From 6a4afb0cb55c23cc84f8bf25ae364461060b6547 Mon Sep 17 00:00:00 2001 From: Arthur Danjou Date: Fri, 6 Jun 2025 20:34:39 +0200 Subject: [PATCH] Delete neural_network.ipynb --- neural_network.ipynb | 567 ------------------------------------------- 1 file changed, 567 deletions(-) delete mode 100644 neural_network.ipynb diff --git a/neural_network.ipynb b/neural_network.ipynb deleted file mode 100644 index 0ded0f1..0000000 --- a/neural_network.ipynb +++ /dev/null @@ -1,567 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import pandas as pd\n", - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "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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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "breast_cancer = pd.read_csv(\"dataR2.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", - " tf.keras.layers.Dense(16, activation='relu', input_shape=(X.shape[1],), kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n", - " tf.keras.layers.Dense(8, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n", - " tf.keras.layers.Dense(1, activation='sigmoid')\n", - " ])\n", - " model.compile(\n", - " optimizer='adam',\n", - " loss='binary_crossentropy',\n", - " metrics=['accuracy']\n", - " )\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "train test split and scaling of the features " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.metrics import f1_score, classification_report\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "\n", - "# Splitting the dataset into training and testing sets\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", - "\n", - "# Scaling the features\n", - "scaler = StandardScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cross validation" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 29ms/step\n", - "Fold 1 - F1-score : 0.7273\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 30ms/step\n", - "Fold 2 - F1-score : 0.6667\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 29ms/step\n", - "Fold 3 - F1-score : 0.5556\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 27ms/step\n", - "Fold 4 - F1-score : 0.7200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 0x16c5f5160> 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 29ms/step\n", - "Fold 5 - F1-score : 0.8000\n", - "\n", - "F1-score moyen sur 5 folds : 0.6939\n" - ] - } - ], - "source": [ - "from sklearn.metrics import f1_score\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from keras.callbacks import EarlyStopping\n", - "from sklearn.preprocessing import StandardScaler\n", - "\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_train_scaled, y_train), 1):\n", - " X_cv_train, X_cv_val = X_train.iloc[train_idx], X_train.iloc[val_idx]\n", - " y_cv_train, y_cv_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n", - " \n", - " model = build_model()\n", - "\n", - " model.compile(\n", - " optimizer='adam',\n", - " loss='binary_crossentropy',\n", - " metrics=[\"f1_score\"]\n", - " )\n", - "\n", - " # EarlyStopping\n", - " callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", - "\n", - " # Entraînement\n", - " history = model.fit(\n", - " X_cv_train, y_cv_train,\n", - " epochs=50,\n", - " batch_size=8,\n", - " validation_data=(X_cv_val, y_cv_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_cv_val) > 0.5).astype(int)\n", - " score = f1_score(y_cv_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": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "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)):\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": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/keras/src/layers/core/dense.py:87: 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 0x174b539d0> 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 28ms/step\n", - " precision recall f1-score support\n", - "\n", - " Sain 0.00 0.00 0.00 11\n", - " Malade 0.54 1.00 0.70 13\n", - "\n", - " accuracy 0.54 24\n", - " macro avg 0.27 0.50 0.35 24\n", - "weighted avg 0.29 0.54 0.38 24\n", - "\n", - "0.7027027027027027\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "/Users/moritzvonsiemens/Library/Python/3.9/lib/python/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" - ] - } - ], - "source": [ - "model = build_model()\n", - "\n", - "model.compile(\n", - " optimizer='adam',\n", - " loss='binary_crossentropy'\n", - ")\n", - "\n", - "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", - "\n", - "history = model.fit(\n", - " X_train_scaled, 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": 12, - "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": "Python 3", - "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": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}