diff --git a/docs/learning.ipynb b/docs/learning.ipynb new file mode 100644 index 0000000..ac4f39a --- /dev/null +++ b/docs/learning.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "faafb9a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "\n", + "sys.path.append(os.path.abspath(\"..\"))\n", + "\n", + "from IPython.core.display_functions import DisplayHandle\n", + "from pandas import read_csv\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.linear_model import LinearRegression\n", + "from IPython.display import Markdown, display\n", + "from src.cleaning import main as cleaning\n", + "from src.scraper import main as scraper\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "\n", + "class Tableau:\n", + " \"\"\"\n", + " classe pour afficher le tableau des scores automatiquements.\n", + " \"\"\"\n", + " def __init__(self, score_title: str = \"R²\") -> None:\n", + " self._table: dict[str, float] = dict[str, float]()\n", + " self._score_title: str = score_title\n", + "\n", + " def ajoutligne(self, methode: str, score: float) -> None:\n", + " self._table[methode] = score\n", + "\n", + " def dessiner(self) -> DisplayHandle | None:\n", + " table = f\"| Méthode | {self._score_title} |\\n| :---: | :---: |\\n\"\n", + "\n", + " for key, value in self._table.items():\n", + " table+= f\"|{key}|{value:0.6f}|\\n\"\n", + "\n", + " return display(Markdown(table))\n", + "\n", + "\n", + "_filename_scraped = \"../data.csv\"\n", + "_filename_cleaned = \"../clean.csv\" \n", + "\n", + "_url = \"bordeaux.html\"\n", + "\n", + "try:\n", + " if not os.path.exists(_filename_scraped):\n", + " print(f\"{_filename_scraped} est absent. Lancement du scraper (~10min):\")\n", + " scraper(_filename_scraped, _url)\n", + " cleaning(_filename_scraped)\n", + "except Exception as e:\n", + " print(f\"ERREUR: {e}\")\n", + "\n", + "dataset = read_csv(_filename_cleaned)" + ] + }, + { + "cell_type": "markdown", + "id": "ed96ca6c", + "metadata": {}, + "source": [ + "## Troisième jalon : Apprentissage\n", + "\n", + "Nous sommes prêts à passer à l’apprentissage à proprement parler. Pour ce faire, nous allons utiliser la bibliothèque scikit-learn.\n", + "\n", + "### Préparation de données d’apprentissage et de test\n", + "Le but est d’estimer le prix d’un produit à partir des attributs. La colonne “Prix” correspond ainsi aux étiquettes (\"targets\"), tandis que les autres colonnes sont les attributs (\"features\").\n", + "\n", + "**Question 15**: Préparez la matrice de données et le vecteur des étiquettes pour votre jeu de données. Séparez les données en un jeu d’entraînement et un jeu de test, sur un base 75%/25%, en utilisant\n", + "le paramètre random_state = 49.\n", + "\n", + "Nous utiliserons les notations suivantes :\n", + "\n", + "- X_train (resp. y_train) fait référence aux données d’apprentissage (resp. à leur étiquette),\n", + "\n", + "- X_test (resp. y_test) fait référence aux données de test (resp. à leur étiquette)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8342340f", + "metadata": {}, + "outputs": [], + "source": [ + "X = dataset.drop(\"Prix\", axis=1).values\n", + "y = dataset[\"Prix\"].values\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.25, random_state=49\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "67685584", + "metadata": {}, + "source": [ + "## Premier modèle : Régression Linéaire\n", + "\n", + "Dans un premier temps, nous allons appliquer une régression linéaire [LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) sur nos données." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9dfdc01f", + "metadata": {}, + "outputs": [], + "source": [ + "lr_table: Tableau = Tableau()\n", + "\n", + "\n", + "def dessiner(lr, X_test, y_test):\n", + " x_test_pred = lr.predict(X_test)\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plt.scatter(x_test_pred, y_test, label=\"Vins\")\n", + "\n", + " mn = min(x_test_pred.min(), y_test.min())\n", + " mx = max(x_test_pred.max(), y_test.max())\n", + "\n", + " plt.plot([mn, mx], [mn, mx], color=\"red\", linestyle=\"--\", label=\"estim_LR=y_test\")\n", + "\n", + " plt.xlabel(\"estim_LR\")\n", + " plt.ylabel(\"y_test\")\n", + " plt.legend()\n", + " plt.grid(True, linestyle=\":\", alpha=0.6)\n", + "\n", + " if hasattr(lr, 'steps'): # Si c'est un Pipeline\n", + " name = lr.steps[-2][1].__class__.__name__\n", + " else:\n", + " name = lr.__class__.__name__\n", + " plt.title(name)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6b6909c3", + "metadata": {}, + "source": [ + "**Question 16**: Évaluez ce modèle sur le jeu de test par r2_score. Rappelons qu’il s’agit de la mesure par défaut pour la fonction score() des méthodes de régression." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "99de3ed7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR score[r^2]: 0.234788\n" + ] + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X_train, y_train)\n", + "score: float = lr.score(X_test, y_test)\n", + "\n", + "# lr_table.ajoutligne(\"LR\", score)\n", + "print(f\"LR score[r^2]: {score:.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f807cb05", + "metadata": {}, + "source": [ + "**Question 17**: Nous allons Faire une comparaison entre les estimations (estim_LR) faites par le modèle pour les données de test et les prix y_test. Dessinez une figure où l’axe x représente estim_LR et l’axe y est y_test. Pensez à utiliser la fonction scatter pour représenter les points. Nous appellerons dans la suite figure de visualisation cette représentation. Rappelons que si votre modèle fonctionne bien pour ce jeu de données, vous devriez avoir les points autour de la diagonale\n", + "où estim_LR est égal à y_test. Que constatez-vous ?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "09eca16d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dessiner(lr, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "4189c565", + "metadata": {}, + "source": [ + "Je constate qu'il y a des bouteilles hors de prix et donc l'algorithme de regression linéaire ne prend pas suffisamment en compte ces valeurs là." + ] + }, + { + "cell_type": "markdown", + "id": "8889cde7", + "metadata": {}, + "source": [ + "**Question 18**: Nous allons maintenant évaluer l’impact du pré-traitement sur nos données. En utilisant la commande make_pipeline, normalisez d’abord les données avant de lancer l’apprentissage, et évaluez le score sur le jeu de test. Répétez l’expérience, cette fois-ci en standardisant les données avant l’apprentissage. Dessinez les figures de visualisation pour les deux cas. Observez-vous les mêmes choses qu’à la question précédente ?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b94a89f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalisation + LR score[r^2]: 0.234788\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standardisation + LR score[r^2]: 0.234788\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scaler_methods = [MinMaxScaler, StandardScaler]\n", + "scaler_names = [\"Normalisation\", \"Standardisation\"]\n", + "\n", + "for name, scaler in zip(scaler_names, scaler_methods):\n", + " pipeline = make_pipeline(scaler(), LinearRegression())\n", + " pipeline.fit(X_train, y_train)\n", + "\n", + " x_test_pred = pipeline.predict(X_test)\n", + " score: float = pipeline.score(X_test, y_test)\n", + "\n", + " # lr_table.ajoutligne(f\"{name} + LR\", score)\n", + " print(f\"{name} + LR score[r^2]: {score:.6f}\")\n", + " \n", + " dessiner(pipeline, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "92a737f0", + "metadata": {}, + "source": [ + "**Question 19**: Trouvez les valeurs minimales et maximales pour les prix dans votre dataset. Que peut-on dire de la dispersion des prix dans votre dataset ?\n", + "Dans le cas où les étiquettes sont trop dispersées, une solution pourrait être de modifier l’échelle des étiquettes. Pour ce faire, on pourrait transformer les prix de sorte que les prix utilisés dans\n", + "l’apprentissage soient ln(y[i]) à l’aide des fonctions ln et exp de la bibliothèque numphy. Quelles sont les nouvelles valeurs pour le prix minimum et maximum de votre dataset ?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4f1c169f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prix Min : 6.20€\n", + "Prix Max : 17280.00€\n", + "Log Min : 1.8245\n", + "Log Max : 9.7573\n" + ] + } + ], + "source": [ + "print(f\"Prix Min : {y.min():.2f}€\")\n", + "print(f\"Prix Max : {y.max():.2f}€\")\n", + "\n", + "y_log = np.log(y)\n", + "\n", + "print(f\"Log Min : {y_log.min():.4f}\")\n", + "print(f\"Log Max : {y_log.max():.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7feda3af", + "metadata": {}, + "source": [ + "**Question 20**: Après avoir éventuellement transformé les prix, remplissez le tableau suivant avec les résultats obtenus grâce à la régression linéaire, avec ou sans pré-traitement. Constatez-vous une amélioration des prédictions due au pré-traitement pour ce jeu de données et cette méthode ?\n", + "Dessinez la figure de visualisation pour la méthode qui donne le meilleur score. Que constatez-vous ?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "91cedffb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "| Méthode | R² |\n", + "| :---: | :---: |\n", + "|LR|0.452908|\n", + "|Normalisation + LR|0.452908|\n", + "|Standardisation + LR|0.452908|\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y_log, test_size=0.25, random_state=49\n", + ")\n", + "\n", + "scaler_methods = [None, MinMaxScaler, StandardScaler]\n", + "scaler_names = [\"LR\", \"Normalisation + LR\", \"Standardisation + LR\"]\n", + "\n", + "best_models = []\n", + "\n", + "for name, scaler in zip(scaler_names, scaler_methods):\n", + " model = (\n", + " LinearRegression()\n", + " if scaler is None\n", + " else make_pipeline(scaler(), LinearRegression())\n", + " )\n", + "\n", + " _ = model.fit(X_train, y_train)\n", + " \n", + " x_test_pred = model.predict(X_test)\n", + " score: float = model.score(X_test, y_test)\n", + " \n", + " lr_table.ajoutligne(f\"{name}\", score)\n", + " best_models.append((model, score))\n", + "\n", + "best_model, best_score = best_models[0]\n", + "for model, score in best_models:\n", + " if score > best_score:\n", + " best_score = score\n", + " best_model = model\n", + "\n", + "lr_table.dessiner()\n", + "dessiner(best_model, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "f42b94f7", + "metadata": {}, + "source": [ + "## Deuxième modèle : Arbre de Décision\n", + "\n", + "Dans la suite, vous utiliserez les prix transformés ou non en fonction de votre dataset. Le deuxième modèle d’apprentissage que nous allons considérer sont les arbres de décision [DecisionTreeRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html)." + ] + }, + { + "cell_type": "markdown", + "id": "d3602df5", + "metadata": {}, + "source": [ + "**Question 21**: En utilisant la méthode de validation croisée (cv) avec cv = 5, déterminez la valeur de la meilleure profondeur maximale (h) parmi max_depth= {3, 4, 5}sur les données X_train (sans pré-traitement).\n", + "\n", + "- Remplissez à nouveau un tableau de trois lignes (une pour la méthode “AD”, une pour “Normalisation + AD” et une pour “Standardisation + AD”) avec les résultats que vous avez obtenus grâce aux arbres de décision avec la profondeur maximale, h, avec ou sans pré-traitement pour les données de test.\n", + "\n", + "- Constatez-vous une amélioration significative suite au pré-traitement ? Les scores obtenus sont-ils satisfaisants ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c21cd56", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3004e1ca", + "metadata": {}, + "source": [ + "## Troisième modèle : Nplus proches voisins\n", + "En guise de troisième méthode d’apprentissage, nous allons nous pencher sur la méthode des plus proches voisins [KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f72f499f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f764f3a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/learning.ipynb b/learning.ipynb deleted file mode 100644 index 8309b62..0000000 --- a/learning.ipynb +++ /dev/null @@ -1,387 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "faafb9a0", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "from pandas import read_csv\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.linear_model import LinearRegression\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "dataset = read_csv(\"clean.csv\")" - ] - }, - { - "cell_type": "markdown", - "id": "ed96ca6c", - "metadata": {}, - "source": [ - "## Troisième jalon : Apprentissage\n", - "\n", - "Nous sommes prêts à passer à l’apprentissage à proprement parler. Pour ce faire, nous allons utiliser la bibliothèque scikit-learn.\n", - "\n", - "### Préparation de données d’apprentissage et de test\n", - "Le but est d’estimer le prix d’un produit à partir des attributs. La colonne “Prix” correspond ainsi aux étiquettes (\"targets\"), tandis que les autres colonnes sont les attributs (\"features\").\n", - "\n", - "**Question 15**: Préparez la matrice de données et le vecteur des étiquettes pour votre jeu de données. Séparez les données en un jeu d’entraînement et un jeu de test, sur un base 75%/25%, en utilisant\n", - "le paramètre random_state = 49.\n", - "\n", - "Nous utiliserons les notations suivantes :\n", - "\n", - "- X_train (resp. y_train) fait référence aux données d’apprentissage (resp. à leur étiquette),\n", - "\n", - "- X_test (resp. y_test) fait référence aux données de test (resp. à leur étiquette)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8342340f", - "metadata": {}, - "outputs": [], - "source": [ - "X = dataset.drop(\"Prix\", axis=1).values\n", - "y = dataset[\"Prix\"].values\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.25, random_state=49\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "67685584", - "metadata": {}, - "source": [ - "### Premier modèle : Régression Linéaire\n", - "\n", - "Dans un premier temps, nous allons appliquer une régression linéaire LinearRegression sur nos données." - ] - }, - { - "cell_type": "markdown", - "id": "6b6909c3", - "metadata": {}, - "source": [ - "**Question 16**: Évaluez ce modèle sur le jeu de test par r2_score. Rappelons qu’il s’agit de la mesure par défaut pour la fonction score() des méthodes de régression." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "99de3ed7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LinearRegression score[r^2]: 0.189665\n" - ] - } - ], - "source": [ - "lr = LinearRegression()\n", - "lr.fit(X_train, y_train)\n", - "print(f\"LinearRegression score[r^2]: {lr.score(X_test, y_test):.6f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f807cb05", - "metadata": {}, - "source": [ - "**Question 17**: Nous allons Faire une comparaison entre les estimations (estim_LR) faites par le modèle pour les données de test et les prix y_test. Dessinez une figure où l’axe x représente estim_LR et l’axe y est y_test. Pensez à utiliser la fonction scatter pour représenter les points. Nous appellerons dans la suite figure de visualisation cette représentation. Rappelons que si votre modèle fonctionne bien pour ce jeu de données, vous devriez avoir les points autour de la diagonale\n", - "où estim_LR est égal à y_test. Que constatez-vous ?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "09eca16d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_test_pred = lr.predict(X_test)\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "plt.scatter(x_test_pred, y_test, label=\"Vins\")\n", - "\n", - "\n", - "mn = min(x_test_pred.min(), y_test.min())\n", - "mx = max(x_test_pred.max(), y_test.max())\n", - "\n", - "plt.plot(\n", - " [mn, mx],\n", - " [mn, mx],\n", - " color=\"red\",\n", - " linestyle=\"--\",\n", - " label=\"estim_LR = y_test\",\n", - ")\n", - "\n", - "# b = lr.intercept_\n", - "# a = lr.coef_[0]\n", - "\n", - "# # coef_tendance = np.polyfit(x_test_pred, y_test, 1)\n", - "# # p = np.poly1d(coef_tendance)\n", - "# # plt.plot([mn, mx], [p(mn), p(mx)], color=\"green\", label=\"Tendance réelle du modèle\")\n", - "\n", - "plt.xlabel(\"estim_LR\")\n", - "plt.ylabel(\"y_test\")\n", - "plt.legend()\n", - "plt.grid(True, linestyle=\":\", alpha=0.6)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4189c565", - "metadata": {}, - "source": [ - "Je constate qu'il y a des bouteilles hors de prix et donc l'algorithme de regression linéaire ne prend pas suffisamment en compte ces valeurs là." - ] - }, - { - "cell_type": "markdown", - "id": "8889cde7", - "metadata": {}, - "source": [ - "**Question 18**: Nous allons maintenant évaluer l’impact du pré-traitement sur nos données. En utilisant la commande make_pipeline, normalisez d’abord les données avant de lancer l’apprentissage, et évaluez le score sur le jeu de test. Répétez l’expérience, cette fois-ci en standardisant les données avant l’apprentissage. Dessinez les figures de visualisation pour les deux cas. Observez-vous les mêmes choses qu’à la question précédente ?" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b94a89f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MinMaxScaler score[r^2]: 0.189665\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for scaler in [MinMaxScaler, StandardScaler]:\n", - " pipeline = make_pipeline(scaler(), LinearRegression())\n", - " pipeline.fit(X_train, y_train)\n", - "\n", - " x_test_pred = pipeline.predict(X_test)\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.scatter(x_test_pred, y_test, label=\"Vins\")\n", - "\n", - "\n", - " mn = min(x_test_pred.min(), y_test.min())\n", - " mx = max(x_test_pred.max(), y_test.max())\n", - "\n", - " plt.plot(\n", - " [mn, mx],[mn, mx], color=\"red\",\n", - " linestyle=\"--\", label=\"estim_LR = y_test\",\n", - " )\n", - "\n", - " # b = lr.intercept_\n", - " # a = lr.coef_[0]\n", - "\n", - " # x_reg = np.linspace(X_test[0].min(), X_test[0].max(), 100)\n", - " # y_reg = a * x_reg + b\n", - " # plt.plot(x_reg, y_reg, color='red', label=f\"y = {a:.2f}x + {b:.2f}\")\n", - "\n", - " plt.xlabel(\"estim_LR\")\n", - " plt.ylabel(\"y_test\")\n", - " plt.title(scaler.__name__)\n", - " plt.legend()\n", - " plt.grid(True, linestyle=\":\", alpha=0.6)\n", - "\n", - " print(f\"{scaler.__name__} score[r^2]: {pipeline.score(X_test, y_test):.6f}\")\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "92a737f0", - "metadata": {}, - "source": [ - "**Question 19**: Trouvez les valeurs minimales et maximales pour les prix dans votre dataset. Que peut-on dire de la dispersion des prix dans votre dataset ?\n", - "Dans le cas où les étiquettes sont trop dispersées, une solution pourrait être de modifier l’échelle des étiquettes. Pour ce faire, on pourrait transformer les prix de sorte que les prix utilisés dans\n", - "l’apprentissage soient ln(y[i]) à l’aide des fonctions ln et exp de la bibliothèque numphy. Quelles sont les nouvelles valeurs pour le prix minimum et maximum de votre dataset ?" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4f1c169f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prix Min : 6.20€\n", - "Prix Max : 17280.00€\n", - "Log Min : 1.8245\n", - "Log Max : 9.7573\n" - ] - } - ], - "source": [ - "print(f\"Prix Min : {y.min():.2f}€\")\n", - "print(f\"Prix Max : {y.max():.2f}€\")\n", - "\n", - "y_log = np.log(y)\n", - "\n", - "print(f\"Log Min : {y_log.min():.4f}\")\n", - "print(f\"Log Max : {y_log.max():.4f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7feda3af", - "metadata": {}, - "source": [ - "**Question 20**: Après avoir éventuellement transformé les prix, remplissez le tableau suivant avec les résultats obtenus grâce à la régression linéaire, avec ou sans pré-traitement. Constatez-vous une amélioration des prédictions due au pré-traitement pour ce jeu de données et cette méthode ?\n", - "Dessinez la figure de visualisation pour la méthode qui donne le meilleur score. Que constatez-vous ?" - ] - }, - { - "cell_type": "markdown", - "id": "e2cc7202", - "metadata": {}, - "source": [ - "|Méthode|r^2|\n", - "|----|----|\n", - "|LR|0.189665|\n", - "|Normalisation + LR|0.189665|\n", - "|Standardisation + LR|0.189665|" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c632df2b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_test_pred = lr.predict(X_test)\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "plt.scatter(x_test_pred, y_test, label=\"Vins\")\n", - "\n", - "\n", - "mn = min(x_test_pred.min(), y_test.min())\n", - "mx = max(x_test_pred.max(), y_test.max())\n", - "\n", - "plt.plot(\n", - " [mn, mx],\n", - " [mn, mx],\n", - " color=\"red\",\n", - " linestyle=\"--\",\n", - " label=\"estim_LR = y_test\",\n", - ")\n", - "\n", - "# b = lr.intercept_\n", - "# a = lr.coef_[0]\n", - "\n", - "# # coef_tendance = np.polyfit(x_test_pred, y_test, 1)\n", - "# # p = np.poly1d(coef_tendance)\n", - "# # plt.plot([mn, mx], [p(mn), p(mx)], color=\"green\", label=\"Tendance réelle du modèle\")\n", - "\n", - "plt.xlabel(\"estim_LR\")\n", - "plt.ylabel(\"y_test\")\n", - "plt.legend()\n", - "plt.grid(True, linestyle=\":\", alpha=0.6)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "74f6654b", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.10.20" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/mkdocs.yml b/mkdocs.yml index f26bb9d..22e3cf1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,6 +7,7 @@ theme: plugins: - search - mkdocstrings + - mkdocs-jupyter extra: generator: false diff --git a/pyproject.toml b/pyproject.toml index 490d929..1e0ef27 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ dependencies = [ "pandas==2.3.3", "tqdm==4.67.3", "scikit-learn==1.7.2", - "matplotlib==3.10.8" + "matplotlib==3.10.8", ] [tool.pytest.ini_options] @@ -16,7 +16,12 @@ testpaths = ["tests"] [project.optional-dependencies] test = ["pytest==8.4.2", "requests-mock==1.12.1", "flake8==7.3.0"] -doc = ["mkdocs<2.0.0", "mkdocs-material==9.6.23", "mkdocstrings[python]"] +doc = [ + "mkdocs<2.0.0", + "mkdocs-material==9.6.23", + "mkdocstrings[python]", + "mkdocs-jupyter==0.26.1", +] [build-system] requires = ["setuptools", "wheel"] diff --git a/src/cleaning.py b/src/cleaning.py index 1b3b3f5..1d00966 100755 --- a/src/cleaning.py +++ b/src/cleaning.py @@ -97,11 +97,12 @@ class Cleaning: return self -def main() -> None: - if len(argv) != 2: - raise ValueError(f"Usage: {argv[0]} ") - - filename = argv[1] +def main(filename: str | None = None) -> None: + if not filename: + if len(argv) != 2: + raise ValueError(f"Usage: {argv[0]} ") + filename = argv[1] + cleaning: Cleaning = ( Cleaning(filename) .drop_empty_appellation() diff --git a/src/learning.py b/src/learning.py index b42d826..285a821 100755 --- a/src/learning.py +++ b/src/learning.py @@ -1,93 +1,65 @@ +# from typing import Any, Callable +# from pandas import DataFrame +# from sklearn.linear_model import LinearRegression +# from sklearn.preprocessing import StandardScaler +# from sklearn.model_selection import train_test_split +# from sklearn.pipeline import make_pipeline +# import matplotlib.pyplot as plt + +# from cleaning import Cleaning -from typing import Any, Callable -from pandas import DataFrame -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split -from sklearn.pipeline import make_pipeline -import matplotlib.pyplot as plt +# class Learning: +# def __init__(self, vins: DataFrame, target: str) -> None: +# self.X = vins.drop(target, axis=1) +# self.y = vins[target] -from cleaning import Cleaning +# self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( +# self.X, self.y, test_size=0.25, random_state=49 +# ) +# def evaluate( +# self, +# estimator, +# pretreatment=None, +# fn_score=lambda m, xt, yt: m.score(xt, yt), +# ): -class Learning: - def __init__(self, vins: DataFrame, target: str) -> None: - self.X = vins.drop(target, axis=1) - self.y = vins[target] +# pipeline = make_pipeline(pretreatment, estimator) if pretreatment else estimator +# pipeline.fit(self.X_train, self.y_train) +# score = fn_score(pipeline, self.X_test, self.y_test) +# prediction = pipeline.predict(self.X_test) - self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( - self.X, self.y, test_size=0.25, random_state=49 - ) +# return score, prediction - def evaluate( - self, - estimator, - pretreatment=None, - fn_score=lambda m, xt, yt: m.score(xt, yt), - ): +# def draw(self, predictions, y_actual): +# plt.figure(figsize=(8, 6)) - pipeline = make_pipeline(pretreatment, estimator) if pretreatment else estimator - pipeline.fit(self.X_train, self.y_train) - score = fn_score(pipeline, self.X_test, self.y_test) - prediction = pipeline.predict(self.X_test) +# plt.scatter( +# predictions, +# y_actual, +# alpha=0.5, +# c="royalblue", +# edgecolors="k", +# label="Vins", +# ) - return score, prediction +# mn = min(predictions.min(), y_actual.min()) +# mx = max(predictions.max(), y_actual.max()) +# plt.plot( +# [mn, mx], +# [mn, mx], +# color="red", +# linestyle="--", +# lw=2, +# label="Prédiction Parfaite", +# ) - def draw(self, predictions, y_actual): - plt.figure(figsize=(8, 6)) +# plt.xlabel("Prix estimés (estim_LR)") +# plt.ylabel("Prix réels (y_test)") +# plt.title("titre") +# plt.legend() +# plt.grid(True, linestyle=":", alpha=0.6) - plt.scatter( - predictions, - y_actual, - alpha=0.5, - c="royalblue", - edgecolors="k", - label="Vins", - ) +# plt.show() - mn = min(predictions.min(), y_actual.min()) - mx = max(predictions.max(), y_actual.max()) - plt.plot( - [mn, mx], - [mn, mx], - color="red", - linestyle="--", - lw=2, - label="Prédiction Parfaite", - ) - - plt.xlabel("Prix estimés (estim_LR)") - plt.ylabel("Prix réels (y_test)") - plt.title("titre") - plt.legend() - plt.grid(True, linestyle=":", alpha=0.6) - - plt.show() - - -df_vins = ( - Cleaning("data.csv") - .drop_empty_appellation() - .fill_missing_scores() - .encode_appellation() - .drop_empty_price() - .getVins() -) - -etude = Learning(df_vins, target="Prix") - -print("--- Question 16 & 17 ---") -score_simple, estim_simple = etude.evaluate(LinearRegression()) -print(f"Score R² (LR Simple) : {score_simple:.4f}") - -etude.draw(estim_simple, etude.y_test) - - -print("\n--- Question 18 ---") -score_std, estim_std = etude.evaluate( - estimator=LinearRegression(), pretreatment=StandardScaler() -) -print(f"Score R² (Standardisation + LR) : {score_std:.4f}") - -etude.draw(estim_std, etude.y_test) diff --git a/src/scraper.py b/src/scraper.py index 8c405ed..f493b05 100755 --- a/src/scraper.py +++ b/src/scraper.py @@ -490,12 +490,13 @@ class Scraper: savestate((page, cache)) -def main() -> None: - if len(argv) != 3: - raise ValueError(f"{argv[0]} ") - filename = argv[1] - suburl = argv[2] - +def main(filename: str | None = None, suburl: str | None = None) -> None: + if filename is None or suburl is None: + if len(argv) != 3: + raise ValueError(f"Usage: python {argv[0]} ") + filename = argv[1] + suburl = argv[2] + scraper: Scraper = Scraper() scraper.getvins(suburl, filename)