diff --git a/code/.idea/.gitignore b/code/.idea/.gitignore
deleted file mode 100644
index 13566b81b018ad684f3a35fee301741b2734c8f4..0000000000000000000000000000000000000000
--- a/code/.idea/.gitignore
+++ /dev/null
@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml
diff --git a/code/.idea/code.iml b/code/.idea/code.iml
deleted file mode 100644
index 456b84dead990d2e2dad38e646b52ae8ab759da4..0000000000000000000000000000000000000000
--- a/code/.idea/code.iml
+++ /dev/null
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
- <component name="NewModuleRootManager">
- <content url="file://$MODULE_DIR$" />
- <orderEntry type="jdk" jdkName="cdbpr-env" jdkType="Python SDK" />
- <orderEntry type="sourceFolder" forTests="false" />
- </component>
- <component name="SonarLintModuleSettings">
- <option name="uniqueId" value="6084c8ba-5729-4f67-aa53-1f1cacc70561" />
- </component>
-</module>
\ No newline at end of file
diff --git a/code/.idea/inspectionProfiles/profiles_settings.xml b/code/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2da2d6447d11dfe32bfb846c3d5b199fc99..0000000000000000000000000000000000000000
--- a/code/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
- <settings>
- <option name="USE_PROJECT_PROFILE" value="false" />
- <version value="1.0" />
- </settings>
-</component>
\ No newline at end of file
diff --git a/code/.idea/misc.xml b/code/.idea/misc.xml
deleted file mode 100644
index 7161c54b1a465b3a67a3dbd057c458daae8a379d..0000000000000000000000000000000000000000
--- a/code/.idea/misc.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="Black">
- <option name="sdkName" value="cdbpr-env" />
- </component>
- <component name="ProjectRootManager" version="2" project-jdk-name="cdbpr-env" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/code/.idea/modules.xml b/code/.idea/modules.xml
deleted file mode 100644
index 23968dc678111b184a2091404b0a05ec67a7b905..0000000000000000000000000000000000000000
--- a/code/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="ProjectModuleManager">
- <modules>
- <module fileurl="file://$PROJECT_DIR$/.idea/code.iml" filepath="$PROJECT_DIR$/.idea/code.iml" />
- </modules>
- </component>
-</project>
\ No newline at end of file
diff --git a/code/.ipynb_checkpoints/Experiments-checkpoint.ipynb b/code/.ipynb_checkpoints/Experiments-checkpoint.ipynb
deleted file mode 100644
index ca54f3255bb8ed3e350a8bb1d627e33b85b32c52..0000000000000000000000000000000000000000
--- a/code/.ipynb_checkpoints/Experiments-checkpoint.ipynb
+++ /dev/null
@@ -1,6563 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "087d8076",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "c0bd6c14",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/\"\n",
- "datasets = ['assist09', 'assist17', 'algebra','math1', 'math2']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1da92e4f",
- "metadata": {},
- "source": [
- "## Table 2: compute ACC, AUC and RMSE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "61c53cb1",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "NB KC 123\n",
- "NB Users 2493\n",
- "NB (question-answer) in the data 17506\n",
- "Epoch 1, Loss: 1.3718266487121582\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Traceback (most recent call last):\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/./binary_model/main.py\", line 223, in <module>\n",
- " acc = bpr_model.train(train, len(dico_kc), epochs, batch_size, y_train, ablation)\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/binary_model/BPR_model.py\", line 105, in train\n",
- " self.optimizer.step()\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/optim/optimizer.py\", line 280, in wrapper\n",
- " out = func(*args, **kwargs)\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/optim/optimizer.py\", line 33, in _use_grad\n",
- " ret = func(self, *args, **kwargs)\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/optim/adam.py\", line 141, in step\n",
- " adam(\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/optim/adam.py\", line 281, in adam\n",
- " func(params,\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/optim/adam.py\", line 391, in _single_tensor_adam\n",
- " denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)\n",
- "KeyboardInterrupt\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "i = 0\n",
- "cmd = 'python ./binary_model/main.py --dataTrain '+path+'data/'+datasets[i]+'/train.csv --dataTest '+path+'data/'+datasets[i]+'/test.csv' \n",
- "os.system(cmd)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6b1c6b4d",
- "metadata": {},
- "source": [
- "## Table 3: compute DOA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "790a43dd",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/home/celine/travail.svn/recherche/phd_subject/Arthur/code\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Traceback (most recent call last):\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/./binary_model/compute_doa.py\", line 11, in <module>\n",
- " doa = compute_doa(data)\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/binary_model/utils.py\", line 163, in compute_doa\n",
- " r = doa_creuse(F, kc_user, kc_user_val, dico_u)\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/binary_model/utils.py\", line 136, in doa_creuse\n",
- " while(ma < len(kc_user[k][ia])) and (mb < len(kc_user[k][ib])):\n",
- "KeyboardInterrupt\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "i = 0\n",
- "'''\n",
- "cmd = 'cd '+path+'binary_model/'\n",
- "cmd = 'ls'\n",
- "os.system(cmd)\n",
- "print(os.getcwd())\n",
- "os.system(cmd)\n",
- "'''\n",
- "print(os.getcwd())\n",
- "cmd = 'python ./binary_model/compute_doa.py --data '+path+'data/'+datasets[i]+'/train' \n",
- "os.system(cmd)\n",
- "\n",
- "#doa = compute_doa(path+datasets[i]+'/train_embed.csv')\n",
- "#print(\"DOA:\", doa)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8d5630b5",
- "metadata": {},
- "source": [
- "## Table 4: ablation --"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "a9e32954",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "NB KC 16\n",
- "NB Users 3911\n",
- "NB (question-answer) in the data 20\n",
- "Epoch 1, Loss: 0.6622111201286316\n",
- "Epoch 11, Loss: 0.5124014616012573\n",
- "DOA: 0.5445884380178283\n",
- "RMSE 0.42956248308434386\n",
- "AUC: 0.8054917274129436\n",
- "ACC: 0.7339260075133254\n",
- "Prec: 0.6877265427850424\n",
- "NB KC 16\n",
- "NB Users 3911\n",
- "NB (question-answer) in the data 20\n",
- "Epoch 1, Loss: 0.6848532557487488\n",
- "Epoch 11, Loss: 0.5197614431381226\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Traceback (most recent call last):\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/./binary_model/main.py\", line 227, in <module>\n",
- " doa = compute_doa(file)\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/binary_model/utils.py\", line 163, in compute_doa\n",
- " r = doa_creuse(F, kc_user, kc_user_val, dico_u)\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/binary_model/utils.py\", line 136, in doa_creuse\n",
- " while(ma < len(kc_user[k][ia])) and (mb < len(kc_user[k][ib])):\n",
- "KeyboardInterrupt\n",
- "Traceback (most recent call last):\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/./binary_model/main.py\", line 2, in <module>\n",
- " import torch\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/__init__.py\", line 1239, in <module>\n",
- " from torch import onnx as onnx\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/__init__.py\", line 12, in <module>\n",
- " from . import ( # usort:skip. Keep the order instead of sorting lexicographically\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/errors.py\", line 9, in <module>\n",
- " from torch.onnx._internal import diagnostics\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/__init__.py\", line 1, in <module>\n",
- " from ._diagnostic import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/_diagnostic.py\", line 10, in <module>\n",
- " from torch.onnx._internal.diagnostics import infra\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/__init__.py\", line 1, in <module>\n",
- " from ._infra import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/_infra.py\", line 10, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra import formatter, sarif\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/formatter.py\", line 7, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra import sarif\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/__init__.py\", line 14, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra.sarif._code_flow import CodeFlow\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_code_flow.py\", line 9, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra.sarif import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow.py\", line 9, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra.sarif import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_thread_flow_location.py\", line 9, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra.sarif import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack.py\", line 9, in <module>\n",
- " from torch.onnx._internal.diagnostics.infra.sarif import (\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/onnx/_internal/diagnostics/infra/sarif/_stack_frame.py\", line 13, in <module>\n",
- " class StackFrame(object):\n",
- " File \"/home/celine/miniconda3/lib/python3.10/dataclasses.py\", line 1184, in dataclass\n",
- " return wrap(cls)\n",
- " File \"/home/celine/miniconda3/lib/python3.10/dataclasses.py\", line 1175, in wrap\n",
- " return _process_class(cls, init, repr, eq, order, unsafe_hash,\n",
- " File \"/home/celine/miniconda3/lib/python3.10/dataclasses.py\", line 1053, in _process_class\n",
- " _cmp_fn('__eq__', '==',\n",
- " File \"/home/celine/miniconda3/lib/python3.10/dataclasses.py\", line 629, in _cmp_fn\n",
- " return _create_fn(name,\n",
- " File \"/home/celine/miniconda3/lib/python3.10/dataclasses.py\", line 432, in _create_fn\n",
- " exec(txt, globals, ns)\n",
- " File \"<string>\", line 1, in <module>\n",
- "KeyboardInterrupt\n",
- "Traceback (most recent call last):\n",
- " File \"/home/celine/travail.svn/recherche/phd_subject/Arthur/code/./binary_model/main.py\", line 2, in <module>\n",
- " import torch\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/__init__.py\", line 934, in <module>\n",
- " from .storage import _StorageBase, TypedStorage, _LegacyStorage, UntypedStorage, _warn_typed_storage_removal\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/torch/storage.py\", line 12, in <module>\n",
- " import numpy as np\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/numpy/__init__.py\", line 139, in <module>\n",
- " from . import core\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/numpy/core/__init__.py\", line 77, in <module>\n",
- " from . import defchararray as char\n",
- " File \"/home/celine/miniconda3/lib/python3.10/site-packages/numpy/core/defchararray.py\", line 1923, in <module>\n",
- " class chararray(ndarray):\n",
- "KeyboardInterrupt\n"
- ]
- }
- ],
- "source": [
- "# 0 no ablation, 1 ablation L2, 2 ablation init, 3 both\n",
- "for abla in range(4):\n",
- " for i in range(5):\n",
- " cmd = 'python ./binary_model/main.py --dataTrain '+ path+'data/'+datasets[i]+'/train.csv --dataTest '+path+'data/'+datasets[i]+'/test.csv --ablation '+str(abla)\n",
- " os.system(cmd)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c5960372",
- "metadata": {},
- "source": [
- "## Table 6 --"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "e46ff5d4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "avg values 0.8171224732461354 0.7558291282588128 0.7514963299272839 0.6211694246268313\n",
- "0\n",
- "avg values 0.8217752573238839 0.8345388354351844 0.8288387755136633 0.5900735950578464\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# pour all\n",
- "cmd = 'python ./nary_model/main_nary_cv.py --data '+ path+'data/covid/initsurvey.csv' \n",
- "print(os.system(cmd))\n",
- "cmd = 'python ./nary_model/main_nary_cv.py --data '+path+'data/covid/psysurvey.csv' \n",
- "print(os.system(cmd))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e586f5cb",
- "metadata": {},
- "source": [
- "## Figure 1 and Table 7"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "4c4b8106",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/home/celine/travail.svn/recherche/phd_subject/Arthur/code/other/dt/train_embed.csv\n",
- "| --- feature_3 <= 0.55\n",
- "| | --- feature_9 <= -0.07\n",
- "| | | --- cluster 0 (151)\n",
- "| | --- feature_9 > -0.07\n",
- "| | | --- feature_15 <= -0.30\n",
- "| | | | --- cluster 1 (111)\n",
- "| | | --- feature_15 > -0.30\n",
- "| | | | --- feature_10 <= -0.25\n",
- "| | | | | --- cluster 2 (57)\n",
- "| | | | --- feature_10 > -0.25\n",
- "| | | | | --- cluster 3 (415)\n",
- "| --- feature_3 > 0.55\n",
- "| | --- feature_8 <= 0.35\n",
- "| | | --- cluster 4 (110)\n",
- "| | --- feature_8 > 0.35\n",
- "| | | --- cluster 5 (73)\n",
- "(array([ 7, 14, 20, 22, 23, 39, 44, 53, 56, 57, 62, 71, 95,\n",
- " 100, 106, 119, 122, 133, 141, 142, 152, 154, 156, 160, 179, 182,\n",
- " 183, 187, 188, 191, 194, 198, 199, 201, 203, 207, 208, 214, 219,\n",
- " 221, 224, 233, 235, 241, 243, 246, 252, 259, 261, 265, 266, 267,\n",
- " 271, 282, 289, 293, 299, 302, 303, 304, 307, 308, 309, 310, 312,\n",
- " 313, 325, 327, 351, 358, 385, 390, 393, 396, 406, 409, 415, 419,\n",
- " 431, 435, 445, 447, 448, 450, 468, 479, 486, 488, 490, 494, 510,\n",
- " 516, 517, 525, 546, 547, 550, 551, 553, 557, 559, 565, 567, 578,\n",
- " 580, 597, 600, 604, 608, 611, 612, 615, 640, 641, 644, 656, 662,\n",
- " 677, 679, 682, 686, 688, 690, 703, 709, 714, 723, 732, 745, 752,\n",
- " 776, 782, 785, 786, 787, 789, 792, 798, 810, 829, 839, 842, 844,\n",
- " 847, 854, 866, 869, 870, 881, 883, 900]),)\n",
- "(array([ 4, 8, 15, 28, 40, 41, 42, 50, 59, 84, 85, 88, 89,\n",
- " 109, 112, 123, 138, 145, 146, 150, 157, 158, 166, 181, 186, 190,\n",
- " 232, 234, 242, 247, 248, 249, 260, 272, 274, 277, 281, 296, 301,\n",
- " 338, 339, 345, 352, 359, 365, 388, 403, 404, 412, 418, 441, 443,\n",
- " 461, 464, 478, 481, 489, 491, 495, 497, 500, 508, 524, 531, 569,\n",
- " 576, 587, 589, 609, 610, 613, 617, 620, 622, 627, 637, 642, 647,\n",
- " 673, 678, 711, 712, 715, 720, 726, 729, 730, 758, 759, 760, 788,\n",
- " 797, 811, 815, 819, 820, 821, 830, 831, 840, 845, 849, 862, 878,\n",
- " 880, 884, 891, 895, 898, 904, 906]),)\n",
- "(array([ 58, 75, 80, 103, 114, 161, 205, 210, 211, 226, 227, 236, 244,\n",
- " 257, 258, 328, 347, 356, 357, 372, 380, 391, 399, 400, 402, 410,\n",
- " 455, 457, 526, 558, 583, 606, 614, 616, 618, 624, 643, 645, 660,\n",
- " 664, 676, 691, 717, 738, 755, 756, 763, 771, 796, 812, 817, 832,\n",
- " 850, 861, 868, 882, 912]),)\n",
- "(array([ 0, 2, 3, 5, 6, 9, 10, 11, 12, 17, 18, 24, 29,\n",
- " 30, 31, 32, 33, 34, 35, 36, 38, 43, 46, 48, 52, 54,\n",
- " 55, 63, 64, 66, 68, 69, 72, 74, 76, 77, 78, 79, 81,\n",
- " 82, 87, 90, 91, 92, 93, 94, 98, 99, 101, 102, 104, 105,\n",
- " 107, 108, 110, 111, 115, 117, 120, 121, 124, 126, 127, 129, 130,\n",
- " 131, 132, 134, 135, 136, 137, 139, 143, 144, 148, 149, 151, 155,\n",
- " 163, 167, 171, 172, 173, 175, 176, 177, 178, 184, 189, 192, 193,\n",
- " 195, 196, 197, 200, 202, 204, 206, 215, 218, 222, 223, 225, 229,\n",
- " 230, 237, 238, 240, 251, 253, 254, 256, 262, 263, 264, 268, 273,\n",
- " 278, 279, 280, 283, 284, 287, 288, 290, 291, 292, 294, 295, 305,\n",
- " 306, 315, 317, 318, 319, 320, 321, 322, 323, 324, 330, 331, 332,\n",
- " 333, 334, 336, 337, 340, 342, 343, 346, 350, 353, 354, 355, 360,\n",
- " 361, 363, 364, 366, 367, 368, 370, 371, 373, 374, 376, 382, 383,\n",
- " 386, 389, 392, 394, 397, 401, 407, 411, 413, 414, 416, 417, 423,\n",
- " 424, 425, 426, 427, 428, 429, 432, 434, 437, 438, 440, 442, 444,\n",
- " 446, 449, 453, 454, 456, 458, 459, 462, 467, 469, 471, 472, 473,\n",
- " 475, 477, 480, 482, 483, 484, 485, 492, 493, 496, 498, 499, 502,\n",
- " 504, 506, 507, 509, 513, 515, 518, 519, 520, 521, 522, 523, 527,\n",
- " 528, 529, 530, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542,\n",
- " 543, 544, 545, 549, 552, 556, 560, 561, 562, 563, 566, 570, 571,\n",
- " 573, 574, 575, 577, 581, 582, 585, 588, 591, 592, 593, 594, 595,\n",
- " 596, 598, 599, 601, 602, 603, 605, 607, 619, 623, 625, 626, 628,\n",
- " 629, 630, 631, 632, 635, 638, 639, 648, 649, 650, 651, 652, 654,\n",
- " 657, 663, 667, 668, 669, 670, 672, 675, 681, 683, 684, 692, 693,\n",
- " 694, 695, 697, 698, 700, 704, 705, 706, 707, 708, 710, 713, 716,\n",
- " 718, 719, 722, 724, 727, 728, 731, 733, 734, 735, 736, 737, 740,\n",
- " 741, 744, 747, 748, 749, 750, 751, 754, 757, 762, 764, 765, 767,\n",
- " 768, 770, 772, 773, 774, 775, 777, 778, 779, 780, 781, 783, 791,\n",
- " 793, 795, 799, 800, 801, 802, 803, 804, 806, 808, 816, 818, 822,\n",
- " 823, 824, 825, 826, 833, 836, 837, 838, 841, 846, 848, 851, 852,\n",
- " 856, 857, 864, 873, 875, 877, 885, 886, 887, 888, 889, 890, 893,\n",
- " 894, 896, 897, 899, 901, 902, 903, 905, 907, 909, 911, 914]),)\n",
- "(array([ 13, 16, 19, 27, 37, 47, 49, 60, 61, 65, 67, 70, 73,\n",
- " 83, 86, 125, 128, 140, 147, 153, 159, 162, 165, 168, 170, 185,\n",
- " 209, 228, 239, 245, 250, 269, 270, 275, 285, 300, 311, 314, 316,\n",
- " 329, 341, 348, 349, 362, 369, 375, 377, 378, 379, 384, 395, 398,\n",
- " 408, 433, 439, 452, 465, 466, 470, 487, 501, 503, 514, 540, 548,\n",
- " 554, 555, 564, 568, 572, 584, 590, 634, 653, 655, 658, 659, 661,\n",
- " 665, 666, 671, 674, 696, 699, 702, 721, 739, 742, 746, 753, 769,\n",
- " 784, 790, 794, 809, 813, 827, 828, 843, 853, 855, 858, 859, 860,\n",
- " 865, 867, 874, 908, 915, 916]),)\n",
- "\n",
- "Purity: 0.8255179934569248\n",
- "0\n",
- "/home/celine/travail.svn/recherche/phd_subject/Arthur/code/other/dt/train_embed.csv\n",
- "| --- feature_3 <= 0.55\n",
- "| | --- feature_9 <= -0.07\n",
- "| | | --- cluster 0 (151)\n",
- "| | --- feature_9 > -0.07\n",
- "| | | --- feature_15 <= -0.30\n",
- "| | | | --- cluster 1 (111)\n",
- "| | | --- feature_15 > -0.30\n",
- "| | | | --- feature_10 <= -0.25\n",
- "| | | | | --- cluster 2 (57)\n",
- "| | | | --- feature_10 > -0.25\n",
- "| | | | | --- cluster 3 (415)\n",
- "| --- feature_3 > 0.55\n",
- "| | --- feature_8 <= 0.35\n",
- "| | | --- cluster 4 (110)\n",
- "| | --- feature_8 > 0.35\n",
- "| | | --- cluster 5 (73)\n",
- "(array([ 7, 14, 20, 22, 23, 39, 44, 53, 56, 57, 62, 71, 95,\n",
- " 100, 106, 119, 122, 133, 141, 142, 152, 154, 156, 160, 179, 182,\n",
- " 183, 187, 188, 191, 194, 198, 199, 201, 203, 207, 208, 214, 219,\n",
- " 221, 224, 233, 235, 241, 243, 246, 252, 259, 261, 265, 266, 267,\n",
- " 271, 282, 289, 293, 299, 302, 303, 304, 307, 308, 309, 310, 312,\n",
- " 313, 325, 327, 351, 358, 385, 390, 393, 396, 406, 409, 415, 419,\n",
- " 431, 435, 445, 447, 448, 450, 468, 479, 486, 488, 490, 494, 510,\n",
- " 516, 517, 525, 546, 547, 550, 551, 553, 557, 559, 565, 567, 578,\n",
- " 580, 597, 600, 604, 608, 611, 612, 615, 640, 641, 644, 656, 662,\n",
- " 677, 679, 682, 686, 688, 690, 703, 709, 714, 723, 732, 745, 752,\n",
- " 776, 782, 785, 786, 787, 789, 792, 798, 810, 829, 839, 842, 844,\n",
- " 847, 854, 866, 869, 870, 881, 883, 900]),)\n",
- "(array([ 4, 8, 15, 28, 40, 41, 42, 50, 59, 84, 85, 88, 89,\n",
- " 109, 112, 123, 138, 145, 146, 150, 157, 158, 166, 181, 186, 190,\n",
- " 232, 234, 242, 247, 248, 249, 260, 272, 274, 277, 281, 296, 301,\n",
- " 338, 339, 345, 352, 359, 365, 388, 403, 404, 412, 418, 441, 443,\n",
- " 461, 464, 478, 481, 489, 491, 495, 497, 500, 508, 524, 531, 569,\n",
- " 576, 587, 589, 609, 610, 613, 617, 620, 622, 627, 637, 642, 647,\n",
- " 673, 678, 711, 712, 715, 720, 726, 729, 730, 758, 759, 760, 788,\n",
- " 797, 811, 815, 819, 820, 821, 830, 831, 840, 845, 849, 862, 878,\n",
- " 880, 884, 891, 895, 898, 904, 906]),)\n",
- "(array([ 58, 75, 80, 103, 114, 161, 205, 210, 211, 226, 227, 236, 244,\n",
- " 257, 258, 328, 347, 356, 357, 372, 380, 391, 399, 400, 402, 410,\n",
- " 455, 457, 526, 558, 583, 606, 614, 616, 618, 624, 643, 645, 660,\n",
- " 664, 676, 691, 717, 738, 755, 756, 763, 771, 796, 812, 817, 832,\n",
- " 850, 861, 868, 882, 912]),)\n",
- "(array([ 0, 2, 3, 5, 6, 9, 10, 11, 12, 17, 18, 24, 29,\n",
- " 30, 31, 32, 33, 34, 35, 36, 38, 43, 46, 48, 52, 54,\n",
- " 55, 63, 64, 66, 68, 69, 72, 74, 76, 77, 78, 79, 81,\n",
- " 82, 87, 90, 91, 92, 93, 94, 98, 99, 101, 102, 104, 105,\n",
- " 107, 108, 110, 111, 115, 117, 120, 121, 124, 126, 127, 129, 130,\n",
- " 131, 132, 134, 135, 136, 137, 139, 143, 144, 148, 149, 151, 155,\n",
- " 163, 167, 171, 172, 173, 175, 176, 177, 178, 184, 189, 192, 193,\n",
- " 195, 196, 197, 200, 202, 204, 206, 215, 218, 222, 223, 225, 229,\n",
- " 230, 237, 238, 240, 251, 253, 254, 256, 262, 263, 264, 268, 273,\n",
- " 278, 279, 280, 283, 284, 287, 288, 290, 291, 292, 294, 295, 305,\n",
- " 306, 315, 317, 318, 319, 320, 321, 322, 323, 324, 330, 331, 332,\n",
- " 333, 334, 336, 337, 340, 342, 343, 346, 350, 353, 354, 355, 360,\n",
- " 361, 363, 364, 366, 367, 368, 370, 371, 373, 374, 376, 382, 383,\n",
- " 386, 389, 392, 394, 397, 401, 407, 411, 413, 414, 416, 417, 423,\n",
- " 424, 425, 426, 427, 428, 429, 432, 434, 437, 438, 440, 442, 444,\n",
- " 446, 449, 453, 454, 456, 458, 459, 462, 467, 469, 471, 472, 473,\n",
- " 475, 477, 480, 482, 483, 484, 485, 492, 493, 496, 498, 499, 502,\n",
- " 504, 506, 507, 509, 513, 515, 518, 519, 520, 521, 522, 523, 527,\n",
- " 528, 529, 530, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542,\n",
- " 543, 544, 545, 549, 552, 556, 560, 561, 562, 563, 566, 570, 571,\n",
- " 573, 574, 575, 577, 581, 582, 585, 588, 591, 592, 593, 594, 595,\n",
- " 596, 598, 599, 601, 602, 603, 605, 607, 619, 623, 625, 626, 628,\n",
- " 629, 630, 631, 632, 635, 638, 639, 648, 649, 650, 651, 652, 654,\n",
- " 657, 663, 667, 668, 669, 670, 672, 675, 681, 683, 684, 692, 693,\n",
- " 694, 695, 697, 698, 700, 704, 705, 706, 707, 708, 710, 713, 716,\n",
- " 718, 719, 722, 724, 727, 728, 731, 733, 734, 735, 736, 737, 740,\n",
- " 741, 744, 747, 748, 749, 750, 751, 754, 757, 762, 764, 765, 767,\n",
- " 768, 770, 772, 773, 774, 775, 777, 778, 779, 780, 781, 783, 791,\n",
- " 793, 795, 799, 800, 801, 802, 803, 804, 806, 808, 816, 818, 822,\n",
- " 823, 824, 825, 826, 833, 836, 837, 838, 841, 846, 848, 851, 852,\n",
- " 856, 857, 864, 873, 875, 877, 885, 886, 887, 888, 889, 890, 893,\n",
- " 894, 896, 897, 899, 901, 902, 903, 905, 907, 909, 911, 914]),)\n",
- "(array([ 13, 16, 19, 27, 37, 47, 49, 60, 61, 65, 67, 70, 73,\n",
- " 83, 86, 125, 128, 140, 147, 153, 159, 162, 165, 168, 170, 185,\n",
- " 209, 228, 239, 245, 250, 269, 270, 275, 285, 300, 311, 314, 316,\n",
- " 329, 341, 348, 349, 362, 369, 375, 377, 378, 379, 384, 395, 398,\n",
- " 408, 433, 439, 452, 465, 466, 470, 487, 501, 503, 514, 540, 548,\n",
- " 554, 555, 564, 568, 572, 584, 590, 634, 653, 655, 658, 659, 661,\n",
- " 665, 666, 671, 674, 696, 699, 702, 721, 739, 742, 746, 753, 769,\n",
- " 784, 790, 794, 809, 813, 827, 828, 843, 853, 855, 858, 859, 860,\n",
- " 865, 867, 874, 908, 915, 916]),)\n",
- "\n",
- "Purity: 0.5986913849509269\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0\n"
- ]
- }
- ],
- "source": [
- "cmd = 'python ./other/decision_tree.py --lower 0'\n",
- "print(os.system(cmd))\n",
- "cmd = 'python ./other/decision_tree.py --lower 1'\n",
- "print(os.system(cmd))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f39f6de3",
- "metadata": {},
- "source": [
- "## Figure 2: radar plots --"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "ceee0ec4",
- "metadata": {},
- "outputs": [],
- "source": [
- "def fromDFtoArray(name, vector, type_value):\n",
- " # Read dataframe and generate a matrix or \n",
- " # a vector of appropriate type\n",
- " df = pd.read_csv(name,index_col=None, header=None)\n",
- " cols = df.columns\n",
- " if(type_value == \"f\"): \n",
- " for col in cols:\n",
- " df[col] = df[col].astype(float)\n",
- " if(type_value == 'i'):\n",
- " for col in cols:\n",
- " df[col] = df[col].astype(int)\n",
- " r = df.values\n",
- " if(vector):\n",
- " r = r.reshape(-1,)\n",
- " return r"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "801e2ab1",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.preprocessing import MinMaxScaler\n",
- "import plotly.graph_objects as go\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import csv\n",
- "from matplotlib import gridspec\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "5a8b764b",
- "metadata": {},
- "outputs": [],
- "source": [
- "H_users = fromDFtoArray(path+ \"other/dt/train_embed.csv\",False,'f') \n",
- "classPsy = fromDFtoArray(path+ \"other/dt/train_user_quest_label.csv\",False,'f') "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "33643887",
- "metadata": {},
- "outputs": [],
- "source": [
- "d = [151,111,57,415,110,73]\n",
- "g = [[7, 14, 20, 22, 23, 39, 44, 53, 56, 57, 62, 71, 95, 100, 106, 119, 122, 133, 141, 142, 152, 154, 156, 160, 179, 182, 183, 187, 188, 191, 194, 198, 199, 201, 203, 207, 208, 214, 219, 221, 224, 233, 235, 241, 243, 246, 252, 259, 261, 265, 266, 267, 271, 282, 289, 293, 299, 302, 303, 304, 307, 308, 309, 310, 312, 313, 325, 327, 351, 358, 385, 390, 393, 396, 406, 409, 415, 419, 431, 435, 445, 447, 448, 450, 468, 479, 486, 488, 490, 494, 510, 516, 517, 525, 546, 547, 550, 551, 553, 557, 559, 565, 567, 578, 580, 597, 600, 604, 608, 611, 612, 615, 640, 641, 644, 656, 662, 677, 679, 682, 686, 688, 690, 703, 709, 714, 723, 732, 745, 752, 776, 782, 785, 786, 787, 789, 792, 798, 810, 829, 839, 842, 844, 847, 854, 866, 869, 870, 881, 883, 900]\n",
- " , [4, 8, 15, 28, 40, 41, 42, 50, 59, 84, 85, 88, 89, 109, 112, 123, 138, 145, 146, 150, 157, 158, 166, 181, 186, 190, 232, 234, 242, 247, 248, 249, 260, 272, 274, 277, 281, 296, 301, 338, 339, 345, 352, 359, 365, 388, 403, 404, 412, 418, 441, 443, 461, 464, 478, 481, 489, 491, 495, 497, 500, 508, 524, 531, 569, 576, 587, 589, 609, 610, 613, 617, 620, 622, 627, 637, 642, 647, 673, 678, 711, 712, 715, 720, 726, 729, 730, 758, 759, 760, 788, 797, 811, 815, 819, 820, 821, 830, 831, 840, 845, 849, 862, 878, 880, 884, 891, 895, 898, 904, 906 ]\n",
- ",[58, 75, 80, 103, 114, 161, 205, 210, 211, 226, 227, 236, 244, 257, 258, 328, 347, 356, 357, 372, 380, 391, 399, 400, 402, 410, 455, 457, 526, 558, 583, 606, 614, 616, 618, 624, 643, 645, 660, 664, 676, 691, 717, 738, 755, 756, 763, 771, 796, 812, 817, 832, 850, 861, 868, 882, 912]\n",
- " ,[0, 2, 3, 5, 6, 9, 10, 11, 12, 17, 18, 24, 29, 30, 31, 32, 33, 34, 35, 36, 38, 43, 46, 48, 52, 54, 55, 63, 64, 66, 68, 69, 72, 74, 76, 77, 78, 79, 81, 82, 87, 90, 91, 92, 93, 94, 98, 99, 101, 102, 104, 105, 107, 108, 110, 111, 115, 117, 120, 121, 124, 126, 127, 129, 130, 131, 132, 134, 135, 136, 137, 139, 143, 144, 148, 149, 151, 155, 163, 167, 171, 172, 173, 175, 176, 177, 178, 184, 189, 192, 193, 195, 196, 197, 200, 202, 204, 206, 215, 218, 222, 223, 225, 229, 230, 237, 238, 240, 251, 253, 254, 256, 262, 263, 264, 268, 273, 278, 279, 280, 283, 284, 287, 288, 290, 291, 292, 294, 295, 305, 306, 315, 317, 318, 319, 320, 321, 322, 323, 324, 330, 331, 332, 333, 334, 336, 337, 340, 342, 343, 346, 350, 353, 354, 355, 360, 361, 363, 364, 366, 367, 368, 370, 371, 373, 374, 376, 382, 383, 386, 389, 392, 394, 397, 401, 407, 411, 413, 414, 416, 417, 423, 424, 425, 426, 427, 428, 429, 432, 434, 437, 438, 440, 442, 444, 446, 449, 453, 454, 456, 458, 459, 462, 467, 469, 471, 472, 473, 475, 477, 480, 482, 483, 484, 485, 492, 493, 496, 498, 499, 502, 504, 506, 507, 509, 513, 515, 518, 519, 520, 521, 522, 523, 527, 528, 529, 530, 532, 533, 534, 535, 536, 537, 538, 539, 541, 542, 543, 544, 545, 549, 552, 556, 560, 561, 562, 563, 566, 570, 571, 573, 574, 575, 577, 581, 582, 585, 588, 591, 592, 593, 594, 595, 596, 598, 599, 601, 602, 603, 605, 607, 619, 623, 625, 626, 628, 629, 630, 631, 632, 635, 638, 639, 648, 649, 650, 651, 652, 654, 657, 663, 667, 668, 669, 670, 672, 675, 681, 683, 684, 692, 693, 694, 695, 697, 698, 700, 704, 705, 706, 707, 708, 710, 713, 716, 718, 719, 722, 724, 727, 728, 731, 733, 734, 735, 736, 737, 740, 741, 744, 747, 748, 749, 750, 751, 754, 757, 762, 764, 765, 767, 768, 770, 772, 773, 774, 775, 777, 778, 779, 780, 781, 783, 791, 793, 795, 799, 800, 801, 802, 803, 804, 806, 808, 816, 818, 822, 823, 824, 825, 826, 833, 836, 837, 838, 841, 846, 848, 851, 852, 856, 857, 864, 873, 875, 877, 885, 886, 887, 888, 889, 890, 893, 894, 896, 897, 899, 901, 902, 903, 905, 907, 909, 911, 914] \n",
- ",[13, 16, 19, 27, 37, 47, 49, 60, 61, 65, 67, 70, 73, 83, 86, 125, 128, 140, 147, 153, 159, 162, 165, 168, 170, 185, 209, 228, 239, 245, 250, 269, 270, 275, 285, 300, 311, 314, 316, 329, 341, 348, 349, 362, 369, 375, 377, 378, 379, 384, 395, 398, 408, 433, 439, 452, 465, 466, 470, 487, 501, 503, 514, 540, 548, 554, 555, 564, 568, 572, 584, 590, 634, 653, 655, 658, 659, 661, 665, 666, 671, 674, 696, 699, 702, 721, 739, 742, 746, 753, 769, 784, 790, 794, 809, 813, 827, 828, 843, 853, 855, 858, 859, 860, 865, 867, 874, 908, 915, 916]\n",
- ",[1, 21, 25, 26, 45, 51, 96, 97, 113, 116, 118, 164, 169, 174, 180, 212, 213, 216, 217, 220, 231, 255, 276, 286, 297, 298, 326, 335, 344, 381, 387, 405, 420, 421, 422, 430, 436, 451, 460, 463, 474, 476, 505, 511, 512, 579, 586, 621, 633, 636, 646, 680, 685, 687, 689, 701, 725, 743, 761, 766, 805, 807, 814, 834, 835, 863, 871, 872, 876, 879, 892, 910, 913] \n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "d190bd89",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "17\n",
- "(917, 17)\n",
- "[array([-0.37748344, -0.50331126, -0.36423841, -0.99337748, -0.39072848,\n",
- " -0.64238411, 0.16556291, 0.33774834, -0.26490066, -0.99337748,\n",
- " 0.36423841, -0.53642384, 0.00662252, -0.41721854, -0.68874172,\n",
- " 0.52317881, -0.65562914]), array([-0.4954955 , -0.62162162, -0.53153153, -1. , -0.69369369,\n",
- " -0.51351351, -0.2972973 , -0.36936937, -0.47747748, 1. ,\n",
- " 0.20720721, -0.58558559, 0.18918919, -0.58558559, -0.45945946,\n",
- " -1. , -0.53153153]), array([-0.43859649, -0.68421053, -0.47368421, -1. , -0.52631579,\n",
- " -0.61403509, -0.05263158, 0.29824561, -0.33333333, 1. ,\n",
- " -0.98245614, -0.40350877, 0.05263158, -0.29824561, -0.57894737,\n",
- " 1. , -0.61403509]), array([-0.70843373, -0.56144578, -0.62891566, -1. , -0.68674699,\n",
- " -0.83614458, 0.2 , 0.4 , -0.56626506, 1. ,\n",
- " 1. , -0.5686747 , 0.36385542, -0.68433735, -0.5373494 ,\n",
- " 1. , -0.58795181]), array([-0.72727273, -0.76363636, -0.72727273, 1. , -0.74545455,\n",
- " -0.07272727, -0.19090909, -0.01818182, -1. , 0.63636364,\n",
- " 0.65454545, -0.78181818, -0.03636364, -0.72727273, -0.70909091,\n",
- " 0.43636364, -0.58181818]), array([-0.53424658, -0.67123288, -0.57534247, 1. , -0.23287671,\n",
- " -0.08219178, -0.31506849, 0.06849315, 1. , 0.64383562,\n",
- " 0.56164384, -0.80821918, 0.08219178, -0.56164384, -0.83561644,\n",
- " 0.63013699, -0.67123288])]\n"
- ]
- }
- ],
- "source": [
- "titre = ['agency', 'anxiety','avoiding', 'depression','ext. control', 'fatigue', 'hyper vigilance',\n",
- " 'trauma','int. control', 'memory', 'problem focused', 'quality of life',\n",
- " 'sadness', 'self efficacy','sleep','social','stress']\n",
- "dim = len(titre)\n",
- "seuils = [ 0.38646466, 0.13056517 , 0.16311094 , 0.5522181 , 0.4068393 , 0.4961518,\n",
- " -0.12576449, -0.16419357 , 0.34581536, -0.06974197, -0.25050393 , 0.17414908,\n",
- " -0.09691429, 0.39290804, 0.37336868, -0.30328006 , 0.23517847]\n",
- "print(len(seuils))\n",
- "\n",
- "indices = []\n",
- "for i in range(17):\n",
- " indices.append([i])\n",
- "print(H_users.shape)\n",
- "radar = []\n",
- "\n",
- "for k in range(len(g)):\n",
- " rows = H_users[g[k]]\n",
- " v = np.full(dim, 0.0)\n",
- " for i in range(len(rows)):\n",
- " #print(len(rows[i]))\n",
- " for j in range(len(rows[i])):\n",
- " \n",
- " if(rows[i][j] > seuils[j]):\n",
- " v[j] = v[j] + 1 #rows[i][j]\n",
- " if(rows[i][j] < seuils[j]):\n",
- " v[j] = v[j] - 1#+ rows[i][j]\n",
- " v = v / len(rows)\n",
- " radar.append(v)\n",
- "print(radar)\n",
- "\n",
- "scaler = MinMaxScaler()\n",
- "radar = scaler.fit_transform(np.array(radar))"
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- "print(classPsy.shape)\n",
- "radar2 = []\n",
- "for k in range(len(g)):\n",
- " rows = classPsy[g[k]]\n",
- " v = np.full(dim, 0.0)\n",
- " for i in range(len(rows)):\n",
- " for j in range(len(rows[i])):\n",
- " if(rows[i][j] == 2):\n",
- " v[j] = v[j] + 1 #rows[i][j]\n",
- " if(rows[i][j] == 0):\n",
- " v[j] = v[j] - 1#+ rows[i][j]\n",
- " v = v / len(rows)\n",
- " radar2.append(v)\n",
- "print(radar2)\n",
- "scaler = MinMaxScaler()\n",
- "radar2 = scaler.fit_transform(np.array(radar2))\n",
- "print(radar2)"
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