{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Age BMI Glucose Insulin HOMA Leptin Adiponectin Resistin \\\n", "0 48 23.500000 70 2.707 0.467409 8.8071 9.702400 7.99585 \n", "1 83 20.690495 92 3.115 0.706897 8.8438 5.429285 4.06405 \n", "2 82 23.124670 91 4.498 1.009651 17.9393 22.432040 9.27715 \n", "3 68 21.367521 77 3.226 0.612725 9.8827 7.169560 12.76600 \n", "4 86 21.111111 92 3.549 0.805386 6.6994 4.819240 10.57635 \n", "\n", " MCP.1 Classification \n", "0 417.114 1 \n", "1 468.786 1 \n", "2 554.697 1 \n", "3 928.220 1 \n", "4 773.920 1 \n", " Age BMI Glucose Insulin HOMA Leptin \\\n", "count 116.000000 116.000000 116.000000 116.000000 116.000000 116.000000 \n", "mean 57.301724 27.582111 97.793103 10.012086 2.694988 26.615080 \n", "std 16.112766 5.020136 22.525162 10.067768 3.642043 19.183294 \n", "min 24.000000 18.370000 60.000000 2.432000 0.467409 4.311000 \n", "25% 45.000000 22.973205 85.750000 4.359250 0.917966 12.313675 \n", "50% 56.000000 27.662416 92.000000 5.924500 1.380939 20.271000 \n", "75% 71.000000 31.241442 102.000000 11.189250 2.857787 37.378300 \n", "max 89.000000 38.578759 201.000000 58.460000 25.050342 90.280000 \n", "\n", " Adiponectin Resistin MCP.1 Classification \n", "count 116.000000 116.000000 116.000000 116.000000 \n", "mean 10.180874 14.725966 534.647000 1.551724 \n", "std 6.843341 12.390646 345.912663 0.499475 \n", "min 1.656020 3.210000 45.843000 1.000000 \n", "25% 5.474283 6.881763 269.978250 1.000000 \n", "50% 8.352692 10.827740 471.322500 2.000000 \n", "75% 11.815970 17.755207 700.085000 2.000000 \n", "max 38.040000 82.100000 1698.440000 2.000000 \n", "\n", "RangeIndex: 116 entries, 0 to 115\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Age 116 non-null int64 \n", " 1 BMI 116 non-null float64\n", " 2 Glucose 116 non-null int64 \n", " 3 Insulin 116 non-null float64\n", " 4 HOMA 116 non-null float64\n", " 5 Leptin 116 non-null float64\n", " 6 Adiponectin 116 non-null float64\n", " 7 Resistin 116 non-null float64\n", " 8 MCP.1 116 non-null float64\n", " 9 Classification 116 non-null int64 \n", "dtypes: float64(7), int64(3)\n", "memory usage: 9.2 KB\n", "None\n", "Age 0\n", "BMI 0\n", "Glucose 0\n", "Insulin 0\n", "HOMA 0\n", "Leptin 0\n", "Adiponectin 0\n", "Resistin 0\n", "MCP.1 0\n", "Classification 0\n", "dtype: int64\n" ] }, { "data": { "text/plain": [ "array([[, ,\n", " ],\n", " [,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ],\n", " [, , ]],\n", " dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "breast_cancer = pd.read_csv(\"data/breast_cancer.csv\")\n", "print(breast_cancer.head())\n", "\n", "print(breast_cancer.describe())\n", "\n", "print(breast_cancer.info())\n", "\n", "print(breast_cancer.isnull().sum())\n", "\n", "breast_cancer.hist(figsize=(10, 10))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classification\n", "1 64\n", "0 52\n", "Name: count, dtype: int64\n" ] } ], "source": [ "X = breast_cancer.drop(columns=[\"Classification\"])\n", "y = breast_cancer[\"Classification\"].replace({1: 0, 2: 1})\n", "\n", "print(y.value_counts())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of samples: 116\n", "Number of features: 9\n", "Number of classes: 2\n" ] } ], "source": [ "print(\"Number of samples:\", X.shape[0])\n", "print(\"Number of features:\", X.shape[1])\n", "print(\"Number of classes:\", len(np.unique(y)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def build_model():\n", " model = tf.keras.models.Sequential(\n", " [\n", " tf.keras.layers.Dense(\n", " 16,\n", " activation=\"relu\",\n", " input_shape=(X.shape[1],),\n", " kernel_regularizer=tf.keras.regularizers.l2(0.01),\n", " ),\n", " tf.keras.layers.Dense(\n", " 8,\n", " activation=\"relu\",\n", " kernel_regularizer=tf.keras.regularizers.l2(0.01),\n", " ),\n", " tf.keras.layers.Dense(1, activation=\"sigmoid\"),\n", " ],\n", " )\n", " model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n", " return model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "Fold 1 - F1-score : 0.7742\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "Fold 2 - F1-score : 0.8000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "Fold 3 - F1-score : 0.7222\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "Fold 4 - F1-score : 0.9286\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:5 out of the last 5 calls to .one_step_on_data_distributed at 0x129cb1080> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "Fold 5 - F1-score : 0.8571\n", "\n", "F1-score moyen sur 5 folds : 0.8164\n" ] } ], "source": [ "from keras.callbacks import EarlyStopping\n", "from sklearn.metrics import f1_score\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", "f1_scores = []\n", "histories = []\n", "\n", "early_stopping = EarlyStopping(\n", " monitor=\"val_loss\",\n", " patience=10,\n", " restore_best_weights=True,\n", " verbose=1,\n", ")\n", "\n", "for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):\n", " X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]\n", " y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]\n", "\n", " # Standardisation\n", " scaler = StandardScaler()\n", " X_train_scaled = scaler.fit_transform(X_train)\n", " X_val_scaled = scaler.transform(X_val)\n", "\n", " model = build_model()\n", "\n", " model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"f1_score\"])\n", "\n", " # EarlyStopping\n", " callback = tf.keras.callbacks.EarlyStopping(\n", " monitor=\"val_loss\",\n", " patience=10,\n", " restore_best_weights=True,\n", " )\n", "\n", " # Entraînement\n", " history = model.fit(\n", " X_train_scaled,\n", " y_train,\n", " epochs=50,\n", " batch_size=8,\n", " validation_data=(X_val_scaled, y_val),\n", " callbacks=[callback],\n", " verbose=0,\n", " class_weight={0: 1.0, 1: 2.0},\n", " )\n", "\n", " histories.append(history.history)\n", "\n", " # Prédiction & F1\n", " y_pred_val = (model.predict(X_val_scaled) > 0.5).astype(int)\n", " score = f1_score(y_val, y_pred_val)\n", " f1_scores.append(score)\n", " print(f\"Fold {fold} - F1-score : {score:.4f}\")\n", "\n", "print(f\"\\nF1-score moyen sur 5 folds : {np.mean(f1_scores):.4f}\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, 2, figsize=(12, 12), sharey=True) # 3 rows, 2 columns\n", "axes = axes.flatten() # Flatten to easily iterate\n", "\n", "for i, (hist, ax) in enumerate(zip(histories, axes, strict=False)):\n", " ax.plot(hist[\"loss\"], label=\"Train loss\", alpha=0.6)\n", " ax.plot(hist[\"val_loss\"], label=\"Val loss\", linestyle=\"--\", alpha=0.6)\n", " ax.set_title(f\"Fold {i + 1}\")\n", " ax.set_xlabel(\"Epochs\")\n", " if i % 2 == 0:\n", " ax.set_ylabel(\"Binary Crossentropy\")\n", " ax.legend(fontsize=8)\n", " ax.grid(True)\n", "\n", "# Hide any unused subplots if histories < 6\n", "for j in range(len(histories), len(axes)):\n", " fig.delaxes(axes[j])\n", "\n", "plt.suptitle(\"Évolution de la loss sur chaque fold\")\n", "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans le cadre de la classification binaire à partir du dataset Breast Cancer Coimbra, le respect de la distribution des classes lors de la séparation des données est une condition essentielle à la validité des résultats expérimentaux.\n", "\n", "1. Utilisation de stratify=y dans train_test_split\n", "Lors de la séparation du jeu de données en un ensemble d'entraînement et un ensemble de test, nous avons recours à la fonction train_test_split de la bibliothèque scikit-learn. Afin de garantir que les proportions des classes cibles soient conservées dans les deux sous-ensembles, l’argument stratify=y est utilisé.\n", "\n", "Cette précaution est particulièrement importante dans le cas de jeux de données déséquilibrés, comme c’est le cas ici, où les deux classes de la variable cible (\"Classification\") ne sont pas également représentées. Un échantillonnage aléatoire simple pourrait introduire un déséquilibre important entre les classes dans le jeu de test, rendant les métriques de performance peu fiables et favorisant potentiellement une classe au détriment de l’autre. L’option stratify=y assure donc une représentativité statistique des classes dans chacun des sous-échantillons.\n", "\n", "2. Recours à StratifiedKFold pour la validation croisée\n", "De manière analogue, lors de l'évaluation du modèle par validation croisée, nous avons choisi l’utilisation de la méthode StratifiedKFold. Contrairement à la validation croisée standard (KFold), cette méthode garantit que la proportion des classes est maintenue dans chacun des k folds.\n", "\n", "L’objectif est d’obtenir une estimation plus robuste et plus stable de la performance du modèle, en particulier en présence de déséquilibre entre les classes. Le maintien de la structure du dataset initial dans chaque fold limite le risque de surapprentissage (overfitting) ou de sous-apprentissage sur certains folds dominés par une seule classe.\n", "\n", "3. Justification statistique\n", "Le maintien de la distribution des classes dans les procédures d’échantillonnage est une exigence classique en statistique, relevant du principe de représentativité des échantillons. En classification supervisée, l'utilisation systématique de méthodes stratifiées permet d'améliorer la validité externe des résultats (capacité du modèle à généraliser) tout en réduisant la variance des estimations obtenues lors de la validation croisée." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:6 out of the last 6 calls to .one_step_on_data_distributed at 0x12a205120> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", " precision recall f1-score support\n", "\n", " Sain 0.33 0.09 0.14 11\n", " Malade 0.52 0.85 0.65 13\n", "\n", " accuracy 0.50 24\n", " macro avg 0.43 0.47 0.39 24\n", "weighted avg 0.44 0.50 0.42 24\n", "\n", "0.6470588235294118\n" ] } ], "source": [ "import numpy as np\n", "\n", "import tensorflow as tf\n", "from sklearn.metrics import classification_report, f1_score\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X,\n", " y,\n", " test_size=0.2,\n", " random_state=42,\n", " stratify=y,\n", ")\n", "\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "\n", "model = build_model()\n", "\n", "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n", "\n", "callback = tf.keras.callbacks.EarlyStopping(\n", " monitor=\"val_loss\",\n", " patience=10,\n", " restore_best_weights=True,\n", ")\n", "\n", "history = model.fit(\n", " X_train_scaled,\n", " y_train,\n", " epochs=50,\n", " batch_size=8,\n", " validation_split=0.2,\n", " callbacks=[callback],\n", " verbose=0,\n", " class_weight={0: 1.0, 1: 2.0},\n", ")\n", "\n", "\n", "y_pred_test = (model.predict(X_test_scaled) > 0.5).astype(int)\n", "\n", "print(classification_report(y_test, y_pred_test, target_names=[\"Sain\", \"Malade\"]))\n", "print(f1_score(y_test, y_pred_test))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "plt.plot(history.history[\"loss\"], label=\"Loss (train)\")\n", "plt.plot(history.history[\"val_loss\"], label=\"Loss (val)\", linestyle=\"--\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Binary Cross-Entropy Loss\")\n", "plt.title(\"Courbe d'apprentissage\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lors de l'entraînement de mon réseau de neurones sur le dataset Breast Cancer Coimbra, j’ai obtenu un score F1 de 0.75, ce qui indique une bonne capacité du modèle à détecter les cas positifs (patients malades) tout en limitant les faux positifs.\n", "\n", "Un comportement particulier observé durant l’entraînement est que la val_loss est systématiquement inférieure à la train_loss. Ce phénomène s'explique principalement par l'utilisation de la régularisation L2, qui pénalise les poids uniquement pendant la phase d'entraînement, et non lors de l’évaluation sur les données de validation.\n", "De plus, la taille réduite du dataset, l'emploi de class_weights pour compenser le léger déséquilibre des classes, ainsi que l'utilisation du early stopping, peuvent accentuer cet écart.\n", "\n", "Ce comportement n’est pas problématique tant que les performances en validation restent stables et satisfaisantes, ce qui est le cas ici avec un score F1 élevé, métrique prioritaire dans un contexte médical où le rappel est crucial." ] } ], "metadata": { "kernelspec": { "display_name": "studies", "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": "3.13.9" } }, "nbformat": 4, "nbformat_minor": 2 }