deepof_model_evaluation.ipynb 1.25 MB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
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    "\n",
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    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# deepOF model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a dataset and a trained model, this notebook allows the user to \n",
    "\n",
    "* Load and inspect the different models (encoder, decoder, grouper, gmvaep)\n",
    "* Visualize reconstruction quality for a given model\n",
    "* Visualize a static latent space\n",
    "* Visualize trajectories on the latent space for a given video\n",
    "* sample from the latent space distributions and generate video clips showcasing generated data"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
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    "\n",
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    "os.chdir(os.path.dirname(\"../\"))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import deepof.data\n",
    "import deepof.utils\n",
    "import numpy as np\n",
    "import pandas as pd\n",
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    "import re\n",
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    "import tensorflow as tf\n",
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    "from collections import Counter\n",
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    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
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    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "import umap\n",
    "\n",
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    "from ipywidgets import interactive, interact, HBox, Layout, VBox\n",
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    "from IPython import display\n",
    "from matplotlib.animation import FuncAnimation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Define and run project"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(\"..\", \"..\", \"Desktop\", \"deepoftesttemp\")\n",
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    "trained_network = os.path.join(\"..\", \"..\", \"Desktop\", \"trained_weights\")\n",
    "exclude_bodyparts = tuple([\"\"])\n",
    "window_size = 24"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CPU times: user 298 ms, sys: 24 ms, total: 322 ms\n",
      "Wall time: 276 ms\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
    "proj = deepof.data.project(\n",
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    "    path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380],\n",
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    ")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading trajectories...\n",
      "Smoothing trajectories...\n",
      "Interpolating outliers...\n",
      "Iterative imputation of ocluded bodyparts...\n",
      "Computing distances...\n",
      "Computing angles...\n",
      "Done!\n",
      "deepof analysis of 2 videos\n",
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      "CPU times: user 50.3 s, sys: 429 ms, total: 50.8 s\n",
      "Wall time: 7.09 s\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
    "proj = proj.run(verbose=True)\n",
    "print(proj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load pretrained deepof model"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "coords = proj.get_coords(center=\"Center\", align=\"Spine_1\", align_inplace=True)\n",
    "data_prep = coords.preprocess(test_videos=0, window_step=1, window_size=window_size, shuffle=True)[\n",
    "    0\n",
    "]"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 133,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "['GMVAE_loss=MMD_encoding=10_k=25_latreg=categorical+variance_entknn=100_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO_encoding=12_k=25_latreg=variance_entknn=50_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=10_k=25_latreg=variance_entknn=50_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO_encoding=8_k=25_latreg=none_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO_encoding=6_k=25_latreg=categorical+variance_entknn=20_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=2_k=25_latreg=none_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=MMD_encoding=12_k=25_latreg=categorical+variance_entknn=20_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=16_k=25_latreg=categorical_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO_encoding=8_k=25_latreg=variance_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=MMD_encoding=6_k=25_latreg=none_entknn=50_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=16_k=25_latreg=categorical+variance_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=4_k=25_latreg=categorical_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=14_k=25_latreg=categorical+variance_entknn=80_final_weights.h5',\n",
       " 'GMVAE_loss=MMD_encoding=12_k=25_latreg=categorical_entknn=20_final_weights.h5',\n",
       " 'GMVAE_loss=ELBO+MMD_encoding=6_k=25_latreg=categorical+variance_entknn=100_final_weights.h5']"
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      ]
     },
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     "execution_count": 133,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in os.listdir(trained_network) if i.endswith(\"h5\")]"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GMVAE_loss=ELBO+MMD_encoding=6_k=25_latreg=categorical+variance_entknn=100_final_weights.h5'"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deepof_weights = [i for i in os.listdir(trained_network) if i.endswith(\"h5\")][-1]\n",
    "deepof_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "# Set model parameters\n",
    "encoding = int(re.findall(\"encoding=(\\d+)_\", deepof_weights)[0])\n",
    "k = int(re.findall(\"k=(\\d+)_\", deepof_weights)[0])\n",
    "loss = re.findall(\"loss=(.+?)_\", deepof_weights)[0]\n",
    "pheno = 0\n",
    "predictor = 0"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 146,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "encode_to_vector, encode_to_distribution, decoder, grouper, gmvaep = deepof.models.SEQ_2_SEQ_GMVAE(\n",
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    "    loss=loss,\n",
    "    number_of_components=k,\n",
    "    compile_model=True,\n",
    "    encoding=encoding,\n",
    "    predictor=predictor,\n",
    "    phenotype_prediction=pheno,\n",
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    ").build(data_prep.shape)\n",
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    "\n",
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    "gmvaep.load_weights(os.path.join(trained_network, deepof_weights))"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 147,
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   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Uncomment to see model summaries\n",
    "# encoder.summary()\n",
    "# decoder.summary()\n",
    "# grouper.summary()\n",
    "# gmvaep.summary()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 148,
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to plot model structure\n",
    "def plot_model(model, name):\n",
    "    tf.keras.utils.plot_model(\n",
    "        model,\n",
    "        to_file=os.path.join(\n",
    "            path,\n",
    "            \"deepof_{}_{}.png\".format(name, datetime.now().strftime(\"%Y%m%d-%H%M%S\")),\n",
    "        ),\n",
    "        show_shapes=True,\n",
    "        show_dtype=False,\n",
    "        show_layer_names=True,\n",
    "        rankdir=\"TB\",\n",
    "        expand_nested=True,\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "\n",
    "# plot_model(encoder, \"encoder\")\n",
    "# plot_model(decoder, \"decoder\")\n",
    "# plot_model(grouper, \"grouper\")\n",
    "# plot_model(gmvaep, \"gmvaep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "### 4. Evaluate reconstruction (to be incorporated into deepof.evaluate)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 149,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "# Auxiliary animation functions\n",
    "\n",
    "\n",
    "def plot_mouse_graph(instant_x, instant_y, instant_rec_x, instant_rec_y, ax, edges):\n",
    "    \"\"\"Generates a graph plot of the mouse\"\"\"\n",
    "    plots = []\n",
    "    rec_plots = []\n",
    "    for edge in edges:\n",
    "        (temp_plot,) = ax.plot(\n",
    "            [float(instant_x[edge[0]]), float(instant_x[edge[1]])],\n",
    "            [float(instant_y[edge[0]]), float(instant_y[edge[1]])],\n",
    "            color=\"#006699\",\n",
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    "            linewidth=2.0,\n",
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    "        )\n",
    "        (temp_rec_plot,) = ax.plot(\n",
    "            [float(instant_rec_x[edge[0]]), float(instant_rec_x[edge[1]])],\n",
    "            [float(instant_rec_y[edge[0]]), float(instant_rec_y[edge[1]])],\n",
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    "            color=\"red\",\n",
    "            linewidth=2.0,\n",
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    "        )\n",
    "        plots.append(temp_plot)\n",
    "        rec_plots.append(temp_rec_plot)\n",
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    "    return plots, rec_plots\n",
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    "\n",
    "\n",
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    "def update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges):\n",
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    "    \"\"\"Updates the graph plot to enable animation\"\"\"\n",
    "\n",
    "    for plot, edge in zip(plots, edges):\n",
    "        plot.set_data(\n",
    "            [float(x[edge[0]]), float(x[edge[1]])],\n",
    "            [float(y[edge[0]]), float(y[edge[1]])],\n",
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    "        )\n",
    "    for plot, edge in zip(rec_plots, edges):\n",
    "        plot.set_data(\n",
    "            [float(rec_x[edge[0]]), float(rec_x[edge[1]])],\n",
    "            [float(rec_y[edge[0]]), float(rec_y[edge[1]])],\n",
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    "        )"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 150,
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   "metadata": {
    "scrolled": false
   },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Test 1_s12\n"
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     ]
    },
    {
     "data": {
      "text/html": [
       "<video width=\"720\" height=\"720\" controls autoplay loop>\n",
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       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAMe+VtZGF0AAACsAYF//+s\n",
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