Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M1/Statistical Learning/neural_network.ipynb

@@ -9,6 +9,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "\n",
     "import tensorflow as tf"
    ]
   },
@@ -187,10 +188,12 @@
     "                kernel_regularizer=tf.keras.regularizers.l2(0.01),\n",
     "            ),\n",
     "            tf.keras.layers.Dense(\n",
-    "                8, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l2(0.01)\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",
     "    )\n",
     "    model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
     "    return model"
@@ -296,7 +299,10 @@
     "histories = []\n",
     "\n",
     "early_stopping = EarlyStopping(\n",
-    "    monitor=\"val_loss\", patience=10, restore_best_weights=True, verbose=1\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",
@@ -314,7 +320,9 @@
     "\n",
     "    # EarlyStopping\n",
     "    callback = tf.keras.callbacks.EarlyStopping(\n",
-    "        monitor=\"val_loss\", patience=10, restore_best_weights=True\n",
+    "        monitor=\"val_loss\",\n",
+    "        patience=10,\n",
+    "        restore_best_weights=True,\n",
     "    )\n",
     "\n",
     "    # Entraînement\n",
@@ -433,13 +441,18 @@
    ],
    "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, y, test_size=0.2, random_state=42, stratify=y\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.2,\n",
+    "    random_state=42,\n",
+    "    stratify=y,\n",
     ")\n",
     "\n",
     "scaler = StandardScaler()\n",
@@ -451,7 +464,9 @@
     "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n",
     "\n",
     "callback = tf.keras.callbacks.EarlyStopping(\n",
-    "    monitor=\"val_loss\", patience=10, restore_best_weights=True\n",
+    "    monitor=\"val_loss\",\n",
+    "    patience=10,\n",
+    "    restore_best_weights=True,\n",
     ")\n",
     "\n",
     "history = model.fit(\n",