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.

M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb

@@ -56,16 +56,15 @@
     "import seaborn as sns\n",
     "\n",
     "sns.set()\n",
-    "import matplotlib.pyplot as plt  # noqa: E402\n",
+    "import matplotlib.pyplot as plt\n",
     "import plotly.express as px\n",
-    "import plotly.graph_objects as gp\n",
-    "from scipy.cluster.hierarchy import dendrogram, linkage  # noqa: E402\n",
+    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
     "\n",
     "# Statistiques\n",
-    "from scipy.stats import chi2_contingency  # noqa: E402, F401\n",
+    "from scipy.stats import chi2_contingency  # noqa: F401\n",
     "\n",
     "# Machine Learning\n",
-    "from sklearn.cluster import AgglomerativeClustering, KMeans  # noqa: E402"
+    "from sklearn.cluster import AgglomerativeClustering, KMeans"
    ]
   },
   {
@@ -899,7 +898,9 @@
    "source": [
     "# Calcul de la partition de l'espace\n",
     "hierarchical_cluster = AgglomerativeClustering(\n",
-    "    n_clusters=3, metric=\"euclidean\", linkage=\"single\"\n",
+    "    n_clusters=3,\n",
+    "    metric=\"euclidean\",\n",
+    "    linkage=\"single\",\n",
     ")\n",
     "\n",
     "labels = hierarchical_cluster.fit_predict(data)\n",
@@ -972,7 +973,9 @@
    "source": [
     "# Calcul de la partition de l'espace\n",
     "hierarchical_cluster = AgglomerativeClustering(\n",
-    "    n_clusters=3, metric=\"euclidean\", linkage=\"complete\"\n",
+    "    n_clusters=3,\n",
+    "    metric=\"euclidean\",\n",
+    "    linkage=\"complete\",\n",
     ")\n",
     "\n",
     "labels = hierarchical_cluster.fit_predict(data)\n",
@@ -1482,7 +1485,7 @@
     "\n",
     "# Group by ZONE_RISQUE and aggregate the necessary columns\n",
     "data = data_retraitee.groupby([\"ZONE_RISQUE\"], as_index=False).agg(\n",
-    "    {\"NB\": \"sum\", \"CHARGE\": \"sum\", \"EXPO\": \"sum\"}\n",
+    "    {\"NB\": \"sum\", \"CHARGE\": \"sum\", \"EXPO\": \"sum\"},\n",
     ")\n",
     "\n",
     "# Calculate derived metrics\n",
@@ -1547,7 +1550,11 @@
    "source": [
     "# Initialisation de l'algorithme\n",
     "kmeans_FREQ = KMeans(\n",
-    "    init=\"random\", n_clusters=5, n_init=1, random_state=42, max_iter=300\n",
+    "    init=\"random\",\n",
+    "    n_clusters=5,\n",
+    "    n_init=1,\n",
+    "    random_state=42,\n",
+    "    max_iter=300,\n",
     ")\n",
     "\n",
     "# Transformation des données : plusieurs échantillons de 1 dimension\n",
@@ -3559,7 +3566,11 @@
    "source": [
     "# Initialisation de l'algorithme\n",
     "kmeans_FREQ_CM = KMeans(\n",
-    "    init=\"random\", n_clusters=5, n_init=1, random_state=42, max_iter=300\n",
+    "    init=\"random\",\n",
+    "    n_clusters=5,\n",
+    "    n_init=1,\n",
+    "    random_state=42,\n",
+    "    max_iter=300,\n",
     ")\n",
     "\n",
     "# Transformation des données : plusieurs échantillons de 1 dimension\n",
@@ -4621,7 +4632,9 @@
    "source": [
     "# Calcul de la partition de l'espace\n",
     "hierarchical_cluster = AgglomerativeClustering(\n",
-    "    n_clusters=5, metric=\"euclidean\", linkage=\"single\"\n",
+    "    n_clusters=5,\n",
+    "    metric=\"euclidean\",\n",
+    "    linkage=\"single\",\n",
     ")\n",
     "\n",
     "labels = hierarchical_cluster.fit_predict(data_x)\n",
@@ -5650,7 +5663,9 @@
    "source": [
     "# Calcul de la partition de l'espace\n",
     "hierarchical_cluster = AgglomerativeClustering(\n",
-    "    n_clusters=5, metric=\"euclidean\", linkage=\"single\"\n",
+    "    n_clusters=5,\n",
+    "    metric=\"euclidean\",\n",
+    "    linkage=\"single\",\n",
     ")\n",
     "\n",
     "labels = hierarchical_cluster.fit_predict(data_x)\n",