diff --git a/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb
index c690af1..e894875 100644
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+++ b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb
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- " EQUIPEMENT_SECURITE | \n",
- " VALEUR_DU_BIEN | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 10 | \n",
- " (0,1] | \n",
- " MENSUEL | \n",
- " [0;20000[ | \n",
- " C | \n",
- " M | \n",
- " False | \n",
- " ESSENCE | \n",
- " VRAI | \n",
- " [15000;20000[ | \n",
- "
\n",
- " \n",
- " | 34 | \n",
- " (-1,0] | \n",
- " MENSUEL | \n",
- " [20000;40000[ | \n",
- " C | \n",
- " M | \n",
- " True | \n",
- " AUTRE | \n",
- " FAUX | \n",
- " [35000;99999[ | \n",
- "
\n",
- " \n",
- " | 36 | \n",
- " (-1,0] | \n",
- " MENSUEL | \n",
- " [20000;40000[ | \n",
- " L | \n",
- " M | \n",
- " False | \n",
- " ESSENCE | \n",
- " VRAI | \n",
- " [0;10000[ | \n",
- "
\n",
- " \n",
- " | 78 | \n",
- " (-1,0] | \n",
- " MENSUEL | \n",
- " [20000;40000[ | \n",
- " B | \n",
- " M | \n",
- " False | \n",
- " DIESEL | \n",
- " FAUX | \n",
- " [15000;20000[ | \n",
- "
\n",
- " \n",
- " | 89 | \n",
- " (1,2] | \n",
- " MENSUEL | \n",
- " [20000;40000[ | \n",
- " C | \n",
- " M | \n",
- " False | \n",
- " ESSENCE | \n",
- " FAUX | \n",
- " [25000;35000[ | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " CONTRAT_ANCIENNETE FREQUENCE_PAIEMENT_COTISATION GROUPE_KM \\\n",
- "10 (0,1] MENSUEL [0;20000[ \n",
- "34 (-1,0] MENSUEL [20000;40000[ \n",
- "36 (-1,0] MENSUEL [20000;40000[ \n",
- "78 (-1,0] MENSUEL [20000;40000[ \n",
- "89 (1,2] MENSUEL [20000;40000[ \n",
- "\n",
- " ZONE_RISQUE GENRE DEUXIEME_CONDUCTEUR ENERGIE EQUIPEMENT_SECURITE \\\n",
- "10 C M False ESSENCE VRAI \n",
- "34 C M True AUTRE FAUX \n",
- "36 L M False ESSENCE VRAI \n",
- "78 B M False DIESEL FAUX \n",
- "89 C M False ESSENCE FAUX \n",
- "\n",
- " VALEUR_DU_BIEN \n",
- "10 [15000;20000[ \n",
- "34 [35000;99999[ \n",
- "36 [0;10000[ \n",
- "78 [15000;20000[ \n",
- "89 [25000;35000[ "
- ]
+ }
},
- "execution_count": 31,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "vars_categorielles.head()"
+ "# Matrice de corrélation pour les variables catégorielles (V de Cramér)\n",
+ "def cramers_v(confusion_matrix):\n",
+ " \"\"\"Calcule le V de Cramér à partir d'une matrice de contingence\"\"\"\n",
+ " chi2 = chi2_contingency(confusion_matrix)[0]\n",
+ " n = confusion_matrix.sum().sum()\n",
+ " phi2 = chi2 / n\n",
+ " r, k = confusion_matrix.shape\n",
+ " phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))\n",
+ " rcorr = r - ((r-1)**2)/(n-1)\n",
+ " kcorr = k - ((k-1)**2)/(n-1)\n",
+ " return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))\n",
+ "\n",
+ "# Créer la matrice de corrélation\n",
+ "categorical_cols = vars_categorielles.columns\n",
+ "n_vars = len(categorical_cols)\n",
+ "cramers_matrix = np.zeros((n_vars, n_vars))\n",
+ "\n",
+ "for i, col1 in enumerate(categorical_cols):\n",
+ " for j, col2 in enumerate(categorical_cols):\n",
+ " if i == j:\n",
+ " cramers_matrix[i, j] = 1.0\n",
+ " else:\n",
+ " confusion_matrix = pd.crosstab(vars_categorielles[col1], vars_categorielles[col2])\n",
+ " cramers_matrix[i, j] = cramers_v(confusion_matrix)\n",
+ "\n",
+ "# Créer le DataFrame de corrélation\n",
+ "correlation_cat = pd.DataFrame(cramers_matrix,\n",
+ " index=categorical_cols,\n",
+ " columns=categorical_cols)\n",
+ "\n",
+ "# Visualiser avec Plotly\n",
+ "fig = px.imshow(correlation_cat,\n",
+ " text_auto='.2f',\n",
+ " aspect=\"auto\",\n",
+ " color_continuous_scale='RdBu_r',\n",
+ " title='Matrice de corrélation des variables catégorielles (V de Cramér)')\n",
+ "fig.show()"
]
},
{
@@ -1447,53 +2131,901 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 34,
"id": "532ca6c4",
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Matrice de corrélation des variables numériques:\n",
- " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS \\\n",
- "ANNEE_CTR 1.000000 0.026613 0.040797 \n",
- "AGE_ASSURE_PRINCIPAL 0.026613 1.000000 0.540899 \n",
- "ANCIENNETE_PERMIS 0.040797 0.540899 1.000000 \n",
- "ANNEE_CONSTRUCTION 0.387562 -0.031655 0.033320 \n",
- "\n",
- " ANNEE_CONSTRUCTION \n",
- "ANNEE_CTR 0.387562 \n",
- "AGE_ASSURE_PRINCIPAL -0.031655 \n",
- "ANCIENNETE_PERMIS 0.033320 \n",
- "ANNEE_CONSTRUCTION 1.000000 \n"
- ]
- },
- {
- "ename": "ValueError",
- "evalue": "\n Invalid value of type 'builtins.str' received for the 'colorscale' property of imshow\n Received value: 'coolwarm'\n\n The 'colorscale' property is a colorscale and may be\n specified as:\n - A list of colors that will be spaced evenly to create the colorscale.\n Many predefined colorscale lists are included in the sequential, diverging,\n and cyclical modules in the plotly.colors package.\n - A list of 2-element lists where the first element is the\n normalized color level value (starting at 0 and ending at 1),\n and the second item is a valid color string.\n (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])\n - One of the following named colorscales:\n ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',\n 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',\n 'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',\n 'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',\n 'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',\n 'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',\n 'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',\n 'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',\n 'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',\n 'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',\n 'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',\n 'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',\n 'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',\n 'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',\n 'ylorrd'].\n Appending '_r' to a named colorscale reverses it.\n",
- "output_type": "error",
- "traceback": [
- "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
- "\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
- "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mMatrice de corrélation des variables numériques:\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(correlation_matrix)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m fig = \u001b[43mpx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mcorrelation_matrix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_auto\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor_continuous_scale\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcoolwarm\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maspect\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mauto\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 8\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m fig.update_layout(title=\u001b[33m\"\u001b[39m\u001b[33mMatrice de corrélation des variables numériques\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 10\u001b[39m fig.show()\n",
- "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/plotly/express/_imshow.py:423\u001b[39m, in \u001b[36mimshow\u001b[39m\u001b[34m(img, zmin, zmax, origin, labels, x, y, animation_frame, facet_col, facet_col_wrap, facet_col_spacing, facet_row_spacing, color_continuous_scale, color_continuous_midpoint, range_color, title, template, width, height, aspect, contrast_rescaling, binary_string, binary_backend, binary_compression_level, binary_format, text_auto)\u001b[39m\n\u001b[32m 420\u001b[39m layout[\u001b[33m\"\u001b[39m\u001b[33myaxis\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mconstrain\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mdomain\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 421\u001b[39m colorscale_validator = ColorscaleValidator(\u001b[33m\"\u001b[39m\u001b[33mcolorscale\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mimshow\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 422\u001b[39m layout[\u001b[33m\"\u001b[39m\u001b[33mcoloraxis1\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mdict\u001b[39m(\n\u001b[32m--> \u001b[39m\u001b[32m423\u001b[39m colorscale=\u001b[43mcolorscale_validator\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalidate_coerce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 424\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcolor_continuous_scale\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m 425\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 426\u001b[39m cmid=color_continuous_midpoint,\n\u001b[32m 427\u001b[39m cmin=zmin,\n\u001b[32m 428\u001b[39m cmax=zmax,\n\u001b[32m 429\u001b[39m )\n\u001b[32m 430\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m labels[\u001b[33m\"\u001b[39m\u001b[33mcolor\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 431\u001b[39m layout[\u001b[33m\"\u001b[39m\u001b[33mcoloraxis1\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mcolorbar\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mdict\u001b[39m(title_text=labels[\u001b[33m\"\u001b[39m\u001b[33mcolor\u001b[39m\u001b[33m\"\u001b[39m])\n",
- "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/_plotly_utils/basevalidators.py:1636\u001b[39m, in \u001b[36mColorscaleValidator.validate_coerce\u001b[39m\u001b[34m(self, v)\u001b[39m\n\u001b[32m 1631\u001b[39m v = [\n\u001b[32m 1632\u001b[39m [e[\u001b[32m0\u001b[39m], ColorValidator.perform_validate_coerce(e[\u001b[32m1\u001b[39m])] \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m v\n\u001b[32m 1633\u001b[39m ]\n\u001b[32m 1635\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m v_valid:\n\u001b[32m-> \u001b[39m\u001b[32m1636\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mraise_invalid_val\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1638\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m v\n",
- "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/_plotly_utils/basevalidators.py:298\u001b[39m, in \u001b[36mBaseValidator.raise_invalid_val\u001b[39m\u001b[34m(self, v, inds)\u001b[39m\n\u001b[32m 295\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m inds:\n\u001b[32m 296\u001b[39m name += \u001b[33m\"\u001b[39m\u001b[33m[\u001b[39m\u001b[33m\"\u001b[39m + \u001b[38;5;28mstr\u001b[39m(i) + \u001b[33m\"\u001b[39m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m298\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 299\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 300\u001b[39m \u001b[33;03m Invalid value of type {typ} received for the '{name}' property of {pname}\u001b[39;00m\n\u001b[32m 301\u001b[39m \u001b[33;03m Received value: {v}\u001b[39;00m\n\u001b[32m 302\u001b[39m \n\u001b[32m 303\u001b[39m \u001b[33;03m{valid_clr_desc}\"\"\"\u001b[39;00m.format(\n\u001b[32m 304\u001b[39m name=name,\n\u001b[32m 305\u001b[39m pname=\u001b[38;5;28mself\u001b[39m.parent_name,\n\u001b[32m 306\u001b[39m typ=type_str(v),\n\u001b[32m 307\u001b[39m v=\u001b[38;5;28mrepr\u001b[39m(v),\n\u001b[32m 308\u001b[39m valid_clr_desc=\u001b[38;5;28mself\u001b[39m.description(),\n\u001b[32m 309\u001b[39m )\n\u001b[32m 310\u001b[39m )\n",
- "\u001b[31mValueError\u001b[39m: \n Invalid value of type 'builtins.str' received for the 'colorscale' property of imshow\n Received value: 'coolwarm'\n\n The 'colorscale' property is a colorscale and may be\n specified as:\n - A list of colors that will be spaced evenly to create the colorscale.\n Many predefined colorscale lists are included in the sequential, diverging,\n and cyclical modules in the plotly.colors package.\n - A list of 2-element lists where the first element is the\n normalized color level value (starting at 0 and ending at 1),\n and the second item is a valid color string.\n (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])\n - One of the following named colorscales:\n ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',\n 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',\n 'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',\n 'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',\n 'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',\n 'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',\n 'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',\n 'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',\n 'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',\n 'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',\n 'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',\n 'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',\n 'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',\n 'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',\n 'ylorrd'].\n Appending '_r' to a named colorscale reverses it.\n"
- ]
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "coloraxis": "coloraxis",
+ "hovertemplate": "x: %{x}
y: %{y}
color: %{z}",
+ "name": "0",
+ "texttemplate": "%{z}",
+ "type": "heatmap",
+ "x": [
+ "ANNEE_CTR",
+ "AGE_ASSURE_PRINCIPAL",
+ "ANCIENNETE_PERMIS",
+ "ANNEE_CONSTRUCTION"
+ ],
+ "xaxis": "x",
+ "y": [
+ "ANNEE_CTR",
+ "AGE_ASSURE_PRINCIPAL",
+ "ANCIENNETE_PERMIS",
+ "ANNEE_CONSTRUCTION"
+ ],
+ "yaxis": "y",
+ "z": {
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+ "dtype": "f8",
+ "shape": "4, 4"
+ }
+ }
+ ],
+ "layout": {
+ "coloraxis": {
+ "colorscale": [
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+ ]
+ },
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
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+ "line": {
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+ "type": "bar"
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+ "barpolar": [
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"source": [
- "# Calcul des corrélations entre variables numériques\n",
- "correlation_matrix = vars_numeriques.corr()\n",
- "print(\"Matrice de corrélation des variables numériques:\")\n",
- "print(correlation_matrix)\n",
- "\n",
- "fig = px.imshow(\n",
- " correlation_matrix, text_auto=True, color_continuous_scale=\"coolwarm\", aspect=\"auto\"\n",
- ")\n",
- "fig.update_layout(title=\"Matrice de corrélation des variables numériques\")\n",
+ "vars_numeriques.corr()\n",
+ "fig = px.imshow(vars_numeriques.corr(),\n",
+ " text_auto=True,\n",
+ " aspect=\"auto\",\n",
+ " color_continuous_scale='RdBu_r',\n",
+ " title='Matrice de corrélation des variables numériques')\n",
"fig.show()"
]
},