diff --git a/notebooks/Predictions_analysis_bckp.ipynb b/notebooks/Predictions_analysis_bckp.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M-41ZfqIHyi2"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gVaa01O5IQke",
+ "outputId": "054b0d9d-148a-4cc6-8616-b9e704eab6ea"
+ },
+ "outputs": [],
+ "source": [
+ "!wget https://geode.liris.cnrs.fr/EDdA-Classification/predictions/dataset_test_predictions_sgd_tfidf.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dYVLgduMIQm4",
+ "outputId": "4e35f288-f81a-428b-8b9e-035c3a1d3c7a"
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"dataset_test_predictions_sgd_tfidf.csv\")\n",
+ "\n",
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 479
+ },
+ "id": "Bp50IA0qIQpf",
+ "outputId": "c4efa4c8-4fac-4349-cc12-a331f89850ad"
+ },
+ "outputs": [],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "3Obah84eIQrm",
+ "outputId": "6971b6d4-b7c5-4029-86b9-83393899dd51"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "# articles dont la première prédiction correspond à la vérité terrain (\n",
+ "df[df[\"ensemble_domaine_enccre\"] == df[\"predict1\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 887
+ },
+ "id": "8eadTEGmJ2BK",
+ "outputId": "f9469880-f3d5-4fb2-8ac3-4db50eb7deb1"
+ },
+ "outputs": [],
+ "source": [
+ "# articles dont la deuxième classe correspond à la vérité terrain (839)\n",
+ "df[(df[\"ensemble_domaine_enccre\"] != df[\"predict1\"]) & (df[\"ensemble_domaine_enccre\"] == df[\"predict2\"])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 939
+ },
+ "id": "W9PzX5DUKbwO",
+ "outputId": "9b851666-3361-452b-cab3-f8e42f06c7e4"
+ },
+ "outputs": [],
+ "source": [
+ "# articles dont ni la première ni la deuxième classe correspondent à la vérité terrain (740)\n",
+ "df[(df[\"ensemble_domaine_enccre\"] != df[\"predict1\"]) & (df[\"ensemble_domaine_enccre\"] != df[\"predict2\"])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "NLcWPZlQK9BM",
+ "outputId": "8baf8f1f-9f36-4779-c12c-47ce96a627da"
+ },
+ "outputs": [],
+ "source": [
+ "# articles de géographie dont la prédiction avec la plus forte proba n'est pas Géographie (seulement la deuxième proba correspond à Géographie) -> 44\n",
+ "\n",
+ "df[(df[\"ensemble_domaine_enccre\"] != df[\"predict1\"]) & (df[\"ensemble_domaine_enccre\"] == df[\"predict2\"]) & (df[\"ensemble_domaine_enccre\"] == \"Géographie\")]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Aq7hmUshMhPh"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 479
+ },
+ "id": "wRv1Nv5-ztyK",
+ "outputId": "94e55eeb-f7a1-4a75-b674-5347092565f1"
+ },
+ "outputs": [],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PYH0M0nddL34"
+ },
+ "source": [
+ "## Word frequency"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RmOViUd-zwe8",
+ "outputId": "e4beff09-2cb2-4cf3-9361-0537e70f484f"
+ },
+ "outputs": [],
+ "source": [
+ "# Liste des ensembles de domaines ENCCRE (classes)\n",
+ "df.ensemble_domaine_enccre.unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "H9YxH28xxMGf"
+ },
+ "outputs": [],
+ "source": [
+ "lst_domaines = sorted(df.ensemble_domaine_enccre.unique())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "26h8-7P-xMI7"
+ },
+ "outputs": [],
+ "source": [
+ "# fonction qui retourne un dictionnaire contenant la fréquence associée à chaque mot de la liste en paramètre\n",
+ "def wordListToFreqDict(wordlist):\n",
+ " wordfreq = [wordlist.count(p) for p in wordlist]\n",
+ " return dict(list(zip(wordlist,wordfreq)))\n",
+ "\n",
+ "def sortFreqDict(freqdict):\n",
+ " aux = [(freqdict[key], key) for key in freqdict]\n",
+ " aux.sort()\n",
+ " aux.reverse()\n",
+ " return aux"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9aNEjYtExMN5"
+ },
+ "outputs": [],
+ "source": [
+ "d = {}\n",
+ "for domaine in lst_domaines:\n",
+ " l_text = [word for line in list(df[df.ensemble_domaine_enccre == domaine].contentWithoutClass.values) for word in line.split()]\n",
+ " print(domaine)\n",
+ " d[domaine] = sortFreqDict(wordListToFreqDict(l_text))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1yYHcDjUY9HG",
+ "outputId": "31e2bb56-46ff-4dab-cc2f-bb117d61fb35"
+ },
+ "outputs": [],
+ "source": [
+ "d['Géographie']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "uchRyb2gqnk0"
+ },
+ "outputs": [],
+ "source": [
+ "path = \"drive/MyDrive/Classification-EDdA/\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oNvSxYxmpqed"
+ },
+ "outputs": [],
+ "source": [
+ "# on créer un fichier csv pour chaque domaine avec la fréquence de chaque mot\n",
+ "for domaine, wordFreq in d.items():\n",
+ "\n",
+ " with open(path+'Wordclouds/frequency_'+domaine+'.csv','w') as file:\n",
+ " csv_out=csv.writer(file)\n",
+ " csv_out.writerow(['frequency','word'])\n",
+ " csv_out.writerows(wordFreq)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tTz8JcdhdHNw"
+ },
+ "source": [
+ "## Wordclouds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "C2NM2ayE9jcR"
+ },
+ "outputs": [],
+ "source": [
+ "from wordcloud import WordCloud"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "EITJqnZ5ecE8",
+ "outputId": "5a0392c9-f9dd-4a6b-b06a-6a2b50aeec27"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "lst_clouds = []\n",
+ "cpt = 1\n",
+ "n_cols = 4\n",
+ "n_rows = 10\n",
+ "\n",
+ "plt.figure(figsize=(30,50))\n",
+ "\n",
+ "for domaine in lst_domaines:\n",
+ " plt.subplot(n_rows, n_cols, cpt)\n",
+ " text = df[df.ensemble_domaine_enccre == domaine].contentWithoutClass.values\n",
+ " cloud_i = WordCloud(width=1080, height=720, background_color='white',\n",
+ " collocations=False, colormap='Set2',\n",
+ " max_words = 100, random_state = 42\n",
+ " ).generate(\" \".join(text))\n",
+ " \n",
+ " # https://matplotlib.org/3.2.1/tutorials/colors/colormaps.html\n",
+ "\n",
+ " plt.axis('off')\n",
+ " plt.title(domaine,fontsize=10)\n",
+ " plt.imshow(cloud_i)\n",
+ "\n",
+ " cloud_i.to_file(path+\"/Wordclouds/Wordclouds_\"+domaine.split(\" \")[0]+\".png\")\n",
+ " cpt += 1\n",
+ "\n",
+ " lst_clouds.append(cloud_i)\n",
+ "\n",
+ "plt.savefig('Domaines_wordclouds.pdf', dpi=300, bbox_inches='tight')\n",
+ "plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rVFvp3owZDPq"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2EIA9mV_ecH8"
+ },
+ "outputs": [],
+ "source": [
+ "# Récupération des mots en communs\n",
+ "m = []\n",
+ "for d1 in lst_clouds :\n",
+ " m2 = []\n",
+ " for d2 in lst_clouds :\n",
+ "\n",
+ " lst_1 = d1.words_.keys()\n",
+ " lst_2 = d2.words_.keys()\n",
+ "\n",
+ " lst_text = [i for i in lst_1 if i in lst_2]\n",
+ " m2.append(len(lst_text))\n",
+ " m.append(m2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 874
+ },
+ "id": "p-D4rLn1TCMV",
+ "outputId": "6dc3c33a-733d-466b-c134-9060e9261973"
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "plt.figure(figsize=(16,13))\n",
+ "\n",
+ "ax = sns.heatmap(m, xticklabels=lst_domaines, yticklabels=lst_domaines, cmap='Blues')\n",
+ "\n",
+ "plt.savefig('Heatmap_commonWords.png', dpi=300, bbox_inches='tight')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZSnHC2IoTqO5",
+ "outputId": "d6249442-119b-4dbd-a764-422d46fcf0f0"
+ },
+ "outputs": [],
+ "source": [
+ "# nombre de mots en commun entre Arts et Métier et Métiers :\n",
+ "\n",
+ "\n",
+ "# 4 et 29\n",
+ "\n",
+ "lst_1 = lst_clouds[4].words_.keys()\n",
+ "lst_2 = lst_clouds[29].words_.keys()\n",
+ "\n",
+ "lst_text = [i for i in lst_1 if i in lst_2]\n",
+ "len(lst_text)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "niAw2OF0bWMi",
+ "outputId": "4f267f2d-8586-44d0-bd54-1c73ebf646bf"
+ },
+ "outputs": [],
+ "source": [
+ "# mots de Arts et métier qui ne sont pas dans les 100 plus fréquents de Métiers\n",
+ "lst_1 = lst_clouds[4].words_.keys()\n",
+ "lst_2 = lst_clouds[29].words_.keys()\n",
+ "\n",
+ "lst_text = [i for i in lst_1 if i not in lst_2]\n",
+ "lst_text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oYT479rsyVvq"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "EDdA-Classification_Analyses_predictions_proba.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('stanza-lexicoscope-py39')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.9.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "68d5f9281eab57a7f4901cb150f4c691b1d08935474a18f188e0e3e8f8f412b7"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}