From 0452e14a1b6c6255dc9147432d4693017eabd1fd Mon Sep 17 00:00:00 2001
From: Arthur BATEL <arthur.batel@insa-lyon.fr>
Date: Fri, 9 Feb 2024 17:12:12 +0100
Subject: [PATCH] code
---
README.md | 6 +-
code/.idea/.gitignore | 8 +
code/.idea/code.iml | 11 +
.../inspectionProfiles/profiles_settings.xml | 6 +
code/.idea/misc.xml | 7 +
code/.idea/modules.xml | 8 +
.../Experiments-checkpoint.ipynb | 6563 +++++++++++++++++
code/Experiments.ipynb | 6493 ++++++++++++++++
code/binary_model/BPR_model.py | 162 +
.../__pycache__/BPR_model.cpython-310.pyc | Bin 0 -> 4245 bytes
.../__pycache__/BPR_model.cpython-311.pyc | Bin 0 -> 9458 bytes
.../__pycache__/utils.cpython-310.pyc | Bin 0 -> 3931 bytes
.../__pycache__/utils.cpython-311.pyc | Bin 0 -> 9414 bytes
code/binary_model/algebra_abla_init_new | 0
code/binary_model/assist09_abla_init_new | 7 +
code/binary_model/assist17_abla_init_new | 0
code/binary_model/compute_doa.py | 12 +
code/binary_model/main.py | 230 +
code/binary_model/math1_abla_init_new | 0
code/binary_model/script_abalation.py | 9 +
code/binary_model/utils.py | 165 +
code/binary_model/utils.pyc | Bin 0 -> 5470 bytes
code/cv/__pycache__/BPR_model.cpython-310.pyc | Bin 0 -> 4839 bytes
code/cv/__pycache__/utils.cpython-310.pyc | Bin 0 -> 3545 bytes
code/nary_model/BPR_model.py | 177 +
.../__pycache__/BPR_model.cpython-310.pyc | Bin 0 -> 4431 bytes
.../__pycache__/utils.cpython-310.pyc | Bin 0 -> 9211 bytes
code/nary_model/main_nary.py | 237 +
code/nary_model/main_nary_cv.py | 107 +
code/nary_model/utils.py | 377 +
code/other/__pycache__/utils.cpython-310.pyc | Bin 0 -> 3671 bytes
code/other/decision_tree.py | 395 +
code/other/dt/train_embed.csv | 917 +++
code/other/dt/train_partitions.csv | 17 +
code/other/dt/train_user_quest_label.csv | 917 +++
code/other/test_3_embed.csv | 917 +++
code/other/test_3_partitions.csv | 17 +
code/other/test_3_partitionsNew.csv | 17 +
code/other/train_embed.csv | 948 +++
code/other/utils.py | 164 +
code/othertest_3_partitionsNew.csv | 17 +
code/radar0_fig.png | Bin 0 -> 93297 bytes
code/radar1_fig.png | Bin 0 -> 100245 bytes
code/radar2_fig.png | Bin 0 -> 94363 bytes
code/radar3_fig.png | Bin 0 -> 93181 bytes
code/radar4_fig.png | Bin 0 -> 97039 bytes
code/radar5_fig.png | Bin 0 -> 91879 bytes
47 files changed, 18909 insertions(+), 2 deletions(-)
create mode 100644 code/.idea/.gitignore
create mode 100644 code/.idea/code.iml
create mode 100644 code/.idea/inspectionProfiles/profiles_settings.xml
create mode 100644 code/.idea/misc.xml
create mode 100644 code/.idea/modules.xml
create mode 100644 code/.ipynb_checkpoints/Experiments-checkpoint.ipynb
create mode 100644 code/Experiments.ipynb
create mode 100644 code/binary_model/BPR_model.py
create mode 100644 code/binary_model/__pycache__/BPR_model.cpython-310.pyc
create mode 100644 code/binary_model/__pycache__/BPR_model.cpython-311.pyc
create mode 100644 code/binary_model/__pycache__/utils.cpython-310.pyc
create mode 100644 code/binary_model/__pycache__/utils.cpython-311.pyc
create mode 100644 code/binary_model/algebra_abla_init_new
create mode 100644 code/binary_model/assist09_abla_init_new
create mode 100644 code/binary_model/assist17_abla_init_new
create mode 100644 code/binary_model/compute_doa.py
create mode 100644 code/binary_model/main.py
create mode 100644 code/binary_model/math1_abla_init_new
create mode 100644 code/binary_model/script_abalation.py
create mode 100644 code/binary_model/utils.py
create mode 100644 code/binary_model/utils.pyc
create mode 100644 code/cv/__pycache__/BPR_model.cpython-310.pyc
create mode 100644 code/cv/__pycache__/utils.cpython-310.pyc
create mode 100644 code/nary_model/BPR_model.py
create mode 100644 code/nary_model/__pycache__/BPR_model.cpython-310.pyc
create mode 100644 code/nary_model/__pycache__/utils.cpython-310.pyc
create mode 100644 code/nary_model/main_nary.py
create mode 100644 code/nary_model/main_nary_cv.py
create mode 100644 code/nary_model/utils.py
create mode 100644 code/other/__pycache__/utils.cpython-310.pyc
create mode 100644 code/other/decision_tree.py
create mode 100644 code/other/dt/train_embed.csv
create mode 100644 code/other/dt/train_partitions.csv
create mode 100644 code/other/dt/train_user_quest_label.csv
create mode 100644 code/other/test_3_embed.csv
create mode 100644 code/other/test_3_partitions.csv
create mode 100644 code/other/test_3_partitionsNew.csv
create mode 100644 code/other/train_embed.csv
create mode 100644 code/other/utils.py
create mode 100644 code/othertest_3_partitionsNew.csv
create mode 100644 code/radar0_fig.png
create mode 100644 code/radar1_fig.png
create mode 100644 code/radar2_fig.png
create mode 100644 code/radar3_fig.png
create mode 100644 code/radar4_fig.png
create mode 100644 code/radar5_fig.png
diff --git a/README.md b/README.md
index b2554fe..f8a8509 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
---
-The current repository contains all the code and data necessary to reproduce the paper results
+The current repository contains all the code and data necessary to reproduce the paper results.
+
+Nonetheless, the cleaning of the code is in porcess to allow the reviewer to reproduce the results.
## Installation
Set the environment with `make`:
@@ -12,4 +14,4 @@ Set the environment with `make`:
R. Céline, A. Batel, M.Plantevit, I. Benouaret
>LIRIS laboratory, UMR5205, Univ Lyon, CNRS, UBL
-## Reference
\ No newline at end of file
+## Reference
diff --git a/code/.idea/.gitignore b/code/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/code/.idea/.gitignore
@@ -0,0 +1,8 @@
+# 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
new file mode 100644
index 0000000..456b84d
--- /dev/null
+++ b/code/.idea/code.iml
@@ -0,0 +1,11 @@
+<?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
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/code/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<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
new file mode 100644
index 0000000..7161c54
--- /dev/null
+++ b/code/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?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
new file mode 100644
index 0000000..23968dc
--- /dev/null
+++ b/code/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?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
new file mode 100644
index 0000000..ca54f32
--- /dev/null
+++ b/code/.ipynb_checkpoints/Experiments-checkpoint.ipynb
@@ -0,0 +1,6563 @@
+{
+ "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",
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+ "]"
+ ]
+ },
+ {
+ "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))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "34d43f1e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(917, 17)\n",
+ "[array([-0.28476821, 0.01986755, 0. , 0.14569536, -0.23178808,\n",
+ " 0.2384106 , 0.01324503, 0.07284768, -0.2384106 , -0.30463576,\n",
+ " 0. , -0.06622517, -0.02649007, -0.15231788, -0.02649007,\n",
+ " 0. , 0.04635762]), array([-0.24324324, 0.07207207, 0. , 0.04504505, -0.0990991 ,\n",
+ " 0.25225225, 0.02702703, 0.05405405, -0.23423423, -0.02702703,\n",
+ " 0. , -0.11711712, 0.10810811, -0.17117117, 0. ,\n",
+ " 0. , 0.18018018]), array([-0.31578947, 0. , 0. , 0.07017544, -0.24561404,\n",
+ " 0.1754386 , -0.03508772, 0.07017544, -0.22807018, -0.03508772,\n",
+ " 0. , -0.12280702, 0.01754386, -0.28070175, -0.15789474,\n",
+ " 0. , 0.10526316]), array([-0.22650602, -0.01686747, 0. , 0.1060241 , -0.14698795,\n",
+ " 0.15421687, -0.02650602, -0.01445783, -0.18795181, -0.01445783,\n",
+ " 0. , -0.09156627, 0.00240964, -0.1060241 , -0.21204819,\n",
+ " 0. , 0.04096386]), array([-0.12727273, 0.00909091, 0. , 0.78181818, -0.13636364,\n",
+ " 0.47272727, 0. , 0.00909091, -0.10909091, -0.04545455,\n",
+ " 0. , -0.04545455, 0.00909091, -0.00909091, -0.05454545,\n",
+ " 0. , 0.00909091]), array([-0.26027397, -0.04109589, 0. , 0.80821918, -0.19178082,\n",
+ " 0.32876712, 0. , -0.01369863, -0.26027397, -0.12328767,\n",
+ " 0. , -0.02739726, -0.05479452, -0.01369863, -0.05479452,\n",
+ " 0. , -0.01369863])]\n",
+ "[[0.16455441 0.53869875 0. 0.13188382 0.0943655 0.26433588\n",
+ " 0.77812042 1. 0.14461525 0. 0. 0.59304052\n",
+ " 0.17375075 0.4726758 0.87507526 0. 0.30976178]\n",
+ " [0.38482645 1. 0. 0. 1. 0.30779335\n",
+ " 1. 0.78473721 0.17223979 0.95668452 0. 0.05963646\n",
+ " 1. 0.40326292 1. 0. 1. ]\n",
+ " [0. 0.36314068 0. 0.03292878 0. 0.06662806\n",
+ " 0. 0.96939204 0.21301194 0.92890608 0. 0.\n",
+ " 0.44405901 0. 0.25538278 0. 0.61358839]\n",
+ " [0.47361018 0.21409258 0. 0.07990188 0.67314696 0.\n",
+ " 0.13815874 0. 0.47837478 1. 0. 0.32743771\n",
+ " 0.35115553 0.64311739 0. 0. 0.28194152]\n",
+ " [1. 0.4434718 0. 0.96540633 0.74566049 1.\n",
+ " 0.5648855 0.26972799 1. 0.89318032 0. 0.81073964\n",
+ " 0.39216942 1. 0.7427686 0. 0.11754528]\n",
+ " [0.29448578 0. 0. 1. 0.36742475 0.54802058\n",
+ " 0.5648855 0.00869591 0. 0.6249548 0. 1.\n",
+ " 0. 0.98303558 0.74159402 0. 0. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "acb7d4df",
+ "metadata": {},
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Depression\"},\"font\":{\"size\":18}}, {\"responsive\": true} ).then(function(){\n",
+ " \n",
+ "var gd = document.getElementById('c5f7ad3d-e342-44e4-8910-3dc3df3edad9');\n",
+ "var x = new MutationObserver(function (mutations, observer) {{\n",
+ " var display = window.getComputedStyle(gd).display;\n",
+ " if (!display || display === 'none') {{\n",
+ " console.log([gd, 'removed!']);\n",
+ " Plotly.purge(gd);\n",
+ " observer.disconnect();\n",
+ " }}\n",
+ "}});\n",
+ "\n",
+ "// Listen for the removal of the full notebook cells\n",
+ "var notebookContainer = gd.closest('#notebook-container');\n",
+ "if (notebookContainer) {{\n",
+ " x.observe(notebookContainer, {childList: true});\n",
+ "}}\n",
+ "\n",
+ "// Listen for the clearing of the current output cell\n",
+ "var outputEl = gd.closest('.output');\n",
+ "if (outputEl) {{\n",
+ " x.observe(outputEl, {childList: true});\n",
+ "}}\n",
+ "\n",
+ " }) }; }); </script> </div>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "name_cluster=['Trauma', 'Sadness, stress and memory','Trauma and memory','Memory','Depression and associated symptoms', 'Depression']\n",
+ "categories = ['processing cost','mechanical properties','chemical stability',\n",
+ " 'thermal stability', 'device integration']\n",
+ "titleGraph = []\n",
+ "for i in range(len(d)):\n",
+ " #titleGraph.append('DTCluster ' + str(i))\n",
+ " titleGraph.append('DTCluster: ' + name_cluster[i])\n",
+ "def radar_fig(i):\n",
+ " fig = go.Figure()\n",
+ "\n",
+ " fig.add_trace(go.Scatterpolar(\n",
+ " r=radar[i],\n",
+ " theta=titre,\n",
+ " fill='toself',\n",
+ " name='H'\n",
+ " ))\n",
+ " fig.add_trace(go.Scatterpolar(\n",
+ " r=radar2[i],\n",
+ " theta=titre,\n",
+ " fill='toself',\n",
+ " name='Class psy'\n",
+ " #text = 'r'\n",
+ " ))\n",
+ "\n",
+ " fig.update_layout(\n",
+ " polar=dict(\n",
+ " radialaxis=dict(\n",
+ " visible=True\n",
+ " #range=[0, 5]\n",
+ " )),\n",
+ " showlegend=False\n",
+ " )\n",
+ " fig.update_layout(title_text = titleGraph[i])\n",
+ " fig.update_layout(\n",
+ " font=dict(\n",
+ " #family=\"Courier New, monospace\",\n",
+ " size=18, # Set the font size here\n",
+ " #color=\"RebeccaPurple\"\n",
+ " )\n",
+ ")\n",
+ " \n",
+ " \n",
+ " fig.show()\n",
+ " fig.write_image(path+\"radar\"+str(i)+\"_fig.png\")\n",
+ " \n",
+ "for i in range(len(d)):\n",
+ " radar_fig(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20ff4dca",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86ca3232",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/code/Experiments.ipynb b/code/Experiments.ipynb
new file mode 100644
index 0000000..e3dbea9
--- /dev/null
+++ b/code/Experiments.ipynb
@@ -0,0 +1,6493 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c0bd6c14",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-09T15:13:01.198176543Z",
+ "start_time": "2024-02-09T15:13:01.181566445Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "path = \"./\"\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": 27,
+ "id": "61c53cb1",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-09T15:52:40.330565696Z",
+ "start_time": "2024-02-09T15:52:39.983890951Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "python: can't open file '/home/arthurb/Téléchargements/code/./script.py': [Errno 2] No such file or directory\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "cmd = 'cd cv/data/bpr_directory && python ./script.py '\n",
+ "!{cmd}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b1c6b4d",
+ "metadata": {},
+ "source": [
+ "## Table 3: compute DOA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "790a43dd",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-09T15:55:06.200772904Z",
+ "start_time": "2024-02-09T15:55:05.836758519Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/arthurb/Téléchargements/code\n",
+ "Traceback (most recent call last):\r\n",
+ " File \"/home/arthurb/Téléchargements/code/./binary_model/compute_doa.py\", line 11, in <module>\r\n",
+ " doa = compute_doa(data)\r\n",
+ " ^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/Téléchargements/code/binary_model/utils.py\", line 161, in compute_doa\r\n",
+ " F = fromDFtoArray(filename+\"_embed.csv\",False,'f')\r\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/Téléchargements/code/binary_model/utils.py\", line 7, in fromDFtoArray\r\n",
+ " df = pd.read_csv(name,index_col=None, header=None)\r\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 948, in read_csv\r\n",
+ " return _read(filepath_or_buffer, kwds)\r\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 611, in _read\r\n",
+ " parser = TextFileReader(filepath_or_buffer, **kwds)\r\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 1448, in __init__\r\n",
+ " self._engine = self._make_engine(f, self.engine)\r\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/parsers/readers.py\", line 1705, in _make_engine\r\n",
+ " self.handles = get_handle(\r\n",
+ " ^^^^^^^^^^^\r\n",
+ " File \"/home/arthurb/anaconda3/envs/cdbpr-env/lib/python3.11/site-packages/pandas/io/common.py\", line 863, in get_handle\r\n",
+ " handle = open(\r\n",
+ " ^^^^^\r\n",
+ "FileNotFoundError: [Errno 2] No such file or directory: './cv/data/math1/train_embed.csv'\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "i = 3\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+'cv/data/'+datasets[i]+'/train' \n",
+ "!{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": 16,
+ "id": "a9e32954",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-09T09:53:25.263346701Z",
+ "start_time": "2024-02-09T09:44:53.793212303Z"
+ }
+ },
+ "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: 1.3062841892242432\n",
+ "Epoch 11, Loss: 1.1247220039367676\n",
+ "DOA: 0.7652701270601657\n",
+ "RMSE 0.4360310956805937\n",
+ "AUC: 0.7970107606624546\n",
+ "ACC: 0.7257046201050292\n",
+ "Prec: 0.6802376862726893\n",
+ "NB KC 16\n",
+ "NB Users 3911\n",
+ "NB (question-answer) in the data 20\n",
+ "Epoch 1, Loss: 0.5947433114051819\n",
+ "Epoch 11, Loss: 0.5205552577972412\n",
+ "DOA: 0.5024588330075522\n",
+ "RMSE 0.42992933328684846\n",
+ "AUC: 0.804911997323218\n",
+ "ACC: 0.7328639144031627\n",
+ "Prec: 0.6869165325362152\n",
+ "NB KC 16\n",
+ "NB Users 3911\n",
+ "NB (question-answer) in the data 20\n",
+ "Epoch 1, Loss: 1.3599088191986084\n",
+ "Epoch 11, Loss: 1.124066948890686\n",
+ "DOA: 0.8408919219715537\n",
+ "RMSE 0.43537672319707393\n",
+ "AUC: 0.7967248771358717\n",
+ "ACC: 0.727651790806994\n",
+ "Prec: 0.6844450698939863\n",
+ "NB KC 16\n",
+ "NB Users 3911\n",
+ "NB (question-answer) in the data 20\n",
+ "Epoch 1, Loss: 0.694855809211731\n",
+ "Epoch 11, Loss: 0.5210095643997192\n",
+ "DOA: 0.501724768179482\n",
+ "RMSE 0.4295033431283873\n",
+ "AUC: 0.8059136173588548\n",
+ "ACC: 0.7314281218653502\n",
+ "Prec: 0.6849403122130395\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",
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+ "]"
+ ]
+ },
+ {
+ "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))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "34d43f1e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(917, 17)\n",
+ "[array([-0.28476821, 0.01986755, 0. , 0.14569536, -0.23178808,\n",
+ " 0.2384106 , 0.01324503, 0.07284768, -0.2384106 , -0.30463576,\n",
+ " 0. , -0.06622517, -0.02649007, -0.15231788, -0.02649007,\n",
+ " 0. , 0.04635762]), array([-0.24324324, 0.07207207, 0. , 0.04504505, -0.0990991 ,\n",
+ " 0.25225225, 0.02702703, 0.05405405, -0.23423423, -0.02702703,\n",
+ " 0. , -0.11711712, 0.10810811, -0.17117117, 0. ,\n",
+ " 0. , 0.18018018]), array([-0.31578947, 0. , 0. , 0.07017544, -0.24561404,\n",
+ " 0.1754386 , -0.03508772, 0.07017544, -0.22807018, -0.03508772,\n",
+ " 0. , -0.12280702, 0.01754386, -0.28070175, -0.15789474,\n",
+ " 0. , 0.10526316]), array([-0.22650602, -0.01686747, 0. , 0.1060241 , -0.14698795,\n",
+ " 0.15421687, -0.02650602, -0.01445783, -0.18795181, -0.01445783,\n",
+ " 0. , -0.09156627, 0.00240964, -0.1060241 , -0.21204819,\n",
+ " 0. , 0.04096386]), array([-0.12727273, 0.00909091, 0. , 0.78181818, -0.13636364,\n",
+ " 0.47272727, 0. , 0.00909091, -0.10909091, -0.04545455,\n",
+ " 0. , -0.04545455, 0.00909091, -0.00909091, -0.05454545,\n",
+ " 0. , 0.00909091]), array([-0.26027397, -0.04109589, 0. , 0.80821918, -0.19178082,\n",
+ " 0.32876712, 0. , -0.01369863, -0.26027397, -0.12328767,\n",
+ " 0. , -0.02739726, -0.05479452, -0.01369863, -0.05479452,\n",
+ " 0. , -0.01369863])]\n",
+ "[[0.16455441 0.53869875 0. 0.13188382 0.0943655 0.26433588\n",
+ " 0.77812042 1. 0.14461525 0. 0. 0.59304052\n",
+ " 0.17375075 0.4726758 0.87507526 0. 0.30976178]\n",
+ " [0.38482645 1. 0. 0. 1. 0.30779335\n",
+ " 1. 0.78473721 0.17223979 0.95668452 0. 0.05963646\n",
+ " 1. 0.40326292 1. 0. 1. ]\n",
+ " [0. 0.36314068 0. 0.03292878 0. 0.06662806\n",
+ " 0. 0.96939204 0.21301194 0.92890608 0. 0.\n",
+ " 0.44405901 0. 0.25538278 0. 0.61358839]\n",
+ " [0.47361018 0.21409258 0. 0.07990188 0.67314696 0.\n",
+ " 0.13815874 0. 0.47837478 1. 0. 0.32743771\n",
+ " 0.35115553 0.64311739 0. 0. 0.28194152]\n",
+ " [1. 0.4434718 0. 0.96540633 0.74566049 1.\n",
+ " 0.5648855 0.26972799 1. 0.89318032 0. 0.81073964\n",
+ " 0.39216942 1. 0.7427686 0. 0.11754528]\n",
+ " [0.29448578 0. 0. 1. 0.36742475 0.54802058\n",
+ " 0.5648855 0.00869591 0. 0.6249548 0. 1.\n",
+ " 0. 0.98303558 0.74159402 0. 0. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "acb7d4df",
+ "metadata": {},
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Depression\"},\"font\":{\"size\":18}}, {\"responsive\": true} ).then(function(){\n",
+ " \n",
+ "var gd = document.getElementById('c5f7ad3d-e342-44e4-8910-3dc3df3edad9');\n",
+ "var x = new MutationObserver(function (mutations, observer) {{\n",
+ " var display = window.getComputedStyle(gd).display;\n",
+ " if (!display || display === 'none') {{\n",
+ " console.log([gd, 'removed!']);\n",
+ " Plotly.purge(gd);\n",
+ " observer.disconnect();\n",
+ " }}\n",
+ "}});\n",
+ "\n",
+ "// Listen for the removal of the full notebook cells\n",
+ "var notebookContainer = gd.closest('#notebook-container');\n",
+ "if (notebookContainer) {{\n",
+ " x.observe(notebookContainer, {childList: true});\n",
+ "}}\n",
+ "\n",
+ "// Listen for the clearing of the current output cell\n",
+ "var outputEl = gd.closest('.output');\n",
+ "if (outputEl) {{\n",
+ " x.observe(outputEl, {childList: true});\n",
+ "}}\n",
+ "\n",
+ " }) }; }); </script> </div>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "name_cluster=['Trauma', 'Sadness, stress and memory','Trauma and memory','Memory','Depression and associated symptoms', 'Depression']\n",
+ "categories = ['processing cost','mechanical properties','chemical stability',\n",
+ " 'thermal stability', 'device integration']\n",
+ "titleGraph = []\n",
+ "for i in range(len(d)):\n",
+ " #titleGraph.append('DTCluster ' + str(i))\n",
+ " titleGraph.append('DTCluster: ' + name_cluster[i])\n",
+ "def radar_fig(i):\n",
+ " fig = go.Figure()\n",
+ "\n",
+ " fig.add_trace(go.Scatterpolar(\n",
+ " r=radar[i],\n",
+ " theta=titre,\n",
+ " fill='toself',\n",
+ " name='H'\n",
+ " ))\n",
+ " fig.add_trace(go.Scatterpolar(\n",
+ " r=radar2[i],\n",
+ " theta=titre,\n",
+ " fill='toself',\n",
+ " name='Class psy'\n",
+ " #text = 'r'\n",
+ " ))\n",
+ "\n",
+ " fig.update_layout(\n",
+ " polar=dict(\n",
+ " radialaxis=dict(\n",
+ " visible=True\n",
+ " #range=[0, 5]\n",
+ " )),\n",
+ " showlegend=False\n",
+ " )\n",
+ " fig.update_layout(title_text = titleGraph[i])\n",
+ " fig.update_layout(\n",
+ " font=dict(\n",
+ " #family=\"Courier New, monospace\",\n",
+ " size=18, # Set the font size here\n",
+ " #color=\"RebeccaPurple\"\n",
+ " )\n",
+ ")\n",
+ " \n",
+ " \n",
+ " fig.show()\n",
+ " fig.write_image(path+\"radar\"+str(i)+\"_fig.png\")\n",
+ " \n",
+ "for i in range(len(d)):\n",
+ " radar_fig(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c03ac5f8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39b0792d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/code/binary_model/BPR_model.py b/code/binary_model/BPR_model.py
new file mode 100644
index 0000000..9937d93
--- /dev/null
+++ b/code/binary_model/BPR_model.py
@@ -0,0 +1,162 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import torch.nn.functional as F
+import pandas as pd
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score
+from sklearn.metrics.cluster import contingency_matrix
+
+dtype = torch.float32
+if torch.cuda.is_available():
+ dev = "cuda:0"
+else:
+ dev = "cpu"
+device = torch.device(dev)
+
+class BPRModel(nn.Module):
+ def __init__(self, batch_size, num_users, num_items, embedding_size, device, cc_exp_row, cc_exp_col, ablation):
+ self.device = device
+ self.batch_size = batch_size
+
+ super(BPRModel, self).__init__()
+ # Model parameters
+ self.user_embeddings = nn.Embedding(num_users, embedding_size).to(self.device)
+ self.item_embeddings = nn.Embedding(num_items, embedding_size).to(self.device)
+
+ # Initialization
+ if(ablation != 2) and (ablation != 3):
+ self.user_embeddings.weight.data.copy_(torch.from_numpy(cc_exp_row))
+ self.item_embeddings.weight.data.copy_(torch.from_numpy(cc_exp_col))
+
+ # Optimizer
+ self.optimizer = optim.Adam(self.parameters(), lr=0.01)
+
+ def forward(self, user, item, k):
+ user_embedding = self.user_embeddings(user)
+ item_embedding = self.item_embeddings(item)
+
+ if (k == -1):
+ user_embedding = F.normalize(user_embedding, p=2, dim=1)
+ item_embedding = F.normalize(item_embedding, p=2, dim=1)
+ return - torch.norm(user_embedding - item_embedding, p=2, dim=1)
+
+ else:
+ user_embedding = F.normalize(user_embedding, p=2, dim=1)
+ return torch.mean(user_embedding[:,k])
+
+ def bpr_loss(self,positive_scores, negative_scores):
+ return -torch.mean(torch.log(torch.sigmoid(positive_scores - negative_scores)))
+
+ def train(self, triplets, num_kc, epochs, batch_size, y, ablation):
+ for epoch in range(epochs):
+ all_labels = None
+ all_decisions = None
+ first = True
+ for k in range(num_kc):
+ trips = triplets[k]
+ y_true = y[k]
+ for i in range(0, len(trips), self.batch_size):
+ users_batch = trips[i:i + self.batch_size, 0]
+ items_batch = trips[i:i + self.batch_size, 1]
+ negatives_batch = trips[i:i + self.batch_size, 2]
+ neg_users_batch = trips[i:i + self.batch_size, 3]
+ y_train = y_true[i:i + self.batch_size]
+
+ # Convert the numpy.ndarray to tensor
+ users_batch = torch.from_numpy(users_batch).to(self.device)
+ items_batch = torch.from_numpy(items_batch).to(self.device)
+ negatives_batch = torch.from_numpy(negatives_batch).to(self.device)
+ neg_users_batch = torch.from_numpy(neg_users_batch).to(self.device)
+
+ # call forward
+ positive_scores = self(users_batch, items_batch, -1)
+ negative_scores = self(users_batch, negatives_batch, -1)
+ loss1 = self.bpr_loss(positive_scores, negative_scores)
+
+ positive_scores_bis = self(users_batch, negatives_batch, k)
+ negative_scores_bis = self(neg_users_batch, negatives_batch, k)
+ loss2 = self.bpr_loss(positive_scores_bis, negative_scores_bis)
+
+ if(ablation != 1) and (ablation != 3):
+ loss = loss1 + loss2
+ else:
+ loss = loss1
+
+ if (first == True):
+ all_labels = y_train
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = comp
+ first = False
+ else:
+ all_labels = np.concatenate((all_labels, y_train), axis=0)
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = np.concatenate((all_decisions, comp), axis=0)
+
+ # Backward
+ loss.backward(retain_graph=True)
+ # Optimizer step
+ self.optimizer.step()
+ # Clear the gradients for the next iteration
+ self.optimizer.zero_grad()
+
+ if(epoch % 10 == 0):
+ print(f"Epoch {epoch + 1}, Loss: {loss.item()}")
+ acc = accuracy_score(all_labels, all_decisions)
+ return acc
+
+ # Evaluate the model
+ def evaluate_model(self, test_triplets, num_kc, y_test):
+ # Initialize counters for metrics
+ all_predictions = None
+ all_labels = None
+ all_decisions = None
+ first = True
+ for k in range(num_kc):
+ trips = test_triplets[k]
+ y = y_test[k]
+ for i in range(0, len(trips), self.batch_size):
+ users_batch = trips[i:i + self.batch_size, 0]
+ items_batch = trips[i:i + self.batch_size, 1]
+ negatives_batch = trips[i:i + self.batch_size, 2]
+ y_true = y[i:i + self.batch_size]
+ # Convert the ndarray to tensor
+ users_batch = torch.from_numpy(users_batch).to(self.device)
+ items_batch = torch.from_numpy(items_batch).to(self.device)
+ negatives_batch = torch.from_numpy(negatives_batch).to(self.device)
+ # Compute distances
+ positive_scores = self(users_batch, items_batch,-1).cpu()
+ negative_scores = self(users_batch, negatives_batch,-1).cpu()
+ # compute probabilities
+ proba = torch.sigmoid(positive_scores - negative_scores )
+
+ if (first == True):
+ all_labels = y_true
+ all_predictions = proba.detach().cpu().numpy()
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = comp
+ first = False
+ else:
+ all_labels = np.concatenate((all_labels, y_true), axis=0)
+ all_predictions = np.concatenate((all_predictions, proba.detach().cpu().numpy()), axis=0)
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = np.concatenate((all_decisions, comp), axis=0)
+
+ mse1 = mean_squared_error(all_labels, all_predictions)
+ print("RMSE", np.sqrt(mse1))#, root_mean_squared_error(all_labels, all_predictions))
+ auc = roc_auc_score(all_labels, all_predictions)
+ print("AUC:", auc)
+ acc = accuracy_score(all_labels, all_decisions)
+ print("ACC:", acc)
+ precision = precision_score(all_labels, all_decisions)
+ print(f'Prec: {precision}')
+ return acc, precision
+
diff --git a/code/binary_model/__pycache__/BPR_model.cpython-310.pyc b/code/binary_model/__pycache__/BPR_model.cpython-310.pyc
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diff --git a/code/binary_model/algebra_abla_init_new b/code/binary_model/algebra_abla_init_new
new file mode 100644
index 0000000..e69de29
diff --git a/code/binary_model/assist09_abla_init_new b/code/binary_model/assist09_abla_init_new
new file mode 100644
index 0000000..5248425
--- /dev/null
+++ b/code/binary_model/assist09_abla_init_new
@@ -0,0 +1,7 @@
+NB KC 123
+NB Users 2493
+NB (question-answer) in the data 17506
+Epoch 1, Loss: 1.3690141439437866
+Epoch 11, Loss: 1.0071520805358887
+NB KC 123
+NB Users 2493
diff --git a/code/binary_model/assist17_abla_init_new b/code/binary_model/assist17_abla_init_new
new file mode 100644
index 0000000..e69de29
diff --git a/code/binary_model/compute_doa.py b/code/binary_model/compute_doa.py
new file mode 100644
index 0000000..19f12bd
--- /dev/null
+++ b/code/binary_model/compute_doa.py
@@ -0,0 +1,12 @@
+import numpy as np
+import pandas as pd
+import argparse
+from utils import *
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-dt", "--data", help="data file")
+ args = parser.parse_args()
+ data = args.data
+
+ doa = compute_doa(data)
+ print("DOA:", doa)
\ No newline at end of file
diff --git a/code/binary_model/main.py b/code/binary_model/main.py
new file mode 100644
index 0000000..bd5e317
--- /dev/null
+++ b/code/binary_model/main.py
@@ -0,0 +1,230 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import torch.nn.functional as F
+import pandas as pd
+from scipy import stats
+import csv
+import json
+import random
+from collections import Counter
+from sklearn.preprocessing import normalize
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score
+from sklearn.metrics.cluster import contingency_matrix
+from utils import *
+from BPR_model import *
+
+dtype = torch.float32
+if torch.cuda.is_available():
+ dev = "cuda:0"
+else:
+ dev = "cpu"
+device = torch.device(dev)
+
+
+def read_file(dataTrain, dataTest):
+ # Compute dictionaries
+ df = pd.read_csv(dataTrain, names=['user_id', 'item_id','correct','knowledge'])
+ dfTest = pd.read_csv(dataTest, names=['user_id', 'item_id','correct','knowledge'])
+ # dico kc
+ kc = df['knowledge']
+ kcT = dfTest['knowledge']
+ kc = flattern_arrays(kc.values, kcT.values)
+ num_kc = len(kc)
+ dico_kc = { k:v for (k,v) in zip(kc, range(len(kc)))}
+ print("NB KC", num_kc)
+ # dico users
+ users = df['user_id']
+ usersT = dfTest['user_id']
+ users = flattern_arrays(users.values, usersT.values)
+ num_users = len(users)
+ dico_users = { k:v for (k,v) in zip(users, range(num_users))}
+ print("NB Users", num_users)
+ # dico items and their associated kc
+ itemsDT = df['item_id']
+ itemsT = dfTest['item_id']
+ items = flattern_arrays(itemsDT.values, itemsT.values)
+ num_items = len(items)
+ dico_items = { k:v for (k,v) in zip(items, range(num_items))}
+ #print("NB Items", num_items)
+ return dico_kc, dico_users, dico_items
+
+def parse_dataframe(data, dico_kc, dico_users, dico_item, is_train = True):
+ df = pd.read_csv(data, names=['user_id', 'item_id','correct','knowledge'])
+ # Compute table of positive and negative items by KC and Users
+ # and the dictionary that associate the KC to a question/answer
+ num_kc = len(dico_kc)
+ num_users = len(dico_users)
+ # Find positive items for each kc/user
+ triplets = []
+ y_true = []
+ for k in range(num_kc):
+ triplets.append([])
+ y_true.append([])
+ k = 0
+ if(is_train):
+ item_users = {}
+ grouped_kc = df.groupby('knowledge')
+ for kc_name, df_kc in grouped_kc:
+ # Find positive items for each user
+ grouped = df_kc.groupby('user_id')
+ for group_name, df_group in grouped:
+ for row_index, row in df_group.iterrows():
+ col = row['item_id']
+ if col not in dico_items:
+ dico_items[col] = len(dico_items)
+ # Warning, all user's answers are positives!
+ q,r = parse_it(col)
+ col_neg = q+'_'+str(1-int(r))
+ if col_neg not in dico_items:
+ dico_items[col_neg] = len(dico_items)
+
+ if(is_train):
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg]])
+ if(dico_items[col] not in item_users):
+ item_users[dico_items[col]] = []
+ item_users[dico_items[col]].append(dico_users[group_name])
+ if(r == 1):
+ y_true[dico_kc[int(kc_name)]].append(r)
+ else:
+ y_true[dico_kc[int(kc_name)]].append(0)
+ else:
+ # reponse q_1, q_0 with y the expected answer
+ if(r==1):
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg]])
+ else:
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col_neg], dico_items[col]])
+ y_true[dico_kc[int(kc_name)]].append(r)
+ for k in range(num_kc):
+ triplets[k] = np.array(triplets[k])
+ y_true[k] = np.array(y_true[k])
+
+ if(is_train):
+ return dico_items, triplets, y_true, item_users
+ else:
+ return dico_items, triplets, y_true
+
+def generate_quad(dico_items, triplets, t_trainy, item_users):
+ l = list(dico_items)
+ quadriplets = []
+ y = []
+ for k in range(len(triplets)):
+ t_quadriplets = []
+ t_y = []
+ for i in range(len(triplets[k])):
+ t = triplets[k][i]
+ q,r = parse_it(l[t[1]])
+ if(t[2] in item_users) and (r == 1):
+ u = random.randint(0,len(item_users[t[2]])-1)
+ uu = item_users[t[2]][u]
+ t_quadriplets.append([t[0], t[1], t[2], uu])
+ t_y.append(t_trainy[k][i])
+ else:
+ t_quadriplets.append([t[0], t[1], t[2], t[0]])
+ t_y.append(t_trainy[k][i])
+ quadriplets.append(np.array(t_quadriplets))
+ y.append(np.array(t_y))
+ return quadriplets, y
+
+def init_frequencielle(train, n, p, dim, dico_items):
+ it = list(dico_items)
+ cc_exp_row = np.random.rand(n,dim)
+ cc_exp_col = np.random.rand(p,dim)
+ for k in range(len(train)):
+ for i in range(len(train[k])):
+ user = train[k][i][0]
+ item = train[k][i][1]
+ q,r = parse_it(it[item])
+ if(r == 1):
+ cc_exp_row[user][k] += 1
+ cc_exp_col[item][k] += 1
+ if(r == 0):
+ cc_exp_row[user][k] -= 1
+ cc_exp_col[item][k] -= 1
+ for i in range(n):
+ cc_exp_row[i] = cc_exp_row[i] / np.sum(cc_exp_row[i])
+ for i in range(p):
+ cc_exp_col[i] = cc_exp_col[i] / np.sum(cc_exp_col[i])
+ return cc_exp_row, cc_exp_col
+
+def write_file_doa(FileName, embed, train, dico_kc, dico_users, dico_items):
+ # write embeddings
+ it = list(dico_items)
+ ut = list(dico_users)
+ nom = FileName+"_embed.csv"
+ f = open(nom, 'w')
+ writer = csv.writer(f)
+ for i in range(embed.shape[0]):
+ row = embed[i]
+ writer.writerow(row)
+ f.close()
+ # write R the responses
+ # user followed by the list of item_resp groupes in block of k
+ nom = FileName+"_responses.csv"
+ f = open(nom, 'w')
+ writer = csv.writer(f)
+ previous_user = -1
+ for k in range(len(dico_kc)):
+ for i in range(len(train[k])):
+ if(previous_user != -1) and (train[k][i][0] != previous_user):
+ positive_items = list(set(positive_items))
+ row = row + positive_items
+ writer.writerow(row)
+ if(train[k][i][0] != previous_user):
+ row = [k]+[train[k][i][0]]
+ positive_items = []
+ previous_user = train[k][i][0]
+ positive_items.append(it[train[k][i][1]])
+ # Remove duplicate positive_items
+ positive_items = list(set(positive_items))
+ row = row + positive_items
+ writer.writerow(row)
+ f.close()
+
+
+#############################
+#############################
+# HP
+epochs = 20
+batch_size = 1024
+ablation = 0
+# 0 no ablation, 1 ablation L2, 2 ablation init, 3 both
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-dtrain", "--dataTrain", help="data file")
+ parser.add_argument("-dtest", "--dataTest", help="data file")
+ parser.add_argument("-ab", "--ablation", help="int")
+ args = parser.parse_args()
+ dataTrain = args.dataTrain
+ dataTest = args.dataTest
+ if args.ablation:
+ ablation = int(args.ablation)
+ file = dataTrain[:-4]
+
+ dico_kc, dico_users, dico_items = read_file(dataTrain, dataTest)
+ embedding_size = len(dico_kc)
+
+ dico_items, t_train, ty_train, item_users = parse_dataframe(dataTrain, dico_kc, dico_users, dico_items, True)
+ train, y_train = generate_quad(dico_items, t_train, ty_train, item_users)
+ dico_items, test, y_test = parse_dataframe(dataTest, dico_kc, dico_users, dico_items, False)
+ print("NB (question-answer) in the data", int(len(dico_items)/2))
+ num_users = len(dico_users)
+ num_items = len(dico_items)
+ cc_exp_row, cc_exp_col = init_frequencielle(train, num_users, num_items, embedding_size, dico_items)
+ bpr_model = BPRModel(batch_size, num_users, num_items, embedding_size, device, cc_exp_row, cc_exp_col, ablation)
+ bpr_model = bpr_model.to(device)
+ # Training loop
+ acc = bpr_model.train(train, len(dico_kc), epochs, batch_size, y_train, ablation)
+ # DOA
+ new_embedding_value = bpr_model.user_embeddings.weight.clone().detach().cpu().numpy()
+ write_file_doa(file, new_embedding_value, train, dico_kc, dico_users, dico_items)
+ doa = compute_doa(file)
+ print("DOA:", doa)
+ # Test
+ acc, precision = bpr_model.evaluate_model(test, len(dico_kc), y_test)
diff --git a/code/binary_model/math1_abla_init_new b/code/binary_model/math1_abla_init_new
new file mode 100644
index 0000000..e69de29
diff --git a/code/binary_model/script_abalation.py b/code/binary_model/script_abalation.py
new file mode 100644
index 0000000..5e56aca
--- /dev/null
+++ b/code/binary_model/script_abalation.py
@@ -0,0 +1,9 @@
+import os
+
+name = ["assist09","assist17","algebra", "math1","math2"]
+for i in range(4):
+ print("Ablation (0 no ablation, 1 ablation L2, 2 ablation init, 3 both) ",i)
+ for a in range(5):
+ print(name[i])
+ cmd = "python main.py --dataTrain ../data/"+name[i]+"/train.csv --dataTest ../data/"+name[i]+"/test.csv --ablation "+str(i)
+ os.system(cmd)
diff --git a/code/binary_model/utils.py b/code/binary_model/utils.py
new file mode 100644
index 0000000..23faa12
--- /dev/null
+++ b/code/binary_model/utils.py
@@ -0,0 +1,165 @@
+import numpy as np
+import pandas as pd
+
+def fromDFtoArray(name, vector, type_value):
+ # Read dataframe and generate a matrix or
+ # a vector of appropriate type
+ df = pd.read_csv(name,index_col=None, header=None)
+ cols = df.columns
+ if(type_value == "f"):
+ for col in cols:
+ df[col] = df[col].astype(float)
+ if(type_value == 'i'):
+ for col in cols:
+ df[col] = df[col].astype(int)
+ r = df.values
+ if(vector):
+ r = r.reshape(-1,)
+ return r
+
+def parse_it(it):
+ # Extract question/answer from label
+ p = it.find('_')
+ q = it[:p]
+ r = it[p+1:]
+ r = int(float(r))
+ return q,r
+
+def flattern_arrays(user_idx, user_idx_test):
+ # Transform two arrays in an array of unique values
+ liste1 = list(user_idx)
+ liste2 = list(user_idx_test)
+ new = list(set(liste2).difference(liste1))
+ liste1.sort()
+ liste1 = np.array(liste1).reshape((-1,1))
+ liste1 = np.unique(liste1).tolist()
+ new.sort()
+ new = np.array(new).reshape((-1,1))
+ new = np.unique(new).tolist()
+ if(len(new) > 0):
+ users = liste1 + new
+ else:
+ users = liste1
+ return users
+
+def flattern_array(user_idx):
+ # Transform an array in an array of unique values
+ users = list(user_idx.reshape((-1,1)))
+ users.sort()
+ users = np.array(users)
+ users = np.unique(users)
+ return users
+
+def parse_kc(s):
+ d = []
+ s = s[1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ return d
+
+def parse(data):
+ d = []
+ for i in range(len(data)):
+ s = data[i][1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ d = flattern_array(np.array(d))
+ return d
+
+def compute_kc_user(name):
+ fileName = name+"_responses.csv"
+ f = open(fileName, "r")
+ lines = f.readlines()
+ kc_user = []
+ kc_user_val = []
+ dico_u = []
+ old_k = -1
+ num_kc = 0
+ for line in lines:
+ r = line.split(',')
+ if(int(r[0]) != old_k):
+ num_kc = num_kc + 1
+ if(old_k != -1):
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ new_kc_user = []
+ new_kc_user_val = []
+ dico_uk = {}
+ old_k = int(r[0])
+ user = int(r[1])
+ if user not in dico_uk:
+ dico_uk[user] = len(dico_uk)
+ new_kc_user.append([])
+ new_kc_user_val.append([])
+ # add item and values
+ for i in range(2,len(r)):
+ q,rep = parse_it(r[i])
+ new_kc_user[dico_uk[user]].append(int(q))
+ new_kc_user_val[dico_uk[user]].append(rep)
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ # sort files
+ for k in range(len(kc_user)):
+ for u in range(len(kc_user[k])):
+ kc_user[k][u], kc_user_val[k][u] = zip(*sorted(zip(kc_user[k][u], kc_user_val[k][u])))
+ return kc_user, kc_user_val, dico_u, num_kc
+
+def doa_creuse(F, kc_user, kc_user_val, dico_u):
+ doa = []
+ user_embed = F
+ num_kc = len(kc_user)
+ for k in range(num_kc):
+ Z1 = 0.0
+ v = 0.0
+ users = list(dico_u[k])
+ for a in range(len(kc_user[k])):
+ for b in range(len(kc_user[k])):
+ Z2 = 0.0
+ Z0 = 0.0
+ ua = users[a]
+ ub = users[b]
+ if(user_embed[ua][k] > user_embed[ub][k]):
+ ia = a
+ ib = b
+ ma = 0
+ mb = 0
+ while(ma < len(kc_user[k][ia])) and (mb < len(kc_user[k][ib])):
+ if(kc_user[k][ia][ma] < kc_user[k][ib][mb]):
+ ma = ma + 1
+ else:
+ if(kc_user[k][ia][ma] > kc_user[k][ib][mb]):
+ mb = mb + 1
+ else:
+ # meme item
+ if(kc_user_val[k][ia][ma] > kc_user_val[k][ib][mb]):
+ Z2 = Z2 + 1
+ Z0 = Z0 + 1
+ else:
+ if(kc_user_val[k][ia][ma] < kc_user_val[k][ib][mb]):
+ Z0 = Z0 + 1
+ ma = ma + 1
+ mb = mb + 1
+ if(Z0 > 0):
+ Z1 = Z1 + 1
+ v = v + Z2/Z0
+ if(Z1 > 0):
+ v = v / Z1
+ doa.append(v)
+ return doa
+
+def compute_doa(filename):
+ F = fromDFtoArray(filename+"_embed.csv",False,'f')
+ kc_user, kc_user_val, dico_u, num_kc = compute_kc_user(filename)
+ r = doa_creuse(F, kc_user, kc_user_val, dico_u)
+ doa = np.mean(r)
+ return doa
diff --git a/code/binary_model/utils.pyc b/code/binary_model/utils.pyc
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diff --git a/code/nary_model/BPR_model.py b/code/nary_model/BPR_model.py
new file mode 100644
index 0000000..3a40c16
--- /dev/null
+++ b/code/nary_model/BPR_model.py
@@ -0,0 +1,177 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import torch.nn.functional as F
+import pandas as pd
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score
+from sklearn.metrics import recall_score
+from sklearn.metrics.cluster import contingency_matrix
+
+
+dtype = torch.float32
+if torch.cuda.is_available():
+ dev = "cuda:0"
+else:
+ dev = "cpu"
+device = torch.device(dev)
+
+class BPRModel(nn.Module):
+ def __init__(self, batch_size, num_users, num_items, embedding_size, device, cc_exp_row=None, cc_exp_col=None):
+ self.device = device
+ self.batch_size = batch_size
+
+ super(BPRModel, self).__init__()
+ # Model parameters
+ self.user_embeddings = nn.Embedding(num_users, embedding_size).to(self.device)
+ self.item_embeddings = nn.Embedding(num_items, embedding_size).to(self.device)
+
+ # Initialization
+ self.user_embeddings.weight.data.copy_(torch.from_numpy(cc_exp_row))
+ self.item_embeddings.weight.data.copy_(torch.from_numpy(cc_exp_col))
+
+ # Optimizer
+ self.optimizer = optim.Adam(self.parameters(), lr=0.01)
+
+
+ def forward(self, user, item, k, is_user):
+ if(is_user == True):
+ user_embedding = self.user_embeddings(user)
+ item_embedding = self.item_embeddings(item)
+
+ if (k == -1):
+ user_embedding = F.normalize(user_embedding, p=2, dim=1)
+ item_embedding = F.normalize(item_embedding, p=2, dim=1)
+ return - torch.norm(user_embedding - item_embedding, p=2, dim=1)
+
+ else:
+ return torch.mean(user_embedding[:,k])
+ else:
+ user_embedding = self.item_embeddings(user)
+ return torch.mean(user_embedding[:,k])
+
+
+ def bpr_loss(self,positive_scores, negative_scores, width):
+ return -torch.mean(torch.log(torch.sigmoid(width*(positive_scores - negative_scores))))
+
+ def train(self, triplets, num_kc, epochs, batch_size, y):
+ for epoch in range(epochs):
+ all_labels = None
+ all_decisions = None
+ first = True
+ for k in range(num_kc):
+ trips = triplets[k]
+ # Set the mask to zero for specific indices (e.g., user 0)
+ masked_indices = [k]
+ y_true = y[k]
+ for i in range(0, len(trips), self.batch_size):
+ users_batch = trips[i:i + self.batch_size, 0]
+ items_batch = trips[i:i + self.batch_size, 1]
+ negatives_batch = trips[i:i + self.batch_size, 2]
+ neg_users_batch = trips[i:i + self.batch_size, 3]
+ width_batch = trips[i:i + self.batch_size, 4]
+ y_train = y_true[i:i + self.batch_size]
+
+ # Convert the numpy.ndarray to tensor
+ users_batch = torch.from_numpy(users_batch).to(self.device)
+ items_batch = torch.from_numpy(items_batch).to(self.device)
+ negatives_batch = torch.from_numpy(negatives_batch).to(self.device)
+ neg_users_batch = torch.from_numpy(neg_users_batch).to(self.device)
+ width_batch = torch.from_numpy(width_batch).to(self.device)
+
+ # Create a mask with the same shape as the embeddings
+ current_batch_size = users_batch.shape[0]
+ oneVect = torch.full((1,current_batch_size),1).to(self.device)
+
+
+ # call forward
+ positive_scores = self(users_batch, items_batch, -1, True)
+ negative_scores = self(users_batch, negatives_batch, -1, True)
+ loss1 = self.bpr_loss(positive_scores, negative_scores, width_batch)
+
+ positive_scores_bis = self(users_batch, negatives_batch, k, True)
+ negative_scores_bis = self(neg_users_batch, negatives_batch, k, True)
+ loss2 = self.bpr_loss(positive_scores_bis, negative_scores_bis, oneVect)
+
+ positive_scores_ter = self(items_batch, negatives_batch, k, False)
+ negative_scores_ter = self(negatives_batch, negatives_batch, k, False)
+ loss3 = self.bpr_loss(positive_scores_ter, negative_scores_ter, width_batch)
+
+ loss = loss1 + loss2 + loss3
+
+ if (first == True):
+ all_labels = y_train
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = comp
+ first = False
+ else:
+ all_labels = np.concatenate((all_labels, y_train), axis=0)
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = np.concatenate((all_decisions, comp), axis=0)
+
+
+ # Backward pass to compute gradients
+ loss.backward(retain_graph=True)
+
+ # Optimizer step
+ self.optimizer.step()
+ # Clear the gradients for the next iteration
+ self.optimizer.zero_grad()
+ #if(epoch % 10 == 0):
+ # print(f"Epoch {epoch + 1}, Loss: {loss.item()}")
+ acc = accuracy_score(all_labels, all_decisions)
+ return acc
+
+ # Evaluate the model
+ def evaluate_model(self, test_triplets, num_kc, y_test):
+ # Initialize counters for metrics
+ precision = 0.0
+ nb_evals = 0.0
+ all_predictions = None
+ all_labels = None
+ all_decisions = None
+ first = True
+ for k in range(num_kc):
+ trips = test_triplets[k]
+ y = y_test[k]
+ for i in range(0, len(trips), self.batch_size):
+ users_batch = trips[i:i + self.batch_size, 0]
+ items_batch = trips[i:i + self.batch_size, 1]
+ negatives_batch = trips[i:i + self.batch_size, 2]
+ y_true = y[i:i + self.batch_size]
+ # Convert the ndarray to tensor
+ users_batch = torch.from_numpy(users_batch).to(self.device)
+ items_batch = torch.from_numpy(items_batch).to(self.device)
+ negatives_batch = torch.from_numpy(negatives_batch).to(self.device)
+
+ # Compute distances
+ positive_scores = self(users_batch, items_batch,-1, True).cpu()
+ negative_scores = self(users_batch, negatives_batch,-1, True).cpu()
+ # compute probabilities
+ proba = torch.sigmoid(positive_scores - negative_scores )
+
+ if (first == True):
+ all_labels = y_true
+ all_predictions = proba.detach().cpu().numpy()
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = comp
+ first = False
+ else:
+ all_labels = np.concatenate((all_labels, y_true), axis=0)
+ all_predictions = np.concatenate((all_predictions, proba.detach().cpu().numpy()), axis=0)
+ comp = negative_scores < positive_scores
+ comp = comp.cpu()
+ all_decisions = np.concatenate((all_decisions, comp), axis=0)
+
+ correct_ranking = sum(negative_scores < positive_scores)# for score in negative_scores)
+ mse1 = mean_squared_error(all_labels, all_predictions)
+ auc = roc_auc_score(all_labels, all_predictions)
+ return accuracy_score(all_labels, all_decisions), precision_score(all_labels, all_decisions), recall_score(all_labels, all_decisions), all_decisions
+
diff --git a/code/nary_model/__pycache__/BPR_model.cpython-310.pyc b/code/nary_model/__pycache__/BPR_model.cpython-310.pyc
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diff --git a/code/nary_model/main_nary.py b/code/nary_model/main_nary.py
new file mode 100644
index 0000000..4e0e442
--- /dev/null
+++ b/code/nary_model/main_nary.py
@@ -0,0 +1,237 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import torch.nn.functional as F
+import pandas as pd
+from scipy import stats
+import csv
+import json
+import random
+import os
+from collections import Counter
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score
+from sklearn.metrics.cluster import contingency_matrix
+from utils import *
+from BPR_model import *
+from sklearn.preprocessing import normalize
+
+dtype = torch.float32
+if torch.cuda.is_available():
+ dev = "cuda:0"
+else:
+ dev = "cpu"
+device = torch.device(dev)
+
+
+def read_file(dataTrain, dataTest):
+ # Compute dictionaries
+ df = pd.read_csv(dataTrain, names=['user_id', 'item_id','correct','knowledge',"max_val"])
+ dfTest = pd.read_csv(dataTest, names=['user_id', 'item_id','correct','knowledge',"max_val"])
+ # dico kc
+ kc = df['knowledge']
+ kcT = dfTest['knowledge']
+ kc = flattern_arrays(kc.values, kcT.values)
+ num_kc = len(kc)
+ dico_kc = { k:v for (k,v) in zip(kc, range(len(kc)))}
+ # dico users
+ users = df['user_id']
+ usersT = dfTest['user_id']
+ users = flattern_arrays(users.values, usersT.values)
+ num_users = len(users)
+ dico_users = { k:v for (k,v) in zip(users, range(num_users))}
+ # dico items and their associated kc
+ itemsDT = df['item_id']
+ nb_item_train = len(flattern_array(itemsDT.values))
+ itemsT = dfTest['item_id']
+ items = flattern_arrays(itemsDT.values, itemsT.values)
+ num_items = len(items)
+ dico_items = { k:v for (k,v) in zip(items, range(num_items))}
+ d_quest_val = {}
+ dico_items, d_quest_val = parse_item_values(df[['item_id', 'max_val']], dico_items, d_quest_val)
+ dico_items, d_quest_val = parse_item_values(dfTest[['item_id', 'max_val']], dico_items, d_quest_val)
+
+ for quest_val in d_quest_val:
+ quest_val = flattern_array(np.array(quest_val))
+ return dico_kc, dico_users, dico_items, d_quest_val, nb_item_train
+
+def parse_dataframe(data, dico_kc, dico_users, dico_items, d_quest_val, nb_item_train, is_train = True):
+ df = pd.read_csv(data, names=['user_id', 'item_id','correct','knowledge','max_val'])
+
+ # Compute table of positive and negative items by KC and Users
+ # and the dictionary that associate the KC to a question/answer
+ num_kc = len(dico_kc)
+ num_users = len(dico_users)
+ # Find positive items for each kc/user
+ triplets = []
+ y_true = []
+ for k in range(num_kc):
+ triplets.append([])
+ y_true.append([])
+ k = 0
+ if(is_train):
+ item_users = {}
+ items_kc = np.full(len(dico_items),0)
+ grouped_kc = df.groupby('knowledge')
+ for kc_name, df_kc in grouped_kc:
+ # Find positive items for each user
+ grouped = df_kc.groupby('user_id')
+ for group_name, df_group in grouped:
+ for row_index, row in df_group.iterrows():
+ col = row['item_id']
+ q,r = parse_it(col)
+ q = int(q)
+ #on en garde une aleatoirement pour le triplet
+ if(is_train):
+ for r_kept in range(1):#len(d_quest_val[q])):
+ r_kept = r
+ while(r_kept == r) and (len(d_quest_val[q]) > 1):
+ r_kept = random.randint(0,len(d_quest_val[q])-1)
+ col_neg_kept = str(q)+'_'+str(d_quest_val[q][r_kept])
+ #print(r_kept, r)
+ amplitude = 1*(np.abs(r-r_kept) + 1)
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg_kept], amplitude])
+ if(dico_items[col] not in item_users):
+ item_users[dico_items[col]] = []
+ item_users[dico_items[col]].append(dico_users[group_name])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ items_kc[dico_items[col]] = int(kc_name)
+ else:
+ # a voir, dans la decision ils doivent etre tous plus loin...
+ # on fait toutes les paires
+ # a bien verifier !!!!
+ for j in range(len(d_quest_val[q])):
+ #r_kept = random.randint(0,len(d_quest_val[q])-1)
+ r_kept = d_quest_val[q][j]
+ col_neg_kept = str(q)+'_'+str(r_kept)
+ amplitude = 1*(np.abs(r-r_kept) + 1 )
+ if(r >= r_kept):
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg_kept], amplitude])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ else:
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col_neg_kept], dico_items[col], amplitude])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ for k in range(num_kc):
+ triplets[k] = np.array(triplets[k])
+ y_true[k] = np.array(y_true[k])
+
+
+ if(is_train):
+ write_file_std(data[:-4]+"_kc.txt", items_kc[0:nb_item_train])
+ return dico_items, triplets, y_true, item_users
+ else:
+ return dico_items, triplets, y_true
+
+def compute_pred(all_predictions, test, y_test, dico_users, dico_items):
+ r = 0
+ all_test = test[0]
+ for i in range(1,len(test)):
+ all_test = np.concatenate((test[i],all_test),axis=0)
+ l = list(dico_items)
+ list_quest = []
+ for i in range(len(all_test)):
+ q,r = parse_it(l[all_test[i,1]])
+ list_quest.append(int(q))
+ all_test = np.concatenate((all_test,np.array(list_quest).reshape(-1,1)), axis=1)
+
+ responses = []
+ list_user = flattern_array(all_test[:,0])
+ for u in list_user:
+ my_users = all_test[:,0] == u
+ list_quest = flattern_array(all_test[my_users,4])
+ for quest in list_quest:
+ rows = all_test[:,4] == quest
+ my_rows = np.logical_and(rows,my_users)
+
+ # we go the rows with all the modalities
+ modality = -1
+ for t in range(len(all_test[my_rows])):
+ ione = all_test[my_rows][t][1]
+ itwo = all_test[my_rows][t][2]
+ q1,r1 = parse_it(l[ione])
+ q2,r2 = parse_it(l[itwo])
+ if(all_predictions[my_rows][t] == 1):
+ modality = r1
+ else:
+ modality = r2
+ responses.append([dico_users[u], quest, modality])
+
+ return responses
+
+
+
+#############################
+#############################
+# HP
+epochs = 20
+batch_size = 1024
+alpha = 1
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-dtrain", "--dataTrain", help="data file")
+ parser.add_argument("-dtest", "--dataTest", help="data file")
+ parser.add_argument("-a", "--alpha", help="float")
+ args = parser.parse_args()
+ dataTrain = args.dataTrain
+ dataTest = args.dataTest
+ filename = dataTrain[:-4]
+ if args.alpha:
+ alpha = int(args.alpha)
+
+ dico_kc, dico_users, dico_items, d_quest_val, nb_item_train = read_file(dataTrain, dataTest)
+ embedding_size = len(dico_kc)
+ dico_items, t_train, ty_train, item_users = parse_dataframe(dataTrain, dico_kc, dico_users, dico_items, d_quest_val, nb_item_train, True)
+ train, y_train = generate_quad(dico_items, t_train, ty_train, item_users, d_quest_val)
+ dico_items, test, y_test = parse_dataframe(dataTest, dico_kc, dico_users, dico_items,d_quest_val, nb_item_train, False)
+
+ num_users = len(dico_users)
+ num_items = len(dico_items)
+ cc_exp_row, cc_exp_col = init_frequencielle(train, num_users, num_items, embedding_size, dico_items, d_quest_val)
+ bpr_model = BPRModel(batch_size, num_users, num_items, embedding_size, device, cc_exp_row, cc_exp_col)
+ bpr_model = bpr_model.to(device)
+ # Training loop
+ acc = bpr_model.train(train, len(dico_kc), epochs, batch_size, y_train)
+
+ new_embedding_value = bpr_model.user_embeddings.weight.clone().detach().cpu().numpy()
+ write_file_doa(filename, new_embedding_value, train, dico_kc, dico_users, dico_items)
+ doa, rdoa = compute_doa(filename)
+ # write embed items
+ #new_embedding_items = bpr_model.item_embeddings.weight.clone().detach().cpu().numpy()
+ #write_file(filename+"embedding_items.csv", new_embedding_items[0:nb_item_train])
+
+ #write_file_std(FileName1+"kc_emb.txt", dico_items)
+ #print("Accuracy and Doa on train dataset:", acc, doa)
+ # Test
+ acc, precision, rappel, all_predictions = bpr_model.evaluate_model(test, len(dico_kc), y_test)
+ s = str(acc) +","+ str( precision)+ ","+str(rappel)+ ","+str(doa)
+ #for i in range(len(rdoa)):
+ # s = s + ','+ str(rdoa[i])
+ print(s)
+
+ # coding users and kc from user_label.csv
+ '''
+ os.getcwd()
+ dir_path = os.path.dirname(os.path.realpath(dataTrain))
+ file = dir_path+'/user_label.csv'
+ dfQE = pd.read_csv(file, names=['user_id', 'knowledge',"value"])
+ # create matrix
+ questEval = np.full((num_users, embedding_size),-1)
+ #fill the matrix
+ uu = flattern_array(dfQE['user_id'].values)
+ for t in dfQE.values:
+ if(int(t[0]) in dico_users):
+ ind = dico_users[int(t[0])]
+ kc = dico_kc[int(t[1])]
+ val = int(t[2])
+ questEval[ind, kc] = val
+ write_file(filename+"_user_quest_label.csv", questEval)
+ '''
+the_predictions = compute_pred(all_predictions, test, y_test, dico_users, dico_items)
+file = "_test_predictions_"+str(alpha)+".csv"
+write_file(filename + file, np.array(the_predictions))
\ No newline at end of file
diff --git a/code/nary_model/main_nary_cv.py b/code/nary_model/main_nary_cv.py
new file mode 100644
index 0000000..609ed98
--- /dev/null
+++ b/code/nary_model/main_nary_cv.py
@@ -0,0 +1,107 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import torch.nn.functional as F
+import pandas as pd
+from scipy import stats
+import csv
+import json
+
+from collections import Counter
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score
+from sklearn.metrics.cluster import contingency_matrix
+from utils import *
+from BPR_model import *
+
+dtype = torch.float32
+
+if torch.cuda.is_available():
+ dev = "cuda:0"
+else:
+ dev = "cpu"
+device = torch.device(dev)
+
+def evaluate_all(dataTrain, dataTest, filename):
+ dico_kc, dico_users, dico_items, d_quest_val, nb_item_train = read_file_cv(dataTrain, dataTest)
+ embedding_size = len(dico_kc)
+ dico_items, t_train, ty_train, item_users = parse_dataframe_cv(dataTrain, dico_kc, dico_users, dico_items, d_quest_val, True)
+ train, y_train = generate_quad(dico_items, t_train, ty_train, item_users, d_quest_val)
+ dico_items, test, y_test = parse_dataframe_cv(dataTest, dico_kc, dico_users, dico_items,d_quest_val, False)
+
+ num_users = len(dico_users)
+ num_items = len(dico_items)
+ cc_exp_row, cc_exp_col = init_frequencielle(train, num_users, num_items, embedding_size, dico_items, d_quest_val)
+ bpr_model = BPRModel(batch_size, num_users, num_items, embedding_size, device, cc_exp_row, cc_exp_col)
+ bpr_model = bpr_model.to(device)
+ # Training loop
+ acc = bpr_model.train(train, len(dico_kc), epochs, batch_size, y_train)
+ new_embedding_value = bpr_model.user_embeddings.weight.clone().detach().cpu().numpy()
+ write_file_doa(filename, new_embedding_value, train, dico_kc, dico_users, dico_items)
+ doa, doatot = compute_doa(filename)
+ # write embed items
+ new_embedding_items = bpr_model.item_embeddings.weight.clone().detach().cpu().numpy()
+ write_file(filename+"embedding_items.csv", new_embedding_items[0:nb_item_train])
+ # Test
+ acc, precision, rappel, all_decisions = bpr_model.evaluate_model(test, len(dico_kc), y_test)
+ '''
+ s = str(acc) +","+ str( precision)+ ","+str(rappel)+ ","+str(doa)
+ for i in range(embedding_size):
+ s = s + ','+ str(doatot[i])
+ print(s)
+ '''
+ return acc, precision, rappel, doa
+
+def cross_validation(data, kfolds, filename):
+ n = data.shape[0]
+ fold_size = n // kfolds
+
+ indices = np.arange(n)
+ np.random.shuffle(indices)
+ doa = []
+ acc = []
+ precision = []
+ rappel = []
+ for i in range(kfolds):
+ start = i * fold_size
+ end = (i + 1) * fold_size if i != kfolds - 1 else n
+ validation_indices = indices[start:end]
+ training_indices = np.concatenate([indices[:start], indices[end:]])
+ validation_data = data[validation_indices]
+ training_data = data[training_indices]
+ dataTrain = pd.DataFrame(data = training_data, columns=['user_id', 'item_id','correct','knowledge',"max_val"])
+ dataTest = pd.DataFrame(data = validation_data, columns=['user_id', 'item_id','correct','knowledge',"max_val"])
+
+ a, p, r, d = evaluate_all(dataTrain, dataTest, filename)
+ acc.append(a)
+ precision.append(p)
+ rappel.append(r)
+ doa.append(d)
+ acc = np.array(acc)
+ precision = np.array(precision)
+ rappel = np.array(rappel)
+ doa = np.array(doa)
+ print("avg values", np.mean(acc), np.mean(precision), np.mean(rappel), np.mean(doa))
+#############################
+#############################
+# HP
+epochs = 30
+batch_size = 1024
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-dt", "--data", help="data file")
+ args = parser.parse_args()
+ data = args.data
+
+ filename = data[:-4]
+ df = pd.read_csv(data)
+ cross_validation(df.values, 5, filename)
+
+
diff --git a/code/nary_model/utils.py b/code/nary_model/utils.py
new file mode 100644
index 0000000..8280c03
--- /dev/null
+++ b/code/nary_model/utils.py
@@ -0,0 +1,377 @@
+import numpy as np
+import pandas as pd
+import csv
+import random
+from sklearn.preprocessing import normalize
+
+def write_file_std(name, data):
+ f = open(name, 'w')
+ for i in range(len(data)):
+ f.write(str(data[i])+"\n")
+ f.close()
+
+def write_file(name, data):
+ f = open(name, 'w')
+ writer = csv.writer(f)
+ for i in range(data.shape[0]):
+ writer.writerow(data[i])
+ f.close()
+
+def fromDFtoArray(name, vector, type_value):
+ # Read dataframe and generate a matrix or
+ # a vector of appropriate type
+ df = pd.read_csv(name,index_col=None, header=None)
+ cols = df.columns
+ if(type_value == "f"):
+ for col in cols:
+ df[col] = df[col].astype(float)
+ if(type_value == 'i'):
+ for col in cols:
+ df[col] = df[col].astype(int)
+ r = df.values
+ if(vector):
+ r = r.reshape(-1,)
+ return r
+
+def parse_it(it):
+ # Extract question/answer from label
+ p = it.find('_')
+ q = it[:p]
+ r = it[p+1:]
+ r = int(float(r))
+ return q,r
+
+def parse_item_values(d, dico_items, d_quest_val):
+ for t in d.values:
+ col = t[0]
+ ma = t[1]
+ if col not in dico_items:
+ dico_items[col] = len(dico_items)
+ # Warning, all user's answers are positives!
+ q,r = parse_it(col)
+ q = int(q)
+ # on rajoute toutes les valeurs avant r jusqu'a 0
+ if(q not in d_quest_val):
+ d_quest_val[q] = []
+
+ for val in range(ma):
+ if(val not in d_quest_val[q]):
+ d_quest_val[q].append(val)
+ col_neg = str(q)+'_'+str(val)
+ if col_neg not in dico_items:
+ dico_items[col_neg] = len(dico_items)
+ return dico_items, d_quest_val
+
+def flattern_arrays(user_idx, user_idx_test):
+ # Transform two arrays in an array of unique values
+ liste1 = list(user_idx)
+ liste2 = list(user_idx_test)
+ new = list(set(liste2).difference(liste1))
+ liste1.sort()
+ liste1 = np.array(liste1).reshape((-1,1))
+ liste1 = np.unique(liste1).tolist()
+ new.sort()
+ new = np.array(new).reshape((-1,1))
+ new = np.unique(new).tolist()
+ if(len(new) > 0):
+ users = liste1 + new
+ else:
+ users = liste1
+ return users
+
+def flattern_array(user_idx):
+ # Transform an array in an array of unique values
+ users = list(user_idx.reshape((-1,1)))
+ users.sort()
+ users = np.array(users)
+ users = np.unique(users)
+ return users
+
+def parse_kc(s):
+ d = []
+ s = s[1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ return d
+
+def parse(data):
+ d = []
+ for i in range(len(data)):
+ s = data[i][1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ d = flattern_array(np.array(d))
+ return d
+
+def compute_kc_user(name):
+ fileName = name+"_responses.csv"
+ f = open(fileName, "r")
+ lines = f.readlines()
+ kc_user = []
+ kc_user_val = []
+ dico_u = []
+ old_k = -1
+ num_kc = 0
+ for line in lines:
+ r = line.split(',')
+ if(int(r[0]) != old_k):
+ num_kc = num_kc + 1
+ if(old_k != -1):
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ new_kc_user = []
+ new_kc_user_val = []
+ dico_uk = {}
+ old_k = int(r[0])
+ user = int(r[1])
+ if user not in dico_uk:
+ dico_uk[user] = len(dico_uk)
+ new_kc_user.append([])
+ new_kc_user_val.append([])
+ # add item and values
+ for i in range(2,len(r)):
+ q,rep = parse_it(r[i])
+ new_kc_user[dico_uk[user]].append(int(q))
+ new_kc_user_val[dico_uk[user]].append(rep)
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ # sort files
+ for k in range(len(kc_user)):
+ for u in range(len(kc_user[k])):
+ kc_user[k][u], kc_user_val[k][u] = zip(*sorted(zip(kc_user[k][u], kc_user_val[k][u])))
+ return kc_user, kc_user_val, dico_u, num_kc
+
+def doa_creuse(F, kc_user, kc_user_val, dico_u):
+ doa = []
+ user_embed = F
+ num_kc = len(kc_user)
+ for k in range(num_kc):
+ Z1 = 0.0
+ v = 0.0
+ users = list(dico_u[k])
+ for a in range(len(kc_user[k])):
+ for b in range(len(kc_user[k])):
+ Z2 = 0.0
+ Z0 = 0.0
+ ua = users[a]
+ ub = users[b]
+ if(user_embed[ua][k] > user_embed[ub][k]):
+ ia = a
+ ib = b
+ ma = 0
+ mb = 0
+ while(ma < len(kc_user[k][ia])) and (mb < len(kc_user[k][ib])):
+ if(kc_user[k][ia][ma] < kc_user[k][ib][mb]):
+ ma = ma + 1
+ else:
+ if(kc_user[k][ia][ma] > kc_user[k][ib][mb]):
+ mb = mb + 1
+ else:
+ # meme item
+ if(kc_user_val[k][ia][ma] > kc_user_val[k][ib][mb]):
+ Z2 = Z2 + 1
+ Z0 = Z0 + 1
+ else:
+ if(kc_user_val[k][ia][ma] < kc_user_val[k][ib][mb]):
+ Z0 = Z0 + 1
+ ma = ma + 1
+ mb = mb + 1
+ if(Z0 > 0):
+ Z1 = Z1 + 1
+ v = v + Z2/Z0
+
+ if(Z1 > 0):
+ v = v / Z1
+ doa.append(v)
+ return doa
+
+def read_file_cv(df, dfTest):
+ kc = df['knowledge']
+ kcT = dfTest['knowledge']
+ kc = flattern_arrays(kc.values, kcT.values)
+ num_kc = len(kc)
+ dico_kc = { k:v for (k,v) in zip(kc, range(len(kc)))}
+ # dico users
+ users = df['user_id']
+ usersT = dfTest['user_id']
+ users = flattern_arrays(users.values, usersT.values)
+ num_users = len(users)
+ dico_users = { k:v for (k,v) in zip(users, range(num_users))}
+ # dico items and their associated kc
+ itemsDT = df['item_id']
+ nb_item_train = len(flattern_array(itemsDT.values))
+ itemsT = dfTest['item_id']
+ items = flattern_arrays(itemsDT.values, itemsT.values)
+ num_items = len(items)
+ dico_items = { k:v for (k,v) in zip(items, range(num_items))}
+ d_quest_val = {}
+ dico_items, d_quest_val = parse_item_values(df[['item_id', 'max_val']], dico_items, d_quest_val)
+ dico_items, d_quest_val = parse_item_values(dfTest[['item_id', 'max_val']], dico_items, d_quest_val)
+
+ for quest_val in d_quest_val:
+ quest_val = flattern_array(np.array(quest_val))
+
+ return dico_kc, dico_users, dico_items, d_quest_val, nb_item_train
+
+def parse_dataframe_cv(df, dico_kc, dico_users, dico_items, d_quest_val, is_train = True):
+ # Compute table of positive and negative items by KC and Users
+ # and the dictionary that associate the KC to a question/answer
+ num_kc = len(dico_kc)
+ num_users = len(dico_users)
+ # Find positive items for each kc/user
+ triplets = []
+ y_true = []
+ for k in range(num_kc):
+ triplets.append([])
+ y_true.append([])
+ k = 0
+ if(is_train):
+ item_users = {}
+ grouped_kc = df.groupby('knowledge')
+ for kc_name, df_kc in grouped_kc:
+ # Find positive items for each user
+ grouped = df_kc.groupby('user_id')
+ for group_name, df_group in grouped:
+ for row_index, row in df_group.iterrows():
+ col = row['item_id']
+ q,r = parse_it(col)
+ q = int(q)
+ if(is_train):
+ for r_kept in range(1):
+ r_kept = r
+ while(r_kept == r) and (len(d_quest_val[q]) > 1):
+ r_kept = random.randint(0,len(d_quest_val[q])-1)
+ col_neg_kept = str(q)+'_'+str(d_quest_val[q][r_kept])
+ amplitude = 1*(np.abs(r-r_kept) + 1)
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg_kept], amplitude])
+ if(dico_items[col] not in item_users):
+ item_users[dico_items[col]] = []
+ item_users[dico_items[col]].append(dico_users[group_name])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ else:
+ # a voir, dans la decision ils doivent etre tous plus loin...
+ # on fait toutes les paires
+ # a bien verifier !!!!
+ for j in range(len(d_quest_val[q])):
+ r_kept = d_quest_val[q][j]
+ col_neg_kept = str(q)+'_'+str(r_kept)
+ amplitude = 1*(np.abs(r-r_kept) + 1 )
+ if(r >= r_kept):
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col], dico_items[col_neg_kept], amplitude])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ else:
+ triplets[dico_kc[int(kc_name)]].append([dico_users[group_name], dico_items[col_neg_kept], dico_items[col], amplitude])
+ y_true[dico_kc[int(kc_name)]].append(r > r_kept)
+ for k in range(num_kc):
+ triplets[k] = np.array(triplets[k])
+ y_true[k] = np.array(y_true[k])
+
+
+ if(is_train):
+ return dico_items, triplets, y_true, item_users
+ else:
+ return dico_items, triplets, y_true
+
+def generate_quad(dico_items, triplets, t_trainy, item_users, d_quest_val):
+ l = list(dico_items)
+ quadriplets = []
+ y = []
+ for k in range(len(triplets)):
+ t_quadriplets = []
+ t_y = []
+ for i in range(len(triplets[k])):
+ t = triplets[k][i]
+ q,r = parse_it(l[t[1]])
+ q = int(q)
+ # on prend une valeur differente pour l'autre user a eloigner...
+ # on prend user sur autre modalite
+ q,val = parse_it(l[t[2]])
+ q = int(q)
+ col_neg = str(q)+'_'+str(val)
+ if(t[2] in item_users):
+ u = random.randint(0,len(item_users[t[2]])-1)
+ uu = item_users[t[2]][u]
+ t_quadriplets.append([t[0], t[1], t[2], uu, t[3]])
+ t_y.append(t_trainy[k][i])
+ else:
+ t_quadriplets.append([t[0], t[1], t[2], t[0], 1])
+ t_y.append(t_trainy[k][i])
+ quadriplets.append(np.array(t_quadriplets))
+ y.append(np.array(t_y))
+ return quadriplets, y
+
+def init_frequencielle(train, n, p, dim, dico_items, d_quest_val):
+ it = list(dico_items)
+ cc_exp_row = np.random.rand(n,dim)
+ cc_exp_col = np.random.rand(p,dim)
+ for k in range(len(train)):
+ for i in range(len(train[k])):
+ user = train[k][i][0]
+ item = train[k][i][1]
+ q,r = parse_it(it[item])
+ q = int(q)
+ mv = int(len(d_quest_val[q])/2)
+ middle_value = d_quest_val[q][mv]
+ if(r > middle_value):
+ cc_exp_row[user][k] += int(r) ## 1
+ cc_exp_col[item][k] += int(r)
+ if(r < middle_value - 1):
+ cc_exp_row[user][k] -= int(r)
+ cc_exp_col[item][k] -= int(r)
+
+ cc_exp_row = normalize(cc_exp_row, axis=1, norm='l1')
+ cc_exp_col = normalize(cc_exp_col, axis=1, norm='l1')
+
+ return cc_exp_row, cc_exp_col
+
+def write_file_doa(FileName, embed, train, dico_kc, dico_users, dico_items):
+ # write embeddings
+ it = list(dico_items)
+ ut = list(dico_users)
+ nom = FileName+"_embed.csv"
+ f = open(nom, 'w')
+ writer = csv.writer(f)
+ for i in range(embed.shape[0]):
+ row = embed[i]
+ writer.writerow(row)
+ f.close()
+ # write R the responses
+ # user followed by the list of item_resp groupes in block of k
+ nom = FileName+"_responses.csv"
+ f = open(nom, 'w')
+ writer = csv.writer(f)
+ previous_user = -1
+ for k in range(len(dico_kc)):
+ for i in range(len(train[k])):
+ if(previous_user != -1) and (train[k][i][0] != previous_user):
+ positive_items = list(set(flattern_array(np.array(positive_items))))
+ row = row + positive_items
+ writer.writerow(row)
+ if(train[k][i][0] != previous_user):
+ row = [k]+[train[k][i][0]]
+ positive_items = []
+ previous_user = train[k][i][0]
+ positive_items.append(it[train[k][i][1]])
+ #remove duplicate positive_items
+ positive_items = list(set(flattern_array(np.array(positive_items))))
+ row = row + positive_items
+ writer.writerow(row)
+ f.close()
+
+def compute_doa(filename):
+ F = fromDFtoArray(filename+"_embed.csv",False,'f')
+ kc_user, kc_user_val, dico_u, num_kc = compute_kc_user(filename)
+ r = doa_creuse(F, kc_user, kc_user_val, dico_u)
+ doa = np.mean(r)
+ return doa,r
+
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diff --git a/code/other/decision_tree.py b/code/other/decision_tree.py
new file mode 100644
index 0000000..bfba35b
--- /dev/null
+++ b/code/other/decision_tree.py
@@ -0,0 +1,395 @@
+import argparse
+import pandas as pd
+import numpy as np
+import csv
+from matplotlib import gridspec
+from sklearn.manifold import TSNE
+import seaborn as sns
+from sklearn import preprocessing
+import matplotlib.pyplot as plt
+from utils import *
+from sklearn.metrics import silhouette_score, silhouette_samples
+from sklearn.tree import DecisionTreeClassifier, export_text
+import os
+
+def new_silhouette(X, cluster_labels):
+ silhouette_avg = silhouette_score(X, cluster_labels)
+ return silhouette_avg
+
+class Tree:
+ def __init__(self, feature, threshold, feats, users, selected_features):
+ self.children_left = None
+ self.children_right = None
+ self.feature = None #feature
+ self.threshold = None #threshold
+ self.attributes = feats
+ self.feat_selected = selected_features
+ self.users = users
+ self.users_quest = []
+
+ def apply_feature(self, feature, threshold, H_users, extend, min_users):
+ self.feature = feature
+ self.threshold = threshold
+ h = H_users[:,feature]
+ part = h <= threshold
+ part = (1 * part).reshape(-1)
+ intersection = self.users * part
+ new_feats = self.attributes.copy()
+ new_feats.remove(feature)
+ new_selected_feats = self.feat_selected.copy()
+ new_selected_feats.append(feature)
+ right_users = self.users - intersection
+ if (extend == True):
+ if(np.sum((intersection)) >= min_users) and (np.sum((right_users)) >= min_users):
+ self.children_left = Tree(feature, threshold, new_feats, intersection, new_selected_feats)
+ self.children_right = Tree(feature, threshold, new_feats, right_users, new_selected_feats)
+ else:
+ self.children_left = None
+ self.children_right = None
+ return self.children_left, self.children_right
+ else:
+ return intersection, right_users
+
+ def findBestAttribute(self, partitions, H_users, min_users):
+ f = np.array(self.feat_selected).reshape(-1)
+ X = H_users[self.users == 1,:]
+ X = X[:,f]
+ iBestAtt = -1
+ best_attribute = 0
+ for i in self.attributes:
+ a = -1
+ b = -1
+ # We test the two classes
+ feature = int(partitions[i,0])
+ threshold = partitions[i,1]
+ left_users, right_users = self.apply_feature(feature, threshold, H_users, False, min_users)
+ group1 = left_users[self.users == 1]
+ group2 = right_users[self.users == 1]
+ if(np.sum(group1) > 0) and (np.sum(group1) < np.sum(self.users)):
+ a = new_silhouette(X, group1)
+ if(np.sum(group2) > 0) and ( np.sum(group2) < np.sum(self.users)):
+ b = new_silhouette(X, group2)
+ if(iBestAtt == -1) or (a > best_attribute):
+ iBestAtt = i
+ best_attribute = a
+ if(iBestAtt == -1) or (b > best_attribute):
+ iBestAtt = i
+ best_attribute = b
+ return iBestAtt
+
+def compute_initial_partition(h, n):
+ h = h.reshape(-1,1)
+ # Sort data
+ h_sort, ind_sort = zip(*sorted(zip(h, list(range(len(h))))))
+ # Find threshold
+ limits = int(len(h) * 20/100.0)
+ labels = np.full(len(h),0)
+ max_silhouette = 0.0
+ max_split = 0.0
+ for i in range(0, limits):
+ labels[ind_sort[i]] = 1
+ for i in range(limits, len(h)-limits):
+ labels[ind_sort[i]] = 1
+ silhouette_avg = new_silhouette(h, labels)
+ if(silhouette_avg > max_silhouette):
+ max_silhouette = silhouette_avg
+ max_split = i
+ best_part = np.copy(labels)
+ threshold = h_sort[max_split]
+ # Reconstruct the partition:
+ part = h <= threshold[0]
+ part = (1 * part).reshape(-1)
+ return float(threshold[0]), max_silhouette
+
+def tree_traversal(tree, min_users, dim):
+ if(tree == None) or (len(tree.feat_selected) == dim ):
+ return
+ else:
+ #left
+ feature = tree.findBestAttribute(partitions_init, H_users, min_users)
+ if(feature != -1):
+ threshold = partitions_init[feature,1]
+ treeL, treeR = tree.apply_feature(feature, threshold, H_users, True, min_users)
+ tree_traversal(treeL, min_users, dim)
+ tree_traversal(treeR, min_users, dim)
+
+def print_node(tree, level, pref, gauche, theClusters):
+ first = 0
+ global nbc
+ if(tree.threshold != None):
+ th = "%.2f" % tree.threshold
+ else:
+ th = "nan"
+ effectif = ''
+ if gauche:
+ sign = '<='
+ if(tree.children_left != None):
+ effectif = str(np.sum(tree.children_left.users))
+ else:
+ if(tree.children_right != None):
+ effectif = str(' cluster ') + str(nbc) + " ("+ str(np.sum(tree.users) - np.sum(tree.children_right.users))+")"
+ theClusters.append((len(theClusters)+1)* (tree.users-tree.children_right.users))
+ print("| "+pref + ""*level+ "---"+ effectif)
+ effectif = ''
+ nbc = nbc + 1
+ else:
+ sign = '>'
+ if(tree.children_right != None):
+ effectif = str(np.sum(tree.children_right.users))
+ else:
+ if(tree.children_left != None):
+ effectif = str(' cluster ') + str(nbc) + " ("+ str(np.sum(tree.users) - np.sum(tree.children_left.users))+")"
+ nbc = nbc + 1
+ theClusters.append((len(theClusters)+1)* (tree.users-tree.children_left.users))
+ first = 1
+ if(tree.children_left == None) and (tree.children_right == None):
+ effectif = str(' cluster ') + str(nbc) + " ("+ str(np.sum(tree.users))+")"
+ theClusters.append((len(theClusters)+1)*tree.users)
+ if(gauche):
+ print("| "+pref + "---"+ effectif)
+ nbc = nbc + 1
+ first = 1
+ return 0
+ if(first == 0):
+ print("| "+pref + "--- feature_"+str(tree.feature)+ " "+sign+" " +str(th))
+ else:
+ print("| "+pref + "--- feature_"+str(tree.feature)+ " "+sign +" "+str(th))
+ print("| "+pref + "| "+ "---")#+ effectif)
+ return 1
+
+
+def print_s(vect):
+ s = '['
+ for i in range(len(vect)-1):
+ if(vect[i] == 1):
+ s = s + str(i) + ", "
+ if(vect[len(vect)-1] == 1):
+ s = s + str(len(vect)-1)
+ s = s +']'
+ s = ''
+ return s
+
+def print_nodeEval(tree, level, pref, gauche, questClusters):
+ first = 0
+ if(tree.threshold != None):
+ th = "%.2f" % tree.threshold
+ else:
+ th = "nan"
+ effectif = ''
+ if gauche:
+ sign = '<='
+ if(tree.children_left != None):
+ effectif = str(np.sum(tree.children_left.users))
+ else:
+ if(tree.children_right != None):
+ questClusters.append(tree.users_quest)
+ diff = tree.users - tree.children_right.users
+ effectif = str('class ') + str(np.sum(tree.users) - np.sum(tree.children_right.users)) + print_s(diff)
+ theClusters.append((len(theClusters)+1) * (tree.users-tree.children_right.users))
+ #print("| "+pref + "---"+ effectif)
+ effectif = ''
+ else:
+ sign = '>'
+ if(tree.children_right != None):
+ effectif = str(np.sum(tree.children_right.users))
+ else:
+ if(tree.children_left != None):
+ questClusters.append(tree.users_quest)
+ diff = tree.users - tree.children_left.users
+ effectif = str('class ') + str(np.sum(tree.users) - np.sum(tree.children_left.users)) + print_s(diff)
+ theClusters.append((len(theClusters)+1)* (tree.users-tree.children_left.users))
+ first = 1
+ if(tree.children_left == None) and (tree.children_right == None):
+ questClusters.append(tree.users_quest)
+ effectif = str('class ') + str(np.sum(tree.users))+ print_s(tree.users_quest)+print_s(tree.users)
+ theClusters.append((len(theClusters)+1)*tree.users)
+ if(gauche):
+ #print("| "+pref + "---"+ effectif)
+ first = 1
+ return 0
+ '''
+ if(first == 0):
+ print("| "+pref + "--- feature"+str(tree.feature)+ sign +str(th) + " "+ effectif) #+ print_s(tree.users_quest))
+ else:
+ print("| "+pref + "--- feature"+str(tree.feature)+ sign +str(th))#+ print_s(tree.users_quest))
+ print("| "+pref + "| "+ "---"+ effectif)
+ '''
+ return 1
+
+def showTreeEval(tree,level, pref, theClusters):
+ if(tree == None):
+ return
+ else:
+ r = print_nodeEval(tree, level, pref, True, theClusters)
+ if(r == 1):
+ showTreeEval(tree.children_left,level+1, pref+"| ", theClusters)
+ r = print_nodeEval(tree, level, pref, False, theClusters)
+ showTreeEval(tree.children_right,level+1, pref+"| ", theClusters )
+
+def showTree(tree,level, pref, theClusters):
+ if(tree == None):
+ return
+ else:
+ r = print_node(tree, level, pref, True, theClusters)
+ if(r == 1):
+ showTree(tree.children_left,level+1, pref+"| ", theClusters)
+ r = print_node(tree, level, pref, False, theClusters)
+ showTree(tree.children_right,level+1, pref+"| ", theClusters)
+
+def tree_eval(tree, quest_user, user_id, questClusters, lower):
+ if(tree == None):
+ return
+ else:
+ feat = tree.feature
+ if(lower == 0):
+ if(feat != None) and ((quest_user[feat] == 1) or (quest_user[feat] == -1) or (quest_user[feat] == 0)):
+ if(tree.children_left != None):
+ tree_eval(tree.children_left, quest_user, user_id, questClusters, lower)
+ else:
+ tree.users_quest.append(user_id)
+ else:
+ if(feat != None) and ((quest_user[feat] == 1) or (quest_user[feat] == -1)):# or (quest_user[feat] == 0)):
+ if(tree.children_left != None):
+ tree_eval(tree.children_left, quest_user, user_id, questClusters, lower)
+ else:
+ tree.users_quest.append(user_id)
+
+ if(lower == 0):
+ if(feat != None) and ((quest_user[feat] == 1) or (quest_user[feat] == -1) or (quest_user[feat] == 2)):
+ if(tree.children_right != None):
+ tree_eval(tree.children_right, quest_user, user_id, questClusters, lower)
+ else:
+ if(tree.children_left != None):
+ tree.users_quest.append(user_id)
+ else:
+ if(feat != None) and ((quest_user[feat] == 1) or (quest_user[feat] == -1)):# or (quest_user[feat] == 2)):
+ if(tree.children_right != None):
+ tree_eval(tree.children_right, quest_user, user_id, questClusters, lower)
+ else:
+ if(tree.children_left != None):
+ tree.users_quest.append(user_id)
+
+def compare_clusters(dtClusters, questClusters, quest):
+ count = 0
+ homogene = np.full((len(questClusters), len(quest[0]),4), 0)
+ for k in range(len(questClusters)):
+ #print(np.sum(dtClusters == (k+1)), len(questClusters[k]))
+ for i in range(len(dtClusters)):
+ if(dtClusters[i] == (k+1)) and (i in questClusters[k]):
+ count = count + 1
+ for d in range(len(quest[0])):
+ v = int(quest[i, d])
+ if(v != -1):
+ homogene[k][d][v] = homogene[k][d][v] + 1
+ #print(count)
+ #print("negatifs")
+ #print(homogene[:,:,0])
+ #print("positifs")
+ #print(homogene[:,:,2])
+ print("Purity:",count / float(len(dtClusters)))
+
+def print_gr(gr):
+ print(gr)
+
+## main
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-lower", "--lower", help="int")
+ #parser.add_argument("-dt", "--dataTest", help="data file")
+ args = parser.parse_args()
+ lower = int(args.lower)
+
+min_users = 50
+os.getcwd()
+path = os.path.dirname(os.path.realpath(__file__))
+print(path+ "/dt/train_embed.csv")
+H_users = fromDFtoArray(path+ "/dt/train_embed.csv",False,'f')
+dim = H_users.shape[1]
+n = H_users.shape[0]
+
+'''
+# step 1: compute initial partitions
+partitions = []
+for j in range(dim):
+ threshold, max_silhouette = compute_initial_partition(H_users[:,j], n)
+ partitions.append([j, threshold, max_silhouette])
+ #comput_init_part(H_users[:,j], n)
+ best_part = H_users[:,j] < threshold
+ best_part = (1 * best_part).reshape(-1)
+ print(threshold, np.sum(best_part), n - np.sum(best_part), max_silhouette)
+# Save the partitions to next time
+nom = path+"/xx_partition.csv"
+print(nom)
+f = open(nom, 'w')
+writer = csv.writer(f)
+for i in range(len(partitions)):
+ row = partitions[i]
+ writer.writerow(row)
+f.close()
+'''
+partitions_init = fromDFtoArray(path+ "/dt/train_partitions.csv",False,'f')
+
+# Step 2: take the partition with max silhouette value
+best_init_part = np.argmax(partitions_init[:,2])
+feature = int(partitions_init[best_init_part,0])
+threshold = partitions_init[best_init_part,1]
+all_users = np.full(n,1)
+root = Tree(None, None, list(range(dim)), all_users, [])
+root.apply_feature(feature, threshold, H_users, True, min_users)
+
+# Step3 : apply the partitions
+tree = root
+tree_traversal(tree.children_left, min_users, dim)
+tree_traversal(tree.children_right, min_users, dim)
+
+#Step 4: vizualization
+theClusters = []
+nbc = 0
+showTree(tree,0,"", theClusters)
+'''
+nbc = 0
+showTreeBis(tree,0,"",1)#level, pref)
+nbc = 0
+tree_traversal_print(tree, 0, 0)
+#tree_rules = export_text(tree, feature_names=list(range(dim)))
+'''
+
+'''
+for i in range(2):
+ #right
+ feature = tree.children_right.findBestAttribute(partitions_init, H_users)
+ threshold = partitions_init[feature,1]
+ tree = tree.children_right.apply_feature(feature, threshold, H_users, True)
+ #tree = tree.children_right
+'''
+
+# Compute clusters
+dtClust = theClusters[0]
+#print(0,np.sum((dtClust==0)))
+for i in range(1,len(theClusters)):
+ dtClust = dtClust + theClusters[i]
+ print_gr(np.where(dtClust==i))
+#print(dtClust)
+
+# Validation with questionnaires
+print("")
+file = path+ "/dt/train_user_quest_label.csv"
+quest = fromDFtoArray(file,False,'f')
+questClusters = []
+for i in range(len(quest)):
+ # we got a user
+ # let us consider the tree
+ # print(quest[i])
+ tree_eval(root, quest[i], i, [], lower)
+
+# Computation of questClusters
+showTreeEval(tree,0,"", questClusters)
+compare_clusters(dtClust, questClusters, quest)
+
+#print(len(questClusters))
+#print("les seuils")
+#print(partitions_init[:,1])
+
+
+#print(partitions_init)
+
\ No newline at end of file
diff --git a/code/other/dt/train_embed.csv b/code/other/dt/train_embed.csv
new file mode 100644
index 0000000..14bb1a3
--- /dev/null
+++ b/code/other/dt/train_embed.csv
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diff --git a/code/other/dt/train_user_quest_label.csv b/code/other/dt/train_user_quest_label.csv
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diff --git a/code/other/test_3_embed.csv b/code/other/test_3_embed.csv
new file mode 100644
index 0000000..14bb1a3
--- /dev/null
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index 0000000..2fbd98a
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diff --git a/code/other/utils.py b/code/other/utils.py
new file mode 100644
index 0000000..4072f57
--- /dev/null
+++ b/code/other/utils.py
@@ -0,0 +1,164 @@
+import numpy as np
+import pandas as pd
+
+def fromDFtoArray(name, vector, type_value):
+ # Read dataframe and generate a matrix or
+ # a vector of appropriate type
+ df = pd.read_csv(name,index_col=None, header=None)
+ cols = df.columns
+ if(type_value == "f"):
+ for col in cols:
+ df[col] = df[col].astype(float)
+ if(type_value == 'i'):
+ for col in cols:
+ df[col] = df[col].astype(int)
+ r = df.values
+ if(vector):
+ r = r.reshape(-1,)
+ return r
+
+def parse_it(it):
+ # Extract question/answer from label
+ p = it.find('_')
+ q = it[:p]
+ r = it[p+1:]
+ r = int(float(r))
+ return q,r
+
+def flattern_arrays(user_idx, user_idx_test):
+ # Transform two arrays in an array of unique values
+ liste1 = list(user_idx)
+ liste2 = list(user_idx_test)
+ new = list(set(liste2).difference(liste1))
+ liste1.sort()
+ liste1 = np.array(liste1).reshape((-1,1))
+ liste1 = np.unique(liste1).tolist()
+ new.sort()
+ new = np.array(new).reshape((-1,1))
+ new = np.unique(new).tolist()
+ if(len(new) > 0):
+ users = liste1 + new
+ else:
+ users = liste1
+ #print(len(users))
+ return users
+
+def flattern_array(user_idx):
+ # Transform an array in an array of unique values
+ users = list(user_idx.reshape((-1,1)))
+ users.sort()
+ users = np.array(users)
+ users = np.unique(users)
+ return users
+
+def parse_kc(s):
+ d = []
+ s = s[1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ return d
+
+def parse(data):
+ d = []
+ for i in range(len(data)):
+ s = data[i][1:]
+ while(s.find(',') > 0):
+ t = s[:s.find(',')]
+ d.append(int(t))
+ s = s[s.find(',')+1:]
+ s = s[:len(s)-1]
+ d.append(int(s))
+ d = flattern_array(np.array(d))
+ return d
+
+def compute_kc_user(name):
+ fileName = name+"_responses.csv"
+ f = open(fileName, "r")
+ lines = f.readlines()
+ kc_user = []
+ kc_user_val = []
+ dico_u = []
+ old_k = -1
+ num_kc = 0
+ for line in lines:
+ r = line.split(',')
+ if(int(r[0]) != old_k):
+ num_kc = num_kc + 1
+ if(old_k != -1):
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ new_kc_user = []
+ new_kc_user_val = []
+ dico_uk = {}
+ old_k = int(r[0])
+ user = int(r[1])
+ if user not in dico_uk:
+ dico_uk[user] = len(dico_uk)
+ new_kc_user.append([])
+ new_kc_user_val.append([])
+ # add item and values
+ for i in range(2,len(r)):
+ q,rep = parse_it(r[i])
+ new_kc_user[dico_uk[user]].append(int(q))
+ new_kc_user_val[dico_uk[user]].append(rep)
+ kc_user.append(new_kc_user)
+ kc_user_val.append(new_kc_user_val)
+ dico_u.append(dico_uk)
+ # sort files ?
+ for k in range(len(kc_user)):
+ for u in range(len(kc_user[k])):
+ kc_user[k][u], kc_user_val[k][u] = zip(*sorted(zip(kc_user[k][u], kc_user_val[k][u])))
+ return kc_user, kc_user_val, dico_u, num_kc
+
+def doa_creuse(F, kc_user, kc_user_val, dico_u):
+ doa = []
+ user_embed = F
+ num_kc = len(kc_user)
+ for k in range(num_kc):
+ Z1 = 0.0
+ v = 0.0
+ users = list(dico_u[k])
+ for a in range(len(kc_user[k])):
+ for b in range(len(kc_user[k])):
+ Z2 = 0.0
+ Z0 = 0.0
+ ua = users[a]
+ ub = users[b]
+ if(user_embed[ua][k] > user_embed[ub][k]):
+ ia = a
+ ib = b
+ ma = 0
+ mb = 0
+ while(ma < len(kc_user[k][ia])) and (mb < len(kc_user[k][ib])):
+ if(kc_user[k][ia][ma] < kc_user[k][ib][mb]):
+ ma = ma + 1
+ else:
+ if(kc_user[k][ia][ma] > kc_user[k][ib][mb]):
+ mb = mb + 1
+ else:
+ # meme item
+ if(kc_user_val[k][ia][ma] > kc_user_val[k][ib][mb]):
+ Z2 = Z2 + 1
+ Z0 = Z0 + 1
+ else:
+ if(kc_user_val[k][ia][ma] < kc_user_val[k][ib][mb]):
+ Z0 = Z0 + 1
+ ma = ma + 1
+ mb = mb + 1
+ #print("Z0 Z2", A, b, Z0, Z2)
+ if(Z0 > 0):
+ Z1 = Z1 + 1
+ v = v + Z2/Z0
+ #print("Z2, Z0",dico_u[k][a],dico_u[k][b],Z2, Z0)
+
+ if(Z1 > 0):
+ v = v / Z1
+ #print("Z1", k, Z1)
+ #print("doa k : ", k, v)
+ doa.append(v)
+ return doa
diff --git a/code/othertest_3_partitionsNew.csv b/code/othertest_3_partitionsNew.csv
new file mode 100644
index 0000000..acf4392
--- /dev/null
+++ b/code/othertest_3_partitionsNew.csv
@@ -0,0 +1,17 @@
+0,0.07093273,0.5555635467122366
+1,-0.07251905,0.6006371514608917
+2,-0.024404738,0.586644840818717
+3,-0.053699624,0.58642469515289
+4,0.02960766,0.621510394267664
+5,0.03977887,0.5617197014066212
+6,0.07516274,0.545949839510865
+7,0.0010237932,0.5751403256245949
+8,-0.06573455,0.5733346958166629
+9,-0.08301515,0.5553507390286373
+10,0.039807074,0.5505901808365979
+11,-0.07010786,0.5913382879804063
+12,0.0735193,0.5634380300398776
+13,0.1259739,0.5648530073124156
+14,-0.0005489956,0.5616242386272854
+15,-0.008212235,0.568164666439042
+16,-0.033589475,0.5684910586600398
diff --git a/code/radar0_fig.png b/code/radar0_fig.png
new file mode 100644
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--
GitLab