+	\end{subfigure}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{Extended Spearman correlation for all feature type sets for an input instance length of 250 characters.}
+	\label{fig:ext_spearman_250}
+\end{figure}
\ No newline at end of file
diff --git a/tex/app_tables.tex b/tex/app_tables.tex
index 083aa4c89b537a0446ca754b0f67db7448c5aa99..f21067ee2bf66563138c961b196a6ee5a148af6f 100644
--- a/tex/app_tables.tex
+++ b/tex/app_tables.tex
@@ -1,7 +1,13 @@
 %gender
 %%baseline 250 chars
-
-%\begin{landscape}
+\FloatBarrier
+\subsection{Data Set Statistics}
+\input{tables/train_dataset.tex}
+\input{tables/val_dataset.tex}
+\input{tables/test_dataset.tex}
+%\begin{landscape}#
+\FloatBarrier
+\clearpage
 \subsection{Baseline}
 \subsubsection{Minimum of Characters: 100}
 
@@ -33,7 +39,7 @@
 
 \FloatBarrier
 \clearpage
-\subsection{POS, TAG, DEP, and NUM}
+\subsection{DIST, CHAR, ASIS, WORD, and LEMMA}
 \subsubsection{Minimum of Characters: 100 \& Cumulated}
 
 \input{tables/gender/latex/results_gender_dwald_direct_100_acc.tex}
@@ -64,36 +70,41 @@
 
 \FloatBarrier
 \clearpage
+
+%%dwald dynAA
 \subsubsection{Minimum of Characters: 100 \& Stacked}
 
-\input{tables/gender/latex/results_gender_dwald_direct_100_acc.tex}
-\input{tables/gender/latex/results_gender_dwald_direct_100_dist.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_100_acc.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_100_dist.tex}
+
+\input{tables/age/latex/results_age_dwald_dynAA_100_acc.tex}
+\input{tables/age/latex/results_age_dwald_dynAA_100_dist.tex}
+
 
-\input{tables/age/latex/results_age_dwald_direct_100_acc.tex}
-\input{tables/age/latex/results_age_dwald_direct_100_dist.tex}
-%%dwald dynAA
 \FloatBarrier
 \clearpage
 \subsubsection{Minimum of Characters: 250 \& Stacked}
 
-\input{tables/gender/latex/results_gender_dwald_direct_250_acc.tex}
-\input{tables/gender/latex/results_gender_dwald_direct_250_dist.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_250_acc.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_250_dist.tex}
 
-\input{tables/age/latex/results_age_dwald_direct_250_acc.tex}
-\input{tables/age/latex/results_age_dwald_direct_250_dist.tex}
+\input{tables/age/latex/results_age_dwald_dynAA_250_acc.tex}
+\input{tables/age/latex/results_age_dwald_dynAA_250_dist.tex}
 
 \FloatBarrier
 \clearpage
 \subsubsection{Minimum of Characters: 500 \& Stacked}
 
-\input{tables/gender/latex/results_gender_dwald_direct_500_acc.tex}
-\input{tables/gender/latex/results_gender_dwald_direct_500_dist.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_500_acc.tex}
+\input{tables/gender/latex/results_gender_dwald_dynAA_500_dist.tex}
 
-\input{tables/age/latex/results_age_dwald_direct_500_acc.tex}
-\input{tables/age/latex/results_age_dwald_direct_500_dist.tex}
+\input{tables/age/latex/results_age_dwald_dynAA_500_acc.tex}
+\input{tables/age/latex/results_age_dwald_dynAA_500_dist.tex}
 \FloatBarrier
 \clearpage
-\subsection{Full Set of Featuretypes}
+
+%full
+\subsection{Full Set of feature types}
 \subsubsection{Minimum of Characters: 100 \& Cumulated}
 \input{tables/gender/latex/results_gender_full_direct_100_acc.tex}
 \input{tables/gender/latex/results_gender_full_direct_100_dist.tex}
@@ -119,14 +130,15 @@
 \input{tables/age/latex/results_age_full_direct_500_acc.tex}
 \input{tables/age/latex/results_age_full_direct_500_dist.tex}
 
+%dynAA
 \FloatBarrier
 \clearpage
 \subsubsection{Minimum of Characters: 100 \& Stacked}
 \input{tables/gender/latex/results_gender_full_dynAA_100_acc.tex}
 \input{tables/gender/latex/results_gender_full_dynAA_100_dist.tex}
 
-\input{tables/age/latex/results_age_full_dynAA_250_acc.tex}
-\input{tables/age/latex/results_age_full_dynAA_250_dist.tex}
+\input{tables/age/latex/results_age_full_dynAA_100_acc.tex}
+\input{tables/age/latex/results_age_full_dynAA_100_dist.tex}
 %%full dynAA
 \FloatBarrier
 \clearpage
@@ -141,6 +153,7 @@
 \subsubsection{Minimum of Characters: 500 \& Stacked}
 \input{tables/gender/latex/results_gender_full_dynAA_500_acc.tex}
 \input{tables/gender/latex/results_gender_full_dynAA_500_dist.tex}
+
 \input{tables/age/latex/results_age_full_dynAA_500_acc.tex}
 \input{tables/age/latex/results_age_full_dynAA_500_dist.tex}
 
diff --git a/tex/appendix.tex b/tex/appendix.tex
index 809fd600a59691d4604863699f5ae09f310a2a54..161badca5dc247e760e66da5eb49c4335a83ef97 100644
--- a/tex/appendix.tex
+++ b/tex/appendix.tex
@@ -5,11 +5,10 @@
 \renewcommand{\thesection}{A\arabic{section}}\setcounter{section}{0}
 %\renewcommand{\thesubsection}{\Roman{section}.\arabic{subsection}}
 \renewcommand{\thesubsection}{A\arabic{section}.\arabic{subsection}}\setcounter{subsection}{0}
-
 \section{Figures}
 \label{sec:att_app_figures}
 \input{app_figures.tex}
-
+\clearpage
 \FloatBarrier
 \section{Tables}
 \label{sec:att_app_tables}
diff --git a/tex/attribution_features.pdf b/tex/attribution_features.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a913677b3c6198ce8d45af1cb32c29042ad51b9c
Binary files /dev/null and b/tex/attribution_features.pdf differ
diff --git a/tex/discussion.tex b/tex/discussion.tex
index 1a5389332631e929d95033168cef9d892608dbcb..a969d88537491b670f6c404a57b05c17e891ebe4 100644
--- a/tex/discussion.tex
+++ b/tex/discussion.tex
@@ -1,16 +1,25 @@
 \section{Discussion}
-Overall, we find that the classifier makes systematic errors on the author-level. For the target \textit{gender}, female authors seem to be difficult to classify in general. The underlying drivers seem to be that the group as such has a very heterogeneous pattern in the features which overlaps with the male authors. In other words, male authors seem so be simpler to classify. That however may also be driven by the fact that the context they are active in is more homogeneous than for female authors. 
-For \textit{age}, we find slightly more stable results as most age brackets exhibit clear patterns that make the distinguishable. 
+Overall, we find that the classifier makes systematic errors at the author level. For the target \textit{gender}, one group of authors (female) seems to be difficult to classify in general. The underlying drivers seem to be that, for the shown dataset, the group as such has a very heterogeneous pattern in the features. In other words, the second group (male) seems to be simpler to classify. That, however, may also be driven by the fact that the context in which they are active is more homogeneous than for female authors.
+For \textit{age}, we find slightly more stable results, as most age brackets exhibit clear patterns that make the individual age brackets distinguishable. 
 In terms of stability, however, the results are more mixed.
-While the features mainly driving the prediction are not \textit{per-se} context-reliant features, increasing the available context does increase performance markedly.
-This is especially evident from the fact that a wide n-gram window for character features yields the greatest relative increase in performance, outstripping the additional information available by including featuretypes of a higher context.
-That, in itself is not directly surprising and not necessarily cause for any raised eyebrows. However, it is relevant to point to the fact that for a low number of authors, about 10\% of the prediction performance stem from an increase in context (e.g. for features CHAR and ASIS when predicting gender, see \autoref{tab:gender_f1_dist_500_baseline_individual}). When looking at the dataset with the largest number of authors, additional information also is what separates the model performance from slightly above random-guess performance and pushes it into the performance ranges found within the literature for comparable data \parencite{wiegmann2019overview}.
-Moreover, building a model on top of a composite of featuretypes (or stacking it on top; see \autoref{tab:gender_f1_dist_500_direct_dwald} and \autoref{tab:age_acc_500_direct_dwald}) is when we see additional performance increases, especially for longer input texts. Thus, giving additional context on top of non-context featuretypes yields a better decision boundary for the classifier. 
-These results, at first seem like technical details. However, in practice they show that the context the model is trained on and in (as reflected simulated by varying the number of authors) largely carries over into its predictive performance. On one hand, that means models trained within one context may not simply be used in another. That is intuitive. What we show here, however, is that even by staying within one group of individuals (creators) and within one domain, an increase in the number of possible targets changes the relevant features and also the information as well as the context encoded within sizably. That becomes evident from the fact that the stability in the relevance of features simply does not exist. 
+While the features mainly driving the prediction are not context-reliant \textit{per-se}, increasing the available context does increase performance markedly.
+This is especially evident from the fact that a wide n-gram window for character features yields the greatest relative increase in performance, outperforming even those models for which additional context information is made available by including additional feature types known to capture context.
+That in itself is not directly surprising and not necessarily cause for any raised eyebrows. However, it is important to note that, for a low number of authors, about 10\% of the prediction performance stems from an increase in context (e.g. for features CHAR and ASIS when predicting gender, see \autoref{tab:gender_f1_dist_500_baseline_individual}). When looking at the dataset with the largest number of authors, additional information is also what makes the model perform slightly better than the random-guess threshold and pushes it into the performance ranges found within the literature for comparable data \parencite{wiegmann2019overview}.
+Moreover, building a model on top of a composite of feature types (or stacking it on top; see \autoref{tab:gender_f1_dist_500_direct_dwald} and \autoref{tab:age_f1_dist_500_direct_dwald}) is when we see additional performance increases, especially for longer input texts. Thus, giving additional context on top of non-context feature types yields a better decision boundary for the classifier.\\
+
+These results at first seem like technical details. However, in practice they show that the context the model is trained on and in (as simulated by varying the number of authors) largely carries over into its predictive performance. That means models trained within one context may not simply be used in another one. That is intuitive. What we show here, however, is that even by staying within one group of individuals (creators) and within one domain (Twitter), an increase in the number of possible targets changes the relevant features, and it also significantly changes the information as well as the context encoded within . That becomes evident from the fact that the stability in the relevance of features simply does not exist. 
 For social sciences, these findings are relevant on two fronts.
-First, the models using the features presented here are indeed well-suited to find a pattern connecting their use to the prediction target. However, that pattern is unstable, changing with the number of authors or features available. Consequently, it hints at the fact that these patterns are merely correlations exploited by the model. Such correlations are difficult to rely upon, as their patterns - as shown by increasing the number of authors - may change at any time.
-Thus, this calls for a careful assessment of the validity when employing pre-trained models within the field, especially when the prediction outcome is used as input for further models or for further analysis. Or spoken differently, a change in behavior by individuals - either over time or by choice - will render the learned context irrelevant. Thus, the environment during training must be carefully compared to the one in which the model is used.
-The second aspect is tied more into the field of law and the wider debate of transparency and proportionality.
-As law enforcement is faced with the problem of combing trough a large amount of online content, searching and assessing such content by hand is untenable. Thus, already today algorithms are employed by law enforcement. However, especially in such environments, it must be clear how much of the findings by an algorithm is merely correlational and also how stable these correlations are in different environments. Only then do law enforcement, the defendant, and also the courts have the possibility to assess the validity of an result \textit{before} acting upon it. After all, how valid is a result identifying traits of a suspect when the features are context-reliant to such a high degree that changing the use of some emojis or some words would alter the result completely. Or that when one is compared to a different number of authors, the result would be driven by completely different features.
-Even more problematic, in real-use, the instances used for training and those used for testing are removed from each other in time. Thus, a real culprit could evade being identified by simply because the context changes. 
-As the findings of this study point towards such an unstable relationship, we argue that the features used in tasks related to authorship profiling and authorship attribution need much more research. Moreover, models should be assessed with a measure for defining the boundaries of their stability. Otherwise establishing a scientifically valid link that goes beyond merely showing that the model yields good correlational predictions on some datasets might be impossible.
\ No newline at end of file
+First, the models using the features presented here are indeed well-suited to find a pattern connecting their use to the prediction target. However, that pattern is unstable, changing with the number of authors or features available. Consequently, it hints at the fact that these patterns are merely correlations exploited by the model. Such correlations are difficult to rely upon, as their patterns -- as shown by increasing the number of authors -- may change at any time.
+Thus, this calls for a careful assessment of the validity when employing pre-trained models within the field, especially when the prediction outcome is used as input for further models or for further analysis. In other words, a change in behavior by individuals -- either over time or by choice -- will render the learned context irrelevant. Thus, the environment during training must be carefully compared to the one in which the model is used.
+In general, the findings thus paint a bleak picture for the social sciences. Our results show that there are authors of certain groups for which one has to expect above-average errors and systematic patterns of misclassification. Taken together with the apparent lack of stability in the predictiveness of features when the dataset changes slightly means that, even when these patterns are assessed during training, the researcher has little chance to assess how they will affect a the result during the time of use. The differences between training data and test data might be difficult to pinpoint. Thus, for cases where it is not clear by how much training context and use-context differ, a social scientist should be very careful in simply adopting pre-trained models as the size of the introduced error is unknown.\\
+
+Another finding is of a normative nature and tied to the wider debate of transparency and proportionality, and thus affects in particular the field of law.
+As law enforcement is faced with the problem of combing through a large amount of online content, searching and assessing such content by hand is untenable. Thus, already today algorithms are employed by law enforcement. However, especially in such environments, it must be clear how much of the findings by an algorithm is relies merely on correlations and especially how stable these correlations are in different environments. That does not even include the fact that there might be some groups of individuals for whom the classifier makes systematic mistakes.
+Only then do law enforcement, the defendant, and also the courts have the possibility to assess the validity of an result \textit{before} acting upon it. After all, how valid is a result identifying traits of a suspect when the features are context-reliant to such a high degree that changing the use of some emojis or some words would alter the result completely? How robust is a result, when the features driving the result change with the number of authors an individual is compared to?
+What is even more problematic in real-world terms is that the instances used for training and those used for during actual application are separated from each other by time.
+Thus, a real culprit could evade being identified simply because the context changes, while innocents could be systematically misidentified as culprits.
+Thus, as the stability in feature importance is already lacking for the relatively small changes introduced here, we should ask ourselves what requirements an algorithm should fulfill before it is being used within the law enforcement context. Optimally, we would ask for causal relationships between input and output. However, that might not be possible. The second-best would then be to have transparency for the model and a some-what stable relationship between input and output. The former assures that users, i.e., the state, as well as affected individuals are able to assess the inner workings of a model. That would enable an individual to judge whether the prediction pertaining to them might be part of a systematic error.\\
+
+A reasonably stable relationship between input and output, i.e., a stable feature importance, guarantees that while there might be systematic errors, the affected groups stay at least constant, although the dateset for the predictions may vary slightly compared to the training dataset, e.g., by number of authors or point-in-time. The alternative would be, of course, to specify a ``half-life'' before a model has to be re-trained and re-assessed.\\
+
+As the findings of this study point towards such an unstable relationship, we argue that the features used in tasks related to authorship profiling and authorship attribution need much more research. Moreover, models should be assessed with a measure for defining the boundaries of their stability. The result of that measure has to be affixed to the model so users may be able to infer its usability. Otherwise, establishing a scientifically valid link -- going beyond merely showing that the model yields good correlational predictions on some datasets -- might be impossible.
\ No newline at end of file
diff --git a/tex/experimental_setup.tex b/tex/experimental_setup.tex
index a578c44a109ba957eeffb2b02fbb9b940ebac8c8..0d268b2e8428f3845389a5ab204d019f9b9fc12c 100644
--- a/tex/experimental_setup.tex
+++ b/tex/experimental_setup.tex
@@ -1,120 +1,131 @@
 \section{Experimental Design and Data}
-In order to conduct our stability analysis, we conduct an experiment as used in the field of machine learning by using introducing controlled variations to an underlying, given dataset. To that end, we use a fixed setup of machine learning (ML) models and test their internal stability, when exposed to these controlled variations.
+In order to conduct our stability analysis, we conduct an experiment as used in the field of machine-learning by introducing controlled variations to an underlying, given dataset. To that end, we use a fixed setup of machine-learning (ML) models and test their internal stability, when they are exposed to these controlled variations.
 
 \paragraph{Synopsis}
-The dataset used for the experiment is the PAN @CLEF 2019 Celebrity Profiling (PAN2019) dataset\footnote{The dataset may be downloaded from the website of the PAN challenge: \href{https://bit.ly/2Yk9so9}{PAN Challenge 2019}.}.
+The dataset used for the experiment is the PAN @CLEF 2019 Celebrity Profiling (PAN2019) dataset.\footnote{The dataset may be downloaded from the website of the PAN challenge: \href{https://bit.ly/2Yk9so9}{PAN Challenge 2019}.}
 As our goal is to assess te performance and the stability of importance in regards to single features, we try to reduce variation present directly within the authors as much as possible.
-Therefore, we focus only on authors from one category, namely those dubbed "creator``. As suggested in the guidelines of the original PAN challenge, we change the age from a numerical variable to their categorical one consisting of five age brackets.
-As prediction target we select ''age" and ''gender", two commonly used characteristics in the social sciences.
-Moreover, in order to further exclude any variation introduced by an imbalanced dataset, we undersample the data in such a way that the genders as well as the age groups are balanced.
-For the comparison in the author dimension, we create four subsets consisting of 50, 150, 500, and 1000 authors respectively.
-The upper limit of 1000 authors reflects the maximum amount of authors for which it is still possible to balance the dataset.
-Furthermore , we repeat the experiment three times for different minimum lenghts per training instance, as the text lengths was shown to impact classifier performance while holding the model setup constant \parencite{custodio2021stacked}. The minimum lengths are 100, 250, and 500 characters respectively. In order to achieve these minimum lengths, tweets from the same author were concatenated.
-As feature types after the text transformation, we use the following, sorted in ascending order in terms of context-information encoded: DIST, CHAR, ASIS, POS, TAG, DEP, LEMMA, WORD, NUM. For all types we apply the n-gram ranges found to be useful by prior research \parencite{custodio2021stacked}. The evaluation is conducted using the 500-score as the performance measure. To assess the stability of features, we use Spearman's Rho for rank order correlation. All evaluations were done on a separate hold-out dataset, the testset. That data was not used during training at any point.
+Therefore, we focus only on authors from one category, namely those dubbed ``creator''. As suggested in the guidelines of the original PAN challenge, we change the age from a numerical variable to their categorical one consisting of five age brackets.
+As a prediction target, we select ``age" and ``gender", two commonly used characteristics in the social sciences.
+Moreover, in order to exclude further any variation introduced by an imbalanced dataset, we undersample the data in such a way that the genders as well as the age groups are balanced.
+For the comparison in the author dimension, we create four subsets consisting of 50, 150, 500, and 1000 authors, respectively.
+The upper limit of 1000 authors reflects the maximum number of authors for whom it is still possible to balance the dataset.
+Furthermore, we repeat the experiment three times for different minimum lenghts per training instance, as the text lengths were shown to impact classifier performance; in doing this, we hold the model setup constant \parencite{custodio2021stacked}.
+The minimum lengths are 100, 250, and 500 characters, respectively. In order to achieve these minimum lengths, tweets from the same author were concatenated.
+As feature types we use the following ones, sorted in ascending order in terms of encoded context information: DIST, CHAR, ASIS, POS, TAG, DEP, LEMMA, WORD, NUM. For all types, we apply the n-gram ranges found to be useful by prior research \parencite{custodio2021stacked}. The evaluation is conducted using the 500-score as the performance measure. To assess the stability of features, we use Spearman's Rho for rank order correlation. All evaluations were done on a separate hold-out dataset, the test set. That data was not used during training at any point.
 In the following paragraphs, we outline our design choices in detail.
 
 \subsection{Data}
 While the literature for authorship analysis is abundant and only increased during recent years, there are no easily identified commonly used datasets across a wide range of studies.
 Comparison between studies is therefore difficult.
-This is well illustrated by \textcite{Neal2017SurveyingApplications} who lists 13 datasets used more than once and a multitude of others used less frequently.
-However, for Twitter\textsuperscript{\textregistered}, they list only two.
-This means that most studies on authorship analysis additionally suffer from one of two limitations.
-Either they focus on authorship analysis while using traditional, longer texts such as articles or blog posts, or they make use of custom datasets \parencite{Neal2017SurveyingApplications}. The latter is sometimes described as a great challenge in the field of authorship analysis making replication as all well verification of results difficult \parencite{Halvani2016AuthorshipTopics}.
-The former implies that past studies not focusing on online short text messages are looking at a fundamentally different research problem compared to Twitter\textsuperscript{\textregistered} texts.
-At the same time, most automated authorship analysis research acknowledges the fact that use cases for these tools will consist of attributing micro-blog texts to an author \parencite[see for example][]{Narayanan2012OnIdentification, Rocha2017AuthorshipForensics, Spitters2016AuthorshipForums}.
-Consequently, the dataset used is from that platform as its prevalence makes it especially relevant. Moreover, it reflects the text data, in style and characteristics commonly found for chat messages. Especially in terms of length it is also similar to text data generated during studies and experiments within the social sciences.\\
+This is well illustrated by \textcite{Neal2017SurveyingApplications} who list 13 datasets used more than once and a multitude of others used less frequently.
+However, for Twitter, they list only two.
+This means that most studies on authorship analysis additionally suffer from at least one of two limitations.
+Either they focus on authorship analysis while using traditional, longer texts, such as articles or blog posts, or they make use of custom datasets \parencite{Neal2017SurveyingApplications}. The latter are sometimes described as a great challenge in the field of authorship analysis, making replication as all well verification of results difficult \parencite{Halvani2016AuthorshipTopics}.
+The former implies that past studies not focusing on online short text messages are looking at a fundamentally different research problem compared to Twitter texts.
+At the same time, most automated authorship analysis research acknowledges the fact that use cases for these tools will consist of attributing micro-blog texts to an author \parencite[see, for example,][]{Narayanan2012OnIdentification, Rocha2017AuthorshipForensics, Spitters2016AuthorshipForums}.
+Consequently, the dataset used is from that platform, as its prevalence makes it especially relevant. Moreover, it reflects the text data, in style and characteristics commonly found for chat messages. Especially in terms of length, it is also similar to text data generated during studies and experiments within the social sciences.\\
 
 %TODO Dateset table with variance and means
 \input{tables/dataset.tex}
-Most studies focusing on short-text online media Twitter\textsuperscript{\textregistered} use different data sets.
-This is due to the fact that the user agreement for the API of this particular platform does generally not give permission to publish a scraped data set online \parencite{Theophilo2019AMicro-messages}.
+Most studies focusing on short-text online media such as Twitter use different data sets.
+This is due to the fact that the user agreement for the API of this particular platform does not generally give permission to publish a scraped data set online \parencite{Theophilo2019AMicro-messages}.
 At this point in time, we know of three public datasets: Twisty \parencite{Verhoeven2016Twisty:Profiling}, ISOT \parencite{Brocardo2015AuthorshipAuthentication}, and PAN \parencite{Stamatatos2015OverviewLab}, a yearly challenge tackling different aspects of authorship analysis.
-The Twisty dataset includes a multitude of languages making it unusable for this task as it was shown that language has major impact on the results \parencite{Halvani2016AuthorshipTopics}.
-Another problem mentioned before concerns the high number of troll-profiles as well as potential alias accounts \parencite{Varol2017OnlineCharacterization} in an arbitrarily captured data set.
+The Twisty dataset includes a multitude of languages, making it unusable for this task as it was shown that language has major impact on the results \parencite{Halvani2016AuthorshipTopics}.
+Another problem mentioned before concerns the high number of troll profiles, as well as potential alias accounts \parencite{Varol2017OnlineCharacterization} in an arbitrarily captured data set.
 This is sometimes referred to as the ground truth problem \parencite{Narayanan2012OnIdentification}.
-As the research question is focused on characteristics of individual people this is particularly problematic.
-For this reason, the ISOT dataset, too, is unusable as neither the problematic of troll-profiles nor the problem of double accounts for a single user can be addressed.\\
+As the research question is focused on characteristics of individual people, this is particularly problematic.
+For this reason, the ISOT dataset, too, is unusable, as neither the problematic of troll profiles nor the problem of double accounts for a single user can be addressed.\\
 
-To overcome this, a special version of the PAN dataset focused on profiling celebrities \parencite{PAN2019CelebrityProfiling} is used.
-For this dataset it can at least be established that the accounts relate to a real, singular human.
-Naturally, there may be new limitations, e.g. that it may not be guaranteed that celebrities always write their own posts.
-However, Twitter\textsuperscript{\textregistered} is more and more considered to be a medium offering the possibility of interacting directly with followers by circumventing the filter, interpretation, and comments of traditional media (thus enabling "authenticity") \parencite{Schmidt2014TwitterPublics}.
+To overcome this, a special version of the PAN dataset focusing on profiling celebrities \parencite{PAN2019CelebrityProfiling} is used.
+For this dataset it can at least be established that the accounts relate to a real, individual human.
+Naturally, there may be new limitations, e.g., it may not be guaranteed that celebrities always write their own posts.
+However, Twitter is more and more considered to be a medium offering the possibility of interacting directly with followers by circumventing the filter, interpretation, and comments of traditional media (thus enabling "authenticity") \parencite{Schmidt2014TwitterPublics}.
 Consequently, the problem of other people messaging instead of the celebrities themselves is considered minor by the authors of the dataset when compared to the problem of having unknown fake profiles.
 
 \subsection{Feature Engineering}
-In order to use text-input for machine learning models, the text has to be transformed into a numerical representation. The chosen representation we call \textit{featuretype} here. Within one featuretype, there may be many features. For an example of two words, each may be mapped to number, so there would be two features.\\
+In order to use text input for machine-learning models, the text has to be transformed into a numerical representation. The chosen representation we call \textit{feature~type} here. Within one feature~type, there may be many features. For an example of two words, each may be mapped to a number, so there would be two features.\\
 For automated authorship analysis, one may in principle choose from or combine a wide range of possible features for prediction.
 The natural approach would be to use word-based features. However, this comes with the limitation that rather than finding features predictive of a certain gender or age, it is more likely that the topic is a latent variable driving the result.
-However, as our goal is to mostly control the information from outside the feature itself, e.g. topic or other context, the selection has to be more nuanced.
+As our goal is to control most of the information from outside the feature itself, e.g., topic or other context, the selection has to be more nuanced.
 This brings us to character-based features.
-Character n-grams, in the form of 1-grams they equal unigrams, are maybe one of the most commonly used feature set within the literature \parencite{Rocha2017AuthorshipForensics}.
+Character n-grams, are based on concatenating characters; in the form of 1-grams they equal uni-grams, i.e., single characters. They are maybe one of the most commonly used feature sets within the literature \parencite{Rocha2017AuthorshipForensics}.
 \textcite{Spitters2016AuthorshipForums} find in their exhaustive review of the literature that most studies employ them in one form or another.
 This is due to the fact that such character n-grams were shown in multiple studies to perform robustly \parencite{Keselj2003N-gram-basedAttribution, Stamatatos2009AMethods, Peng2003LanguageModels}.
 Some authors like \textcite{Forstall2010FeaturesSound} link this performance to the fact that n-grams are very closely related to pronunciation.
-Moreover, due to the fact that the n-gram length may be reduced, many outside influences which introduce context in terms of topics, text type and even language as a whole may be removed.
-For example, the cross-domain analysis by \textcite{Stamatatos2013OnFeatures} shows that compared to traditional word-based features, character n-grams outperform them in terms of cross-domain stability.
+Moreover, due to the fact that the n-gram length may be reduced, many outside influences which introduce context in terms of topics, text type, and even language as a whole may be removed.
+For example, the cross-domain analysis by \textcite{Stamatatos2013OnFeatures} shows that, compared to traditional word-based features, character n-grams outperform them in terms of cross-domain stability.
 N-grams were also shown to capture many different features such as punctuation or spelling mistakes.
-Regarding the length of n-grams, it must be noted that for English those with a length of three and above are shown to partially capture content again and thus become topic-dependent \parencite{Narayanan2012OnIdentification, Spitters2016AuthorshipForums}.
-Thus, not only is the type of feature important when controlling for the relevance of topic and content but also the n-grams used. 
-As such, we have a layered approach, controlling for the featuretype on one hand, while also varying the n-grams employed within one featuretype.
-In the following, we construct a hierarchy ranging from the type of features mostly removed from content to the ones which partially capture content. In between we can place those features still related to style and structure but necessitating a certain amount of text.
-In terms of the actual featuretypes as well as range of the ngrams, this study mainly follows \textcite{custodio2021stacked} with the numerical features taken from \textcite{huang2020contribution}. It gives us the following types as input in ascending order when compared on their context-dependency.
+Regarding the length of n-grams, it must be noted that for English, those with a length of three and above are shown to capture content again partially, thus becoming topic-dependent \parencite{Narayanan2012OnIdentification, Spitters2016AuthorshipForums}.
+Therefore, not only is the type of feature important when controlling for the relevance of topic and content but also the n-grams themselves are crucial. 
+As such, we have a layered approach, controlling for the feature~type, while also varying the n-grams employed within one feature~type.
+In the following, we construct a hierarchy ranging from the type of features mostly removed from content to the ones which partially capture content. In between, we can place those features which are still related to style and structure, but which necessitate a certain amount of text.
+In terms of the actual feature types as well as range of the n-grams, this study mainly follows \textcite{custodio2021stacked} with the numerical features taken from \textcite{huang2020contribution}. It gives us the following types as input in ascending order, when compared on their context-dependency.
 
 \paragraph{Text Distortion}
-Usually symbols within text are disregarded for the standard approaches of text-based models. However, past research has shown that these features serve as valuable information in the context of authorship analysis \parencite{Stamatatos2017authorship}. For this featuretype all a-z characters are mapped to '*' which only leaves punctuation and other markers. We call this type of feature \textit{DIST} and apply ngrams $\in[2,5]$
+Symbols within text are usually disregarded for the standard approaches of text-based models. However, past research has shown that these features serve as valuable information in the context of authorship analysis \parencite{Stamatatos2017authorship}. For this feature~type, all a-z characters are mapped to ``*'', which only leaves punctuation and other markers. We call this type of feature \textit{DIST} and apply n-grams $\in[2,5]$
 
 \paragraph{Character}
-Character n-grams were shown to capture many idiosyncrasies present in text while yielding a stable performance \parencite{Stamatatos2013OnFeatures}. Moreover, the amount of context present in the n-grams can easily be adjusted by their range \parencite{Rocha2017AuthorshipForensics}. Thus, we include them in the range of [2,5], we refer to them as \textit{CHAR}.
+Character n-grams were shown to capture many idiosyncrasies present in text, while yielding a stable performance \parencite{Stamatatos2013OnFeatures}. Moreover, the amount of context present in the n-grams can easily be adjusted by their range \parencite{Rocha2017AuthorshipForensics}. Thus, we include them in the range of [2,5], referring to them as \textit{CHAR}.
 
 \paragraph{Unprocessed Text}
-In essence, this featuretype is a combination of text distortion and character ngrams. As input the unprocessed text, including all special characters and punctuation is transformed into ngrams. The ngram-range is also [2,5]. This featuretype we refer to as \textit{ASIS}.
+In essence, this feature~type is a combination of text distortion and character n-grams. As input, the unprocessed text, including all special characters and punctuation, is transformed into n-grams. The n-gram range is also [2,5]. This feature~type we refer to as \textit{ASIS}.
 
 \paragraph{Part-of-Speech}
-Part-of-Speech tags capture linguistic style patters and general information such as grammatical classes. We tag the text by employing the SpaCy\footnote{https://spacy.io} tagger. This featuretype is called \textit{POS} and the ngram-range is [1,3].
+Part-of-Speech tags capture linguistic style patters and general information such as grammatical classes, e.g. ``noun'' or ``verb''. We tag the text by employing the SpaCy\footnote{https://spacy.io} tagger. This feature~type is called \textit{POS} and the n-gram range is [1,3].
 
 \paragraph{Language-specific morphological features}
-Within a language, one is also able to find more fine-grained features related to morphology. Such features concern, for example, the gender of word, tenses and others. To extract this, the tags generated by SpaCy on its \textit{TAG}-level are used. Following this, we call this featuretype \textit{TAG} and employ the ngram-range [1,3]
+Within a language, one is also able to find more fine-grained features related to morphology. Such features concern, for example, the gender of a word, tenses and others. To extract these, the tags generated by SpaCy on its \textit{TAG} level are used. Following this, we call this feature~type \textit{TAG} and employ the n-gram range [1,3].
 
 \paragraph{Syntactic Dependencies}
-This type of feature captures structural information, e.g. the use of passive over active. The dependencies are generated using SpaCy's dependency parser. We will refer to it as \textit{DEP} and the ngram-range is [1,3] as well.
+This type of feature captures structural information, e.g., the use of the passive over the active voice. The dependencies are generated using SpaCy's dependency parser. We refer to it as \textit{DEP} and the n-gram range is [1,3] as well.
 
 \paragraph{Lemma}
-Lemmas are essentially word-like features although the words are reduced to a common, lowercase form. For example, "I'm" would be converted to "i" and "am" while "played" and "playing" would both be mapped onto "play". In such a way, words but not their transformation are captured. We call this feature \textit{LEMMA} and include it with [1,2]-grams.
+Lemmas are essentially word-like features, although the words are reduced to a common, lowercase form. For example, "I'm" would be converted to "i" and "am", while "played" and "playing" would both be mapped onto "play". In such a way, words are captured but not their transformations. We call this feature \textit{LEMMA} and include it with [1,2]-grams.
 
 \paragraph{Words}
-This featuretype is created by forming the ngrams directly from the words without any preprocessing besides lowercasing and removing characters not from within the a-z range. Again we employ [1,2]-grams, the featuretype is called \textit{WORD}.
+This feature~type is created by forming the n-grams directly from the words without any preprocessing besides lowercasing, and removing all characters that are not within the A-Z range. Again, we employ [1,2]-grams, the feature~type is called \textit{WORD}.
 
 \paragraph{Numerical Features}
-\textcite{huang2020contribution} additionally suggest numerical features describing the content and the form of the tweet. The featuretype \textit{NUM} is thus comprised of the following attributes: average tweet length, number of URLs, number of dates and times, number of emoticons, number of emojis as well as polarity and subjectivity.
+\textcite{huang2020contribution} additionally suggest numerical features describing the content and the form of the tweet. The feature~type \textit{NUM} is thus comprised of the following attributes: average tweet length, number of URLs, number of dates and times, number of emoticons, number of emojis, as well as polarity and subjectivity.
 
 \paragraph{Preprocessing, Models, and Targets}
-For the preprocessing we apply the featuretype-specific ones as outlined above. In general, emojis and emoticons were always counted as one singular feature and marked by an $<EMOJI>$ or $<EMOTICON>$ token in the beginning and end. Furthermore, we replaced the unicode string by the textual description using the package demoji\footnote{\url{https://pypi.org/project/demoji/}}. For all text-based features we apply count vectorization and tf-idf scaling. The individual features were kept when they appeared in more than 1\% of the samples. For the featuretype NUM we apply scaling and centering.
-For the model there is the option of linear and non-linear models. While neuronal nets and transfer-learning models gain huge popularity, in the case of authorship analysis it turns out that simple linear models regularly outperform those more complex ones \parencite{Rocha2017AuthorshipForensics, custodio2021stacked}. Moreover, as we also address the social sciences as well as the law community, interpretability and transparency is a key aspect. Thus, we focus here on well-researched models which also enable a global interpretation as well as attribution of outcome to individual features of the input. While there is a wide range of models employed, the most common ones are a SVM, a logitic classifier and Naive Bayes Classifiers \parencite{Rocha2017AuthorshipForensics}. We test all three of them and use the overall best-performing for the evaluation. For the SVM, we reduce ourselves to a linear kernel as only this type enables us to interpret the weight-matrix directly in terms of feature importance.
+For the preprocessing, we apply the specific ones outlined above to each feature~type. In general, emojis and emoticons were always counted as one singular feature and marked by an $<EMOJI>$ or $<EMOTICON>$ token in the beginning and end. Furthermore, we replaced the unicode string by the textual description using the package demoji.\footnote{\url{https://pypi.org/project/demoji/}}
+For all text-based features, we apply count vectorization and tf-idf scaling. The individual features were kept when they appeared in more than 1\% of the samples. For the feature~type NUM, we apply scaling and centering.
+For the model, there is the option of linear and non-linear models. While neuronal nets and transfer-learning models gain huge popularity, for our case of authorship analysis, it turns out that simple linear models regularly outperform the more complex ones \parencite{Rocha2017AuthorshipForensics, custodio2021stacked}. Moreover, as we also address the social sciences as well as the law community, interpretability and transparency are key aspects. Thus, we focus here on well-researched models which also enable a mathematically global interpretation, as well as attribution of outcome to individual features of the input. While there is a wide range of models employed, the most common ones are a SVM, a logistic classifier, and Naive Bayes Classifiers \parencite{Rocha2017AuthorshipForensics}. We test all three of them and use the overall best-performing one for the evaluation. For the SVM, the analysis is limited to a linear kernel as only this type enables us to interpret the weight matrix directly in terms of feature importance.
 As target, we selected two author characteristics of high relevance for the social sciences, namely \textit{age} and \textit{gender}. 
 
 
 \subsection{Experimental Setup}
-In order to assess how different combinations of featuretypes impact the outcome, we use three different approaches to feed them  into a classifier.
+In order to assess how different combinations of feature types impact the outcome, we use three different approaches to feed them  into a classifier.
 \begin{enumerate}
-	\item Baseline: Here the model gets only one featuretype (although with varying ngram-ranges). Thus it enables us to compare the performance of individual featuretypes against one-another.
-	\item Cumulated: For this approach we feed the classifier combinations of featuretypes such that we combine them in an ascending order in terms of context-content.
-	\item Stacked: Here too, we use different featuretypes as input. However, we first make predictions using individual featuretypes (as in baseline) and then apply a second classifier, a logistic one, on top, using the predictions as input to predict the target again. That, in essence, is an ensemble approach \parencite{dietterich2000ensemble} and a variation of the successful DynAA model by \textcite{custodio2021stacked}.
+	\item Baseline: Here, the model gets only one feature~type (although with varying n-gram ranges). Thus, it enables us to compare the performance of individual feature types against one another.
+	\item Cumulated: For this approach, we feed the classifier combinations of feature types such that we combine them in an ascending order in terms of context-content.
+	\item Stacked: Here, too, we use different feature types as input. However, we first make predictions using individual feature types (as in the baseline setup) and then apply a second classifier, a logistic one, on top, using the predictions as input to predict the target again. That, in essence, is an ensemble approach \parencite{dietterich2000ensemble} and a variation of the successful DynAA model by \textcite{custodio2021stacked}.
 \end{enumerate}
 
-To assess the stability in performance as well as relevance we compare the different featuretypes we introduce a small, controlled variation on the input data. To that end, we vary the number of authors within the dataet by constructing four subsets from our dataset. Each subset is comprised of a different number of authors (50, 150, 500, 1000). Furthermore, the sets are constructed in such a way that all authors present the smaller set are also present in all the larger ones. That means the 50 authors from the smallest set are part of all three larger sets as well. We chose that approach as to increase the number, and thus the potential variation, while at the same time keeping prior information.
-Moreover, the authors are balanced in gender as well as in age. That is necessary so that the model has no advantage by focusing on one class to the detriment of others. That gives us a cleaner result when analyzing the impact of the individual featuretypes as well as n-grams.\\
-We conduct the whole experiment three times, varying the input-length of the individual text-instances each time. As previous research has shown that text length greatly influences the outcome \parencite{custodio2021stacked}, we construct input instances of different minimum lengths. We do so by concatenating different tweets by the same author together till the minimum length is reached. No ngrams are constructed in such a way that they would contain information from two different tweets. Naturally, when we increase the minimum length, the number of individual training instances declines as more tweets are needed to form one training instance. As minimum lengths we use 150, 250, and 500 characters.\\
-In order to have no spillover of information between evaluation and training, we split the dataset into three subsets, training, testing. All training was done on the training dataset while all evaluations shown here are done on the hold-out testset. For the stacked approach, we split the testset into a validation and testset. Here, the first layer of the model is trained with the training set, which is the same for all classifiers. The second layer is then trained on the validation set. Finally the evaluations are conducted on the hold-out testset.
+To assess the stability in performance as well as relevance, we compare the different feature types we introduce a small, controlled variation on the input data. In order to simulate (possible) shifting variations in the patterns of feature use, we vary the number of authors within the dataset. To that end, we construct four subsets from our dataset. Each subset is comprised of a different number of authors (50, 150, 500, 1000). Furthermore, the sets are constructed in such a way that all authors present in the smaller set are also present in all the larger ones. That means the 50 authors from the smallest set are part of all three larger sets as well. We chose that approach in order to increase the number, and thus the potential variation, while at the same time keeping prior information.
+Moreover, the authors are balanced in gender as well as in age. That is necessary so that the model has no advantage by focusing on one class to the detriment of others. That gives us a cleaner result when analyzing the impact of the individual feature types as well as n-grams.\\
+
+We conduct the whole experiment three times, varying the input length of the individual text instances each time. As previous research has shown that text length greatly influences the outcome \parencite{custodio2021stacked}, we construct input instances of different minimum lengths. We do so by concatenating different tweets by the same author together until the minimum length is reached. No n-grams are constructed in such a way that they would contain information from two different tweets. Naturally, when we increase the minimum length, the number of individual training instances declines, as more tweets are needed to form one training instance. As minimum lengths we use 150, 250, and 500 characters. The summary statistics for the dataset may be found in \autoref{tab:dataset}.\\
+
+In order to have no spillover of information between evaluation and training, we split the dataset into two subsets, training and testing. All training was done on the training dataset, while all evaluations shown here are done on the hold-out test set. For the stacked approach, we split the test again, this time into a validation and test set. Here, the first layer of the model is trained with the training set, which is the same for all classifiers. The second layer is then trained on the validation set. Finally the evaluations are conducted on the hold-out test set.
 Due to this setup, all classifiers are trained on exactly the same first-layer input in order to increase comparability. 
 
 
 %%%%%%%%%%%%%%%%%%%%
 \subsection{Evaluation Measures}
-We seek to help answering the question about the feature stability as well as featuretype relevance. The latter means comparing the different featuretypes and their performance against each other, especially in terms of the amount of context they introduce and by how much this improves the models performance. As the evaluation metric of choice we use the macro F1-score. The score is an equally weighted mean of precision\footnote{$True Positives * (Predicted Positives)^{-1}$} and recall\footnote{$True Positives * Positives^{-1}$}. Moreover, on a balanced dataset, the score is nearly equal to the accuracy. The score is bounded between 0 and 1 th 1 being the optimum.\\
-The other, more central aspect is that of stability. The question we try to answer here is by what degree are features used constant when the classifier input-set used for training is slightly changed. The change introduced here is the increase of the number of authors. What we want to assess is by how much the importance of individual features shifts when such a change occurs. 
-We developed the following approach: First, we extract the weight matrix of the two relevant models. When all input features are scaled to the same range as well as centered, the matrix contains the information about the relative importance of each feature when predicting the outcome. As we are using linear models these weights are global, i.e. the importance assigned to a feature is the same no matter which individual instance is assessed.
-To assess the potential shift in importance, we rank the individual features in terms of weights assigned. In a second step we then calculate Spearman's $\rho$ in order to assess  by how much the importance placed on individual input features shifts when introducing a small variation in the underlying data. The reason why this works is because the ranking of a feature directly reflects the weight the classifier places on it. Within our linear models, this is a direct mapping on its importance to the prediction result. Thus, when this coefficient is 1, the distribution of importance across features is completely identical, if it is -1 it would be completely inverse. Consequently, we say the stability is high for values going towards 1 while values close to 0 imply that there is no recognizable relationship and thus very high instability.
-However, when increasing the number of authors, the underlying featureset might also increase and thus the two matrices do not have the same dimensionality anymore. To tackle that problem, we follow two approaches: The first is to expand the smaller matrix by adding columns of $\infty$ for the missing features. That ensures that these will always be assigned the highest possible rank in the smaller matrix (the ranking are sorted in ascending order during comparison). We call this \textit{extended} Spearman correlation and it enables us to assess the absolute feature importance ranking. The second option is to only assess the features present in both matrices and therefore to reduce the dimensionality of the larger one. That ensures that we assess relative importance but ignore that additional features might have great influence on the outcome. That is referred to as \textit{reduced} Spearman correlation.
+We seek to answer the question of stability in predictions as well as stability on the level of features.
+For the former, we test the predictive power of the classifier for both targets on different inputs and sets varying in the number of authors. 
+Our analysis compares different feature types and their performance against each other. Their difference lies in what they capture, especially in terms of the amount of context. We also analyze by how much their inclusion improves the model's performance. As the evaluation metric of choice, we use the \textit{macro} F1-score. The score is an equally weighted mean of precision\footnote{$True Positives * (Predicted Positives)^{-1}$.} and recall\footnote{$True Positives * Positives^{-1}$.}. Moreover, on a balanced dataset, the score is nearly equal to the accuracy. The score is bounded between 0 and 1, with 1 being the optimum.\\
+Moreover, we analyze the performance on the author level. That helps us to test whether the models make systematic errors for specific authors. That is indeed important as it tells us something about how patterns, found to be predictive for target categories, may systematically disadvantage some individuals compared to others. For that part of the analysis, we look at author level accuracy as well as the stability in classifications patterns. For the latter, we evaluate confusion matrices. The results for that analysis may be found in \Cref{att:sec:prediction}.\\
+
+The second, central aspect in this study is that of stability on the feature level. The question we try to answer here is by which degree stays the importance assigned to single features constant, when the classifier input-set used for training is slightly changed. The change introduced here is the increase in the number of authors. What we want to assess is by how much the importance of individual features shifts when such a change occurs. 
+We developed the following approach: First, we extract the weight matrix of the two relevant models. When all input features are scaled to the same range as well as centered, the matrix contains the information about the relative importance of each feature when predicting the outcome. As we are using linear models, these weights are global, i.e., the importance assigned to a feature is the same no matter which individual instance is assessed.\\
+
+To assess the potential shift in importance, we rank the individual features in terms of weights assigned. In a second step, we then calculate Spearman's $\rho$ in order to assess  by how much the importance placed on individual input features shifts when introducing a small variation in the underlying data. The reason why this works is because the ranking of a feature directly reflects the weight the classifier places on it. Within our linear models, this is a direct mapping on its importance to the prediction result. Thus, when this coefficient is 1, the distribution of importance across features is completely identical; if it were -1, it would be completely inverse. Consequently, we say the stability is high for values going towards 1, while values close to 0 imply that there is no recognizable relationship, and thus very high instability.
+Moreover, we selected an ordinal measure, as it allows for more latitude. While the absolute values of the coefficients might change (and indeed have to when more features are included), their ordering may still be constant. Consequently, their importance when compared to each other can still be stable. That means that any stability found here is to be considered the upper bound.\\
+
+However, when increasing the number of authors, the underlying feature set might also increase and thus the two matrices do not have the same dimensionality anymore. To tackle that problem, we follow two approaches: The first is to expand the smaller matrix by adding columns of $\infty$ for the missing features. This ensures, that these will always be assigned the highest possible rank in the smaller matrix (the rankings are sorted in ascending order during comparison). We call this the \textit{extended} Spearman correlation and it enables us to assess the absolute feature importance ranking. The second option is to assess only the features present in both matrices, and therefore to reduce the dimensionality of the larger one. This ensures that we assess relative importance, but ignore that additional features might have great influence on the outcome. That we refer to as the \textit{reduced} Spearman correlation. The result for the analysis of the feature importance and its stability may be found in \Cref{att:sec:importance}.
 
 
 
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diff --git a/tex/introduction.tex b/tex/introduction.tex
index dfd9249dd4be65f1630020941d8a441ab0fe3d89..2aaa2141eddf0bfc46469460d263c29555da9fb8 100644
--- a/tex/introduction.tex
+++ b/tex/introduction.tex
@@ -1,37 +1,41 @@
 \section{Introduction}
 \label{sec:intro}
-In traditional studies within the social sciences, characteristics such as age or gender are key traits as they have proven to be central to understanding and modeling human behavior \parencite[e.g.][]{lahey2000age, Gneezy2004GenderAge,Charness2012StrongTaking,Booth2012GenderMatter,Sutter2014GenderPersist, Bian2017GenderInterests}.
-While traditionally studies within the field focused on lab experiments as well as questionnaires, large-scale datasets have, until recently, been of limited availability. Especially, when it comes to text data, the analysis often proved resource-intensive and the results are difficult to assess.
-However, in general that data is a wealth of information, even more so since the amount of text data generated by individuals increased massively with the advent of services such as Facebook\textsuperscript{\textregistered} or Twitter\textsuperscript{\textregistered} \parencite{InternetLiveStats2019TwitterStatistics}.
-With such large amounts of data, new methods in machine learning and natural language processing (NLP) gained great popularity, especially within the social sciences.
-In order to mine that treasure-trove researchers increasingly turn to the application of NLP to answer prevailing questions in the social sciences \parencite[e.g.]{bail2016combining, pavlick2016gun, costa2019analysis, burley2020nlp}.
+In traditional studies within the social sciences, characteristics such as age or gender are key traits as they have proven to be central to understanding and modeling human behavior \parencite[e.g.,][]{lahey2000age, Gneezy2004GenderAge,Charness2012StrongTaking,Booth2012GenderMatter,Sutter2014GenderPersist, Bian2017GenderInterests}.
+While traditionally studies within the field focused on lab experiments as well as questionnaires, large-scale datasets have, until recently, been of limited availability. Especially when it comes to text data, the analysis often proved resource-intensive and the results are difficult to assess.
+However, in general, that data is a wealth of information, even more so since the amount of text data generated by individuals increased massively with the advent of services such as Facebook or Twitter \parencite{InternetLiveStats2019TwitterStatistics}.
+With such large amounts of data, new methods in machine-learning and natural language processing (NLP) gained great popularity, especially within the social sciences.
+In order to mine that treasure trove, researchers increasingly turn to the application of NLP to answer prevailing questions in the social sciences \parencite[e.g.,]{bail2016combining, pavlick2016gun, costa2019analysis, burley2020nlp}.
 The technology as such is, depending on the algorithms, comparatively simple to use in terms of know-how when one considers the textual features for identification as well as the difficulty of setting up and training the respective algorithms \parencite{Narayanan2012OnIdentification}.
-By reducing the entrance cost in such way, this technology has certainly great potential. 
-Here, the section of authorship analysis is becoming an area of special relevance to the social scientists. The reason is simply that while a large amount of text data is available, often characteristics of the individual, e.g. age, gender but also their identity as such, is not. The main goal of that particular area therefore is to profile characteristics such as age, gender but also political orientation or even the author's identity from texts written by the individual. 
-As such, while studies within the social sciences making use of that new type of data continue to include these characteristics due to their proven relevance in past research \parencite[e.g.][]{bail2016combining, colleoni2014echo}, authorship analysis is used to compensate for the lack of ground truth. That is often done by using a layered approach, first inferring the missing characteristics with a trained classifier, and then in turn using that as input to their research approach \parencite{barbera2015understanding, huang2020contribution}. 
-Moreover, these characteristics are not only important as an input for further research but also as identifying information. In the adjacent field where NLP and behavioral sciences intersect, the law community is making use of such analysis for the targeting and researching of incriminating online behavior, e.g. hate speech \parencite{Djuric2015HateEmbeddings,Laub2019HateComparisons,zufall2020operationalizing}.
-In that, also the practical application of authorship analysis becomes relevant when pursuing offenses.
+By reducing the entrance cost in such a way, this technology certainly has great potential. 
+Here, the section of authorship analysis is becoming an area of special relevance to the social scientists. The reason is simply that, while a large amount of text data is available, characteristics of the individual, such as age and gender, but also their identity, often are not. The main goal of that particular area is therefore to profile characteristics such as age and gender, but also political orientation or even the author's identity from texts written by the individual. 
+As such, while studies within the social sciences, in making use of that new type of data, continue to include these characteristics due to their proven relevance in past research \parencite[e.g.][]{bail2016combining, colleoni2014echo}, authorship analysis is used to compensate for the lack of ground truth. That is often done by using a layered approach, first inferring the missing characteristics with a trained classifier, and then in turn using that as input to their research approach \parencite{barbera2015understanding, huang2020contribution}. 
+Moreover, these characteristics are not only important as an input for further research, but also as identifying information. In the adjacent field where NLP and behavioral sciences intersect, the law community is making use of such analysis for the targeting and researching of incriminating online behavior, e.g., hate speech \parencite{Djuric2015HateEmbeddings,Laub2019HateComparisons,zufall2020operationalizing}.
+The practical application of authorship analysis also becomes relevant when pursuing offenses.
 Often, online users do not use their real names, and finding out their identities becomes difficult either because it is not available or because the companies having access to the information are not willing to share it \parencite{facebook}.
-Consequently, often during such an investigation \textit{forensic} authorship analysis is employed to gain additional information on the potential offender. Due to the high volume of data, these processes become increasingly automated and as such the research field of automated forensic authorship analysis is well established \parencite{Rocha2017AuthorshipForensics}.\\
+Consequently, often during such an investigation, \textit{forensic} authorship analysis is employed to gain additional information on the potential offender. Due to the high volume of data, these processes become increasingly automated and as such the research field of automated forensic authorship analysis is well established \parencite{Rocha2017AuthorshipForensics}.\\
 
-However, while state-of-the-art methods regularly manage to achieve a high accuracy for authorship analysis \parencite{Rocha2017AuthorshipForensics}, some call the field's scientific character into question  \parencite{Chaski2001EmpiricalTechniques}.
+However, while state-of-the-art methods regularly manage to achieve a high accuracy for authorship analysis \parencite{Rocha2017AuthorshipForensics}, some call the field's scientific character into question \parencite{Chaski2001EmpiricalTechniques}.
 This is due to the fact that current research of automated authorship analysis mostly focuses on correct predictions, using a wide set of features which often varies between different papers \parencite{Rocha2017AuthorshipForensics}.
-The focus seems to lie on achieving the best results, showing the viability of automated authorship analysis often to the detriment of rigorous explainability and transparency \parencite{Chaski2012BestIdentification}. This not only generally concerns research employing such models but, when used by law enforcement, it is also directly related to the admissibility of findings from automated authorship analysis in the courts as transparency as well as rigor might not satisfy the demands before the law \parencite{Chaski2012BestIdentification}. Consequently, the technology is in need of further assessment before being used even more widely than already the case.\\
-That result is somewhat surprising as one of the more extensive surveys on literature targeting \textit{forensic} authorship analysis notes the importance of and need for a \textquote{well-defined process} \parencite{Rocha2017AuthorshipForensics} in order to satisfy the high requirements for explainability before the law.
+The focus seems to lie on achieving the best results, showing the viability of automated authorship analysis often to the detriment of rigorous explainability and transparency \parencite{Chaski2012BestIdentification}. This not only generally concerns research employing such models but, when used by law enforcement, it is also directly related to the admissibility of findings from automated authorship analysis in the courts, as transparency and rigor might not satisfy the demands before the law \parencite{Chaski2012BestIdentification}. 
+As such, this topic is very much of normative interest. Explainability and transparency are the central aspects when a decision made by some model affects individuals. Within the field of law, as outlined above, that may affect the individual directly as she comes under suspicion when identified by the machine. In social sciences, they may be more indirectly affected. One way would be when predicted labels on a datasset are used for further analysis. The result from such an analysis may inform policy decisions. However, the error will propagate. As the underlying labels assigned by the machine were systematically faulty, the policy design will be as well.
+On the one hand, it is therefore paramount that, when exposed to such machine-informed decisions and label assignments, there are no systematic patterns of errors. On the other hand, the things driving the prediction result should be more than just spurious correlations, present in one dataset, absent in the next.
+Consequently, the technology is in need of further assessment before being used even more widely than is already the case.\\
+%That result is somewhat surprising, as one of the more extensive surveys on literature targeting \textit{forensic} authorship analysis notes the importance of and need for a \textquote{well-defined process} \parencite{Rocha2017AuthorshipForensics} in order to satisfy the high requirements for explainability before the law.\\
+
 Besides understanding the algorithm, such an explainability would require two things:
-One concerns the topic-independence which means that the features predictive of an author should not depend on the content of the text \parencite{Narayanan2012OnIdentification}.
+One concerns the topic independence, which means that the features predictive of an author should not depend on the content of the text \parencite{Narayanan2012OnIdentification}.
 The second aspect is rarely mentioned. 
-Most models are trained at one point in time, on one particular text corpus related to one domain.
-Using this at a later point in time, however, assumes that in the meantime there was no shift in the underlying features used or shifts in the set used by a particular author.
-If one looks for example at age, it may be that at the moment older people are using more grammatically correct language in the online environment compared to younger people \parencite{Flekova2016ExploringTwitter}.
-Naturally that is merely a snapshot of the current environment.
-It does not imply that the next generation features the same pattern and consequently, any model trained on the old pattern might inadvertently miss-classify when confronted with the new pattern.
+Most models are trained, at one point in time, on one particular text corpus related to one domain.
+Using the trained model at a later point in time, however, assumes that in the meantime there was no shift in the underlying features used or shifts in the set used by a particular author.
+If one looks at age, for example, it may be that older people are currently using more grammatically correct language in the online environment compared to younger people \parencite{Flekova2016ExploringTwitter}.
+Naturally, that is merely a snapshot of the current environment.
+It does not imply that the next generation features the same pattern, and consequently, any model trained on the old pattern might inadvertently misclassify when confronted with the new pattern.
 For that reason, it is necessary to assess the stability of individual features used by the classifier, when the underlying data and thus the patterns change slightly.
-There are only very few forays seeking to address such problems, for example like \textcite{Azarbonyad2015Time-awareStreams} do with their temporal weighting of features.
-As stylometry is rooted in the humanities and thus the social sciences \parencite{Neal2017SurveyingApplications} it is surprising that not more efforts have been made so far to see whether some author characteristics result in some stable topic- and domain-independent features and feature importance.\\
+There are only very few forays seeking to address such problems, for example as \textcite{Azarbonyad2015Time-awareStreams} do with their temporal weighting of features.
+As stylometry is rooted in the humanities and thus the social sciences \parencite{Neal2017SurveyingApplications}, it is surprising that not more efforts have been made so far to see whether some author characteristics result in some stable topic- and domain-independent features and feature importance.\\
 
-Therefore the central aspect of our approach is helping to answer that question about the feature stability as well as their relevance.  We seek to extend the understanding about the fundamentals of language and communication. We think that such a stability analysis would aid immensely in assessing the rigor of predictions therefore making them safer to use in the legal context. Moreover, it would also help to better establish the boundaries of transferability and stability of models and their predictions which is needed when used as input for further research.
-This contribution is therefore interdisciplinary in nature as it tries to address an issue affecting multiple fields.
-While the lack of contributions was already pointed out \textcite{Rocha2017AuthorshipForensics}, only recently has there been any notable forays. In general, there has been an effort to make model predictions more explainable \parencite{Ribeiro2016WhyClassifier, samek2019explainable}. A systematic approach , however, looking at changes when features are systematically varied is as of yet limited. The study by \textcite{koppel2011authorship} look at authorship analysis "in the wild`` and systemically vary the number of authors as well as the number of features to assess quantify performance gains and losses. They do not focus on featuretypes and do not extend their study towards analyzing the changes in within the model. Recently, \textcite{boenninghoff2019explainable} showed a method to make a complex model based on a neuronal net explainable. Their approach is limited to their specific model and does also not analyze what the decisive features correspond to, i.e. how much context they encode. In that vein, \textcite{sanchez2017comparison} is closer to our approach. They also seek to limit topic-dependency and focus on featuretypes. However, their goal is to find a good subset of ngrams for their featureset with high predictive power. The paper closest to ours is the one by \textcite{sage2020investigating}. Their analysis is focused on different featuretypes and the influence of varying ngram-lengths. They systematically vary both in order to find the impact on performance. However, they do not extend their analysis to different input sets and also focus on longer news articles instead of the more common data of microblog texts. Moreover, we also extend that analysis into the domain of stability, assessing whether there are shifts in feature-importance.
+Therefore, the central aspect of our approach is helping to answer that question about the stability. We assess the stability in terms of predictions and in terms of feature importance. We seek to extend the understanding about the fundamentals of language and communication. We think that such a stability analysis would aid immensely in assessing the rigor of predictions, therefore making them safer to use in the legal context. Moreover, it would also help us to better establish the boundaries of transferability and stability of models and their predictions, which is needed predictions of such models are used as input for further research.
+This contribution is therefore interdisciplinary in nature, as it tries to address an issue affecting multiple fields.
+While the lack of contributions has already been pointed out \textcite{Rocha2017AuthorshipForensics}, only recently, have there been any notable forays. In general, there has been an effort to make model predictions more explainable \parencite{Ribeiro2016WhyClassifier, samek2019explainable}. A systematic approach , however, looking at changes when features are systematically varied, is still limited. The study by \textcite{koppel2011authorship} looks at authorship analysis ``in the wild'' and systemically varies the number of authors as well as the number of features to assess and quantify gains and losses in performance. However, the authors do not focus on feature types and do not extend their study towards analyzing the changes in within the model. Recently, \textcite{boenninghoff2019explainable} showed a method to make a complex model based on a neuronal net explainable. Their approach is limited to their specific model and does not analyze either what the decisive features correspond to, i.e., how much context they encode. In that vein, \textcite{sanchez2017comparison} is closer to our approach. They also seek to limit topic dependency and focus on feature types. However, their goal is to find a good subset of n-grams for their feature set with high predictive power. The paper closest to ours is the one by \textcite{sage2020investigating}. Their analysis is focused on different feature types and the influence of varying n-gram lengths. They systematically vary both in order to find the impact on performance. However, they do not extend their analysis to different input sets and also focus on longer news articles instead of the more common data of microblog texts. Moreover, we also extend that analysis into the domain of stability, assessing whether there are shifts in feature importance.
 
 
diff --git a/tex/main.aux b/tex/main.aux
index 5868dc75e5b6c18bba1a4ddbf8a1a4374601b876..2237bf304513bd43a502bd53d1fba589173e53bc 100644
--- a/tex/main.aux
+++ b/tex/main.aux
@@ -1,5 +1,10 @@
 \relax 
 \providecommand\hyper@newdestlabel[2]{}
+\providecommand*\new@tpo@label[2]{}
+\@writefile{toc}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
+\@writefile{lof}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
+\@writefile{lot}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
+\abx@aux@refcontext{nyt/global//global/global}
 \providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
 \HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
 \global\let\oldcontentsline\contentsline
@@ -16,21 +21,9 @@
 \gdef\HyperFirstAtBeginDocument#1{#1}
 \providecommand\HyField@AuxAddToFields[1]{}
 \providecommand\HyField@AuxAddToCoFields[2]{}
-\@writefile{toc}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
-\@writefile{lof}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
-\@writefile{lot}{\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax }
-\abx@aux@refcontext{nyt/global//global/global}
+\providecommand\BKM@entry[2]{}
 \providecommand \oddpage@label [2]{}
-\abx@aux@cite{InternetLiveStats2019TwitterStatistics}
-\abx@aux@segm{0}{0}{InternetLiveStats2019TwitterStatistics}
-\abx@aux@cite{bail2016combining}
-\abx@aux@segm{0}{0}{bail2016combining}
-\abx@aux@cite{pavlick2016gun}
-\abx@aux@segm{0}{0}{pavlick2016gun}
-\abx@aux@cite{costa2019analysis}
-\abx@aux@segm{0}{0}{costa2019analysis}
-\abx@aux@cite{burley2020nlp}
-\abx@aux@segm{0}{0}{burley2020nlp}
+\BKM@entry{id=1,dest={73656374696F6E2E31},srcline={1}}{496E74726F64756374696F6E}
 \abx@aux@cite{lahey2000age}
 \abx@aux@segm{0}{0}{lahey2000age}
 \abx@aux@cite{Gneezy2004GenderAge}
@@ -43,6 +36,18 @@
 \abx@aux@segm{0}{0}{Sutter2014GenderPersist}
 \abx@aux@cite{Bian2017GenderInterests}
 \abx@aux@segm{0}{0}{Bian2017GenderInterests}
+\abx@aux@cite{InternetLiveStats2019TwitterStatistics}
+\abx@aux@segm{0}{0}{InternetLiveStats2019TwitterStatistics}
+\abx@aux@cite{bail2016combining}
+\abx@aux@segm{0}{0}{bail2016combining}
+\abx@aux@cite{pavlick2016gun}
+\abx@aux@segm{0}{0}{pavlick2016gun}
+\abx@aux@cite{costa2019analysis}
+\abx@aux@segm{0}{0}{costa2019analysis}
+\abx@aux@cite{burley2020nlp}
+\abx@aux@segm{0}{0}{burley2020nlp}
+\abx@aux@cite{Narayanan2012OnIdentification}
+\abx@aux@segm{0}{0}{Narayanan2012OnIdentification}
 \abx@aux@cite{bail2016combining}
 \abx@aux@segm{0}{0}{bail2016combining}
 \abx@aux@cite{colleoni2014echo}
@@ -61,8 +66,29 @@
 \abx@aux@segm{0}{0}{facebook}
 \abx@aux@cite{Rocha2017AuthorshipForensics}
 \abx@aux@segm{0}{0}{Rocha2017AuthorshipForensics}
-\abx@aux@cite{Narayanan2012OnIdentification}
-\abx@aux@segm{0}{0}{Narayanan2012OnIdentification}
+\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}{section.1}\protected@file@percent }
+\newlabel{sec:intro}{{1}{2}{Introduction}{section.1}{}}
+\newlabel{sec:intro@cref}{{[section][1][]1}{[1][2][]2}}
+\abx@aux@page{1}{2}
+\abx@aux@page{2}{2}
+\abx@aux@page{3}{2}
+\abx@aux@page{4}{2}
+\abx@aux@page{5}{2}
+\abx@aux@page{6}{2}
+\abx@aux@page{7}{2}
+\abx@aux@page{8}{2}
+\abx@aux@page{9}{2}
+\abx@aux@page{10}{2}
+\abx@aux@page{11}{2}
+\abx@aux@page{12}{2}
+\abx@aux@page{13}{2}
+\abx@aux@page{14}{2}
+\abx@aux@page{15}{2}
+\abx@aux@page{16}{2}
+\abx@aux@page{17}{2}
+\abx@aux@page{18}{2}
+\abx@aux@page{19}{2}
+\abx@aux@page{20}{2}
 \abx@aux@cite{Rocha2017AuthorshipForensics}
 \abx@aux@segm{0}{0}{Rocha2017AuthorshipForensics}
 \abx@aux@cite{Chaski2001EmpiricalTechniques}
@@ -73,8 +99,6 @@
 \abx@aux@segm{0}{0}{Chaski2012BestIdentification}
 \abx@aux@cite{Chaski2012BestIdentification}
 \abx@aux@segm{0}{0}{Chaski2012BestIdentification}
-\abx@aux@cite{Rocha2017AuthorshipForensics}
-\abx@aux@segm{0}{0}{Rocha2017AuthorshipForensics}
 \abx@aux@cite{Narayanan2012OnIdentification}
 \abx@aux@segm{0}{0}{Narayanan2012OnIdentification}
 \abx@aux@cite{Flekova2016ExploringTwitter}
@@ -83,8 +107,16 @@
 \abx@aux@segm{0}{0}{Azarbonyad2015Time-awareStreams}
 \abx@aux@cite{Neal2017SurveyingApplications}
 \abx@aux@segm{0}{0}{Neal2017SurveyingApplications}
-\abx@aux@cite{Narayanan2012OnIdentification}
-\abx@aux@segm{0}{0}{Narayanan2012OnIdentification}
+\abx@aux@page{21}{3}
+\abx@aux@page{22}{3}
+\abx@aux@page{23}{3}
+\abx@aux@page{24}{3}
+\abx@aux@page{25}{3}
+\abx@aux@page{26}{3}
+\abx@aux@page{27}{3}
+\abx@aux@page{28}{3}
+\abx@aux@page{29}{3}
+\abx@aux@page{30}{3}
 \abx@aux@cite{Rocha2017AuthorshipForensics}
 \abx@aux@segm{0}{0}{Rocha2017AuthorshipForensics}
 \abx@aux@cite{Ribeiro2016WhyClassifier}
@@ -99,34 +131,74 @@
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 \abx@aux@defaultrefcontext{0}{Azarbonyad2015Time-awareStreams}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{bail2016combining}{nyt/global//global/global}
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 \abx@aux@defaultrefcontext{0}{Booth2012GenderMatter}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{Brocardo2015AuthorshipAuthentication}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{burley2020nlp}{nyt/global//global/global}
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 \abx@aux@defaultrefcontext{0}{Charness2012StrongTaking}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{Chaski2001EmpiricalTechniques}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{Chaski2012BestIdentification}{nyt/global//global/global}
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 \abx@aux@defaultrefcontext{0}{koppel2011authorship}{nyt/global//global/global}
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 \abx@aux@defaultrefcontext{0}{Neal2017SurveyingApplications}{nyt/global//global/global}
 \abx@aux@defaultrefcontext{0}{PAN2019CelebrityProfiling}{nyt/global//global/global}
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 \abx@aux@defaultrefcontext{0}{Stamatatos2015OverviewLab}{nyt/global//global/global}
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+\newlabel{tab:Gender_dist_500_dynAA_full@cref}{{[table][61][2147483647]A.61}{[1][81][]81}}
+\@writefile{lot}{\defcounter {refsection}{0}\relax }\@writefile{lot}{\contentsline {table}{\numberline {A.62}{\ignorespaces 'Accuracy and F1-scores for the prediction of age on a minimal input instance length of 500 characters using a stacked model on the ordered, full feature set\relax }}{82}{table.62}\protected@file@percent }
+\newlabel{tab:Age_acc_500_dynAA_full}{{A.62}{82}{'Accuracy and F1-scores for the prediction of age on a minimal input instance length of 500 characters using a stacked model on the ordered, full feature set\relax }{table.62}{}}
+\newlabel{tab:Age_acc_500_dynAA_full@cref}{{[table][62][2147483647]A.62}{[1][81][]82}}
+\@writefile{lot}{\defcounter {refsection}{0}\relax }\@writefile{lot}{\contentsline {table}{\numberline {A.63}{\ignorespaces Stability of feature importance for the prediction of age on a minimal input instance length of 500 characters using a stacked model on the ordered, full feature set\relax }}{82}{table.63}\protected@file@percent }
+\newlabel{tab:Age_dist_500_dynAA_full}{{A.63}{82}{Stability of feature importance for the prediction of age on a minimal input instance length of 500 characters using a stacked model on the ordered, full feature set\relax }{table.63}{}}
+\newlabel{tab:Age_dist_500_dynAA_full@cref}{{[table][63][2147483647]A.63}{[1][81][]82}}
diff --git a/tex/main.bbl b/tex/main.bbl
index 17095aced958c1cc3aac34ed342f0db68112e669..666563cce79c5d17e900cfd518e22ea8d3536545 100644
--- a/tex/main.bbl
+++ b/tex/main.bbl
@@ -394,6 +394,39 @@
       \field{pages}{152\bibrangedash 159}
       \range{pages}{8}
     \endentry
+    \entry{Chan2018SocialTwitter}{inproceedings}{}
+      \name{author}{2}{}{%
+        {{un=0,uniquepart=base,hash=b201feca42a0d06c48c5984458b57c5f}{%
+           family={Chan},
+           familyi={C\bibinitperiod},
+           given={Sophia},
+           giveni={S\bibinitperiod},
+           givenun=0}}%
+        {{un=0,uniquepart=base,hash=08b40b43ab255b3f5a2b4fb6a7728c23}{%
+           family={Fyshe},
+           familyi={F\bibinitperiod},
+           given={Alona},
+           giveni={A\bibinitperiod},
+           givenun=0}}%
+      }
+      \strng{namehash}{3b179682a1e1838776cdcec5aa4f1899}
+      \strng{fullhash}{3b179682a1e1838776cdcec5aa4f1899}
+      \strng{bibnamehash}{3b179682a1e1838776cdcec5aa4f1899}
+      \strng{authorbibnamehash}{3b179682a1e1838776cdcec5aa4f1899}
+      \strng{authornamehash}{3b179682a1e1838776cdcec5aa4f1899}
+      \strng{authorfullhash}{3b179682a1e1838776cdcec5aa4f1899}
+      \field{sortinit}{C}
+      \field{sortinithash}{4c244ceae61406cdc0cc2ce1cb1ff703}
+      \field{extradatescope}{labelyear}
+      \field{labeldatesource}{year}
+      \field{labelnamesource}{author}
+      \field{labeltitlesource}{title}
+      \field{booktitle}{Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media}
+      \field{title}{{Social and Emotional Correlates of Capitalization on Twitter}}
+      \field{year}{2018}
+      \field{pages}{10\bibrangedash 15}
+      \range{pages}{6}
+    \endentry
     \entry{Charness2012StrongTaking}{article}{}
       \name{author}{2}{}{%
         {{un=0,uniquepart=base,hash=134e92f09e5ba63f519ccd0a63b0dcff}{%
@@ -1206,40 +1239,6 @@
       \verb https://www.cfr.org/backgrounder/hate-speech-social-media-global-comparisons
       \endverb
     \endentry
-    \entry{facebook}{online}{}
-      \name{author}{1}{}{%
-        {{un=0,uniquepart=base,hash=308f06c5fc394642d1ced66a1c157c5e}{%
-           family={Nachrichten},
-           familyi={N\bibinitperiod},
-           given={Stuttgarter},
-           giveni={S\bibinitperiod},
-           givenun=0}}%
-      }
-      \strng{namehash}{308f06c5fc394642d1ced66a1c157c5e}
-      \strng{fullhash}{308f06c5fc394642d1ced66a1c157c5e}
-      \strng{bibnamehash}{308f06c5fc394642d1ced66a1c157c5e}
-      \strng{authorbibnamehash}{308f06c5fc394642d1ced66a1c157c5e}
-      \strng{authornamehash}{308f06c5fc394642d1ced66a1c157c5e}
-      \strng{authorfullhash}{308f06c5fc394642d1ced66a1c157c5e}
-      \field{sortinit}{N}
-      \field{sortinithash}{98cf339a479c0454fe09153a08675a15}
-      \field{extradatescope}{labelyear}
-      \field{labeldatesource}{year}
-      \field{labelnamesource}{author}
-      \field{labeltitlesource}{title}
-      \field{title}{Bitte, Facebook, Hilf uns}
-      \field{urlday}{22}
-      \field{urlmonth}{7}
-      \field{urlyear}{2021}
-      \field{year}{2017}
-      \field{urldateera}{ce}
-      \verb{urlraw}
-      \verb https://www.stuttgarter-nachrichten.de/inhalt.polizei-als-bittsteller-bei-straftaten-bitte-facebook-hilf-uns.a3a743a3-d41e-479c-aba7-d5bc4da25286.html
-      \endverb
-      \verb{url}
-      \verb https://www.stuttgarter-nachrichten.de/inhalt.polizei-als-bittsteller-bei-straftaten-bitte-facebook-hilf-uns.a3a743a3-d41e-479c-aba7-d5bc4da25286.html
-      \endverb
-    \endentry
     \entry{Narayanan2012OnIdentification}{inproceedings}{}
       \name{author}{7}{}{%
         {{un=0,uniquepart=base,hash=95dc795a2be14c50fb6a924acc135ff8}{%
@@ -1991,6 +1990,37 @@
       \field{pages}{518\bibrangedash 538}
       \range{pages}{21}
     \endentry
+    \entry{facebook}{online}{}
+      \name{author}{1}{}{%
+        {{un=0,uniquepart=base,hash=9e3da02e903645b8c7fcb8a67c7cbe7e}{%
+           family={{Stuttgarter Nachrichten}},
+           familyi={S\bibinitperiod}}}%
+      }
+      \strng{namehash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \strng{fullhash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \strng{bibnamehash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \strng{authorbibnamehash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \strng{authornamehash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \strng{authorfullhash}{9e3da02e903645b8c7fcb8a67c7cbe7e}
+      \field{sortinit}{S}
+      \field{sortinithash}{c319cff79d99c853d775f88277d4e45f}
+      \field{extradatescope}{labelyear}
+      \field{labeldatesource}{year}
+      \field{labelnamesource}{author}
+      \field{labeltitlesource}{title}
+      \field{title}{Bitte, Facebook, Hilf uns}
+      \field{urlday}{22}
+      \field{urlmonth}{7}
+      \field{urlyear}{2021}
+      \field{year}{2017}
+      \field{urldateera}{ce}
+      \verb{urlraw}
+      \verb https://www.stuttgarter-nachrichten.de/inhalt.polizei-als-bittsteller-bei-straftaten-bitte-facebook-hilf-uns.a3a743a3-d41e-479c-aba7-d5bc4da25286.html
+      \endverb
+      \verb{url}
+      \verb https://www.stuttgarter-nachrichten.de/inhalt.polizei-als-bittsteller-bei-straftaten-bitte-facebook-hilf-uns.a3a743a3-d41e-479c-aba7-d5bc4da25286.html
+      \endverb
+    \endentry
     \entry{Sutter2014GenderPersist}{article}{}
       \name{author}{2}{}{%
         {{un=0,uniquepart=base,hash=848a999889a2e54a98fd472b1ba29520}{%
diff --git a/tex/main.bcf b/tex/main.bcf
index a6f8e0cf0562bb0013d58ba512f70d0078598da4..7734cdea9cd0254dfacdd1f978b6fcf5d4321871 100644
--- a/tex/main.bcf
+++ b/tex/main.bcf
@@ -2351,89 +2351,91 @@
     <bcf:datasource type="file" datatype="bibtex" glob="false">references.bib</bcf:datasource>
   </bcf:bibdata>
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-    <bcf:citekey order="1">InternetLiveStats2019TwitterStatistics</bcf:citekey>
-    <bcf:citekey order="2">bail2016combining</bcf:citekey>
-    <bcf:citekey order="2">pavlick2016gun</bcf:citekey>
-    <bcf:citekey order="2">costa2019analysis</bcf:citekey>
-    <bcf:citekey order="2">burley2020nlp</bcf:citekey>
-    <bcf:citekey order="3">lahey2000age</bcf:citekey>
-    <bcf:citekey order="3">Gneezy2004GenderAge</bcf:citekey>
-    <bcf:citekey order="3">Charness2012StrongTaking</bcf:citekey>
-    <bcf:citekey order="3">Booth2012GenderMatter</bcf:citekey>
-    <bcf:citekey order="3">Sutter2014GenderPersist</bcf:citekey>
-    <bcf:citekey order="3">Bian2017GenderInterests</bcf:citekey>
-    <bcf:citekey order="4">bail2016combining</bcf:citekey>
-    <bcf:citekey order="4">colleoni2014echo</bcf:citekey>
-    <bcf:citekey order="5">barbera2015understanding</bcf:citekey>
-    <bcf:citekey order="5">huang2020contribution</bcf:citekey>
-    <bcf:citekey order="6">Djuric2015HateEmbeddings</bcf:citekey>
-    <bcf:citekey order="6">Laub2019HateComparisons</bcf:citekey>
-    <bcf:citekey order="6">zufall2020operationalizing</bcf:citekey>
-    <bcf:citekey order="7">facebook</bcf:citekey>
-    <bcf:citekey order="8">Rocha2017AuthorshipForensics</bcf:citekey>
-    <bcf:citekey order="9">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="1">lahey2000age</bcf:citekey>
+    <bcf:citekey order="1">Gneezy2004GenderAge</bcf:citekey>
+    <bcf:citekey order="1">Charness2012StrongTaking</bcf:citekey>
+    <bcf:citekey order="1">Booth2012GenderMatter</bcf:citekey>
+    <bcf:citekey order="1">Sutter2014GenderPersist</bcf:citekey>
+    <bcf:citekey order="1">Bian2017GenderInterests</bcf:citekey>
+    <bcf:citekey order="2">InternetLiveStats2019TwitterStatistics</bcf:citekey>
+    <bcf:citekey order="3">bail2016combining</bcf:citekey>
+    <bcf:citekey order="3">pavlick2016gun</bcf:citekey>
+    <bcf:citekey order="3">costa2019analysis</bcf:citekey>
+    <bcf:citekey order="3">burley2020nlp</bcf:citekey>
+    <bcf:citekey order="4">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="5">bail2016combining</bcf:citekey>
+    <bcf:citekey order="5">colleoni2014echo</bcf:citekey>
+    <bcf:citekey order="6">barbera2015understanding</bcf:citekey>
+    <bcf:citekey order="6">huang2020contribution</bcf:citekey>
+    <bcf:citekey order="7">Djuric2015HateEmbeddings</bcf:citekey>
+    <bcf:citekey order="7">Laub2019HateComparisons</bcf:citekey>
+    <bcf:citekey order="7">zufall2020operationalizing</bcf:citekey>
+    <bcf:citekey order="8">facebook</bcf:citekey>
+    <bcf:citekey order="9">Rocha2017AuthorshipForensics</bcf:citekey>
     <bcf:citekey order="10">Rocha2017AuthorshipForensics</bcf:citekey>
     <bcf:citekey order="11">Chaski2001EmpiricalTechniques</bcf:citekey>
     <bcf:citekey order="12">Rocha2017AuthorshipForensics</bcf:citekey>
     <bcf:citekey order="13">Chaski2012BestIdentification</bcf:citekey>
     <bcf:citekey order="14">Chaski2012BestIdentification</bcf:citekey>
-    <bcf:citekey order="15">Rocha2017AuthorshipForensics</bcf:citekey>
-    <bcf:citekey order="16">Narayanan2012OnIdentification</bcf:citekey>
-    <bcf:citekey order="17">Flekova2016ExploringTwitter</bcf:citekey>
-    <bcf:citekey order="18">Azarbonyad2015Time-awareStreams</bcf:citekey>
-    <bcf:citekey order="19">Neal2017SurveyingApplications</bcf:citekey>
-    <bcf:citekey order="20">Narayanan2012OnIdentification</bcf:citekey>
-    <bcf:citekey order="21">Rocha2017AuthorshipForensics</bcf:citekey>
-    <bcf:citekey order="22">Ribeiro2016WhyClassifier</bcf:citekey>
-    <bcf:citekey order="22">samek2019explainable</bcf:citekey>
-    <bcf:citekey order="23">koppel2011authorship</bcf:citekey>
-    <bcf:citekey order="24">boenninghoff2019explainable</bcf:citekey>
-    <bcf:citekey order="25">sanchez2017comparison</bcf:citekey>
-    <bcf:citekey order="26">sage2020investigating</bcf:citekey>
-    <bcf:citekey order="27">Rocha2017AuthorshipForensics</bcf:citekey>
-    <bcf:citekey order="28">Spitters2016AuthorshipForums</bcf:citekey>
-    <bcf:citekey order="29">Keselj2003N-gram-basedAttribution</bcf:citekey>
-    <bcf:citekey order="29">Stamatatos2009AMethods</bcf:citekey>
-    <bcf:citekey order="29">Peng2003LanguageModels</bcf:citekey>
-    <bcf:citekey order="30">Forstall2010FeaturesSound</bcf:citekey>
-    <bcf:citekey order="31">Stamatatos2013OnFeatures</bcf:citekey>
-    <bcf:citekey order="32">Narayanan2012OnIdentification</bcf:citekey>
-    <bcf:citekey order="32">Spitters2016AuthorshipForums</bcf:citekey>
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-    <bcf:citekey order="35">Stamatatos2017authorship</bcf:citekey>
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-    <bcf:citekey order="43">Rocha2017AuthorshipForensics</bcf:citekey>
-    <bcf:citekey order="44">Neal2017SurveyingApplications</bcf:citekey>
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-    <bcf:citekey order="50">Brocardo2015AuthorshipAuthentication</bcf:citekey>
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-    <bcf:citekey order="52">Halvani2016AuthorshipTopics</bcf:citekey>
-    <bcf:citekey order="53">Varol2017OnlineCharacterization</bcf:citekey>
-    <bcf:citekey order="54">Narayanan2012OnIdentification</bcf:citekey>
-    <bcf:citekey order="55">PAN2019CelebrityProfiling</bcf:citekey>
-    <bcf:citekey order="56">Schmidt2014TwitterPublics</bcf:citekey>
+    <bcf:citekey order="15">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="16">Flekova2016ExploringTwitter</bcf:citekey>
+    <bcf:citekey order="17">Azarbonyad2015Time-awareStreams</bcf:citekey>
+    <bcf:citekey order="18">Neal2017SurveyingApplications</bcf:citekey>
+    <bcf:citekey order="19">Rocha2017AuthorshipForensics</bcf:citekey>
+    <bcf:citekey order="20">Ribeiro2016WhyClassifier</bcf:citekey>
+    <bcf:citekey order="20">samek2019explainable</bcf:citekey>
+    <bcf:citekey order="21">koppel2011authorship</bcf:citekey>
+    <bcf:citekey order="22">boenninghoff2019explainable</bcf:citekey>
+    <bcf:citekey order="23">sanchez2017comparison</bcf:citekey>
+    <bcf:citekey order="24">sage2020investigating</bcf:citekey>
+    <bcf:citekey order="25">custodio2021stacked</bcf:citekey>
+    <bcf:citekey order="26">custodio2021stacked</bcf:citekey>
+    <bcf:citekey order="27">Neal2017SurveyingApplications</bcf:citekey>
+    <bcf:citekey order="28">Neal2017SurveyingApplications</bcf:citekey>
+    <bcf:citekey order="29">Halvani2016AuthorshipTopics</bcf:citekey>
+    <bcf:citekey order="30">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="30">Rocha2017AuthorshipForensics</bcf:citekey>
+    <bcf:citekey order="30">Spitters2016AuthorshipForums</bcf:citekey>
+    <bcf:citekey order="31">Theophilo2019AMicro-messages</bcf:citekey>
+    <bcf:citekey order="32">Verhoeven2016Twisty:Profiling</bcf:citekey>
+    <bcf:citekey order="33">Brocardo2015AuthorshipAuthentication</bcf:citekey>
+    <bcf:citekey order="34">Stamatatos2015OverviewLab</bcf:citekey>
+    <bcf:citekey order="35">Halvani2016AuthorshipTopics</bcf:citekey>
+    <bcf:citekey order="36">Varol2017OnlineCharacterization</bcf:citekey>
+    <bcf:citekey order="37">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="38">PAN2019CelebrityProfiling</bcf:citekey>
+    <bcf:citekey order="39">Schmidt2014TwitterPublics</bcf:citekey>
+    <bcf:citekey order="40">Rocha2017AuthorshipForensics</bcf:citekey>
+    <bcf:citekey order="41">Spitters2016AuthorshipForums</bcf:citekey>
+    <bcf:citekey order="42">Keselj2003N-gram-basedAttribution</bcf:citekey>
+    <bcf:citekey order="42">Stamatatos2009AMethods</bcf:citekey>
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+    <bcf:citekey order="45">Narayanan2012OnIdentification</bcf:citekey>
+    <bcf:citekey order="45">Spitters2016AuthorshipForums</bcf:citekey>
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+    <bcf:citekey order="51">huang2020contribution</bcf:citekey>
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+    <bcf:citekey order="54">dietterich2000ensemble</bcf:citekey>
+    <bcf:citekey order="55">custodio2021stacked</bcf:citekey>
+    <bcf:citekey order="56">custodio2021stacked</bcf:citekey>
     <bcf:citekey order="57">custodio2021stacked</bcf:citekey>
     <bcf:citekey order="58">custodio2021stacked</bcf:citekey>
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-    <bcf:citekey order="62">DeJonge2012TextmessageStudents</bcf:citekey>
-    <bcf:citekey order="62">Hovy2015TaggingAge</bcf:citekey>
-    <bcf:citekey order="63">wiegmann2019overview</bcf:citekey>
+    <bcf:citekey order="59">wiegmann2019overview</bcf:citekey>
+    <bcf:citekey order="60">Flekova2016ExploringTwitter</bcf:citekey>
+    <bcf:citekey order="61">DeJonge2012TextmessageStudents</bcf:citekey>
+    <bcf:citekey order="62">Chan2018SocialTwitter</bcf:citekey>
+    <bcf:citekey order="63">Rocha2017AuthorshipForensics</bcf:citekey>
+    <bcf:citekey order="64">DeJonge2012TextmessageStudents</bcf:citekey>
+    <bcf:citekey order="64">Hovy2015TaggingAge</bcf:citekey>
+    <bcf:citekey order="65">wiegmann2019overview</bcf:citekey>
   </bcf:section>
   <!-- SORTING TEMPLATES -->
   <bcf:sortingtemplate name="nyt">
diff --git a/tex/main.blg b/tex/main.blg
index 407070f5307dc45efca5888953fe9da73afac739..582eb392deaa625cf20a45addd1b21a51a9ca8be 100644
--- a/tex/main.blg
+++ b/tex/main.blg
@@ -1,17 +1,17 @@
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-[105] biber-MSWIN64:330> INFO - === 
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-[265] Biber.pm:4334> INFO - Processing section 0
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-[1023] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
-[1023] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
-[1023] Biber.pm:4162> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
-[1023] Biber.pm:4168> INFO - No sort tailoring available for locale 'en-US'
-[1107] bbl.pm:651> INFO - Writing 'main.bbl' with encoding 'UTF-8'
-[1152] bbl.pm:754> INFO - Output to main.bbl
-[1153] Biber.pm:128> INFO - WARNINGS: 1
+[102] biber-MSWIN64:330> INFO - === 
+[122] Biber.pm:415> INFO - Reading 'main.bcf'
+[239] Biber.pm:952> INFO - Found 52 citekeys in bib section 0
+[256] Biber.pm:4334> INFO - Processing section 0
+[272] Biber.pm:4523> INFO - Looking for bibtex format file 'references.bib' for section 0
+[282] bibtex.pm:1682> INFO - LaTeX decoding ...
+[585] bibtex.pm:1494> INFO - Found BibTeX data source 'references.bib'
+[916] Utils.pm:395> WARN - ISBN '2163-8284' in entry 'Forstall2010FeaturesSound' is invalid - run biber with '--validate_datamodel' for details.
+[1069] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
+[1069] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
+[1069] Biber.pm:4162> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
+[1069] Biber.pm:4168> INFO - No sort tailoring available for locale 'en-US'
+[1137] bbl.pm:651> INFO - Writing 'main.bbl' with encoding 'UTF-8'
+[1179] bbl.pm:754> INFO - Output to main.bbl
+[1179] Biber.pm:128> INFO - WARNINGS: 1
diff --git a/tex/main.log b/tex/main.log
index c09143781926a3853470351917b78fc9670991ad..d700aafe833241ab4878ebb53950476c5ea74727 100644
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diff --git a/tex/main.out b/tex/main.out
index 93ef75aa47473ed0852d2374548fae823b3b61ad..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
--- a/tex/main.out
+++ b/tex/main.out
@@ -1,13 +0,0 @@
-\BOOKMARK [1][-]{section.1}{Introduction}{}% 1
-\BOOKMARK [1][-]{section.2}{Experimental Design and Data}{}% 2
-\BOOKMARK [2][-]{subsection.2.1}{Experimental Setup}{section.2}% 3
-\BOOKMARK [2][-]{subsection.2.2}{Evaluation Measures}{section.2}% 4
-\BOOKMARK [2][-]{subsection.2.3}{Target}{section.2}% 5
-\BOOKMARK [2][-]{subsection.2.4}{Data}{section.2}% 6
-\BOOKMARK [1][-]{section.3}{Results}{}% 7
-\BOOKMARK [2][-]{subsection.3.1}{Baseline}{section.3}% 8
-\BOOKMARK [2][-]{subsection.3.2}{Cumulated}{section.3}% 9
-\BOOKMARK [1][-]{section.4}{Discussion}{}% 10
-\BOOKMARK [1][-]{section*.20}{Appendix}{}% 11
-\BOOKMARK [1][-]{appendix.A}{Figures}{}% 12
-\BOOKMARK [1][-]{appendix.B}{Tables}{}% 13
diff --git a/tex/main.pdf b/tex/main.pdf
index 9a3577778ccb82904b454a45d6c2a54997d93c90..88516f1dbd5d79fe217000069e6b52f0978afaba 100644
Binary files a/tex/main.pdf and b/tex/main.pdf differ
diff --git a/tex/main.synctex.gz b/tex/main.synctex.gz
index fadf21f5cd13ac12c647b456b9e0b6adf51c193f..43f49441640bf65149a00ba262da48baa4cbdeb3 100644
Binary files a/tex/main.synctex.gz and b/tex/main.synctex.gz differ
diff --git a/tex/main.tex b/tex/main.tex
index a87a7c2cd69694651f6f923c4af83242ac464e4a..1969d6768ccc8efd94f13a6230e5d3ff9b47777e 100644
--- a/tex/main.tex
+++ b/tex/main.tex
@@ -1,20 +1,20 @@
 % !TeX spellcheck = en_US
-\documentclass[a4paper, 11pt]{article}
+\documentclass[a4paper, 11pt]{scrartcl}
 \usepackage[utf8]{inputenc}
-\usepackage{fullpage} % changes the margin
+%\usepackage{fullpage} % changes the margin
 \usepackage{url}
 \usepackage{amsmath}
 \usepackage{booktabs}
 \usepackage{multirow}
-\usepackage{titling}
-\usepackage{caption}
 \usepackage{graphicx}
 \usepackage{adjustbox}
+%\usepackage{titling}
+\usepackage{caption}
 \usepackage{subcaption}
 \usepackage{filecontents}
 \usepackage{enumitem}
-\usepackage[flushleft]{threeparttable}
-\usepackage{longtable, tabularx}
+%\usepackage[flushleft]{threeparttable}
+\usepackage{tabularx}
 \usepackage{multirow}
 \usepackage[toc,page]{appendix}
 \usepackage{fancyvrb} %for txt input
@@ -35,9 +35,10 @@
 \definecolor{LightCyan}{rgb}{0.88,1,1}
 %\renewcommand\theadfont{\small\bfseries} % for bold in table using \small
 \renewcommand\theadgape{}
-\title{Working Title: Feature Attribution}
-\author[1]{Marcel H. Schubert\thanks{Corresponding author. schubert@coll.mpg.de}}
-\affil[1]{Max-Planck Institute for Research on Collective Goods}
+\title{Systematic Errors and the Stability of Feature Importance}
+\subtitle{An Assessment for the Social Scientists}
+\author[1]{Marcel H. Schubert}
+\affil[1]{\scriptsize Max-Planck Institute for Research on Collective Goods}
 \date{ }
 
 \usepackage[autostyle]{csquotes} 
@@ -45,9 +46,19 @@
 \usepackage[backend=biber, style=authoryear, natbib= true, url=false, doi=false, eprint=false, isbn=false]{biblatex}
 \addbibresource{references.bib}
 \usepackage[hidelinks]{hyperref}
+\usepackage{cleveref}
+
 \begin{document}
-\maketitle
-\clearpage
+
+\begin{titlepage}
+	
+	\maketitle  
+	
+	\begin{abstract}
+		\noindent
+		Machine-learning is used ever more widely, particularly in the social sciences and in law. Here, Natural Language Processing (NLP) finds many cases of use as it enables the processing of large-scale online text data. However,  often the focus only lies on a model's performance and not on its transparency. That lack of transparency is especially troubling as the users of those models may often not be its designers. As such, they have no way of assessing whether there are systematic errors or whether the performance hinges on unknown, possibly unstable, factors. Here, we offer an in-depth analysis of the effect small variations in the input have on systematic errors and feature stability. Our aim is to enable social scientists and practitioners using the technology to assess whether they may invite either normative or unwanted systematic errors into their results when using current technologies.
+	\end{abstract}
+\end{titlepage}
 \input{introduction.tex}
 \FloatBarrier
 \input{experimental_setup.tex}
@@ -61,10 +72,10 @@
 \printbibliography
 
 \clearpage
-%\begin{appendix}
+\begin{appendix}
 	\section*{Appendix}
 	\addcontentsline{toc}{section}{Appendix}
 	\input{appendix.tex}
-%\end{appendix}
+\end{appendix}
 
 \end{document}
\ No newline at end of file
diff --git a/tex/main_old.pdf b/tex/main_old.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e17006873add93fc28699c6dacbd38dcdac38279
Binary files /dev/null and b/tex/main_old.pdf differ
diff --git a/tex/references.bib b/tex/references.bib
index 9f6cc24335adecabfbea53969e3e38f4fa379518..be9515d4556986d7ad673c2087555f49f072b6b1 100644
--- a/tex/references.bib
+++ b/tex/references.bib
@@ -1911,7 +1911,7 @@
 
 
 @online{facebook,
-	author = {Stuttgarter Nachrichten},
+	author = {{Stuttgarter Nachrichten}},
 	title = {Bitte, Facebook, Hilf uns},
 	year = 2017,
 	url={https://www.stuttgarter-nachrichten.de/inhalt.polizei-als-bittsteller-bei-straftaten-bitte-facebook-hilf-uns.a3a743a3-d41e-479c-aba7-d5bc4da25286.html},
diff --git a/tex/results.tex b/tex/results.tex
index 9ada759d6e92c5f39d0fe5c64ae80700f52f27f5..78d5b4f868e564385b93f4289848f95352973e52 100644
--- a/tex/results.tex
+++ b/tex/results.tex
@@ -1,132 +1,134 @@
-\section{Results}
-The results were computed on HPC system making use of 72 cores with 256GB of RAM.\footnote{\href{https://docs.mpcdf.mpg.de/doc/computing/raven-user-guide.html}{MPCDF HPC System "Raven"}}
-Of our classifiers, the SVM outperformed the logistic classifier as well as the Naive Bayes Classifier as a firts-layer model.
+\section{Stability of Predictions}
+\label{att:sec:prediction}
+The results were computed on HPC system, making use of 72 cores with 256GB of RAM.\footnote{\href{https://docs.mpcdf.mpg.de/doc/computing/raven-user-guide.html}{MPCDF HPC System "Raven".}}
+Of our classifiers, the SVM outperformed the logistic classifier as well as the Naive Bayes Classifier as a first-layer model.
 Thus, for all results a SVM was used as the first-layer model. For the stacked model, a logistic classifier was used for the second layer to stay close to the setup by \textcite{custodio2021stacked}.
-Overall, per target the result of the experimental setup is comprised of 1200 SVM as well stacked models, i.e. 2400 models in total for both targets.
-To enable a concise analysis, we opted to showcase the results for the textset with an inputlength of 500 characters. The results for the other textsets may be found in the Appendix.
+Overall, per target, the result of the experimental setup is comprised of 1200 SVMs as well stacked models, i.e., 2400 models in total for both targets.
+To enable a concise analysis, we opted to showcase the results for the text set for which individual inputs have a length of 500 characters. The results for the other text sets may be found in the Appendix.
 
-\subsection{Aggregate Overview}
-First, we will look at the aggregated results for our experiments. In \autoref{fig:f1_500}, we see the f1-scores for the classifiers trained on the dataset with a minimum character length of 500 per instance. As in previous results, the score declines markedly in the number of authors. However, especially the models trained either with different featuretypes as input (labeled "cumulated") or those trained similarly to the DynAA model by \textcite{custodio2021stacked} (labeled "stacked") perform consistently. 
+\subsection{Aggregate Overview for Feature-Stability}
+First, we will look at the aggregated results for our experiments. In \autoref{fig:f1_500}, we see the F1-scores for the classifiers trained on the dataset with a minimum character length of 500 per instance. As in previous results, the score declines markedly in the number of authors. However, especially the models trained either with different feature types as input (labeled "cumulated") or those trained similarly to the DynAA model by \textcite{custodio2021stacked} (labeled "stacked") perform consistently. 
 Moreover, overall the results are in line with previous top-performing results on the PAN2019 dataset \parencite{wiegmann2019overview}.
-The baseline models trained on featuresets consisting of singular types have a high variance in performance. That, is not surprising as we look at individual models each trained on one type of features. As can be seen in \autoref{tab:gender_f1_dist_500_baseline_individual} and \autoref{tab:age_f1_dist_500_baseline_individual}, the performance is on the upper end for the featuretypes such as CHAR with an $F1_{50}^{CHAR-2}$ up to 0.88/0.82  and on the lower end for featuretypes such as NUM with an $F1_{50}^{NUM}$ of 0.59/0.29 for the targets gender/age.
-Consequently, the type of feature used and their combinations does not only have a great impact on the outcome but some features do encode little or next to no information for our classification task. Hence, we exclude those from our further analysis.
+The baseline models trained on feature sets consisting of singular types have a high variance in performance. That is not surprising, when we look at individual models, each trained on one type of features. As can be seen in \autoref{tab:gender_f1_dist_500_baseline_individual} and \autoref{tab:age_f1_dist_500_baseline_individual}, the performance is on the upper end for the feature types such as CHAR with an $F1_{50}^{CHAR-2}$ up to 0.88/0.82  and on the lower end for feature types such as NUM with an $F1_{50}^{NUM}$ of 0.59/0.29 for the targets gender/age.
+Consequently, the feature types used, as well as their combinations, not only have a great impact on the outcome, but some features do encode little or next to no information for our classification task. Hence, we exclude those from our further analysis.
 The same findings apply to the models trained on instances with a minimum length of 100 characters and 250 characters respectively (\autoref{fig:f1_100} and \autoref{fig:f1_250} in the Appendix).
 \begin{figure}[!htpb]
 	\centering
 	\begin{subfigure}[b]{0.49\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/f1_scores_500.pdf}
-		\caption{Results for Target Gender.}
+		\caption{Results for target \textit{gender}.}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{0.49\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/f1_scores_500.pdf}
-		\caption{Results for Target Age.}
+		\caption{Results for target \textit{age}.}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows boxplots for the F1-score of all models estimated for a given combination of featuretypes used and way of input, i.e. baseline, cumulated, or stacked.}
-		\caption{F1-Score instance-length of 500 Characters.}
+	\caption*{\scriptsize \textit{Notes}: The figure shows boxplots for the F1-score of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+		\caption{F1-score input instance length of 500 characters.}
 	\label{fig:f1_500}
 \end{figure}
 Having comparable results in terms of accuracy and f1-score to what is found in the literature for this dataset serves as a basis for our following evaluation.
 A high performance lends credence to the assumption that our classifier is indeed working well and extracting the relevant information from the input data.
 In reverse, we may therefore assume that the features used are indeed those holding the relevant information for the respective task. Thus, our approach of extracting the feature importance via the associated weights is sensible.\\
 \autoref{fig:ext_spearman_500} shows the results for the extended distortion calculated via Spearman's Rho.
-The comparison is always done between two models varying in the amount of authors the respective model is trained on.
-In the comparison, model 1 is trained on the lower number of authors (e.g. 50) whereas model 2 is trained on the next higher number of authors (e.g. 150). Overall we thus have 3 comparisons in the number of authors.\\
-For target \textit{gender}, on average and irrespective of the model we find only little correlation when increasing the number of authors from 50 to 150 ($0.05 < \rho < 0.20$). For the others, when increasing the number of authors correlation goes down to at maximum 0.05. At the same time, we show that the performance in terms of f1-score remains relatively stable.
-That implies that the stability in performance comes at the expense of the stability in feature importance. When increasing the number of authors, different features are therefore predictive in terms of the target. It does not generally imply that the previous features lose their importance only that when ordering all features in terms of the associated importance, the ordering changes fundamentally.That fact is reflected in Spearman's Rho.
-Irregardless by how much we increase the number of authors, the correlation is, on average between 5\% and 15\% for all models.
-That is interesting, as the number of features additionally available when increasing the number of authors is never above 30\%. Thus, it cannot be that mainly those additional features are the ones being used for predictions as the correlation coefficient would then still be higher than what we find. 
-Rather it seems to be the case that the model weighs the previous and new features in such a way that the new ordering imposed  differs completely from the previous one.
+The comparison is always done between two models, varying in the amount of authors the respective model is trained on.
+In the comparison, model 1 is trained on the lower number of authors (e.g., 50) whereas model 2 is trained on the next-highest number of authors (e.g., 150). Overall, we thus have 3 comparisons in the number of authors.\\
+For target \textit{gender}, on average, and irrespective of the model, we find only little correlation when increasing the number of authors from 50 to 150 ($0.05 < \rho < 0.20$). For the others, when increasing the number of authors, correlation goes down to a maximum of $0.05$. At the same time, we show that the performance in terms of f1-score remains relatively stable.
+This implies that the stability in performance comes at the expense of the stability in feature importance. When increasing the number of authors, different features are therefore predictive in terms of the target. These findings do not generally imply that the previous features lose their importance. The do however mean that, when ordering all features in terms of the associated importance, the ordering changes fundamentally. That fact is reflected in Spearman's Rho.
+Regardless by how much we increase the number of authors, on average the correlation lies between 5\% and 15\% for all models.
+That is interesting, as the number of features additionally available when increasing the number of authors is never above 30\%. Thus, it cannot be that mainly those additional features are the ones being used for predictions, as the correlation coefficient would then still be higher than what we find. 
+Rather, it seems to be the case that the model weighs the previous and new features in such a way that the new ordering imposed  differs completely from the previous one.
 
 \begin{figure}[!htpb]
 	\centering
 	\begin{subfigure}[b]{0.49\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/spearman_ext_500.pdf}
-		\caption{Results for Target Gender.}
+		\caption{Results for target \textit{gender}.}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{0.49\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/spearman_ext_500.pdf}
-		\caption{Results for Target Age.}
+		\caption{Results for target \textit{age}.}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of featuretypes used and way of input, i.e. baseline, cumulated, or stacked.}
-	\caption{Extended Spearman Correlation instance-length of 500 Characters.}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the boxplots for the extended $\rho$ of all models estimated for a given combination of feature types used and way of input, i.e., baseline, cumulated, or stacked.}
+	\caption{Extended Spearman correlation input instance length of 500 characters.}
 	\label{fig:ext_spearman_500}
 \end{figure}
 Similar results are found for the target \textit{age}. However, here the decline in correlation is even more unidirectional when increasing the number of authors (increase in authors yields a decline in correlation).\\
 
-The only outlier to these results is the baseline model trained on individual featuretypes. While on average, the correlation is the same as for the models on combinations of featuretypes, the outliers show a very high positive correlation (up to $\rho_{150|50}^{NUM}: 0.42$, see \autoref{tab:age_f1_dist_500_baseline_individual}). 
-While that may look like it stands in opposition to the other results, their low predictive power helps to explain this phenomenon. \autoref{tab:age_f1_dist_500_baseline_individual} shows that the F1-score for the prediction of \textit{age} is only at $F1_{150}^{NUM}:0.25$ with the random guess benchmark being 0.2. Thus, while the feature importance remains stable for some features their predictive power is negligible. Thus the importance assigned to these features may simply be random noise without any signal.
-As such, when looking at the average feature stability over all featuretype sets the conclusion is that the features are, on average, not stable and the distortion of importance when increasing the number of authors in a dataset is already high for a low number of authors (from 50 to 150).
-These findings hold irregardless of the character length of the input text, as \autoref{fig:ext_spearman_100} and \autoref{fig:ext_spearman_250} in the appendix show.
+The only outlier to these results is the baseline model trained on individual feature types. While on average the correlation is the same as for the models on combinations of feature types, the outliers show a very high positive correlation (up to $\rho_{150|50}^{NUM}: 0.42$, see \autoref{tab:age_f1_dist_500_baseline_individual}). 
+While that may look like it stands in opposition to the other results, their low predictive power helps to explain this phenomenon. \autoref{tab:age_f1_dist_500_baseline_individual} shows that the F1-score for the prediction of \textit{age} is only at $F1_{150}^{NUM}:0.25$ with the random guess benchmark being 0.2. Thus, while the feature importance remains stable for some features, their predictive power is negligible. Thus, the importance assigned to these features may simply be random noise without any signal.
+As such, when looking at the average feature stability over all feature type sets, the conclusion is that the features are, on average, not stable and the distortion of importance when increasing the number of authors in a dataset is already high for a low number of authors (from 50 to 150).
+These findings hold regardless of the character length of the input text, as \autoref{fig:ext_spearman_100} and \autoref{fig:ext_spearman_250} in the Appendix show.
 
 \subsection{Author-Level Analysis}
-For the author-level analysis we look at the results gained by feeding the classifier the full set of featuretypes in a cumulated way. That choice assures that our results are predicted making use of the full information. Furthermore, cumulating the input yields the best results overall (see \autoref{sec:att_app_tables} in the Appendix). In that way, the analysis is done using the best possible set. However, the findings hold for any of the featuretype-approach combinations.\\
+For the author-level analysis, we look at the results gained by feeding the classifier the full set of feature types in a cumulated way. That choice assures that our results are predicted making use of the full information. Furthermore, cumulating the input yields the best results overall (compare \autoref{sec:att_app_tables}). In that way, the analysis is done using the best possible set. However, the findings hold for any of the feature-type approach combinations.\\
 
-\autoref{fig:acc_auth_500}a shows the error on the author-level when predicting gender. We see, that overall we have very few authors for which the accuracy is lower than the random-guess accuracy (0.5), i.e, for which our prediction error is higher than 0.5. By eyeballing one is able to see that the relative number of authors, when compared to the number of authors in the set, stays constant. Consequently, the relative performance on author-level for the classifier stays stable even when increasing the performance. However, there is a small but stable number of authors for which the classifier is \textit{systematically unable} to predict the target correctly.
+\autoref{fig:acc_auth_500}a shows the error at the author level when predicting gender. We see, that overall we have very few authors for which the accuracy is lower than the random-guess accuracy (0.5), i.e, for which our prediction error is higher than 0.5. We see that the relative number of authors for which the classifier performs below the random-guess threshold stays stable, when compared to the number of authors in the set. Consequently, the relative overall-classification performance at the author level stays stable, even when increasing the the number of authors in the set. However, there is a small but stable proportion of authors for which the classifier is \textit{systematically unable} to predict the target correctly.
 \begin{figure}[!htpb]
 	\centering
 	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/500/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_color_gender.pdf}
-		\caption{Author-Level Errors for target \textit{gender}.}
+		\caption{Author-level errors for target \textit{gender}.}
 	\end{subfigure}
 	\hfill
 	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/500/bars/proportions_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_color_centered_age.pdf}
-		\caption{Author-Level Errors for target \textit{age}.}
+		\caption{Author-level errors for target \textit{age}.}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full featureset as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000). The result per author shows the result over all subsets.}
-	\caption{Author-Level Results for the Full Featureset in an Instance-Length of 500 Characters.}
+	\caption*{\scriptsize \textit{Notes}: The figure shows the results when using the full feature set as cumulated input. Each author is a unique instance on the x-axis. The proportion per author is then shown as the y-value. The authors are sorted by their appearance in the respective subsets (i.e., 50, 150, 500, 1000) and according to the proportion of errors within those subsets. The result per author shows the result over all subsets.}
+	\caption{Author-level results for the full feature set with an input instance length of 500 characters.}
 	\label{fig:acc_auth_500}
 \end{figure}
-When looking at the distribution of the errors across genders, we find that a higher number of those authors for which our classifier makes systematic errors, seems to be female. 
-On the high-level, we find that the performance remains relatively stable when increasing the number of authors as depicted in \autoref{fig:gender_conf_auth_500}a. However, as soon as we reach the upper to brackets of authors, we see that while the result for male authors remains relatively stable, the outcome for female authors declines markedly from $acc_{150}^{Female}:0.82$ to $acc_{1000}^{Female}:0.69$. For the same sets the accuracy for males declines only from $acc_{150}^{Male}:0.83$ to $acc_{1000}^{Male}:0.80$. Looking closer, we can see that that drop for females happens when increasing the number of authors from 150 to 500. The implication is thus that we add female authors which are systematically difficult to classify. Bringing this together with our observations from before, we include only those authors in \autoref{fig:gender_conf_auth_500}b for which the classifier performs worse than the random-guess threshold. Here, we find some support for our previous assumption. When looking at the errors we make for female authors, we see that starting from 500 authors onward, the number of those below the random-guess threshold jumps up.\\
-
-While that seems to point to the fact that apparently female authors are difficult to classify, the true reason may be slightly more nuanced. When looking at the male authors in \autoref{fig:gender_conf_auth_500}b, we see that, while obviously low in absolute numbers, the pattern is inverse for the datsets comprised of 50 and 150 authors. Consequently, while female may be indeed be more slightly more difficult to classify for unknown reasons, the main driver seems to be feature stability or the lack thereof. Due to the fact that we limited the amount of features for each featuretype to those appearing at least in 1\% of all training instances, the number of features for word-based featuretypes increases only \textit{sublinearly} relative to the number of authors. Thus, the amount of author-individual fitting the classifier is able to achieve into account declines. Indeed, that is the very essence of reducing overfitting. However, as shown in \autoref{fig:ext_spearman_500}a, the stability of feature-importance is low. Taken together, that simply means that systematic patterns within a limited number of features for authors of the same gender decline when the number of authors increases, i.e. the correlational patterns break down or become unstable and more complex. The underlying reason is that additional authors introduce new features while using the old features in a different way. As the classifier is only able to estimate one weight per feature and the number of additional features is limited, the ability to represent all the necessary information declines.\\
+When looking at the distribution of the errors across genders, we find that a higher number of those authors for whom our classifier makes systematic errors seems to be female. 
+On the high level, we find that the performance remains relatively stable when we increase the number of authors as depicted in \autoref{fig:gender_conf_auth_500}a. However, as soon as we reach the two upper-most brackets of authors, we see that the result for male authors remains relatively stable, while the outcome for female authors declines markedly from $acc_{150}^{Female}:0.82$ to $acc_{1000}^{Female}:0.69$. For the same sets, the mostly stable accuracy for males declines only from $acc_{150}^{Male}:0.83$ to $acc_{1000}^{Male}:0.80$. Looking closer, we can see that that drop for females happens when the number of authors increases from 150 to 500. The implication is thus that we add female authors who are systematically difficult to classify. Bringing this together with our observations from before, we include only those authors in \autoref{fig:gender_conf_auth_500}b for whom the classifier performs worse than the random-guess threshold. Here, we find some support for our previous assumption. When looking at the errors we make for female authors, we see that, starting from 500 authors onward, the number of those below the random-guess threshold jumps up.\\
 
+While that seems to point to the fact that apparently female authors are difficult to classify, the true reason may be slightly more nuanced. When looking at the male authors in \autoref{fig:gender_conf_auth_500}b, we see that, while obviously low in absolute numbers, the pattern is inverse for the datsets comprised of 50 and 150 authors. The underlying driver, however, does not seem to be the gender \textit{per se}, but rather the lack of stability in regards to feature importance. Per design, we limited the amount of features for each feature type to those appearing at least in 1\% of all training instances. Hence, the number of features for word-based feature types increases only \textit{sublinearly} relative to the number of authors. Thus, the amount of author-individual fitting the classifier is able to achieve declines. Indeed, that is the very essence of reducing overfitting. However, as shown in \autoref{fig:ext_spearman_500}a, the stability of feature importance is low. Taken together, that simply means that systematic patterns within a limited number of features for authors of the same gender decline when the number of authors increases, i.e., the patterns seem to be merely correlational -- they start to break down or become unstable and more complex. The underlying reason is that additional authors introduce new features, while using the old features in a different way. As the classifier is only able to estimate one weight per feature and the number of additional features is limited, the ability to represent all the necessary information declines.\\
 \begin{figure}[!htpb]
 	\centering
-	\begin{subfigure}[b]{\textwidth}
+	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/500/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_mode_basic.pdf}
-		\caption{All Authors (Row-Wise Normalization).}
+		\caption{All authors (row-wise normalization).}
 	\end{subfigure}
 	\hfill
-	\begin{subfigure}[b]{\textwidth}
+	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/gender/500/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_mode_major_false.pdf}
-		\caption{Below Random Guess Accuracy (Matrix-Wise Normalization).}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full featureset as cumulated input on an instance-length of 500 Characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
-	\caption{Confusion Matrices for Target \textit{gender}.}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 500 Characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{gender}.}
 	\label{fig:gender_conf_auth_500}
 \end{figure}
-For target \textit{age}, we also find only comparatively few authors with an average accuracy below the random-guess threshold of $0.2$ (see \autoref{fig:acc_auth_500}b). When comparing it to the results of the target \textit{gender}, it becomes clear that the number of those below the threshold jumps up significantly when the number of authors increases from 150 to 500. Moreover, it seems to be the case that authors of the intermediate age brackets (\textit{1975} and \textit{1985}) seem to be more difficult to classify. Overall, the patterns seem to be less pronounce when compared to the ones found for \textit{gender}.
-Looking at the category-wise analysis presented in \autoref{fig:age_conf_auth_500}a, we find that overall there are only few pronounced patterns of confusion. As already suggested by \autoref{fig:acc_auth_500}b, only the intermediate age brackets have a systematic pattern. Especially for the datasets consisting of 500 and 100 authors, the confusion between the true age bracket \textit{1985} and the youngest age~bracket \textit{1995} is pronounced. Here only 38\% of the instances are classified correctly as \textit{1985} while 28\% are confused as \textit{1995}. While here the interpretation might be that the distinction between the youngest authors might be difficult, the results of \textit{1975}, the intermediate category, make it more difficult. We see that the confusion with the category \textit{1963} as well as \textit{1995} is of similar size. It might be, that instead of predicting only \textit{age}, the classifier picks up on a proxy in the way people express themselves. While certainly dependent on age in terms of punctuation for older authors \parencite{Flekova2016ExploringTwitter} as well as stability of language use in younger authors \parencite{DeJonge2012TextmessageStudents}, the way of expression also depends on the groups we are part of \parencite{Chan2018SocialTwitter}. Consequently, some of those authors confused might simply be part of peer-groups where the mode of expression is reflective of younger age brackets. As a consequence they get misclassified.
+
+For the target \textit{age}, we also find only comparatively few authors with an average accuracy below the random-guess threshold of $0.2$ (see \autoref{fig:acc_auth_500}b). When comparing it to the results of the target \textit{gender}, it becomes clear that the number of those below the threshold jumps up significantly when the number of authors increases from 150 to 500. Moreover, it seems to be the case that authors of the intermediate age brackets (\textit{1975} and \textit{1985}) seem to be more difficult to classify. Overall, the patterns seem to be less pronounced when compared to the ones found for \textit{gender}.
+Looking at the category-wise analysis presented in \autoref{fig:age_conf_auth_500}a, we find that, overall there are only few pronounced patterns of confusion. As already suggested by \autoref{fig:acc_auth_500}b, only the intermediate age brackets have a systematic pattern. Especially for the datasets consisting of 500 and 100 authors, the confusion between the true age bracket \textit{1985} and the youngest age~bracket \textit{1995} is pronounced. Here, only 38\% of the instances are classified correctly as \textit{1985}, while 28\% are confused as \textit{1995}. While here the interpretation might be that the distinction between the youngest authors might be difficult, the results of \textit{1975}, the intermediate category, make it more difficult. We see that the confusion with the category \textit{1963} as well as \textit{1995} is of similar size. It might be that, instead of predicting only \textit{age}, the classifier picks up on a proxy in the way people express themselves. While certainly dependent on age in terms of punctuation for older authors \parencite{Flekova2016ExploringTwitter} as well as on stability of language use in younger authors \parencite{DeJonge2012TextmessageStudents}, the way of expression also depends on the groups to which we belong \parencite{Chan2018SocialTwitter}. Consequently, some of those authors confused might simply be part of peer groups where the mode of expression is reflective of younger age brackets. As a consequence, they get misclassified.
 These relatively distinctive patterns for the oldest and youngest authors are also most likely the reason why the prediction accuracy for those is markedly high, even for the set with the highest number of authors.\\
+
+When looking only at those authors below the random-guess threshold, as depicted in \autoref{fig:age_conf_auth_500}b, we find that there are only two age brackets for which we have a systematic and pronounced confusion. For the set with 150 authors, this is the age bracket \textit{1947}, which is most often confused with the two adjacent age brackets \textit{1963} and \textit{1975}. For the set with 1000 authors, the age bracket \textit{1985} is mostly confused with belonging either to the youngest or the oldest age bracket.
+As that bracket is also overall the most confused one, it stands to reason that the variance in expression is the highest. Consequently, there is no pronounced pattern in the features on which the classifier is able to pick up.\\
 \begin{figure}[!htpb]
 	\centering
-	\begin{subfigure}[b]{\textwidth}
+	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/500/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_mode_basic.pdf}
-		\caption{All Authors (Row-Wise Normalization).}
+		\caption{All authors (row-wise normalization).}
 	\end{subfigure}
 	\hfill
-	\begin{subfigure}[b]{\textwidth}
+	\begin{subfigure}[b]{0.8\textwidth}
 		\centering
 		\includegraphics[width=\textwidth]{figures/age/500/confusion/confusion_cumulated_dist_char_asis_pos_tag_dep_lemma_word_emoticon_c_polarity_num_1_500_mode_major_false.pdf}
-		\caption{Below Random Guess Accuracy (Matrix-Wise Normalization).}
+		\caption{Below random guess accuracy (matrix-wise normalization).}
 	\end{subfigure}
-	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full featureset as cumulated input on an instance-length of 500 Characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
-	\caption{Confusion Matrices for Target \textit{age}.}
+	\caption*{\scriptsize \textit{Notes}: The figure shows confusion matrices for the results produced by using the full feature set as cumulated input on an input instance length of 500 Characters. The matrix for the respective set of authors is calculated by looking at the respective set in isolation.}
+	\caption{Confusion matrices for target \textit{age}.}
 	\label{fig:age_conf_auth_500}
 \end{figure}
-When looking only at those authors below the random-guess threshold, as depicted in \autoref{fig:age_conf_auth_500}b we find that there are only two age brackets for which we have a systematic an pronounced confusion. For the set with 150 authors, this is the age bracket \textit{1947} which gets mostly confused with the two adjacent age brackets \textit{1963} and \textit{1975}. For the set with 1000 authors, the age bracket \textit{1985} is mostly confused with belonging either to the youngest or the oldest age bracket.m
-As that bracket is also overall the most confused one, it stands to reason that the variance in expression is the highest. Consequently, there is no pronounced pattern in the features the classifier is able to pick up on. \\
 
diff --git a/tex/robustness.tex b/tex/robustness.tex
index 7fc89c5c9ff366856faa51b745d9b07ba1fc17fc..e083c8bd69e41a8cc1e642392d9acdbab8fd1355 100644
--- a/tex/robustness.tex
+++ b/tex/robustness.tex
@@ -1,43 +1,44 @@
 \section{Robustness of Feature-Importance}
-In order to assess how the aggregate results from the previous section hold, when looking at individual featuretypes as well as different classifier approaches, this section presents a more fine-grained insight. In this section, we focus on the input in terms of featuretypes and ngrams. We analyse these aspects with a special look on the stability of the feature-importance.
+\label{att:sec:importance}
+In order to assess how the aggregate results from the previous section hold, when looking at individual feature types as well as different classifier approaches, this section presents a more fine-grained insight. In this section, we focus on the input in terms of feature types and n-grams. We analyze these aspects with a special look on the stability of the feature importance.
 
 \subsection{Baseline}
-First, we now have a look at the results on featureset 1, i.e. the results gained by using each featuretype individually to predict the target. \autoref{tab:gender_f1_dist_500_baseline_individual} and \autoref{tab:age_f1_dist_500_baseline_individual} show the experimental results for targets \textit{gender} and \textit{age} respectively. Each row shows the result for a featuretype and the corresponding ngrams. The featuretypes themselves are sorted in an ascending order such that featuretypes in lower rows capture more context, e.g. the type CHAR (characters) captures, in principle, less contextual information, such as topic or structural information, when compared to, for example, word-based ngrams \parencite{Rocha2017AuthorshipForensics}.
-Naturally, when the ngram-window is increased, e.g. for character-based features from 2 to 4, the character-ngrams also start to capture contextual information.
-Consequently, also the ngram-combinations within the individual featuretypes are sorted in an ascending fashion.\\
+First, we now take a look at the results in feature set 1, i.e., the results gained when using each feature type individually to predict the target. \autoref{tab:gender_f1_dist_500_baseline_individual} and \autoref{tab:age_f1_dist_500_baseline_individual} show the experimental results for targets \textit{gender} and \textit{age}, respectively. Each row shows the result for a feature type and the corresponding n-grams. The feature types themselves are sorted in an ascending order such that feature types in lower rows capture more context. The type CHAR (characters), for example, captures, in principle, less contextual information (such as topic or structural information) compared to, for example, word-based n-grams \parencite{Rocha2017AuthorshipForensics}.
+Naturally, when the n-gram window is increased, e.g., for character-based features from 2 to 4, the character n-grams also start to capture contextual information.
+Consequently, the n-gram combinations within the individual feature types are also sorted in an ascending fashion.\\
 
 \input{tables/gender/latex/results_gender_individual_baseline_500_f1_dist.tex}
 
-Looking at results for the target \textit{gender} we first find that the most predictive featuretypes are those most closely related to the words but not necessarily the structure of the text. That can be seen from the fact that the latter is captured by POS, TAG, and DEP which show low predictive power no matter the number of authors within the subset. 
-Moreover, already CHAR-2-grams perform well ($F1_{50}^{CHAR-2}: 0.80$) on the small dataset comprised of 50 authors. However, when we increase the number of authors, the performance declines markedly ($F1_{1000}^{CHAR-5}: 0.67$), especially when compared to CHAR-(2,5)-grams ($F1_{1000}^{CHAR-5}:0.741$) which are close to the top performance ($F1_{1000}^{ASIS-5}: 0.748$). The same pattern, although on a lower overall performance level, is visible for the text-distortion features DIST capturing punctuation and other stylistic markers.
-For lower ngram-sizes, the performance is only negligibly above or below the random guess threshold while for higher ngrams the performance is higher ($F1_{50}^{DIST-5}: 0.67$) but then decreases again in the number of authors. Consequently, the results show that there seems little cause to think that there are patterns in the style of authors related to gender.
-On the other side, CHAR-2-grams have a reliable performance ($0.67 < F1^{CHAR-2} <0.80$) and increasing the ngram window only by 1 increases performance markedly in turn. Consequently, it can be assumed that the there seems to be a discerable pattern related to gender within the character-combinations used. The underlying assumption would be that certain topics might be reflected by the use similar words or that certain synonyms are preferred by one group over the other. 
-We can compare this with the result for the WORD-grams. Here we see, that while we the performance is high, it is still worse when compared to CHAR-(2,5)-grams. The latter would also capture words up to five characters long. However, if that overlap would be the sole driver of performance, then WORD-grams should not be outperformed. As such, we can conclude that there is a discernible pattern related to gender in low-context CHAR-ngrams.
-In terms of the stability of the feature importance, the results are sobering. As in the aggregate before, the correlation tends towards zero when increasing the number of authors. Besides that, in some cases the correlation even flips signs. That implies features which were useful for predicting group A before are now either irrelevant or relevant for predicting group B (see for example \autoref{tab:gender_f1_dist_500_baseline_individual}, $\rho_{500}^{POS-2}: -0.15$). While mostly small, all correlation coefficients are significant on the 1\%-level.\\
+Looking at results for the target \textit{gender}, we first find that the most predictive feature types are those most closely related to the words of the text, but not necessarily the structure. That can be seen from the fact that structure-capturing feature types, such as POS, TAG, and DEP, show low predictive power no matter what the number of authors within the subset is. 
+Moreover, CHAR-2-grams already perform well ($F1_{50}^{CHAR-2}: 0.80$) on the small dataset comprised of 50 authors. However, when we increase the number of authors, the performance declines markedly ($F1_{1000}^{CHAR-5}: 0.67$), especially when compared to CHAR-(2,5)-grams ($F1_{1000}^{CHAR-5}:0.741$), which are close to the top performance ($F1_{1000}^{ASIS-5}: 0.748$). The same pattern, although on a lower overall performance level, is visible for the text distortion features DIST capturing punctuation and other stylistic markers.
+For lower n-gram sizes, the performance is only negligibly above or below the random guess threshold, while for higher n-grams the performance is higher ($F1_{50}^{DIST-5}: 0.67$), but then decreases again in the number of authors. Consequently, the results show that there seems little cause to think that there are patterns in the style of authors related to gender.
+On the other side, CHAR-2-grams have a reliable performance ($0.67 < F1^{CHAR-2} <0.80$); increasing the n-gram window only by 1 increases performance even more. Consequently, it can be assumed that the there seems to be a discernible pattern related to gender within the character combinations used. The underlying assumption would be that certain topics might be reflected by the use of similar words or that certain synonyms are preferred by one group over the other. 
+We can compare this with the result for the WORD-grams. Here we see that, while we the performance is high, it is still worse when compared to CHAR-(2,5)-grams. The latter would also capture words up to five characters long. However, if that overlap is the sole driver of performance, then WORD-grams should not be outperformed. As such, we can conclude that there is a discernible pattern related to gender in low-context CHAR-n-grams.
+In terms of the stability of the feature importance, the results are sobering. As in the aggregate before, the correlation tends towards zero when increasing the number of authors. Besides that, in some cases the correlation even flips signs. That implies features which were useful for predicting group A before are now either relevant for neither group or relevant for predicting group B (see, for example, \autoref{tab:gender_f1_dist_500_baseline_individual}, $\rho_{500}^{POS-2}: -0.15$). While mostly small, all correlation coefficients are significant at the 1\%-level.\\
 
 When looking at age, the results shown in \autoref{tab:age_f1_dist_500_baseline_individual} reflect the overall findings for gender.
-Text distortion alone such as punctuation reflected in the features of type DIST does hold some but not the majority of the information relevant to the prediction of age. That is evident from the stark decline towards random-guess accuracy, especially for low-level-ngrams.
+Text distortion alone, such as punctuation reflected in the features of type DIST, does hold some, but not the majority of the information relevant to the prediction of age. That is evident from the stark decline towards random-guess accuracy, especially for low-level n-grams.
 
 \input{tables/age/latex/results_age_individual_baseline_500_f1_dist.tex}
 
-When combined with CHAR, then especially higher-order ngrams (which is reflected in the featuretype ASIS) hold the most information about an authors age. That seems to be in line with findings linking age to a higher adherence to linguistic rules, even in an online environment \parencite{DeJonge2012TextmessageStudents, Hovy2015TaggingAge}.
-However, also the content of the tweets seems to set the age categories apart, as illustrated by the fact that TAG alone has a relatively high predictive power even for the dataset comprised of 1000 authors (F1: 0.31). The same holds true for LEMMA implying that age groups are also set apart by the use of one set of words over another.
+When combined with CHAR, then especially higher-order n-grams (which is reflected in the feature type ASIS) hold the most information about an author's age. That seems to be in line with findings linking age to a higher adherence to linguistic rules, even in an online environment \parencite{DeJonge2012TextmessageStudents, Hovy2015TaggingAge}.
+However, the content of the tweets also seems to set the age categories apart, as illustrated by the fact that TAG alone has a relatively high predictive power even for the dataset comprised of 1000 authors (F1: 0.31). The same holds true for LEMMA, implying that age groups are also set apart by the use of one set of words over another.
 Here again, the feature stability is low, with a $ \rho \in [0, 0.05]$.\\
 
-Thus, we can conclude that for singular feature sets, the model is able to extract information from the features, especially those with higher context as evident from the increase in predictive performance when the n-gram range is increased.
-However the relevant information is not stable in the number of authors which means that additional authors introduce a wider variation which needs to be separated differently than the smaller range.
-As the number of characters is limited overall (and thus the number of features in the lower n-gram range), that automatically implies that the the content and therefore the relevant features change. That seems to lead to an overall change in the way individual features are predictive. Thus, the rank correlation is low.
+Thus, we can conclude that, for singular feature sets, the model is able to extract information from the features, especially those with higher context, as evident from the increase in predictive performance when the n-gram range is increased.
+However, the relevant information is not stable in the number of authors, which means that additional authors introduce a wider variation, that needs to be separated differently than the smaller range.
+As the number of characters is limited overall (and thus the number of features in the lower n-gram range), that automatically implies that the  content and therefore the relevant features change. That seems to lead to an overall change in the way individual features are predictive. Thus, the rank correlation is low.
 
 \input{tables/gender/latex/results_gender_dwald_direct_500_f1_dist.tex}
 
 \subsection{Cumulated}
-The previous analysis has shown that some featuretypes, such as POS, TAG, DEP, and NUM do hold little relevant information. We therefore constructed a subset of featuretypes which excludes them. That subset includes the types ASIS, CHAR, LEMMA, and WORD.
-Compared to the previous analysis, we now give the model the possibility to include additional information, i.e. information stemming from different featuretypes, in the model.
+The previous analysis has shown that some feature types, such as POS, TAG, DEP, and NUM do hold little relevant information. We therefore constructed a subset of feature types which excludes them. That subset includes the types ASIS, CHAR, LEMMA, and WORD.
+Compared to the previous analysis, we now give the model the possibility to include additional information, i.e., information stemming from different feature types, in the model.
 As shown by the results in \autoref{tab:gender_f1_dist_500_direct_dwald} and \autoref{tab:age_f1_dist_500_direct_dwald}, the additional information yields to overall increase in performance. How much additional information leads to an improvement differs by target.
-For \textit{age}, we find that some additional contextual information increases the outcome. However, when the contextual information becomes larger, e.g. by including LEMMA and WORD, the result does not improve anymore. The result is consistent across a different number of authors. Consequently, the information for \textit{age} seems to be less reliant on contextual information and content. Already single-word content and context as captured by CHAR-(2,5) and ASIS-(2,5), is enough for a high prediction score. 
-When we compare the outcome for \textit{gender} with the the results in \autoref{tab:gender_f1_dist_500_baseline_individual}, we see that using a cumulated input improves the results overall. It is especially important to note that when faced with a high number of individual authors, increasing the context by using additional featuretypes such as LEMMA or WORD in addition to high-level ngrams increases performance.
-When taken together, our findings show that context and underlying data structure is an important driver behind the predictions of a model as shown by the fact that the relevant features in terms of predictiveness change. 
+For \textit{age}, we find that some additional contextual information increases the outcome. However, when the contextual information becomes larger, e.g., by including LEMMA and WORD, the result does not improve anymore. The result is consistent across a different number of authors. Consequently, the information for \textit{age} seems to be less reliant on contextual information and content. Already single-word content and context as captured by CHAR-(2,5) and ASIS-(2,5), is enough for a high prediction score. 
+When we compare the outcome for \textit{gender} with the the results in \autoref{tab:gender_f1_dist_500_baseline_individual}, we see that using a cumulated input improves the results overall. It is especially important to note that, when faced with a high number of individual authors, increasing the context by using additional feature types such as LEMMA or WORD in addition to high-level n-grams increases performance.
+When taken together, our findings show that context and underlying data structure is an important driver behind the predictions of a model, as shown by the fact that the relevant features in terms of predictiveness change. 
 At the same time, we show that the weight placed on individual features (and thus individual inputs reflecting certain contexts) is not stable.
 \input{tables/age/latex/results_age_dwald_direct_500_f1_dist.tex}
 That is evident by the correlation scores across different author sets. The scores are $\rho_{150|50}^{NUM}: 0.42$ at the highest for target gender and $\rho_{150|50}^{DEP-2}: -0.1$ at their lowest.
-Consequently, while the predictive accuracy is high, the model seems to rely on correlational patterns which are not only not invariant but also quite unstable when the dataset is changed only slightly.\footnote{All anaylsis -stacked as well as cumulated - was also done on the full number of featuretypes as well as the different numbers of authors and the different input lengths. The results may be found in the Appendix \autoref{sec:att_app_tables}}.
\ No newline at end of file
+Consequently, while the predictive accuracy is high, the model seems to rely on correlational patterns which are not only not invariant, but also quite unstable when the dataset is changed only slightly.\footnote{All analyses --stacked as well as cumulated-- were also done on the full number of feature types, as well as the different numbers of authors and the different input instance lengths. The results may be found in the \autoref{sec:att_app_tables}.}
\ No newline at end of file
diff --git a/tex/tables/age/html/results_age_dwald_direct_100_acc.html b/tex/tables/age/html/results_age_dwald_direct_100_acc.html
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diff --git a/tex/tables/age/html/results_age_dwald_direct_250_acc.html b/tex/tables/age/html/results_age_dwald_direct_250_acc.html
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diff --git a/tex/tables/age/html/results_age_dwald_direct_250_dist.html b/tex/tables/age/html/results_age_dwald_direct_250_dist.html
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diff --git a/tex/tables/age/html/results_age_dwald_direct_500_acc.html b/tex/tables/age/html/results_age_dwald_direct_500_acc.html
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diff --git a/tex/tables/age/html/results_age_dwald_dynAA_100_acc.html b/tex/tables/age/html/results_age_dwald_dynAA_100_acc.html
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diff --git a/tex/tables/age/html/results_age_full_direct_100_f1_dist.html b/tex/tables/age/html/results_age_full_direct_100_f1_dist.html
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diff --git a/tex/tables/age/html/results_age_full_direct_250_f1_dist.html b/tex/tables/age/html/results_age_full_direct_250_f1_dist.html
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       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_direct_500_acc.html b/tex/tables/age/html/results_age_full_direct_500_acc.html
index 0da3809417de02b147e1067fd7b42a5bac0e7f37..bf2d1e907e9bda28fb20f411d98a17e4c38596c7 100644
--- a/tex/tables/age/html/results_age_full_direct_500_acc.html
+++ b/tex/tables/age/html/results_age_full_direct_500_acc.html
@@ -12,7 +12,7 @@
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     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_direct_500_dist.html b/tex/tables/age/html/results_age_full_direct_500_dist.html
index aa628cf8b7fddd71f9f378db73d359288b87a88b..7770bc0c612c3f8d68c2fd2fea7a5279ae36090c 100644
--- a/tex/tables/age/html/results_age_full_direct_500_dist.html
+++ b/tex/tables/age/html/results_age_full_direct_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_full_direct_500_f1_dist.html b/tex/tables/age/html/results_age_full_direct_500_f1_dist.html
index cadce79f628022e2c27d7d96acd49dce7ccfbab5..db52dc73719e503c0b45796d6cca96b23bed65dc 100644
--- a/tex/tables/age/html/results_age_full_direct_500_f1_dist.html
+++ b/tex/tables/age/html/results_age_full_direct_500_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_100_acc.html b/tex/tables/age/html/results_age_full_dynAA_100_acc.html
index 39de89cf4ec5219fae8ea1d9cd4abaed9a2a9446..17f0fe2dcc025764eaad98f5b76a23523e5c7734 100644
--- a/tex/tables/age/html/results_age_full_dynAA_100_acc.html
+++ b/tex/tables/age/html/results_age_full_dynAA_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_100_dist.html b/tex/tables/age/html/results_age_full_dynAA_100_dist.html
index dc703d89d86bc77aa1078bf66363ff306a02d53b..9a7b65e5e4bd0c04e1b7ca86f48051aef27231b0 100644
--- a/tex/tables/age/html/results_age_full_dynAA_100_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_100_f1_dist.html b/tex/tables/age/html/results_age_full_dynAA_100_f1_dist.html
index c04978e96689f0f771a24883e50bd481ba6c041e..2e0a70eaf509d42d7e0dbd661748db8b0c4f6fd9 100644
--- a/tex/tables/age/html/results_age_full_dynAA_100_f1_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_100_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_250_acc.html b/tex/tables/age/html/results_age_full_dynAA_250_acc.html
index 8046b7bebcf57877f5731ee017278f294159c784..3f968110a68654feacc729970eeb6f82c226ca2e 100644
--- a/tex/tables/age/html/results_age_full_dynAA_250_acc.html
+++ b/tex/tables/age/html/results_age_full_dynAA_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_250_dist.html b/tex/tables/age/html/results_age_full_dynAA_250_dist.html
index f1c118a0106d6da998b3f5b50092a0e2ffaa04d1..c31aed6aed468cdab7dc08025e99768d5c4c28b1 100644
--- a/tex/tables/age/html/results_age_full_dynAA_250_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_250_f1_dist.html b/tex/tables/age/html/results_age_full_dynAA_250_f1_dist.html
index 0e62012257e0f45cb46d624034b047749e0e4693..10db2860560d38f9bd81638b3c3471cd6f118f72 100644
--- a/tex/tables/age/html/results_age_full_dynAA_250_f1_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_250_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_500_acc.html b/tex/tables/age/html/results_age_full_dynAA_500_acc.html
index d10a875a7a1ab89eb9f43e26d36723e2548f6f95..68face7a8638a0506144425f8fbe8481f59daede 100644
--- a/tex/tables/age/html/results_age_full_dynAA_500_acc.html
+++ b/tex/tables/age/html/results_age_full_dynAA_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_500_dist.html b/tex/tables/age/html/results_age_full_dynAA_500_dist.html
index 633417deeb43423aaa88cfffe9a3bc00fd974771..a0906c80d58cf44d3b1eca707adbb56e260c0739 100644
--- a/tex/tables/age/html/results_age_full_dynAA_500_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_full_dynAA_500_f1_dist.html b/tex/tables/age/html/results_age_full_dynAA_500_f1_dist.html
index d07003549b0ccd62197a8433fc76cd62affae2b1..27e5d3514402012a23f30341dd549c6d57122581 100644
--- a/tex/tables/age/html/results_age_full_dynAA_500_f1_dist.html
+++ b/tex/tables/age/html/results_age_full_dynAA_500_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_100_acc.html b/tex/tables/age/html/results_age_individual_baseline_100_acc.html
index 70b41b80d588a673fb3e9b75ecff90a57eb29ba0..1101433d1428f0f8b29acc8d167c38c84ca24455 100644
--- a/tex/tables/age/html/results_age_individual_baseline_100_acc.html
+++ b/tex/tables/age/html/results_age_individual_baseline_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_100_dist.html b/tex/tables/age/html/results_age_individual_baseline_100_dist.html
index c9e5bb3cbafd4448d8cf495a174e1f4fa52d051a..bc59e5069c5504297192541b2faf29e02056711c 100644
--- a/tex/tables/age/html/results_age_individual_baseline_100_dist.html
+++ b/tex/tables/age/html/results_age_individual_baseline_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_250_acc.html b/tex/tables/age/html/results_age_individual_baseline_250_acc.html
index de66eda0d5ca2abde62ec6b22d536b2ddf0c395a..6a98dafb299c52625c9a77d32be517b8fba79f97 100644
--- a/tex/tables/age/html/results_age_individual_baseline_250_acc.html
+++ b/tex/tables/age/html/results_age_individual_baseline_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_250_dist.html b/tex/tables/age/html/results_age_individual_baseline_250_dist.html
index 854dde04d44e6e6305b264fa8fe12010e510ebfa..8f2114cf3b357b98dc6f3841990cf4d002c4432d 100644
--- a/tex/tables/age/html/results_age_individual_baseline_250_dist.html
+++ b/tex/tables/age/html/results_age_individual_baseline_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_500_acc.html b/tex/tables/age/html/results_age_individual_baseline_500_acc.html
index 83be911693e7f5e8f3788f01ca7c0fd656d4bb3e..a2aa7168a736b3836754e493670e3c433e812647 100644
--- a/tex/tables/age/html/results_age_individual_baseline_500_acc.html
+++ b/tex/tables/age/html/results_age_individual_baseline_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/age/html/results_age_individual_baseline_500_dist.html b/tex/tables/age/html/results_age_individual_baseline_500_dist.html
index 40573960b126206edbb1b4c9f10b02b17fa02175..ff04b864f12ca765a5e0f5b8f5837de5b765781c 100644
--- a/tex/tables/age/html/results_age_individual_baseline_500_dist.html
+++ b/tex/tables/age/html/results_age_individual_baseline_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/age/latex/results_age_dwald_direct_100_acc.tex b/tex/tables/age/latex/results_age_dwald_direct_100_acc.tex
index cf80b2dfd739912dd9c7b609f3c1ff091c6127e4..38745649d2f50d1181a6eeb951388b55d04620f7 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_100_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_100_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5983 &   0.5994 &   0.4887 &   0.4861 &   0.3739 &   0.3555 &   0.3088 &   0.2673 \\
                           & 2-3 &   0.6907 &   0.6908 &   0.5595 &   0.5583 &   0.4424 &   0.4382 &   0.3888 &   0.3706 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_100_dist.tex b/tex/tables/age/latex/results_age_dwald_direct_100_dist.tex
index 9631a1d864a8f99289dc984df2aacd0c0c0fc8c8..487187b616efeb3db29527722cbfbd82d99b2696 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_100_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 100 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_100_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0348 &                       -0.0149 &                        0.0099 &                        0.0315 &                       -0.0141 &                        0.0174 \\
                           & 2-3 &                       -0.0051 &                       -0.0070 &                       -0.0063 &                       -0.0108 &                        0.0126 &                        0.0458 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_250_acc.tex b/tex/tables/age/latex/results_age_dwald_direct_250_acc.tex
index cef3e89cb25e1bd79ebc64eec327d7cd0a04d65a..07ab016488214614f87d49f2dde652ad19746d09 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_250_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_250_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.6772 &   0.6770 &   0.5772 &   0.5772 &   0.4543 &   0.4439 &   0.3928 &   0.3686 \\
                           & 2-3 &   0.7627 &   0.7629 &   0.6684 &   0.6689 &   0.5239 &   0.5115 &   0.4643 &   0.4581 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_250_dist.tex b/tex/tables/age/latex/results_age_dwald_direct_250_dist.tex
index 234e13533644653abf2eb54c052eee3b728f2307..3d92ba1a8de69953db6d2e670d193f1f015bcb87 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_250_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 250 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_250_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0416 &                        0.1495 &                        0.0072 &                        0.0051 &                        0.0153 &                        0.0489 \\
                           & 2-3 &                       -0.0223 &                        0.0252 &                       -0.0132 &                        0.0114 &                        0.0086 &                       -0.0058 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_500_acc.tex b/tex/tables/age/latex/results_age_dwald_direct_500_acc.tex
index d5dfb5b0646094db64daf011cf526b98d571fb42..47a167603d9211165822012c8ef9e381306ff905 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_500_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_500_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7262 &   0.7271 &   0.6350 &   0.6349 &   0.5133 &   0.5123 &   0.4465 &   0.4434 \\
                           & 2-3 &   0.8192 &   0.8214 &   0.7342 &   0.7331 &   0.5938 &   0.5898 &   0.5199 &   0.5177 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_500_dist.tex b/tex/tables/age/latex/results_age_dwald_direct_500_dist.tex
index d243a290432e26774f66040667c52a11b4851d3c..6662db7652b3c483b1a9558fd443d1d9135b1d62 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_500_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 500 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_500_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1872 &                        0.0727 &                        0.0083 &                        0.0109 &                        0.0192 &                        0.0501 \\
                           & 2-3 &                        0.1142 &                        0.0359 &                        0.0275 &                        0.0294 &                        0.0478 &                        0.0012 \\
diff --git a/tex/tables/age/latex/results_age_dwald_direct_500_f1_dist.tex b/tex/tables/age/latex/results_age_dwald_direct_500_f1_dist.tex
index 30a2c8170f7fb2e425ee70593092a13b7153f1a7..6cdf8451dce2a5c6b448e2ba6496af13aeff8ab4 100644
--- a/tex/tables/age/latex/results_age_dwald_direct_500_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_direct_500_f1_dist.tex
@@ -10,7 +10,7 @@
                           & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
                           & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+feature types & subgrams &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7271 &                        -- &   0.6349 &                        0.1366 &   0.5123 &                        0.0244 &   0.4434 &                        0.0048 \\
                           & 2-3 &   0.8214 &                        -- &   0.7331 &                        0.1364 &   0.5898 &                        0.0286 &   0.5177 &                        0.0117 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_100_acc.tex b/tex/tables/age/latex/results_age_dwald_dynAA_100_acc.tex
index be329f166becfdf91343b1e4057bce905560d8c3..b3c48da673704d0b5bc32103058a00031211a048 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_100_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_100_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5814 &   0.5806 &   0.4549 &   0.4544 &   0.2092 &   0.0892 &   0.2029 &   0.0707 \\
                           & 2-3 &   0.6560 &   0.6565 &   0.5434 &   0.5426 &   0.4313 &   0.4281 &   0.2607 &   0.1626 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_100_dist.tex b/tex/tables/age/latex/results_age_dwald_dynAA_100_dist.tex
index 745621fe593337beeb4b85fe077fa5906e9ffdbd..0a03a59195bc641a5c7cdb145c2e8349c1568b3e 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_100_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 100 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_100_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                       -0.0261 &                       -0.0180 &                       -0.0041 &                        0.0083 &                        0.0156 &                        0.0209 \\
                           & 2-3 &                       -0.0058 &                       -0.0031 &                       -0.0170 &                       -0.0032 &                        0.0285 &                        0.0297 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_250_acc.tex b/tex/tables/age/latex/results_age_dwald_dynAA_250_acc.tex
index 7140d3320db6c4a372c0cc9646e141c5c29a5e76..c98035f28aedabe5ea10a7c998744636a6c54165 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_250_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_250_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.6667 &   0.6672 &   0.5623 &   0.5621 &   0.4216 &   0.4205 &   0.3466 &   0.3196 \\
                           & 2-3 &   0.7480 &   0.7484 &   0.6506 &   0.6498 &   0.5183 &   0.5180 &   0.4535 &   0.4513 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_250_dist.tex b/tex/tables/age/latex/results_age_dwald_dynAA_250_dist.tex
index ed99abf76a3f7204db7d70a807b82e0fbde8633d..35eba19105e7e66a8d62124037f3125c86038312 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_250_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 250 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_250_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0132 &                       -0.0179 &                       -0.0008 &                        0.0021 &                        0.0057 &                        0.0131 \\
                           & 2-3 &                        0.0076 &                       -0.0155 &                        0.0017 &                        0.0131 &                        0.0161 &                        0.0122 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_500_acc.tex b/tex/tables/age/latex/results_age_dwald_dynAA_500_acc.tex
index 94252dd35f9937f66ab671394bc3dd5b40393c39..37959d56a1d0bd9d182710afa9129de83060de37 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_500_acc.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_acc_500_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7413 &   0.7422 &   0.6347 &   0.6350 &   0.4899 &   0.4848 &   0.4263 &   0.4239 \\
                           & 2-3 &   0.8250 &   0.8252 &   0.7357 &   0.7356 &   0.5868 &   0.5840 &   0.5140 &   0.5114 \\
diff --git a/tex/tables/age/latex/results_age_dwald_dynAA_500_dist.tex b/tex/tables/age/latex/results_age_dwald_dynAA_500_dist.tex
index 3fb342b85cdb6bdb33e130ac8969ef13c901ba74..211f7b30327ce88b70b8bd5c614ae4548744b413 100644
--- a/tex/tables/age/latex/results_age_dwald_dynAA_500_dist.tex
+++ b/tex/tables/age/latex/results_age_dwald_dynAA_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 500 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Age_dist_500_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{age} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Age} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0733 &                        0.0228 &                        0.0064 &                        0.0045 &                       -0.0029 &                        0.0157 \\
                           & 2-3 &                        0.0645 &                       -0.0005 &                        0.0075 &                        0.0223 &                       -0.0016 &                        0.0015 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_100_acc.tex b/tex/tables/age/latex/results_age_full_direct_100_acc.tex
index 2560b4b4e5d78442ff24dcaa88b79e4da0f537f1..fb1be0c5194e1b8fed3d7a46049d534af3463292 100644
--- a/tex/tables/age/latex/results_age_full_direct_100_acc.tex
+++ b/tex/tables/age/latex/results_age_full_direct_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_acc_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5983 &   0.5994 &   0.4887 &   0.4861 &   0.3739 &   0.3555 &   0.3088 &   0.2673 \\
                                       & 2-3 &   0.6907 &   0.6908 &   0.5595 &   0.5583 &   0.4424 &   0.4382 &   0.3888 &   0.3706 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_100_dist.tex b/tex/tables/age/latex/results_age_full_direct_100_dist.tex
index a0c98a5f6e6c3dd5888ae788fd78f85f72fef9e9..272fccd7c05db0b23137fb7c3a02b2c046546d39 100644
--- a/tex/tables/age/latex/results_age_full_direct_100_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_dist_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0348 &                       -0.0149 &                        0.0099 &                        0.0315 &                       -0.0141 &                        0.0174 \\
                                       & 2-3 &                       -0.0051 &                       -0.0070 &                       -0.0063 &                       -0.0108 &                        0.0126 &                        0.0458 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_100_f1_dist.tex b/tex/tables/age/latex/results_age_full_direct_100_f1_dist.tex
index b87ad67f054cda71c06103fcee794dccf4232eef..f7f88c5d1093a7dab645b46e6af4ddf93959d6b4 100644
--- a/tex/tables/age/latex/results_age_full_direct_100_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_100_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 100 characters using a cumulated
-                                 model on the ordered, full Featureset}
-\label{tab:age_f1_dist_100_direct_full}
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Age_f1_dist_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5994 &                        -- &   0.4861 &                        0.0013 &   0.3555 &                        0.0077 &   0.2673 &                        0.0047 \\
                                       & 2-3 &   0.6908 &                        -- &   0.5583 &                        0.0064 &   0.4382 &                       -0.0084 &   0.3706 &                        0.0196 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_250_acc.tex b/tex/tables/age/latex/results_age_full_direct_250_acc.tex
index 49ee6c960488c673e4d42c45bc8e50f8428bf316..4e712609ca2f447fd7e32a5dfe3233f5a1a4c8e7 100644
--- a/tex/tables/age/latex/results_age_full_direct_250_acc.tex
+++ b/tex/tables/age/latex/results_age_full_direct_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_acc_250_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.6772 &   0.6770 &   0.5772 &   0.5772 &   0.4543 &   0.4439 &   0.3928 &   0.3686 \\
                                       & 2-3 &   0.7627 &   0.7629 &   0.6684 &   0.6689 &   0.5239 &   0.5115 &   0.4643 &   0.4581 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_250_dist.tex b/tex/tables/age/latex/results_age_full_direct_250_dist.tex
index 99dd3f1a054887e82522d078bb6944b4720677b2..a9cf72be0330970603d15016f2d9db665a1b3a0c 100644
--- a/tex/tables/age/latex/results_age_full_direct_250_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_dist_250_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0416 &                        0.1495 &                        0.0072 &                        0.0051 &                        0.0153 &                        0.0489 \\
                                       & 2-3 &                       -0.0223 &                        0.0252 &                       -0.0132 &                        0.0114 &                        0.0086 &                       -0.0058 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_250_f1_dist.tex b/tex/tables/age/latex/results_age_full_direct_250_f1_dist.tex
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..51cfc2bbfbde5eaebfcf48ecc29b7e760eda9447 100644
--- a/tex/tables/age/latex/results_age_full_direct_250_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_250_f1_dist.tex
@@ -0,0 +1,45 @@
+\begin{table}[!ht]
+\centering
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Age_f1_dist_250_direct_full}
+\begin{adjustbox}{max width=\textwidth}
+\begin{tabular}{llcccccccc}
+\toprule
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
+\midrule
+\multirow{4}{*}{DIST\_CHAR} & 2 &   0.6770 &                        -- &   0.5772 &                        0.0620 &   0.4439 &                        0.0122 &   0.3686 &                        0.0181 \\
+                                      & 2-3 &   0.7629 &                        -- &   0.6689 &                        0.0686 &   0.5115 &                        0.0189 &   0.4581 &                        0.0152 \\
+                                      & 2-3-4 &   0.7555 &                        -- &   0.6732 &                        0.0693 &   0.5440 &                        0.0045 &   0.4795 &                        0.0072 \\
+                                      & 2-3-4-5 &   0.7413 &                        -- &   0.6848 &                        0.0496 &   0.5361 &                        0.0093 &   0.4875 &                       -0.0054 \\
+\cline{1-10}
+\multirow{4}{*}{DIST\_CHAR\_ASIS} & 2 &   0.7751 &                        -- &   0.6996 &                        0.0390 &   0.5567 &                        0.0187 &   0.4712 &                        0.0050 \\
+                                      & 2-3 &   0.7920 &                        -- &   0.7067 &                        0.0346 &   0.5705 &                        0.0025 &   0.4942 &                        0.0027 \\
+                                      & 2-3-4 &   0.7996 &                        -- &   0.7131 &                        0.0333 &   0.5736 &                        0.0004 &   0.4902 &                        0.0168 \\
+                                      & 2-3-4-5 &   0.7941 &                        -- &   0.7071 &                        0.0450 &   0.5845 &                       -0.0004 &   0.5020 &                        0.0206 \\
+\cline{1-10}
+\multirow{3}{*}{DIST\_CHAR\_ASIS\_POS} & 1 &   0.7611 &                        -- &   0.6963 &                        0.0487 &   0.5785 &                        0.0226 &   0.4899 &                        0.0268 \\
+                                      & 1-2 &   0.7846 &                        -- &   0.7104 &                        0.0371 &   0.5838 &                        0.0278 &   0.4921 &                        0.0101 \\
+                                      & 1-2-3 &   0.7854 &                        -- &   0.7056 &                        0.0359 &   0.5842 &                        0.0253 &   0.4968 &                        0.0106 \\
+\cline{1-10}
+\multirow{3}{*}{DIST\_CHAR\_ASIS\_POS\_TAG} & 1 &   0.7851 &                        -- &   0.7068 &                        0.0467 &   0.5713 &                        0.0249 &   0.4799 &                        0.0238 \\
+                                      & 1-2 &   0.7764 &                        -- &   0.7016 &                        0.0567 &   0.5649 &                        0.0192 &   0.4960 &                        0.0132 \\
+                                      & 1-2-3 &   0.7829 &                        -- &   0.7012 &                        0.0444 &   0.5635 &                        0.0295 &   0.4999 &                        0.0359 \\
+\cline{1-10}
+\multirow{3}{*}{DIST\_CHAR\_ASIS\_POS\_TAG\_DEP} & 1 &   0.7747 &                        -- &   0.6970 &                        0.0169 &   0.5749 &                        0.0155 &   0.4950 &                        0.0337 \\
+                                      & 1-2 &   0.7721 &                        -- &   0.6960 &                        0.0212 &   0.5829 &                        0.0235 &   0.4925 &                        0.0254 \\
+                                      & 1-2-3 &   0.7825 &                        -- &   0.6980 &                        0.0463 &   0.5815 &                        0.0254 &   0.5003 &                        0.0086 \\
+\cline{1-10}
+\multirow{2}{*}{DIST\_CHAR\_ASIS\_POS\_TAG\_DEP\_LEMMA} & 1 &   0.7668 &                        -- &   0.6956 &                        0.0495 &   0.5709 &                        0.0171 &   0.5031 &                        0.0171 \\
+                                      & 1-2 &   0.7647 &                        -- &   0.7019 &                        0.0392 &   0.5673 &                        0.0085 &   0.5060 &                        0.0258 \\
+\cline{1-10}
+\multirow{2}{*}{DIST\_CHAR\_ASIS\_POS\_TAG\_DEP\_LEMMA\_WORD} & 1 &   0.7674 &                        -- &   0.6883 &                        0.0342 &   0.5821 &                        0.0089 &   0.5042 &                        0.0285 \\
+                                      & 1-2 &   0.7692 &                        -- &   0.6875 &                        0.0310 &   0.5842 &                        0.0136 &   0.5019 &                        0.0133 \\
+\bottomrule
+
+\end{tabular}\end{adjustbox}
+\end{table}
diff --git a/tex/tables/age/latex/results_age_full_direct_500_acc.tex b/tex/tables/age/latex/results_age_full_direct_500_acc.tex
index d1053b035c5de51163d8f35592ae2080166a03d7..077d435d133c09d963b158d953023bef325be700 100644
--- a/tex/tables/age/latex/results_age_full_direct_500_acc.tex
+++ b/tex/tables/age/latex/results_age_full_direct_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_acc_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7262 &   0.7271 &   0.6350 &   0.6349 &   0.5133 &   0.5123 &   0.4465 &   0.4434 \\
                                       & 2-3 &   0.8192 &   0.8214 &   0.7342 &   0.7331 &   0.5938 &   0.5898 &   0.5199 &   0.5177 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_500_dist.tex b/tex/tables/age/latex/results_age_full_direct_500_dist.tex
index 7b4f220da1a6d3f845d8d3d2ef8ccf05fceb9ecf..55c94305bdc5e1f13147e849cc7e5ead9718a609 100644
--- a/tex/tables/age/latex/results_age_full_direct_500_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Age_dist_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1872 &                        0.0727 &                        0.0083 &                        0.0109 &                        0.0192 &                        0.0501 \\
                                       & 2-3 &                        0.1142 &                        0.0359 &                        0.0275 &                        0.0294 &                        0.0478 &                        0.0012 \\
diff --git a/tex/tables/age/latex/results_age_full_direct_500_f1_dist.tex b/tex/tables/age/latex/results_age_full_direct_500_f1_dist.tex
index 3d5d44e1e9f8e748b9749019d210fd8890d1c822..f636811cfe63b4e38a3748abfbb674566e2f90c8 100644
--- a/tex/tables/age/latex/results_age_full_direct_500_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_direct_500_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 500 characters using a cumulated
-                                 model on the ordered, full Featureset}
-\label{tab:age_f1_dist_500_direct_full}
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Age_f1_dist_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7271 &                        -- &   0.6349 &                        0.1366 &   0.5123 &                        0.0244 &   0.4434 &                        0.0048 \\
                                       & 2-3 &   0.8214 &                        -- &   0.7331 &                        0.1364 &   0.5898 &                        0.0286 &   0.5177 &                        0.0117 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_100_acc.tex b/tex/tables/age/latex/results_age_full_dynAA_100_acc.tex
index b457857dc214093eb8e7d0f3e3a68c5f8e583056..3a1fc321c042d31d10b01a56c8e0645daef80ecb 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_100_acc.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_acc_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5814 &   0.5806 &   0.4549 &   0.4544 &   0.2092 &   0.0892 &   0.2029 &   0.0707 \\
                                       & 2-3 &   0.6560 &   0.6565 &   0.5434 &   0.5426 &   0.4313 &   0.4281 &   0.2607 &   0.1626 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_100_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_100_dist.tex
index 6d510b6a735aaed1d3d317932f82345524a88a28..5c5ef6143269fdf5e0ab71680448407c4fa11e38 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_100_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_dist_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                       -0.0261 &                       -0.0180 &                       -0.0041 &                        0.0083 &                        0.0156 &                        0.0209 \\
                                       & 2-3 &                       -0.0058 &                       -0.0031 &                       -0.0170 &                       -0.0032 &                        0.0285 &                        0.0297 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_100_f1_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_100_f1_dist.tex
index d9e9d63a21ccc7cf05201aece753cfde7cc88228..cb2101ef8806dcc929f9e96b77ea60d88efb0400 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_100_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_100_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 100 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:age_f1_dist_100_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
+\label{tab:Age_f1_dist_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.5806 &                        -- &   0.4544 &                       -0.0261 &   0.0892 &                       -0.0041 &   0.0707 &                        0.0156 \\
                                       & 2-3 &   0.6565 &                        -- &   0.5426 &                       -0.0058 &   0.4281 &                       -0.0170 &   0.1626 &                        0.0285 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_250_acc.tex b/tex/tables/age/latex/results_age_full_dynAA_250_acc.tex
index 40e0aab870767fa53129fffc23aa17296d8fe63b..04561712e7d6c81300be10900977fa7c1d47cb10 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_250_acc.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_acc_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.6667 &   0.6672 &   0.5623 &   0.5621 &   0.4216 &   0.4205 &   0.3466 &   0.3196 \\
                                       & 2-3 &   0.7480 &   0.7484 &   0.6506 &   0.6498 &   0.5183 &   0.5180 &   0.4535 &   0.4513 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_250_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_250_dist.tex
index 327dd534b74a0abe2c3b9578bf08832daf721df1..13fc8c80ad654facd98cbf1bd1e6b4ebe595e2ed 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_250_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_dist_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0132 &                       -0.0179 &                       -0.0008 &                        0.0021 &                        0.0057 &                        0.0131 \\
                                       & 2-3 &                        0.0076 &                       -0.0155 &                        0.0017 &                        0.0131 &                        0.0161 &                        0.0122 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_250_f1_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_250_f1_dist.tex
index 702864d488afaf561bcbe800e9fa2d13ea3587f0..08927844b31ddc010947414c1f384d6b85297ab9 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_250_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_250_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 250 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:age_f1_dist_250_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
+\label{tab:Age_f1_dist_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.6672 &                        -- &   0.5621 &                        0.0132 &   0.4205 &                       -0.0008 &   0.3196 &                        0.0057 \\
                                       & 2-3 &   0.7484 &                        -- &   0.6498 &                        0.0076 &   0.5180 &                        0.0017 &   0.4513 &                        0.0161 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_500_acc.tex b/tex/tables/age/latex/results_age_full_dynAA_500_acc.tex
index c820f92f3417f527acac6ef37ee9b8ea7c3ac552..ed01d9fed17c6ab8a9502a3091a044530fd422a8 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_500_acc.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_acc_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7413 &   0.7422 &   0.6347 &   0.6350 &   0.4899 &   0.4848 &   0.4263 &   0.4239 \\
                                       & 2-3 &   0.8250 &   0.8252 &   0.7357 &   0.7356 &   0.5868 &   0.5840 &   0.5140 &   0.5114 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_500_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_500_dist.tex
index 810f6c23c554e111baa93eaad0771cf7c609bf80..75c1445ed5ba9bda0c99a90dc49a30fde5ffece4 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_500_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
 \label{tab:Age_dist_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0733 &                        0.0228 &                        0.0064 &                        0.0045 &                       -0.0029 &                        0.0157 \\
                                       & 2-3 &                        0.0645 &                       -0.0005 &                        0.0075 &                        0.0223 &                       -0.0016 &                        0.0015 \\
diff --git a/tex/tables/age/latex/results_age_full_dynAA_500_f1_dist.tex b/tex/tables/age/latex/results_age_full_dynAA_500_f1_dist.tex
index 6b39e7c396c46bb6eda536d98f45799a14518a1e..c84c1efda7f39514395ad01a10dd9c915dee6a53 100644
--- a/tex/tables/age/latex/results_age_full_dynAA_500_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_full_dynAA_500_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 500 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:age_f1_dist_500_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
+\label{tab:Age_f1_dist_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{age} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Age} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7422 &                        -- &   0.6350 &                        0.0733 &   0.4848 &                        0.0064 &   0.4239 &                       -0.0029 \\
                                       & 2-3 &   0.8252 &                        -- &   0.7356 &                        0.0645 &   0.5840 &                        0.0075 &   0.5114 &                       -0.0016 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_100_acc.tex b/tex/tables/age/latex/results_age_individual_baseline_100_acc.tex
index 454022be43767eb3cab62d8112caf6d55b0b85dd..3ccf2ca6204d811bf3419550daccb2da1c12687c 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_100_acc.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 100 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_acc_100_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.3648 &   0.3249 &   0.2771 &   0.2136 &   0.2645 &   0.2207 &   0.2114 &   0.1008 \\
     & 2-3 &   0.3689 &   0.3502 &   0.3350 &   0.3266 &   0.2162 &   0.1061 &   0.2082 &   0.0899 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_100_dist.tex b/tex/tables/age/latex/results_age_individual_baseline_100_dist.tex
index ac982bf27df63fdd749bc09e7d77aae250b2bdf5..2de8fe7d88d1ad61ac41341b996173bf10c1a2dc 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_100_dist.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 100 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 100 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_dist_100_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0348 &                       -0.0149 &                        0.0099 &                        0.0315 &                       -0.0141 &                        0.0174 \\
     & 2-3 &                       -0.0051 &                       -0.0070 &                       -0.0063 &                       -0.0108 &                        0.0126 &                        0.0458 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_250_acc.tex b/tex/tables/age/latex/results_age_individual_baseline_250_acc.tex
index 3c951736737eac2717ae7c9f4d38b24e6e354e38..272317bd9590c2e63cd28cf54c959f6aee7d6c09 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_250_acc.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 250 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_acc_250_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.3910 &   0.3670 &   0.2839 &   0.2198 &   0.2414 &   0.1601 &   0.2796 &   0.2179 \\
     & 2-3 &   0.4489 &   0.4159 &   0.3855 &   0.3728 &   0.2272 &   0.1273 &   0.3201 &   0.3105 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_250_dist.tex b/tex/tables/age/latex/results_age_individual_baseline_250_dist.tex
index a3ebeb234fc089f923f5b50286cb68de607c44f0..784057d536b6d21a5998ac8466142d92b0cd56fa 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_250_dist.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 250 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 250 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_dist_250_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0416 &                        0.1495 &                        0.0072 &                        0.0051 &                        0.0153 &                        0.0489 \\
     & 2-3 &                       -0.0223 &                        0.0252 &                       -0.0132 &                        0.0114 &                        0.0086 &                       -0.0058 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_500_acc.tex b/tex/tables/age/latex/results_age_individual_baseline_500_acc.tex
index 7856fd2bcba757a58de9d6defb1c45a8b5d22746..960bfb9d5d1e0296ec57dcdc599ec9dfe0e3ed10 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_500_acc.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of age on a minimal input instance
+                                        length of 500 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_acc_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.4831 &   0.4797 &   0.2924 &   0.2249 &   0.3224 &   0.2808 &   0.2819 &   0.2196 \\
     & 2-3 &   0.4969 &   0.4809 &   0.3987 &   0.3711 &   0.3089 &   0.2633 &   0.3029 &   0.2736 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_500_dist.tex b/tex/tables/age/latex/results_age_individual_baseline_500_dist.tex
index 18fab38cebdbb0a83016619574bd9dc06c2fa248..407396d50bb1d96301ece3ffa9e9cbdd4026603c 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_500_dist.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of age on a Minimal Instance Length of 500 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of age on a minimal input instance
+                                        length of 500 characters using a feature-wise model on the individual feature types}
 \label{tab:Age_dist_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{age} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Age} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1872 &                        0.0727 &                        0.0083 &                        0.0109 &                        0.0192 &                        0.0501 \\
     & 2-3 &                        0.1142 &                        0.0359 &                        0.0275 &                        0.0294 &                        0.0478 &                        0.0012 \\
diff --git a/tex/tables/age/latex/results_age_individual_baseline_500_f1_dist.tex b/tex/tables/age/latex/results_age_individual_baseline_500_f1_dist.tex
index 2b0e627956f5e78b9c0568dee3bb93f8237b6c28..57cdd60e840f78ae3b5a503c4c6a3fddf9ab475a 100644
--- a/tex/tables/age/latex/results_age_individual_baseline_500_f1_dist.tex
+++ b/tex/tables/age/latex/results_age_individual_baseline_500_f1_dist.tex
@@ -1,7 +1,7 @@
 \begin{table}
 \centering
 \caption{F1-Score and Average Distortion of Features for the Prediction of Age on a Minimal Tweet Length of 500 characters using a feature-wise
-                                 model on the individual  Featuretypes}
+                                 model on the individual  feature types}
 \label{tab:age_f1_dist_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
@@ -10,7 +10,7 @@
     & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
     & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+feature types & subgrams &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.4797 &                        -- &   0.2249 &                        0.1872 &   0.2808 &                        0.0083 &   0.2196 &                        0.0192 \\
     & 2-3 &   0.4809 &                        -- &   0.3711 &                        0.1142 &   0.2633 &                        0.0275 &   0.2736 &                        0.0478 \\
diff --git a/tex/tables/dataset.tex b/tex/tables/dataset.tex
index 2801499c4226b662eb027d6fff5b7d438ee8ab68..6e5b23157d4300b5ea656bc868a9110f3134403c 100644
--- a/tex/tables/dataset.tex
+++ b/tex/tables/dataset.tex
@@ -7,7 +7,7 @@
 \toprule
     & {} & \multicolumn{2}{l}{avg\_instance} & \multicolumn{2}{l}{avg\_tweet} & \multicolumn{2}{l}{avg\_tweet\_per\_instance} \\
     & Target &          age & gender &       age & gender &                    age & gender \\
-\# Chars in Input & \# Authors &              &        &           &        &                        &        \\
+No. of Characters & No. of Authors &              &        &           &        &                        &        \\
 \midrule
 \multirow{4}{*}{100} & 50   &       160.30 & 162.94 &    109.58 & 111.92 &                   1.46 &   1.46 \\
     & 150  &       160.52 & 162.37 &    107.38 & 112.94 &                   1.49 &   1.44 \\
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_100_acc.html b/tex/tables/gender/html/results_gender_dwald_direct_100_acc.html
index 6054e718a408eb1d5508e2dd31334b9cac2b94fa..94dc4c22e64af0adf9c173158b19900d4d5759ad 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_100_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_100_dist.html b/tex/tables/gender/html/results_gender_dwald_direct_100_dist.html
index 37beb8dbe5d54756c4947ee76fcdb21856050765..68f2524a43e26bdf1f0ae65b3179873b8a28116d 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_100_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_250_acc.html b/tex/tables/gender/html/results_gender_dwald_direct_250_acc.html
index de6dd7987ceb032c6c40e907f9c4e696ebaa0cd0..fdd0a40f11c5276b802b392ac657f2b50da30c50 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_250_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_250_dist.html b/tex/tables/gender/html/results_gender_dwald_direct_250_dist.html
index 9d2c11adc4043fa7136e6b18b35e64fa95a94b94..48ced1d3c48a8fa2a038034e62fd13474c0abfbe 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_250_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_500_acc.html b/tex/tables/gender/html/results_gender_dwald_direct_500_acc.html
index b9e20e848ee0efe83baa54d421e9514104277222..627762f1d74082bf9b9170b2cb79b0eafa7670b6 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_500_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_direct_500_dist.html b/tex/tables/gender/html/results_gender_dwald_direct_500_dist.html
index 3ca6e29c052eaa0341f9174b12bc24d7fbc46562..68259c9e8bc1627c28149544aba91e8dd7e296c0 100644
--- a/tex/tables/gender/html/results_gender_dwald_direct_500_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_direct_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_100_acc.html b/tex/tables/gender/html/results_gender_dwald_dynAA_100_acc.html
index 4f574cec3e0888d3ce358ea0a0312b8f21d05065..27a189b8d4b61fbbba7bebd3fb4bec602dfca5b1 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_100_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_100_dist.html b/tex/tables/gender/html/results_gender_dwald_dynAA_100_dist.html
index 037e7d9f03879c0c1666a68a0411b3cfa2bbdb59..f2b8373663861ac6173f96f9cf7559c1d08c45d9 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_100_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_250_acc.html b/tex/tables/gender/html/results_gender_dwald_dynAA_250_acc.html
index f0efcbafb063f9c3fbc0ccde3c83d83c85192b66..5ffd5f6ed4307c22b7940ba671bfcc206bcd8b7e 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_250_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_250_dist.html b/tex/tables/gender/html/results_gender_dwald_dynAA_250_dist.html
index 97d7178a4f5574512888704218103b3c52a2ebe3..f07b9628e2e5bc6e4194209b902934d9f71b7e2f 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_250_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_500_acc.html b/tex/tables/gender/html/results_gender_dwald_dynAA_500_acc.html
index d563dc104f5fcae38f0c8820824c2c4867b6c249..c6164ee17cd6e7c3c409a055bb706ad770ca4afe 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_500_acc.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_dwald_dynAA_500_dist.html b/tex/tables/gender/html/results_gender_dwald_dynAA_500_dist.html
index 72ee2773cb4645c8e1a9d740e9ddeb07326d6db3..b44bd53fcff4acaf0c6a0691775c4002258d4eb9 100644
--- a/tex/tables/gender/html/results_gender_dwald_dynAA_500_dist.html
+++ b/tex/tables/gender/html/results_gender_dwald_dynAA_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_100_acc.html b/tex/tables/gender/html/results_gender_full_direct_100_acc.html
index 0319427810c8f14bf2e4485b4b73a738107d31f4..0a0b60c7ce9cb7f5b697aa7ebdb0500e5fb140b9 100644
--- a/tex/tables/gender/html/results_gender_full_direct_100_acc.html
+++ b/tex/tables/gender/html/results_gender_full_direct_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_100_dist.html b/tex/tables/gender/html/results_gender_full_direct_100_dist.html
index 476808ff31465dbb4de42dc9fcbb2ff67abe4629..81e9515180c05f9675b18be0a61ea662eeb67110 100644
--- a/tex/tables/gender/html/results_gender_full_direct_100_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_100_f1_dist.html b/tex/tables/gender/html/results_gender_full_direct_100_f1_dist.html
index 57b7e6cf51c60e44d6f6b20ed7b3693d87f6700c..4f67741791ab9e65252e26b68516c21eef20e638 100644
--- a/tex/tables/gender/html/results_gender_full_direct_100_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_100_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_250_acc.html b/tex/tables/gender/html/results_gender_full_direct_250_acc.html
index 4a4d88eea559db7572dbbc972b466816127da096..4c9fc5a39c41cc8f90b5df39ab464d057301893a 100644
--- a/tex/tables/gender/html/results_gender_full_direct_250_acc.html
+++ b/tex/tables/gender/html/results_gender_full_direct_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_250_dist.html b/tex/tables/gender/html/results_gender_full_direct_250_dist.html
index eb0eaf73210f8e34909792d2f25c8a396919b485..b7030ca9fb6d46def753685842b3c773b2dab401 100644
--- a/tex/tables/gender/html/results_gender_full_direct_250_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_250_f1_dist.html b/tex/tables/gender/html/results_gender_full_direct_250_f1_dist.html
index e2eac376e43f18db2f3707eef855cd58175a1de3..ef8220be8378eedd2cec702d7e36f13b39f6cea5 100644
--- a/tex/tables/gender/html/results_gender_full_direct_250_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_250_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_500_acc.html b/tex/tables/gender/html/results_gender_full_direct_500_acc.html
index 0b88f6bfaea6a6df492f2c15ed65049fd36af569..83e93dbcbd1a8a4372ed70201ac526eb28619500 100644
--- a/tex/tables/gender/html/results_gender_full_direct_500_acc.html
+++ b/tex/tables/gender/html/results_gender_full_direct_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_500_dist.html b/tex/tables/gender/html/results_gender_full_direct_500_dist.html
index 3435489843081d719be68a3274fd4210ccc092b7..32a23068863f3fad5e9b3b77d13f50a420231ba1 100644
--- a/tex/tables/gender/html/results_gender_full_direct_500_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_direct_500_f1_dist.html b/tex/tables/gender/html/results_gender_full_direct_500_f1_dist.html
index 5b7b9d745b67d81fb04bacb44b41cb254c90d133..889248dd58efb135b3ae35d96112ec469c6458c8 100644
--- a/tex/tables/gender/html/results_gender_full_direct_500_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_direct_500_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_100_acc.html b/tex/tables/gender/html/results_gender_full_dynAA_100_acc.html
index c501e1d9a2591bd3fe4e421d4d38e99d0f7ea6cd..23c261f6a5bdcd3559ac5e95803ce60549290332 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_100_acc.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_100_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_100_dist.html
index 6f9fc6535284ff2c6eb741f95900eea515835c47..fe38efcd421a7b5101400a318d77bb0d89e493b4 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_100_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_100_f1_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_100_f1_dist.html
index 49ccc9f02b5693d7447b09683e19bfc449cb6b69..589b34133c83bfa4aa6f97038de0f0c2fcd38e81 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_100_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_100_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_250_acc.html b/tex/tables/gender/html/results_gender_full_dynAA_250_acc.html
index 82486cf1c158c7597951549d8317cc7f9a1caa90..51d3c89a4adbd7a92e38d082783091718126f5af 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_250_acc.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_250_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_250_dist.html
index 4fad31d88e56ef399f2627a28d7d605ba03f1441..6379bab6dffb1c625e3fc9f31d52ef3b9cd07747 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_250_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_250_f1_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_250_f1_dist.html
index b29c14e1e1ecc33670fa9338b3af83b1725fd3cd..7419cdd4cc4d5aa2b363fe46ec53567d4d748525 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_250_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_250_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_500_acc.html b/tex/tables/gender/html/results_gender_full_dynAA_500_acc.html
index 351d17d2e8d5beb8efffcc0cdde1f612c1ff0044..2df2eaa7118f4e543769379cd4a4c052e79b3ff2 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_500_acc.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_500_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_500_dist.html
index a2e9ff3970abe48f9209d7347b0899c454624d92..8d77deb50d672d65ff9de6d27d4771b8a44a3ce9 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_500_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_full_dynAA_500_f1_dist.html b/tex/tables/gender/html/results_gender_full_dynAA_500_f1_dist.html
index a8f62a68d7baf8ef9e6241ce4f7b0d8ee972b6d0..0eba9cc81b71035fc781d1aa69f94f6c9d41a8c1 100644
--- a/tex/tables/gender/html/results_gender_full_dynAA_500_f1_dist.html
+++ b/tex/tables/gender/html/results_gender_full_dynAA_500_f1_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_100_acc.html b/tex/tables/gender/html/results_gender_individual_baseline_100_acc.html
index e473fe9d5d530abeac727cd7a7215baa7872d3a3..4edc2e7d445954981dd26222729334f6af593e88 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_100_acc.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_100_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_100_dist.html b/tex/tables/gender/html/results_gender_individual_baseline_100_dist.html
index 978758da28e268a3d0db789907a93fd629763fd9..284da6e552103fb9e92fe969ac025c7372e66dca 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_100_dist.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_100_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_250_acc.html b/tex/tables/gender/html/results_gender_individual_baseline_250_acc.html
index 81852aaa191b5538d0a4a7917d8c6a1ba3c16e1f..6ca7dffbfd6b572fc219f5cdbe8c4e8a83a26960 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_250_acc.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_250_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_250_dist.html b/tex/tables/gender/html/results_gender_individual_baseline_250_dist.html
index 73714a7b609e43bab751ddbb10e261a9b1aa31ec..b7a66050a0fb975a834719a29ee17b5ff95c154c 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_250_dist.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_250_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_500_acc.html b/tex/tables/gender/html/results_gender_individual_baseline_500_acc.html
index 71e62ea48bd2ed2f44bb46547f720fe3c2f3ee91..6c0acfcd42779a3ee38ae92dbe6684dfbd83b13b 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_500_acc.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_500_acc.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">50</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
diff --git a/tex/tables/gender/html/results_gender_individual_baseline_500_dist.html b/tex/tables/gender/html/results_gender_individual_baseline_500_dist.html
index cc3c416602b5703ef1da7d2a04be0514c6465622..5d5c6fc57c03eefe4bd5b4aebb78b4116e15088a 100644
--- a/tex/tables/gender/html/results_gender_individual_baseline_500_dist.html
+++ b/tex/tables/gender/html/results_gender_individual_baseline_500_dist.html
@@ -12,7 +12,7 @@
     </tr>
     <tr>
       <th></th>
-      <th># Authors</th>
+      <th>No. of Authors</th>
       <th colspan="2" halign="left">150</th>
       <th colspan="2" halign="left">500</th>
       <th colspan="2" halign="left">1000</th>
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_100_acc.tex b/tex/tables/gender/latex/results_gender_dwald_direct_100_acc.tex
index bb911d05577c41304bde78c73be03c72b43e74ac..4216da3aefc881c4f6a878f00d6fc42b91c5e0b7 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_100_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_100_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7343 &   0.7338 &   0.6967 &   0.6947 &   0.6284 &   0.6276 &   0.6089 &   0.5966 \\
                           & 2-3 &   0.7746 &   0.7738 &   0.7331 &   0.7330 &   0.6647 &   0.6645 &   0.6387 &   0.6301 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_100_dist.tex b/tex/tables/gender/latex/results_gender_dwald_direct_100_dist.tex
index c28caf1d7eddbdb5418b3a6067c2de2c01ae9a74..042f31408a7ce8bc4391871237a133b4b06d6a58 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_100_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_100_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1683 &                        0.2888 &                       -0.0348 &                       -0.0259 &                       -0.0312 &                       -0.0785 \\
                           & 2-3 &                        0.0446 &                        0.0485 &                        0.0274 &                        0.0075 &                        0.1002 &                        0.0619 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_250_acc.tex b/tex/tables/gender/latex/results_gender_dwald_direct_250_acc.tex
index 53696bbf60633723d14917d95403ad3f526cf97f..42e81941ebcc5d8adbb8ba698d250bfcfe58ab52 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_250_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_250_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7844 &   0.7841 &   0.7495 &   0.7493 &   0.6736 &   0.6736 &   0.6514 &   0.6511 \\
                           & 2-3 &   0.8249 &   0.8247 &   0.7771 &   0.7738 &   0.7219 &   0.7218 &   0.6916 &   0.6916 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_250_dist.tex b/tex/tables/gender/latex/results_gender_dwald_direct_250_dist.tex
index 8e2669ef3ecca68d0096e3ddce7d97e3d95e5246..eae1ca81a35bdd18d0bbdf48a527e749219f00bc 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_250_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_250_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0390 &                        0.1945 &                        0.0539 &                        0.0938 &                        0.2805 &                        0.2587 \\
                           & 2-3 &                        0.1318 &                        0.1969 &                        0.0012 &                        0.1452 &                        0.1234 &                        0.1113 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_500_acc.tex b/tex/tables/gender/latex/results_gender_dwald_direct_500_acc.tex
index c9979a423ea7d7c7686880c5123da241d0583a04..63c51c2500a1b8caf891c55d33a2fadab14fe644 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_500_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_500_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8231 &   0.8230 &   0.7868 &   0.7868 &   0.7062 &   0.7058 &   0.6795 &   0.6769 \\
                           & 2-3 &   0.8802 &   0.8802 &   0.8396 &   0.8392 &   0.7698 &   0.7696 &   0.7313 &   0.7311 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_500_dist.tex b/tex/tables/gender/latex/results_gender_dwald_direct_500_dist.tex
index 6f75c454b914b63fc2c9ad29b7c0efbcb663724a..dc0925f40c272077373c8aa7bf9f1632c8bb2b91 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_500_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a cumulated model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_500_direct_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0858 &                        0.1370 &                        0.1528 &                        0.2654 &                        0.2834 &                        0.2834 \\
                           & 2-3 &                        0.1535 &                        0.1134 &                        0.1350 &                        0.1049 &                        0.1451 &                        0.1875 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_direct_500_f1_dist.tex b/tex/tables/gender/latex/results_gender_dwald_direct_500_f1_dist.tex
index 0aa40f3deaa9799ee559841fea8f768038b19ad8..d5cd205642685bd8c5b71072c10f93df39941596 100644
--- a/tex/tables/gender/latex/results_gender_dwald_direct_500_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_direct_500_f1_dist.tex
@@ -10,7 +10,7 @@
                           & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
                           & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+feature types & subgrams &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8230 &                        -- &   0.7868 &                        0.0911 &   0.7058 &                        0.0598 &   0.6769 &                        0.0998 \\
                           & 2-3 &   0.8802 &                        -- &   0.8392 &                        0.0903 &   0.7696 &                        0.0331 &   0.7311 &                        0.0178 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_100_acc.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_100_acc.tex
index 0991a7587880f7f8257c46fdbd34ebd8bdb4c76c..51b27825fef2e87ecbe4c0339f59366dca55bd5b 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_100_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_100_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7223 &   0.7220 &   0.6879 &   0.6864 &   0.6232 &   0.6230 &   0.6142 &   0.6136 \\
                           & 2-3 &   0.7658 &   0.7658 &   0.7159 &   0.7139 &   0.6594 &   0.6594 &   0.6426 &   0.6424 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_100_dist.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_100_dist.tex
index 7aaab26885bd05c90e5dc187bba34801ee74ea5e..768e7a4c1047f731cf70e3835638c5e13fe5a556 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_100_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_100_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                       -0.0387 &                       -0.0005 &                        0.0055 &                       -0.0134 &                        0.0438 &                        0.0420 \\
                           & 2-3 &                       -0.0308 &                       -0.0107 &                       -0.0109 &                       -0.0139 &                        0.0442 &                        0.0409 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_250_acc.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_250_acc.tex
index b711ca1b60632bb2ae26bb22f2edf6a81f59df15..21c0f04be21e9230cd2aaf7806d2ee0b6af98cbd 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_250_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_250_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7737 &   0.7735 &   0.7414 &   0.7414 &   0.6647 &   0.6642 &   0.6455 &   0.6436 \\
                           & 2-3 &   0.8138 &   0.8130 &   0.7813 &   0.7813 &   0.7156 &   0.7156 &   0.6858 &   0.6849 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_250_dist.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_250_dist.tex
index b1eea436eaa34172e683ff463acc0464fded7124..b0c1c09be165f7c7dad40cbb7a496c8f0ef27573 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_250_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_250_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0315 &                        0.0538 &                        0.0180 &                        0.0214 &                        0.0739 &                        0.0638 \\
                           & 2-3 &                        0.0250 &                        0.0498 &                        0.0184 &                        0.0121 &                        0.0694 &                        0.0464 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_500_acc.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_500_acc.tex
index af005fb08ceddd2139bc3b8cfa3db052cbde016b..6ec9125ade85e43ed15889514e6287890bd0b857 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_500_acc.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_acc_500_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                          & Target & \multicolumn{8}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                          & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                          & Target & \multicolumn{8}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8192 &   0.8179 &   0.7780 &   0.7778 &   0.6948 &   0.6935 &   0.6765 &   0.6764 \\
                           & 2-3 &   0.8743 &   0.8740 &   0.8369 &   0.8369 &   0.7639 &   0.7636 &   0.7286 &   0.7285 \\
diff --git a/tex/tables/gender/latex/results_gender_dwald_dynAA_500_dist.tex b/tex/tables/gender/latex/results_gender_dwald_dynAA_500_dist.tex
index c381a11860b2e0061ddc17d62d25dcdde7cca73b..485ae0f39a9a4687a6b47532c9842c7be9d99fc1 100644
--- a/tex/tables/gender/latex/results_gender_dwald_dynAA_500_dist.tex
+++ b/tex/tables/gender/latex/results_gender_dwald_dynAA_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Reduced Featureset (DIST, CHAR, LEMMA, WORD)}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a stacked model on the reduced feature set (DIST, CHAR, LEMMA, WORD)}
 \label{tab:Gender_dist_500_dynAA_dwald}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                          & Target & \multicolumn{6}{l}{gender} \\
-                          & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                          & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                          & Target & \multicolumn{6}{l}{Gender} \\
+                          & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                          & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                           & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.1274 &                        0.0535 &                        0.0573 &                        0.0718 &                        0.0552 &                        0.0749 \\
                           & 2-3 &                        0.1182 &                        0.0385 &                        0.0420 &                        0.0543 &                        0.0388 &                        0.0574 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_100_acc.tex b/tex/tables/gender/latex/results_gender_full_direct_100_acc.tex
index ed6d2e4b915af1d7c0188809a0bd686477901e23..0269631560de09a079e8e4ad9b1cd78748e7ad55 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_100_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_acc_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7343 &   0.7338 &   0.6967 &   0.6947 &   0.6284 &   0.6276 &   0.6089 &   0.5966 \\
                                       & 2-3 &   0.7746 &   0.7738 &   0.7331 &   0.7330 &   0.6647 &   0.6645 &   0.6387 &   0.6301 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_100_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_100_dist.tex
index cd136f52b1f0067431be5396ad993d68a357c679..032d13bd813c1ca6b1c09feeb677c4e0c4f43689 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_100_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_dist_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1683 &                        0.2888 &                       -0.0348 &                       -0.0259 &                       -0.0312 &                       -0.0785 \\
                                       & 2-3 &                        0.0446 &                        0.0485 &                        0.0274 &                        0.0075 &                        0.1002 &                        0.0619 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_100_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_100_f1_dist.tex
index 7b23b1aaa2e594e8ae8cd014738ec9c0cc100f80..867462688ff9fdf6aca6f9a26ab5626dbf4d3663 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_100_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_100_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 100 characters using a cumulated
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_100_direct_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_100_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7338 &                        -- &   0.6947 &                       -0.0481 &   0.6276 &                       -0.0020 &   0.5966 &                        0.0686 \\
                                       & 2-3 &   0.7738 &                        -- &   0.7330 &                       -0.0355 &   0.6645 &                        0.0120 &   0.6301 &                        0.0665 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_250_acc.tex b/tex/tables/gender/latex/results_gender_full_direct_250_acc.tex
index 88fe6c8cf46527d63c13b9752965ea0e93e892c7..6ee93e096b6e945b83a078441a8f14aaa2b498af 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_250_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_acc_250_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7844 &   0.7841 &   0.7495 &   0.7493 &   0.6736 &   0.6736 &   0.6514 &   0.6511 \\
                                       & 2-3 &   0.8249 &   0.8247 &   0.7771 &   0.7738 &   0.7219 &   0.7218 &   0.6916 &   0.6916 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_250_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_250_dist.tex
index afb7e61bdab5b6ea6fa8dfbeb4be297e8bc16654..c5a31ca82184a9ff54d01920df181ffd4914d662 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_250_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_dist_250_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0390 &                        0.1945 &                        0.0539 &                        0.0938 &                        0.2805 &                        0.2587 \\
                                       & 2-3 &                        0.1318 &                        0.1969 &                        0.0012 &                        0.1452 &                        0.1234 &                        0.1113 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_250_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_250_f1_dist.tex
index 5315482016f1e4360cbec79b38bb90a1b145baa2..3f4822e0464a22327ea1eb806411a09f99e0e901 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_250_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_250_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 250 characters using a cumulated
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_250_direct_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_250_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7841 &                        -- &   0.7493 &                        0.0017 &   0.6736 &                        0.0348 &   0.6511 &                        0.0363 \\
                                       & 2-3 &   0.8247 &                        -- &   0.7738 &                       -0.0013 &   0.7218 &                       -0.0077 &   0.6916 &                        0.0245 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_500_acc.tex b/tex/tables/gender/latex/results_gender_full_direct_500_acc.tex
index 9d0fb1a8852003020e2a9a6b0784e8a307e3fdf0..d897af87588894859978902f3fe9ea544c8b4e95 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_500_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_acc_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8231 &   0.8230 &   0.7868 &   0.7868 &   0.7062 &   0.7058 &   0.6795 &   0.6769 \\
                                       & 2-3 &   0.8802 &   0.8802 &   0.8396 &   0.8392 &   0.7698 &   0.7696 &   0.7313 &   0.7311 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_500_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_500_dist.tex
index a7539f78dd946ba24be76a488915cb2534c7742f..1fbb0724485993b71eec4080658dcaa04082eb3c 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_500_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Cumulated
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
 \label{tab:Gender_dist_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0858 &                        0.1370 &                        0.1528 &                        0.2654 &                        0.2834 &                        0.2834 \\
                                       & 2-3 &                        0.1535 &                        0.1134 &                        0.1350 &                        0.1049 &                        0.1451 &                        0.1875 \\
diff --git a/tex/tables/gender/latex/results_gender_full_direct_500_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_direct_500_f1_dist.tex
index 92282ca5600c72eb71a85eae814ed6de4b8f60c8..5c3d2f6b6cb72ad00c2b3d8ba782782ba9a92e7e 100644
--- a/tex/tables/gender/latex/results_gender_full_direct_500_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_direct_500_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 500 characters using a cumulated
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_500_direct_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a cumulated model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_500_direct_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8230 &                        -- &   0.7868 &                        0.0911 &   0.7058 &                        0.0598 &   0.6769 &                        0.0998 \\
                                       & 2-3 &   0.8802 &                        -- &   0.8392 &                        0.0903 &   0.7696 &                        0.0331 &   0.7311 &                        0.0178 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_100_acc.tex b/tex/tables/gender/latex/results_gender_full_dynAA_100_acc.tex
index 71d359db400b9b52089f2fc3d6f1e2ffdab78552..2211efdb92055ab1f68fa3386313bd21f8cf09f3 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_100_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_acc_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7223 &   0.7220 &   0.6879 &   0.6864 &   0.6232 &   0.6230 &   0.6142 &   0.6136 \\
                                       & 2-3 &   0.7658 &   0.7658 &   0.7159 &   0.7139 &   0.6594 &   0.6594 &   0.6426 &   0.6424 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_100_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_100_dist.tex
index f9b2be122f3954de5a0b93e00fb2c87f538324c0..25768893e01b18925743be9b7ce96d206f669fcc 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_100_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_dist_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                       -0.0387 &                       -0.0005 &                        0.0055 &                       -0.0134 &                        0.0438 &                        0.0420 \\
                                       & 2-3 &                       -0.0308 &                       -0.0107 &                       -0.0109 &                       -0.0139 &                        0.0442 &                        0.0409 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_100_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_100_f1_dist.tex
index af95a8fa530ff8a6cb15747032dc5e380d226bbd..b05c107a3eb6328d56a6b3899b0c08854347005f 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_100_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_100_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 100 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_100_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a stacked model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_100_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7220 &                        -- &   0.6864 &                       -0.0387 &   0.6230 &                        0.0055 &   0.6136 &                        0.0438 \\
                                       & 2-3 &   0.7658 &                        -- &   0.7139 &                       -0.0308 &   0.6594 &                       -0.0109 &   0.6424 &                        0.0442 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_250_acc.tex b/tex/tables/gender/latex/results_gender_full_dynAA_250_acc.tex
index c4461747b1661a82f00d362748dea8a9e492df78..a77fdd768d3e70416cb1586cf28a3378111b2f9f 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_250_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_acc_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7737 &   0.7735 &   0.7414 &   0.7414 &   0.6647 &   0.6642 &   0.6455 &   0.6436 \\
                                       & 2-3 &   0.8138 &   0.8130 &   0.7813 &   0.7813 &   0.7156 &   0.7156 &   0.6858 &   0.6849 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_250_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_250_dist.tex
index c1cbaca7fea017644c50a26cf0460c5de579fa90..a34bf21771fcdbf232a42a731bde48a72db1ee0c 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_250_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_dist_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.0315 &                        0.0538 &                        0.0180 &                        0.0214 &                        0.0739 &                        0.0638 \\
                                       & 2-3 &                        0.0250 &                        0.0498 &                        0.0184 &                        0.0121 &                        0.0694 &                        0.0464 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_250_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_250_f1_dist.tex
index 00e25516905d97d34df27e4323ee55c34a9bea4f..160c773fa0bf488ea661f68235283b02d94a1cd7 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_250_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_250_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 250 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_250_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a stacked model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_250_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.7735 &                        -- &   0.7414 &                        0.0315 &   0.6642 &                        0.0180 &   0.6436 &                        0.0739 \\
                                       & 2-3 &   0.8130 &                        -- &   0.7813 &                        0.0250 &   0.7156 &                        0.0184 &   0.6849 &                        0.0694 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_500_acc.tex b/tex/tables/gender/latex/results_gender_full_dynAA_500_acc.tex
index 2db86e1ed427e4abe247f8062af6b3c608460008..d8565ec7a5d821bbd508b66a256e40608f617c23 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_500_acc.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_acc_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8192 &   0.8179 &   0.7780 &   0.7778 &   0.6948 &   0.6935 &   0.6765 &   0.6764 \\
                                       & 2-3 &   0.8743 &   0.8740 &   0.8369 &   0.8369 &   0.7639 &   0.7636 &   0.7286 &   0.7285 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_500_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_500_dist.tex
index d659b7409a67a86969751c3d0fbb1d97fca8bfd6..3fa9035f27cbf25cf3bcda58899df88f0744df04 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_500_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Stacked
-                                     Model on the Ordered, Full Featureset}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
 \label{tab:Gender_dist_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-                                      & Target & \multicolumn{6}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Target & \multicolumn{6}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
                                       & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &                        0.1274 &                        0.0535 &                        0.0573 &                        0.0718 &                        0.0552 &                        0.0749 \\
                                       & 2-3 &                        0.1182 &                        0.0385 &                        0.0420 &                        0.0543 &                        0.0388 &                        0.0574 \\
diff --git a/tex/tables/gender/latex/results_gender_full_dynAA_500_f1_dist.tex b/tex/tables/gender/latex/results_gender_full_dynAA_500_f1_dist.tex
index 8e29dad4d35046c53eec1e8dc70d3b1356675154..801bb6675e68ebb7572efc4a3138494c7b2aaf11 100644
--- a/tex/tables/gender/latex/results_gender_full_dynAA_500_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_full_dynAA_500_f1_dist.tex
@@ -1,16 +1,16 @@
-\begin{table}
+\begin{table}[!ht]
 \centering
-\caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 500 characters using a stacked
-                                 model on the ordered, full Featureset}
-\label{tab:gender_f1_dist_500_dynAA_full}
+\caption{Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a stacked model on the ordered, full feature set}
+\label{tab:Gender_f1_dist_500_dynAA_full}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-                                      & Target & \multicolumn{8}{l}{gender} \\
-                                      & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-                                      & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-                                      & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+                                      & Target & \multicolumn{8}{l}{Gender} \\
+                                      & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+                                      & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+                                      & Score & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & F1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
+Feature types & N-gram ranges &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST\_CHAR} & 2 &   0.8179 &                        -- &   0.7778 &                        0.1274 &   0.6935 &                        0.0573 &   0.6764 &                        0.0552 \\
                                       & 2-3 &   0.8740 &                        -- &   0.8369 &                        0.1182 &   0.7636 &                        0.0420 &   0.7285 &                        0.0388 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_100_acc.tex b/tex/tables/gender/latex/results_gender_individual_baseline_100_acc.tex
index 4694c1ce045c0f48d488022f1e824f7585605c3b..083f99bed616bc1da140075d1d2f02663082e870 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_100_acc.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_100_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_acc_100_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{100} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{100} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.5886 &   0.5711 &   0.5455 &   0.4567 &   0.5639 &   0.5594 &   0.5387 &   0.4881 \\
     & 2-3 &   0.6071 &   0.5727 &   0.5992 &   0.5881 &   0.5358 &   0.4757 &   0.5549 &   0.5379 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_100_dist.tex b/tex/tables/gender/latex/results_gender_individual_baseline_100_dist.tex
index 1864e0c3a765c19e970c959c11c7f28111064d85..a4fb095d5370779720c007a6b0ca9c876910f9f5 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_100_dist.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_100_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 100 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 100 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_dist_100_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{100} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{100} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.1683 &                        0.2888 &                       -0.0348 &                       -0.0259 &                       -0.0312 &                       -0.0785 \\
     & 2-3 &                        0.0446 &                        0.0485 &                        0.0274 &                        0.0075 &                        0.1002 &                        0.0619 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_250_acc.tex b/tex/tables/gender/latex/results_gender_individual_baseline_250_acc.tex
index 982b1e8ce424aeb363aef37f0074cd34e7eb7716..8ecc99c510404cc46f7cbdcf4da47112d5a33b99 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_250_acc.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_250_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_acc_250_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{250} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{250} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.6002 &   0.5633 &   0.6026 &   0.6026 &   0.5649 &   0.5558 &   0.5543 &   0.5311 \\
     & 2-3 &   0.6261 &   0.6071 &   0.5801 &   0.5326 &   0.5771 &   0.5764 &   0.5541 &   0.5267 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_250_dist.tex b/tex/tables/gender/latex/results_gender_individual_baseline_250_dist.tex
index aa4f7a79cca1a9db7a0e6d2143bfc3dd32bda657..84818584872148333e0545634c2cd024d399c8c4 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_250_dist.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_250_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 250 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 250 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_dist_250_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{250} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{250} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0390 &                        0.1945 &                        0.0539 &                        0.0938 &                        0.2805 &                        0.2587 \\
     & 2-3 &                        0.1318 &                        0.1969 &                        0.0012 &                        0.1452 &                        0.1234 &                        0.1113 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_500_acc.tex b/tex/tables/gender/latex/results_gender_individual_baseline_500_acc.tex
index 135f7cf0de24e6285747f6e769d5d1ee53967d64..be19d97f259aa856e8c4e8010c02e9e726e0f01a 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_500_acc.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_500_acc.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Accuracy and F1-Sscores for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{'Accuracy and F1-scores for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_acc_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
 \toprule
-    & Target & \multicolumn{8}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
-    & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
-    & Score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score & accuracy & f1-score \\
-featuretypes & subgrams &          &          &          &          &          &          &          &          \\
+    & Target & \multicolumn{8}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{8}{l}{500} \\
+    & No. of Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score & Accuracy & F1-score \\
+Feature types & N-gram ranges &          &          &          &          &          &          &          &          \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.6367 &   0.6322 &   0.5043 &   0.3481 &   0.5095 &   0.3680 &   0.5582 &   0.5403 \\
     & 2-3 &   0.6531 &   0.6477 &   0.5202 &   0.3788 &   0.5278 &   0.4365 &   0.5518 &   0.5173 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_500_dist.tex b/tex/tables/gender/latex/results_gender_individual_baseline_500_dist.tex
index 9b1c0fc1b23ef92cb9e523844daa1178c3050928..01fdc96e635510f0d8ea2c8355653df5bb999111 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_500_dist.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_500_dist.tex
@@ -1,16 +1,16 @@
 \begin{table}[!ht]
 \centering
-\caption{Average Distortion of Features for the Prediction of gender on a Minimal Instance Length of 500 Chars Using a Feature-Wise
-                                     Model on the Individual  Featuretypes}
+\caption{Stability of feature importance for the prediction of gender on a minimal input instance
+                                        length of 500 characters using a feature-wise model on the individual feature types}
 \label{tab:Gender_dist_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccc}
 \toprule
-    & Target & \multicolumn{6}{l}{gender} \\
-    & Min. \# Chars in Input & \multicolumn{6}{l}{500} \\
-    & \# Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
+    & Target & \multicolumn{6}{l}{Gender} \\
+    & Min. No. of Characters & \multicolumn{6}{l}{500} \\
+    & No. of Authors & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & \thead{Avg. \\ Spearman's  $\rho$ (red.)} \\
-featuretypes & subgrams &                               &                               &                               &                               &                               &                               \\
+Feature types & N-gram ranges &                               &                               &                               &                               &                               &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &                        0.0858 &                        0.1370 &                        0.1528 &                        0.2654 &                        0.2834 &                        0.2834 \\
     & 2-3 &                        0.1535 &                        0.1134 &                        0.1350 &                        0.1049 &                        0.1451 &                        0.1875 \\
diff --git a/tex/tables/gender/latex/results_gender_individual_baseline_500_f1_dist.tex b/tex/tables/gender/latex/results_gender_individual_baseline_500_f1_dist.tex
index 400f05b6c1f9d96fb7440b64810e4f1fda7866bd..b3c8509a034d6a5af8a94d0d90766222671e6948 100644
--- a/tex/tables/gender/latex/results_gender_individual_baseline_500_f1_dist.tex
+++ b/tex/tables/gender/latex/results_gender_individual_baseline_500_f1_dist.tex
@@ -1,7 +1,7 @@
 \begin{table}
 \centering
 \caption{F1-Score and Average Distortion of Features for the Prediction of Gender on a Minimal Tweet Length of 500 characters using a feature-wise
-                                 model on the individual  Featuretypes}
+                                 model on the individual  feature types}
 \label{tab:gender_f1_dist_500_baseline_individual}
 \begin{adjustbox}{max width=\textwidth}
 \begin{tabular}{llcccccccc}
@@ -10,7 +10,7 @@
     & Min. \# Chars in Input & \multicolumn{8}{l}{500} \\
     & \# Authors & \multicolumn{2}{l}{50} & \multicolumn{2}{l}{150} & \multicolumn{2}{l}{500} & \multicolumn{2}{l}{1000} \\
     & Score & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} & f1-score & \thead{Avg. \\ Spearman's $\rho$ (ext.)} \\
-featuretypes & subgrams &          &                               &          &                               &          &                               &          &                               \\
+feature types & subgrams &          &                               &          &                               &          &                               &          &                               \\
 \midrule
 \multirow{4}{*}{DIST} & 2 &   0.6322 &                        -- &   0.3481 &                        0.0858 &   0.3680 &                        0.1528 &   0.5403 &                        0.2834 \\
     & 2-3 &   0.6477 &                        -- &   0.3788 &                        0.1535 &   0.4365 &                        0.1350 &   0.5173 &                        0.1451 \\
diff --git a/tex/tables/test_dataset.tex b/tex/tables/test_dataset.tex
new file mode 100644
index 0000000000000000000000000000000000000000..f317a2e75c78d9b0c1bcf8c9210a431474a1b81f
--- /dev/null
+++ b/tex/tables/test_dataset.tex
@@ -0,0 +1,29 @@
+\begin{table}[!ht]
+\centering
+\caption{Statistics of the Dataset}
+\label{tab:dataset_test}
+\begin{adjustbox}{max width=\textwidth}
+\begin{tabular}{llrrrrrr}
+\toprule
+    & {} & \multicolumn{2}{l}{avg\_instance} & \multicolumn{2}{l}{avg\_tweet} & \multicolumn{2}{l}{avg\_tweet\_per\_instance} \\
+    & Target &          age & gender &       age & gender &                    age & gender \\
+No. of Characters & No. of Authors &              &        &           &        &                        &        \\
+\midrule
+\multirow{4}{*}{100} & 50   &       159.93 & 162.32 &    109.42 & 111.75 &                   1.46 &   1.45 \\
+    & 150  &       161.05 & 162.54 &    107.75 & 112.95 &                   1.49 &   1.44 \\
+    & 500  &       160.74 & 161.81 &    109.27 & 111.92 &                   1.47 &   1.45 \\
+    & 1000 &       160.09 & 161.03 &    109.28 & 111.96 &                   1.47 &   1.44 \\
+\cline{1-8}
+\multirow{4}{*}{250} & 50   &       313.19 & 315.01 &    108.30 & 111.55 &                   2.89 &   2.82 \\
+    & 150  &       313.55 & 315.26 &    107.24 & 112.85 &                   2.92 &   2.79 \\
+    & 500  &       313.39 & 314.64 &    108.98 & 111.86 &                   2.88 &   2.81 \\
+    & 1000 &       313.23 & 314.55 &    109.23 & 111.85 &                   2.87 &   2.81 \\
+\cline{1-8}
+\multirow{4}{*}{500} & 50   &       566.58 & 569.37 &    109.40 & 112.97 &                   5.18 &   5.04 \\
+    & 150  &       566.12 & 568.69 &    107.69 & 112.78 &                   5.26 &   5.04 \\
+    & 500  &       566.17 & 567.78 &    109.24 & 111.45 &                   5.18 &   5.09 \\
+    & 1000 &       566.14 & 567.36 &    109.12 & 111.69 &                   5.19 &   5.08 \\
+\bottomrule
+
+\end{tabular}\end{adjustbox}
+\end{table}
diff --git a/tex/tables/train_dataset.tex b/tex/tables/train_dataset.tex
new file mode 100644
index 0000000000000000000000000000000000000000..2d2943734997629cc2b0693ee6ce1c437cb1839a
--- /dev/null
+++ b/tex/tables/train_dataset.tex
@@ -0,0 +1,29 @@
+\begin{table}[!ht]
+\centering
+\caption{Statistics of the Dataset}
+\label{tab:dataset_train}
+\begin{adjustbox}{max width=\textwidth}
+\begin{tabular}{llrrrrrr}
+\toprule
+    & {} & \multicolumn{2}{l}{avg\_instance} & \multicolumn{2}{l}{avg\_tweet} & \multicolumn{2}{l}{avg\_tweet\_per\_instance} \\
+    & Target &          age & gender &       age & gender &                    age & gender \\
+No. of Characters & No. of Authors &              &        &           &        &                        &        \\
+\midrule
+\multirow{4}{*}{100} & 50   &       159.94 & 162.93 &    109.21 & 111.71 &                   1.46 &   1.46 \\
+    & 150  &       160.56 & 162.01 &    107.39 & 112.88 &                   1.50 &   1.44 \\
+    & 500  &       160.83 & 161.64 &    109.30 & 111.84 &                   1.47 &   1.45 \\
+    & 1000 &       160.04 & 160.92 &    109.26 & 111.80 &                   1.46 &   1.44 \\
+\cline{1-8}
+\multirow{4}{*}{250} & 50   &       313.46 & 315.85 &    109.39 & 112.14 &                   2.87 &   2.82 \\
+    & 150  &       313.21 & 315.31 &    107.38 & 112.75 &                   2.92 &   2.80 \\
+    & 500  &       313.21 & 314.61 &    109.06 & 111.62 &                   2.87 &   2.82 \\
+    & 1000 &       313.33 & 314.29 &    109.23 & 111.81 &                   2.87 &   2.81 \\
+\cline{1-8}
+\multirow{4}{*}{500} & 50   &       565.52 & 568.76 &    108.88 & 112.09 &                   5.19 &   5.07 \\
+    & 150  &       565.67 & 568.60 &    107.37 & 112.89 &                   5.27 &   5.04 \\
+    & 500  &       566.35 & 567.42 &    109.17 & 111.80 &                   5.19 &   5.08 \\
+    & 1000 &       566.01 & 567.42 &    109.16 & 111.92 &                   5.19 &   5.07 \\
+\bottomrule
+
+\end{tabular}\end{adjustbox}
+\end{table}
diff --git a/tex/tables/val_dataset.tex b/tex/tables/val_dataset.tex
new file mode 100644
index 0000000000000000000000000000000000000000..1be0bdd15f6fcce7ed0b60e1d4bd840932cf38e2
--- /dev/null
+++ b/tex/tables/val_dataset.tex
@@ -0,0 +1,29 @@
+\begin{table}[!ht]
+\centering
+\caption{Statistics of the Dataset}
+\label{tab:dataset_val}
+\begin{adjustbox}{max width=\textwidth}
+\begin{tabular}{llrrrrrr}
+\toprule
+    & {} & \multicolumn{2}{l}{avg\_instance} & \multicolumn{2}{l}{avg\_tweet} & \multicolumn{2}{l}{avg\_tweet\_per\_instance} \\
+    & Target &          age & gender &       age & gender &                    age & gender \\
+No. of Characters & No. of Authors &              &        &           &        &                        &        \\
+\midrule
+\multirow{4}{*}{100} & 50   &       161.16 & 163.39 &    110.30 & 112.38 &                   1.46 &   1.45 \\
+    & 150  &       160.11 & 162.66 &    107.11 & 112.99 &                   1.49 &   1.44 \\
+    & 500  &       160.73 & 161.51 &    109.04 & 111.86 &                   1.47 &   1.44 \\
+    & 1000 &       160.10 & 161.06 &    109.31 & 111.83 &                   1.46 &   1.44 \\
+\cline{1-8}
+\multirow{4}{*}{250} & 50   &       312.62 & 316.00 &    108.98 & 112.14 &                   2.87 &   2.82 \\
+    & 150  &       313.13 & 316.26 &    107.63 & 113.00 &                   2.91 &   2.80 \\
+    & 500  &       313.25 & 314.76 &    109.22 & 111.64 &                   2.87 &   2.82 \\
+    & 1000 &       313.29 & 314.28 &    109.07 & 111.87 &                   2.87 &   2.81 \\
+\cline{1-8}
+\multirow{4}{*}{500} & 50   &       565.07 & 568.36 &    109.31 & 110.78 &                   5.17 &   5.13 \\
+    & 150  &       566.11 & 568.92 &    107.51 & 112.82 &                   5.27 &   5.04 \\
+    & 500  &       565.73 & 567.60 &    109.05 & 111.69 &                   5.19 &   5.08 \\
+    & 1000 &       566.23 & 567.34 &    109.21 & 111.85 &                   5.18 &   5.07 \\
+\bottomrule
+
+\end{tabular}\end{adjustbox}
+\end{table}