From 991c797ce17f22ca13d7cb3e5c37ce3a8408b0ee Mon Sep 17 00:00:00 2001
From: Luigi Sbailo <sbailo@fhi-berlin.mpg.de>
Date: Mon, 28 Sep 2020 18:31:53 +0200
Subject: [PATCH] First prototype of the notebook
---
tetradymite_PRM2020.ipynb | 539 ++++++++++++++++++++----------
tetradymite_PRM2020/visualizer.py | 55 +--
2 files changed, 398 insertions(+), 196 deletions(-)
diff --git a/tetradymite_PRM2020.ipynb b/tetradymite_PRM2020.ipynb
index 384ff31..6fd6baf 100644
--- a/tetradymite_PRM2020.ipynb
+++ b/tetradymite_PRM2020.ipynb
@@ -45,8 +45,71 @@
"execution_count": 1,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T12:14:16.892451Z",
- "start_time": "2020-09-26T12:14:16.862246Z"
+ "end_time": "2020-09-28T16:29:21.821237Z",
+ "start_time": "2020-09-28T16:29:21.811355Z"
+ },
+ "init_cell": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<script>\n",
+ " code_show=true; \n",
+ " function code_toggle() {\n",
+ " if (code_show)\n",
+ " {\n",
+ " $('div.input').hide();\n",
+ " } \n",
+ " else \n",
+ " {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ " } \n",
+ " $( document ).ready(code_toggle);\n",
+ " window.runCells(\"startup\");\n",
+ "</script>\n",
+ "The raw code for this notebook is by default hidden for easier reading.\n",
+ "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%HTML\n",
+ "<script>\n",
+ " code_show=true; \n",
+ " function code_toggle() {\n",
+ " if (code_show)\n",
+ " {\n",
+ " $('div.input').hide();\n",
+ " } \n",
+ " else \n",
+ " {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ " } \n",
+ " $( document ).ready(code_toggle);\n",
+ " window.runCells(\"startup\");\n",
+ "</script>\n",
+ "The raw code for this notebook is by default hidden for easier reading.\n",
+ "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-09-28T15:50:43.008324Z",
+ "start_time": "2020-09-28T15:50:42.991266Z"
}
},
"outputs": [],
@@ -60,9 +123,10 @@
"execution_count": 2,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T12:14:18.033512Z",
- "start_time": "2020-09-26T12:14:17.339188Z"
- }
+ "end_time": "2020-09-28T16:29:22.312658Z",
+ "start_time": "2020-09-28T16:29:21.823064Z"
+ },
+ "init_cell": true
},
"outputs": [],
"source": [
@@ -78,25 +142,11 @@
"execution_count": 3,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T12:14:36.475512Z",
- "start_time": "2020-09-26T12:14:18.035077Z"
+ "end_time": "2020-09-28T15:50:45.224040Z",
+ "start_time": "2020-09-28T15:50:45.202499Z"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number queries entries: 1581\n",
- "Number of entries loaded in the last api call: 100\n",
- "Bytes loaded in the last api call: 502327341\n",
- "Bytes loaded from this query: 502327341\n",
- "Number of downloaded entries: 100\n",
- "Number of made api calls: 1\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from nomad import client, config\n",
"config.client.url = 'http://nomad-lab.eu/prod/rae/api'\n",
@@ -109,26 +159,28 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T12:16:04.914649Z",
- "start_time": "2020-09-26T12:14:36.477143Z"
- }
+ "end_time": "2020-09-28T16:29:22.335555Z",
+ "start_time": "2020-09-28T16:29:22.314325Z"
+ },
+ "init_cell": true
},
"outputs": [],
"source": [
- "df_train = pd.read_csv(\"./data/tetradymite_PRM2020/train.csv\", index_col=0).astype(float)"
+ "df_train = pd.read_pickle('./data/tetradymite_PRM2020/training_set')"
]
},
{
"cell_type": "code",
- "execution_count": 125,
+ "execution_count": 6,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T15:03:37.344693Z",
- "start_time": "2020-09-26T15:03:37.315508Z"
- }
+ "end_time": "2020-09-28T15:50:51.751396Z",
+ "start_time": "2020-09-28T15:50:51.727769Z"
+ },
+ "scrolled": true
},
"outputs": [],
"source": [
@@ -136,30 +188,15 @@
"try:\n",
" os.mkdir(path_structure)\n",
"except OSError:\n",
- " !rm ./data/tetradymite_PRM2020/structures/*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 137,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-26T15:11:48.378665Z",
- "start_time": "2020-09-26T15:11:45.885626Z"
- },
- "scrolled": true
- },
- "outputs": [],
- "source": [
+ " !rm ./data/tetradymite_PRM2020/structures/*\n",
"compounds=df_train.index.to_list()\n",
"scale_factor = 10**10\n",
"alist = []\n",
"for compound in compounds:\n",
" for entry in range (1581):\n",
- " \n",
" labels = query[entry].section_run[0].section_system[-1].atom_labels\n",
" if (len(labels)>5):\n",
- " continue\n",
+ " continue\n",
" \n",
" labels_1 = str(labels[0])+'_'+str(labels[1])+'_'+str(labels[3])+'_'+str(labels[4])+'_'+str(labels[2])\n",
" labels_2 = str(labels[0])+'_'+str(labels[1])+'_'+str(labels[4])+'_'+str(labels[3])+'_'+str(labels[2])\n",
@@ -192,11 +229,11 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-24T15:05:00.227743Z",
- "start_time": "2020-09-24T15:05:00.213980Z"
+ "end_time": "2020-09-28T15:50:53.305285Z",
+ "start_time": "2020-09-28T15:50:53.291671Z"
}
},
"outputs": [],
@@ -205,45 +242,19 @@
"# col_01 = 10000\n",
"# col_10 = 95000\n",
"# col_11 = 100000\n",
- "# df_red = pd.concat([df_train[df_train.columns[col_00:col_01].to_list()],df_train[df_train.columns[col_10:col_11].to_list()],df_train['Class']],axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-24T15:05:00.433166Z",
- "start_time": "2020-09-24T15:05:00.418153Z"
- }
- },
- "outputs": [],
- "source": [
+ "# df_red = pd.concat([df_train[df_train.columns[col_00:col_01].to_list()],df_train[df_train.columns[col_10:col_11].to_list()],df_train['Class']],axis=1)\n",
"# df_red.to_pickle('./data/tetradymite_PRM2020/training_set')"
]
},
{
"cell_type": "code",
- "execution_count": 173,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-25T12:37:48.925176Z",
- "start_time": "2020-09-25T12:37:48.876683Z"
- }
- },
- "outputs": [],
- "source": [
- "df_train = pd.read_pickle('./data/tetradymite_PRM2020/training_set')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
+ "execution_count": 4,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-24T18:05:41.584081Z",
- "start_time": "2020-09-24T18:05:41.427515Z"
- }
+ "end_time": "2020-09-28T16:29:22.463936Z",
+ "start_time": "2020-09-28T16:29:22.337130Z"
+ },
+ "init_cell": true
},
"outputs": [],
"source": [
@@ -258,11 +269,12 @@
" ])\n",
"for comp in df_train.index:\n",
" ablmn = comp.split('_')\n",
- " df_feat.loc[comp] = pd.Series({'A':ablmn[0],\n",
- " 'B':ablmn[1],\n",
- " 'L':ablmn[2],\n",
- " 'M':ablmn[3],\n",
- " 'N':ablmn[4],\n",
+ " df_feat.loc[comp] = pd.Series({\n",
+ "# 'A':ablmn[0],\n",
+ "# 'B':ablmn[1],\n",
+ "# 'L':ablmn[2],\n",
+ "# 'M':ablmn[3],\n",
+ "# 'N':ablmn[4],\n",
" 'z_A':zeta[ablmn[0]],\n",
" 'z_B':zeta[ablmn[1]],\n",
" 'z_L':zeta[ablmn[2]],\n",
@@ -278,133 +290,226 @@
" 'l_L':lambd[ablmn[2]],\n",
" 'l_M':lambd[ablmn[3]],\n",
" 'l_N':lambd[ablmn[4]],\n",
- " }) \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 138,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-26T15:14:04.072271Z",
- "start_time": "2020-09-26T15:14:04.041798Z"
- }
- },
- "outputs": [],
- "source": [
- "# df_feat"
+ " }) \n",
+ "\n",
+ "df_feat['Class'] = df_train['Class']"
]
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 5,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T15:04:11.632031Z",
- "start_time": "2020-09-26T15:04:07.252979Z"
- }
+ "end_time": "2020-09-28T16:29:22.470868Z",
+ "start_time": "2020-09-28T16:29:22.465327Z"
+ },
+ "init_cell": true
},
"outputs": [],
"source": [
- "phi_0, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds = generate_phi_0_from_csv(\n",
- " df_train, \"Class\", cols=\"all\", task_key=None, leave_out_frac=0.0\n",
- ")\n",
+ "def get_feat_space_and_sr(\n",
+ " df,\n",
+ " ops= ['add', 'sub', 'abs_diff', 'mult', 'div', 'exp', 'neg_exp', 'inv', 'sq', 'cb', \n",
+ " 'sqrt', 'cbrt', 'log', 'abs'],\n",
+ " cols=\"all\",\n",
+ " max_phi=2,\n",
+ " n_sis_select=50,\n",
+ " remove_double_divison=True,\n",
+ " max_dim=3,\n",
+ " n_residual=1,\n",
+ " default=True,\n",
+ "):\n",
"\n",
- "feat_space = generate_fs(\n",
- " phi_0, \n",
- " prop, \n",
- " task_sizes_train, \n",
- " [\"add\", \"sub\", \"mult\", \"div\", \"abs_diff\", \"sq\", \"cb\", \"sqrt\", \"cbrt\", \"inv\", \"abs\"], \n",
- " \"classification\",\n",
- " 0, \n",
- " 50\n",
- ")\n",
+ " if default:\n",
+ " phi_0, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds = generate_phi_0_from_csv(\n",
+ " df_train, \"Class\", cols='all', task_key=None, leave_out_frac=0.0\n",
"\n",
- "sisso = SISSOClassifier(\n",
- " feat_space,\n",
- " prop_unit,\n",
- " prop,\n",
- " prop_test,\n",
- " task_sizes_train,\n",
- " task_sizes_test,\n",
- " leave_out_inds,\n",
- " 2,\n",
- " 10,\n",
- " 10,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 130,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-26T15:04:28.767213Z",
- "start_time": "2020-09-26T15:04:11.633705Z"
- }
- },
- "outputs": [],
- "source": [
- "sisso.fit()"
+ " )\n",
+ " feat_space = generate_fs(\n",
+ " phi_0, \n",
+ " prop, \n",
+ " task_sizes_train, \n",
+ " [\"add\", \"sub\", \"mult\", \"div\", \"abs_diff\", \"sq\", \"cb\", \"sqrt\", \"cbrt\", \"inv\", \"abs\"], \n",
+ " \"classification\",\n",
+ " 0, \n",
+ " 50\n",
+ " )\n",
+ " else:\n",
+ " phi_0, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds = generate_phi_0_from_csv(\n",
+ " df_feat, \"Class\", cols=cols, task_key=None, leave_out_frac=0.0, leave_out_inds=None\n",
+ " )\n",
+ " feat_space = generate_fs(\n",
+ " phi_0, \n",
+ " prop, \n",
+ " task_sizes_train, \n",
+ " ops,\n",
+ " \"classification\",\n",
+ " max_phi, \n",
+ " n_sis_select\n",
+ " )\n",
+ " \n",
+ " sisso = SISSOClassifier(\n",
+ " feat_space,\n",
+ " prop_unit,\n",
+ " prop,\n",
+ " prop_test,\n",
+ " task_sizes_train,\n",
+ " task_sizes_test,\n",
+ " leave_out_inds,\n",
+ " max_dim,\n",
+ " 10,\n",
+ " 10\n",
+ " )\n",
+ " return feat_space, sisso"
]
},
{
"cell_type": "code",
- "execution_count": 131,
+ "execution_count": 6,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T15:04:28.784474Z",
- "start_time": "2020-09-26T15:04:28.768858Z"
+ "end_time": "2020-09-28T16:29:22.491252Z",
+ "start_time": "2020-09-28T16:29:22.472488Z"
},
- "scrolled": true
+ "init_cell": true
},
"outputs": [],
"source": [
- "model = sisso.models[1][0]\n",
- "feat_1=model.feats[0].value\n",
- "feat_0=model.feats[1].value\n",
- "classified=model.prop_train\n",
- "compounds=df_train.index.to_list()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 132,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-09-26T15:04:28.802666Z",
- "start_time": "2020-09-26T15:04:28.786184Z"
- }
- },
- "outputs": [],
- "source": [
- "df=pd.DataFrame(data={\"Compound\":compounds,\n",
- " \"Classification\":classified})\n",
+ "from ipywidgets import widgets, interactive\n",
+ "from IPython.display import HTML, clear_output\n",
+ "\n",
+ "def plot_2d_solution(b):\n",
+ " with out2:\n",
+ " model = sisso.models[1][0]\n",
+ " classified=model.prop_train\n",
+ " compounds = df_train.index.to_list()\n",
+ " df=pd.DataFrame(data={\n",
+ " \"Compound\":compounds,\n",
+ " \"Classification\":classified})\n",
+ " for feat in sisso.models[sisso.n_dim-1][0].feats:\n",
+ " df[str(feat)]=feat.value\n",
+ " visualizer=Visualizer(df, sisso)\n",
+ " visualizer.show()\n",
+ " \n",
+ "def prm_select(change):\n",
+ " if change['new'] == 'PRM2015':\n",
+ " default_operations = ['add', 'sub', 'abs_diff', 'mult', 'div', 'exp', 'neg_exp', 'inv', 'sq', 'cb', \n",
+ " 'sqrt', 'cbrt', 'log', 'abs']\n",
+ " default_features = ['z_AB','chi_AB','lambda_AB','z_LMN','chi_LMN','lambda_LMN']\n",
+ "\n",
+ " for op, widget in zip(possible_operations, op_list):\n",
+ " widget.value = op in default_operations\n",
+ " widget.disabled = True\n",
+ " for feat, widget in zip(possible_features, feat_list):\n",
+ " widget.value = feat in default_features\n",
+ " widget.disabled = True\n",
+ " tier_selection.value = 'PRM2015'\n",
+ " feat_per_iter_selection.value = 50\n",
+ " dimension_selection.value = 2 \n",
+ " else:\n",
+ " for widget in op_list+feat_list:\n",
+ " widget.disabled = False\n",
"\n",
- "for feat in sisso.models[sisso.n_dim - 1][0].feats:\n",
- " df[str(feat)]=feat.value"
+ "def default_selection(b):\n",
+ " \n",
+ " default_operations = ['add', 'sub', 'abs_diff', 'mult', 'div', 'exp', 'neg_exp', 'inv', 'sq', 'cb', \n",
+ " 'sqrt', 'cbrt', 'log', 'abs']\n",
+ " default_features = ['z_AB','chi_AB','lambda_AB','z_LMN','chi_LMN','lambda_LMN']\n",
+ " for op, widget in zip(possible_operations, op_list):\n",
+ " widget.value = op in default_operations\n",
+ " widget.disabled = True\n",
+ " for feat, widget in zip(possible_features, feat_list):\n",
+ " widget.value = feat in default_features\n",
+ " widget.disabled = True\n",
+ " tier_selection.value = 'PRM2020'\n",
+ " feat_per_iter_selection.value = 50\n",
+ " dimension_selection.value = 2\n",
+ " \n",
+ "def find_descriptors(b):\n",
+ " with out2:\n",
+ " clear_output() \n",
+ " with out1:\n",
+ " clear_output()\n",
+ " print('Calculating...', flush=True)\n",
+ " selected_features = []\n",
+ " allowed_operations = []\n",
+ " for op, widget in zip(possible_operations, op_list):\n",
+ " if widget.value:\n",
+ " allowed_operations.append(op)\n",
+ " for sel_feat, widget in zip(possible_features, feat_list):\n",
+ " if widget.value:\n",
+ " feat = sel_feat.split('_')[0]\n",
+ " for char in sel_feat.split('_')[1]:\n",
+ " selected_features.append(feat + '_'+ char) \n",
+ " \n",
+ " if tier_selection.value == 'PRM2020':\n",
+ " selected_features = \"all\"\n",
+ " tier = 0\n",
+ " default = True\n",
+ " else:\n",
+ " tier = tier_selection.value\n",
+ " default = False\n",
+ " \n",
+ " global feat_space\n",
+ " global sisso\n",
+ " \n",
+ "# try:\n",
+ " feat_space, sisso = get_feat_space_and_sr(\n",
+ " df = df_train,\n",
+ " ops = allowed_operations,\n",
+ " cols = selected_features,\n",
+ " max_phi = tier,\n",
+ " n_sis_select = feat_per_iter_selection.value,\n",
+ " remove_double_divison=True,\n",
+ " max_dim = dimension_selection.value,\n",
+ " n_residual = 1,\n",
+ " default = default)\n",
+ " clear_output()\n",
+ " if (dimension_selection.value>1):\n",
+ " plot_button.disabled=False\n",
+ " else:\n",
+ " plot_button.disabled=True\n",
+ "\n",
+ " print(\"Number of features generated: \" + str(feat_space.n_feat))\n",
+ "\n",
+ " try:\n",
+ " sisso.fit()\n",
+ " for i in range(dimension_selection.value):\n",
+ " print(str(i+1)+'D model')\n",
+ "# print(\"RMSE: {:.4} | Descriptor: {}\".format(sisso.models[i][0].rmse, sisso.models[i][0]))\n",
+ " string = \"c0:{:.4}\".format(sisso.models[i][0].coefs[0][-1])\n",
+ " for j in range(i+1):\n",
+ " string = string + str(\" | a\"+str(j)+\":{:.4}\".format(sisso.models[i][0].coefs[0][j]))\n",
+ " print(string + '\\n')\n",
+ " global df\n",
+ "\n",
+ " except RuntimeError:\n",
+ " print(\"\\nThe number of selected features per SIS iteration is bigger than the number of features available. Please reduce the number of selected features per SIS iteration (number of features generated / max number of dimensions) or increase the number of selected features and operations.\")\n",
+ "# except:\n",
+ "# print('The present selection does not lead to the creation of any derived features in the highest selected rung, please select at least one binary or power operator, or reduce the maximum rung')"
]
},
{
"cell_type": "code",
- "execution_count": 139,
+ "execution_count": 7,
"metadata": {
"ExecuteTime": {
- "end_time": "2020-09-26T15:14:36.063894Z",
- "start_time": "2020-09-26T15:14:35.462458Z"
+ "end_time": "2020-09-28T16:29:22.749289Z",
+ "start_time": "2020-09-28T16:29:22.492553Z"
},
+ "init_cell": true,
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ec3056dff7ad49ceaeea04bcd583f307",
+ "model_id": "db66a946fda449bf9fb691a810eedb38",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "VBox(children=(HBox(children=(VBox(children=(Dropdown(description='x-axis', options=('[[[Z11A+Z11B]*[Z11C+Z11D…"
+ "VBox(children=(HBox(children=(VBox(children=(Label(value=''), Checkbox(value=True, disabled=True, indent=False…"
]
},
"metadata": {},
@@ -412,11 +517,91 @@
}
],
"source": [
- "Visualizer(df, sisso).show()"
+ "cb_layout = widgets.Layout(width = '15px')\n",
+ "thin_layout = widgets.Layout(width = '100px')\n",
+ "mid_layout = widgets.Layout(width = '200px')\n",
+ "wide_layout = widgets.Layout(width = '300px')\n",
+ "\n",
+ "possible_operations = ['add', 'sub', 'abs_diff', 'mult', 'div', 'exp', 'neg_exp', 'inv', 'sq', 'cb', \n",
+ " 'sqrt', 'cbrt', 'log', 'abs']\n",
+ "\n",
+ "possible_features = ['z_AB','chi_AB','lambda_AB','z_LMN','chi_LMN','lambda_LMN']\n",
+ "\n",
+ "tooltips = {\n",
+ " \"z_AB\" : \"Atomic number cations\",\n",
+ " \"chi_AB\" : \"Pauling electronegativit cations\",\n",
+ " \"lambda_AB\" : \"Atomic SOC constant cations\",\n",
+ " \"z_LMN\" : \"Atomic number anions\",\n",
+ " \"chi_LMN\" : \"Pauling electronegativity anions\",\n",
+ " \"lambda_LMN\" : \"Atomic SOC constant anions\",\n",
+ "}\n",
+ "\n",
+ "labels = {\n",
+ " 'add' : '$x + y$', 'sub' : '$x - y$', 'abs_diff' : '$|x - y|$', 'mult' : '$x \\cdot y$', 'div' : '$x / y$',\n",
+ " 'exp' : '$\\exp(x)$', 'neg_exp' : '$\\exp(-x)$', 'inv' : '$1/x$', 'sq' : '$x^2$', 'cb' : '$x^3$', \n",
+ " 'six_pow' : '$x^6$', 'sqrt' : '$\\sqrt{x}$', 'cbrt' : '$\\sqrt[3]{x}$', 'log' : '$\\log(x)$',\n",
+ " 'abs' : '$|x|$', 'sin' : '$\\sin(x)$', 'cos' : '$\\cos(x)$', 'z_AB' : '$Z_{AB}$', 'chi_AB' : '$\\chi_{AB}$', \n",
+ " 'lambda_AB' : '$\\lambda_{AB}$', 'z_LMN' : '$Z_{LMN}$', 'chi_LMN' : '$\\chi_{LMN}$', 'lambda_LMN' : '$\\lambda_{LMN}$' \n",
+ "}\n",
+ "\n",
+ "op_list = []\n",
+ "op_labels = []\n",
+ "feat_list = []\n",
+ "feat_labels = []\n",
+ "for operation in possible_operations:\n",
+ " op_list.append(widgets.Checkbox(description='', value=True, indent=False, layout=cb_layout))\n",
+ " op_labels.append(widgets.Label(value=labels[operation]))\n",
+ "for feature in possible_features:\n",
+ " feat_list.append(widgets.Checkbox(description=tooltips[feature], value=True, indent=False, layout=cb_layout))\n",
+ " feat_labels.append(widgets.Label(value=labels[feature]))\n",
+ " \n",
+ "op_box = widgets.VBox([widgets.Label()]+op_list)\n",
+ "op_label_box = widgets.VBox([widgets.Label(value='Operations:', layout=thin_layout)]+op_labels)\n",
+ "feat_box = widgets.VBox([widgets.Label()]+feat_list)\n",
+ "feat_label_box = widgets.VBox([widgets.Label(value='Features:', layout=thin_layout)]+feat_labels)\n",
+ "\n",
+ "tier_selection = widgets.Dropdown(options=['PRM2020', 1,2,3], layout=thin_layout)\n",
+ "feat_per_iter_selection = widgets.BoundedIntText(value=26, min=1, max=100, step=1, layout=thin_layout)\n",
+ "dimension_selection = widgets.BoundedIntText(value = 3, min=1, max=4, step=1, layout = thin_layout)\n",
+ "settings_box = widgets.VBox([\n",
+ " widgets.Label(value='Settings:', layout=wide_layout),\n",
+ " widgets.Label(value='SISSO rung:', layout=wide_layout),\n",
+ " tier_selection,\n",
+ " widgets.Label(value='Number of selected features per SIS iteration:', layout=wide_layout),\n",
+ " feat_per_iter_selection,\n",
+ " widgets.Label(value='Maximum number of dimensions:', layout=wide_layout),\n",
+ " dimension_selection])\n",
+ "\n",
+ "default_button = widgets.Button(description = 'Default selection', layout=mid_layout)\n",
+ "descriptor_button = widgets.Button(description = 'Run', layout=mid_layout)\n",
+ "plot_button = widgets.Button(description = 'Plot interactive map', disabled=True, layout=mid_layout)\n",
+ "default_button.on_click(default_selection)\n",
+ "descriptor_button.on_click(find_descriptors)\n",
+ "plot_button.on_click(plot_2d_solution)\n",
+ "button_box = widgets.VBox([default_button, descriptor_button, plot_button])\n",
+ "\n",
+ "out1 = widgets.Output()\n",
+ "out2 = widgets.Output()\n",
+ "\n",
+ "gui_box = widgets.HBox([op_box, op_label_box, feat_box, feat_label_box, settings_box, button_box])\n",
+ "out_box = widgets.VBox([gui_box, out1, out2])\n",
+ "\n",
+ "tier_selection.observe(prm_select, names='value')\n",
+ "\n",
+ "default_selection('')\n",
+ "display(out_box)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
+ "celltoolbar": "Initialization Cell",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
diff --git a/tetradymite_PRM2020/visualizer.py b/tetradymite_PRM2020/visualizer.py
index 6483cf5..c58a2fd 100644
--- a/tetradymite_PRM2020/visualizer.py
+++ b/tetradymite_PRM2020/visualizer.py
@@ -80,7 +80,8 @@ class Visualizer:
x_cls1 = df[self.features[0]][self.df_cls1]
y_cls1 = df[self.features[1]][self.df_cls1]
line_x, line_y = self.f_x(self.features[0], self.features[1])
- hullx_cls0, hully_cls0, hullx_cls1, hully_cls1 = self.make_hull(self.features[0], self.features[1])
+
+
# custom_cls0 = np.dstack((self.target_train[self.df_cls0],
# self.target_predict[self.df_cls0]))[0]
# custom_cls1 = np.dstack((self.target_train[self.df_cls1],
@@ -122,25 +123,41 @@ class Visualizer:
marker=dict(color=self.colors_cls1),
)
))
- self.fig.add_trace(
- go.Scatter(
- x=hullx_cls0,
- y=hully_cls0,
- line=dict(color='Grey', width=1, dash=self.line_styles[0]),
- name=r'Convex' + '<br>' + 'hull 0',
- visible=False
- ),
- )
- self.fig.add_trace(
- go.Scatter(
- x=hullx_cls1,
- y=hully_cls1,
- line=dict(color='Grey', width=1, dash=self.line_styles[0]),
- name=r'Convex' + '<br>' + 'hull 1',
- visible=False
- ),
+ try:
+ hullx_cls0, hully_cls0, hullx_cls1, hully_cls1 = self.make_hull(self.features[0], self.features[1])
+
+ self.fig.add_trace(
+ go.Scatter(
+ x=hullx_cls0,
+ y=hully_cls0,
+ line=dict(color='Grey', width=1, dash=self.line_styles[0]),
+ name=r'Convex' + '<br>' + 'hull 0',
+ visible=False
+ ),
+ )
+ self.fig.add_trace(
+ go.Scatter(
+ x=hullx_cls1,
+ y=hully_cls1,
+ line=dict(color='Grey', width=1, dash=self.line_styles[0]),
+ name=r'Convex' + '<br>' + 'hull 1',
+ visible=False
+ ),
+
+ )
+ except:
+ self.fig.add_trace(
+ go.Scatter(
+ visible=False
+ ),
+ )
+ self.fig.add_trace(
+ go.Scatter(
+ visible=False
+ ),
+
+ )
- )
self.fig.add_trace(
go.Scatter(
x=line_x,
--
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