From a0cc98744f84b3480989eec086671af12c3ef9ec Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Thu, 23 Oct 2025 15:02:34 +0200 Subject: [PATCH] Implement code changes to enhance functionality and improve performance --- .../TP_4/2025_M2_ISF_TP_4.ipynb | 51644 +++++++++++++++- 1 file changed, 50235 insertions(+), 1409 deletions(-) diff --git a/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb b/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb index 0ca383e..4fcd6a1 100644 --- a/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb +++ b/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "97d58527", "metadata": {}, "outputs": [], @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 103, "id": "c67db932", "metadata": {}, "outputs": [], @@ -96,7 +96,7 @@ " crosstab = np.array(pd.crosstab(var1,var2, rownames=None, colnames=None)) # Cross table building\n", " stat = chi2_contingency(crosstab)[0] # Keeping of the test statistic of the Chi2 test\n", " obs = np.sum(crosstab) # Number of observations\n", - " mini = min(crosstab.shape)-1 # Take the minimum value between the columns and the rows of the cross table\n", + " mini = min(crosstab.shape)-1 # Take the minimum value between the colmns and the rows of the cross table\n", " return (stat/(obs*mini))" ] }, @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 104, "id": "c9597b48", "metadata": {}, "outputs": [], @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 105, "id": "8051b5f4", "metadata": {}, "outputs": [], @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 106, "id": "b9b98d36", "metadata": {}, "outputs": [ @@ -237,27 +237,12 @@ "type": "string" }, { - "name": "NB", - "rawType": "int64", - "type": "integer" - }, - { - "name": "CHARGE", - "rawType": "float64", - "type": "float" - }, - { - "name": "EXPO", - "rawType": "float64", - "type": "float" - }, - { - "name": "sinistré", - "rawType": "int64", - "type": "integer" + "name": "SINISTRE", + "rawType": "bool", + "type": "boolean" } ], - "ref": "b979eb39-686f-4927-8f14-5b4f00e866e5", + "ref": "7ddcc588-b097-4f77-b9f1-5915ff78dabd", "rows": [ [ "0", @@ -274,10 +259,7 @@ "ESSENCE", "FAUX", "[10000;15000[", - "0", - "0.0", - "245.3278688524592", - "0" + "False" ], [ "1", @@ -294,10 +276,7 @@ "DIESEL", "VRAI", "[20000;25000[", - "0", - "0.0", - "230.36885245901655", - "0" + "False" ], [ "2", @@ -314,10 +293,7 @@ "ESSENCE", "FAUX", "[15000;20000[", - "0", - "0.0", - "300.0", - "0" + "False" ], [ "3", @@ -334,10 +310,7 @@ "ESSENCE", "VRAI", "[35000;99999[", - "0", - "0.0", - "303.99999999999994", - "0" + "False" ], [ "4", @@ -354,915 +327,12 @@ "DIESEL", "FAUX", "[10000;15000[", - "0", - "0.0", - "365.0", - "0" - ], - [ - "5", - "2019", - "(2,5]", - "MENSUEL", - "[0;20000[", - "A", - "67", - "M", - "False", - "22", - "2015.0", - "ESSENCE", - "VRAI", - "[10000;15000[", - "0", - "0.0", - "364.5874316939892", - "0" - ], - [ - "6", - "2016", - "(0,1]", - "MENSUEL", - "[0;20000[", - "C", - "37", - "F", - "False", - "15", - "2016.0", - 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" NB\n", - " CHARGE\n", - " EXPO\n", - " sinistré\n", + " SINISTRE\n", " \n", " \n", " \n", @@ -1319,10 +386,7 @@ " ESSENCE\n", " FAUX\n", " [10000;15000[\n", - " 0\n", - " 0.0\n", - " 245.327869\n", - " 0\n", + " False\n", " \n", " \n", " 1\n", @@ -1339,10 +403,7 @@ " DIESEL\n", " VRAI\n", " [20000;25000[\n", - " 0\n", - " 0.0\n", - " 230.368852\n", - " 0\n", + " False\n", " \n", " \n", " 2\n", @@ -1359,10 +420,7 @@ " ESSENCE\n", " FAUX\n", " [15000;20000[\n", - " 0\n", - " 0.0\n", - " 300.000000\n", - " 0\n", + " False\n", " \n", " \n", " 3\n", @@ -1379,10 +437,7 @@ " ESSENCE\n", " VRAI\n", " [35000;99999[\n", - " 0\n", - " 0.0\n", - " 304.000000\n", - " 0\n", + " False\n", " \n", " \n", " 4\n", @@ -1399,202 +454,54 @@ " DIESEL\n", " FAUX\n", " [10000;15000[\n", - " 0\n", - " 0.0\n", - " 365.000000\n", - " 0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - 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count=%{y}", + "hovertemplate": "SINISTRE=%{x}
count=%{y}", "legendgroup": "", "marker": { "color": "#636efa", @@ -1632,10 +1110,14244 @@ "orientation": "v", "showlegend": false, "type": "histogram", - "x": { - "bdata": "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       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[ - "(14236, 16)" + "(14236, 13)" ] }, "execution_count": 109, @@ -2483,7 +16197,7 @@ } ], "source": [ - "data_set = data_retraitee.drop(\"sinistré\", axis=1)\n", + "data_set = data_model.drop(\"SINISTRE\", axis=1)\n", "data_set.shape" ] }, @@ -2499,20 +16213,20 @@ "variables_numeriques = []\n", "variables_01 = []\n", "variables_categorielles = []\n", - "for colu in data_set.columns:\n", - " if True in data_set[colu].isna().unique():\n", - " variables_na.append(data_set[colu])\n", + "for col in data_set.columns:\n", + " if True in data_model[col].isna().unique():\n", + " variables_na.append(data_model[col])\n", " else:\n", - " if str(data_set[colu].dtypes) in [\"int32\", \"int64\", \"float64\"]:\n", - " if len(data_set[colu].unique()) == 2:\n", - " variables_categorielles.append(data_set[colu])\n", + " if str(data_model[col].dtypes) in [\"int32\", \"int64\", \"float64\"]:\n", + " if len(data_model[col].unique()) == 2:\n", + " variables_categorielles.append(data_model[col])\n", " else:\n", - " variables_numeriques.append(data_set[colu])\n", + " variables_numeriques.append(data_model[col])\n", " else:\n", - " if len(data_set[colu].unique()) == 2:\n", - " variables_categorielles.append(data_set[colu])\n", + " if len(data_model[col].unique()) == 2:\n", + " variables_categorielles.append(data_model[col])\n", " else:\n", - " variables_categorielles.append(data_set[colu])\n" + " variables_categorielles.append(data_model[col])\n" ] }, { @@ -2594,7 +16308,7 @@ "type": "float" } ], - "ref": "0d7eb6cc-5877-455f-9d93-0374286dc27c", + "ref": "76f9f21f-6f19-4d9a-a69d-3e090e8cd298", "rows": [ [ "CONTRAT_ANCIENNETE", @@ -2924,7 +16638,7 @@ " index=vars_categorielles.columns,\n", ")\n", "\n", - "v_cramer_resultats\n" + "v_cramer_resultats" ] }, { @@ -2939,11 +16653,7 @@ " for j in range(i + 1, v_cramer_resultats.shape[0]):\n", " if v_cramer_resultats.iloc[i, j] > 0.7:\n", " print(\n", - " v_cramer_resultats.index.to_numpy()[i]\n", - " + \" et \"\n", - " + v_cramer_resultats.columns[j]\n", - " + \" sont trop dépendantes, V-CRAMER = \"\n", - " + str(v_cramer_resultats.iloc[i, j])\n", + " f\"{v_cramer_resultats.index.to_numpy()[i]} et {v_cramer_resultats.colmns[j]} sont trop dépendantes, V-CRAMER = {v_cramer_resultats.iloc[i, j]}\"\n", " )\n" ] }, @@ -3007,99 +16717,42 @@ "name": "ANNEE_CONSTRUCTION", "rawType": "float64", "type": "float" - }, - { - "name": "NB", - "rawType": "float64", - "type": "float" - }, - { - "name": "CHARGE", - "rawType": "float64", - "type": "float" - }, - { - "name": "EXPO", - "rawType": "float64", - "type": "float" } ], - "ref": "6775fec4-a2fa-4d45-a7e7-55334dc80d4d", + "ref": "4d9d1f6e-75c3-46f7-aef5-41fca2b9955d", "rows": [ [ "ANNEE_CTR", "1.0", "0.048023234802924315", "0.043983174120495815", - "0.3615499864845018", - "-0.05775190894636334", - "-0.028901069139582642", - "-0.04770515515535773" + "0.3615499864845018" ], [ "AGE_ASSURE_PRINCIPAL", "0.048023234802924315", "1.0", "0.4987430846753776", - "-0.0591835157827114", - "-0.012425345899111317", - "-0.020907992524227155", - "0.06096340138959582" + "-0.0591835157827114" ], [ "ANCIENNETE_PERMIS", "0.043983174120495815", "0.4987430846753776", "1.0", - "-0.0298138263902136", - "-0.008703999957333864", - "-0.011347002839350888", - "0.0324606537737922" + "-0.0298138263902136" ], [ "ANNEE_CONSTRUCTION", "0.3615499864845018", "-0.0591835157827114", "-0.0298138263902136", - "1.0", - "-0.01437673371578632", - "-0.0012301736578250726", - "-0.07395284013392618" - ], - [ - "NB", - "-0.05775190894636334", - "-0.012425345899111317", - "-0.008703999957333864", - "-0.01437673371578632", - "1.0", - "0.5071071150738479", - "0.0507022890091039" - ], - [ - "CHARGE", - "-0.028901069139582642", - "-0.020907992524227155", - "-0.011347002839350888", - "-0.0012301736578250726", - "0.5071071150738479", - "1.0", - "-0.021418687122216843" - ], - [ - "EXPO", - "-0.04770515515535773", - "0.06096340138959582", - "0.0324606537737922", - "-0.07395284013392618", - "0.0507022890091039", - "-0.021418687122216843", "1.0" ] ], "shape": { - "columns": 7, - "rows": 7 + "columns": 4, + "rows": 4 } }, "text/html": [ @@ -3125,9 +16778,6 @@ " AGE_ASSURE_PRINCIPAL\n", " ANCIENNETE_PERMIS\n", " ANNEE_CONSTRUCTION\n", - " NB\n", - " CHARGE\n", - " EXPO\n", " \n", " \n", " \n", @@ -3137,9 +16787,6 @@ " 0.048023\n", " 0.043983\n", " 0.361550\n", - " -0.057752\n", - " -0.028901\n", - " -0.047705\n", " \n", " \n", " AGE_ASSURE_PRINCIPAL\n", @@ -3147,9 +16794,6 @@ " 1.000000\n", " 0.498743\n", " -0.059184\n", - " -0.012425\n", - " -0.020908\n", - " 0.060963\n", " \n", " \n", " ANCIENNETE_PERMIS\n", @@ -3157,9 +16801,6 @@ " 0.498743\n", " 1.000000\n", " -0.029814\n", - " -0.008704\n", - " -0.011347\n", - " 0.032461\n", " \n", " \n", " ANNEE_CONSTRUCTION\n", @@ -3167,39 +16808,6 @@ " -0.059184\n", " -0.029814\n", " 1.000000\n", - " -0.014377\n", - " -0.001230\n", - " -0.073953\n", - " \n", - " \n", - " NB\n", - " -0.057752\n", - " -0.012425\n", - " -0.008704\n", - " -0.014377\n", - " 1.000000\n", - " 0.507107\n", - " 0.050702\n", - " \n", - " \n", - " CHARGE\n", - " -0.028901\n", - " -0.020908\n", - " -0.011347\n", - " -0.001230\n", - " 0.507107\n", - " 1.000000\n", - " -0.021419\n", - " \n", - " \n", - " EXPO\n", - " -0.047705\n", - " 0.060963\n", - " 0.032461\n", - " -0.073953\n", - " 0.050702\n", - " -0.021419\n", - " 1.000000\n", " \n", " \n", "\n", @@ -3211,18 +16819,12 @@ "AGE_ASSURE_PRINCIPAL 0.048023 1.000000 0.498743 \n", "ANCIENNETE_PERMIS 0.043983 0.498743 1.000000 \n", "ANNEE_CONSTRUCTION 0.361550 -0.059184 -0.029814 \n", - "NB -0.057752 -0.012425 -0.008704 \n", - "CHARGE -0.028901 -0.020908 -0.011347 \n", - "EXPO -0.047705 0.060963 0.032461 \n", "\n", - " ANNEE_CONSTRUCTION NB CHARGE EXPO \n", - "ANNEE_CTR 0.361550 -0.057752 -0.028901 -0.047705 \n", - "AGE_ASSURE_PRINCIPAL -0.059184 -0.012425 -0.020908 0.060963 \n", - "ANCIENNETE_PERMIS -0.029814 -0.008704 -0.011347 0.032461 \n", - "ANNEE_CONSTRUCTION 1.000000 -0.014377 -0.001230 -0.073953 \n", - "NB -0.014377 1.000000 0.507107 0.050702 \n", - "CHARGE -0.001230 0.507107 1.000000 -0.021419 \n", - "EXPO -0.073953 0.050702 -0.021419 1.000000 " + " ANNEE_CONSTRUCTION \n", + "ANNEE_CTR 0.361550 \n", + "AGE_ASSURE_PRINCIPAL -0.059184 \n", + "ANCIENNETE_PERMIS -0.029814 \n", + "ANNEE_CONSTRUCTION 1.000000 " ] }, "execution_count": 115, @@ -3249,12 +16851,8 @@ " for j in range(i + 1, nb_variables):\n", " if abs(correlations_num.iloc[i, j]) > 0.7:\n", " print(\n", - " correlations_num.index.to_numpy()[i]\n", - " + \" et \"\n", - " + correlations_num.columns[j]\n", - " + \" sont trop dépendantes, corr = \"\n", - " + str(correlations_num.iloc[i, j])\n", - " )" + " f\"{correlations_num.index.to_numpy()[i]} et {correlations_num.columns[j]} sont trop dépendantes, corr = {correlations_num.iloc[i, j]}\"\n", + " )\n" ] }, { @@ -3475,7 +17073,7 @@ "type": "float" } ], - "ref": "babc19df-3fb0-454f-b931-5edcdd6c6a55", + "ref": "9ba6f62d-da03-4f68-bd27-371767efa9b7", "rows": [ [ "0", @@ -3970,78 +17568,48 @@ "name": "ANNEE_CONSTRUCTION", "rawType": "float64", "type": "float" - }, - { - "name": "NB", - "rawType": "float64", - "type": "float" - }, - { - "name": "CHARGE", - "rawType": "float64", - "type": "float" - }, - { - "name": "EXPO", - "rawType": "float64", - "type": "float" } ], - "ref": "46a8d9a1-3a1b-4f12-80a5-7301880114ee", + "ref": "9dfd57b5-53a6-4a5d-834b-64070a4d03e2", "rows": [ [ "0", "0.1393559608666301", "0.6582867283271144", "0.5635879287137437", - "0.1740107784615837", - "-0.24202868219585674", - "-0.181253980627111", - "-0.289146035458737" + "0.1740107784615837" ], [ "1", "0.1393559608666301", "3.1516280073827847", "0.9874335016275682", - "0.7442069902648635", - "-0.24202868219585674", - "-0.181253980627111", - "-0.42709265252699025" + "0.7442069902648635" ], [ "2", "1.3471924655222902", "-0.7350510452628191", "-1.078813666327326", - "0.45910888436322356", - "-0.24202868219585674", - "-0.181253980627111", - "0.215020504730438" + "0.45910888436322356" ], [ "3", "1.3471924655222902", "0.0716181920787214", "0.40464583887105954", - "0.7442069902648635", - "-0.24202868219585674", - "-0.181253980627111", - "0.25190705219855114" + "0.7442069902648635" ], [ "4", "-0.4645622914611999", "0.0716181920787214", "-0.28410321711390524", - "-1.8216759628498953", - "-0.24202868219585674", - "-0.181253980627111", - "0.8144269010872852" + "-1.8216759628498953" ] ], "shape": { - "columns": 7, + "columns": 4, "rows": 5 } }, @@ -4068,9 +17636,6 @@ " AGE_ASSURE_PRINCIPAL\n", " ANCIENNETE_PERMIS\n", " ANNEE_CONSTRUCTION\n", - " NB\n", - " CHARGE\n", - " EXPO\n", " \n", " \n", " \n", @@ -4080,9 +17645,6 @@ " 0.658287\n", " 0.563588\n", " 0.174011\n", - " -0.242029\n", - " -0.181254\n", - " -0.289146\n", " \n", " \n", " 1\n", @@ -4090,9 +17652,6 @@ " 3.151628\n", " 0.987434\n", " 0.744207\n", - " -0.242029\n", - " -0.181254\n", - " -0.427093\n", " \n", " \n", " 2\n", @@ -4100,9 +17659,6 @@ " -0.735051\n", " -1.078814\n", " 0.459109\n", - " -0.242029\n", - " -0.181254\n", - " 0.215021\n", " \n", " \n", " 3\n", @@ -4110,9 +17666,6 @@ " 0.071618\n", " 0.404646\n", " 0.744207\n", - " -0.242029\n", - " -0.181254\n", - " 0.251907\n", " \n", " \n", " 4\n", @@ -4120,28 +17673,18 @@ " 0.071618\n", " -0.284103\n", " -1.821676\n", - " -0.242029\n", - " -0.181254\n", - " 0.814427\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS ANNEE_CONSTRUCTION \\\n", - "0 0.139356 0.658287 0.563588 0.174011 \n", - "1 0.139356 3.151628 0.987434 0.744207 \n", - "2 1.347192 -0.735051 -1.078814 0.459109 \n", - "3 1.347192 0.071618 0.404646 0.744207 \n", - "4 -0.464562 0.071618 -0.284103 -1.821676 \n", - "\n", - " NB CHARGE EXPO \n", - "0 -0.242029 -0.181254 -0.289146 \n", - "1 -0.242029 -0.181254 -0.427093 \n", - "2 -0.242029 -0.181254 0.215021 \n", - "3 -0.242029 -0.181254 0.251907 \n", - "4 -0.242029 -0.181254 0.814427 " + " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS ANNEE_CONSTRUCTION\n", + "0 0.139356 0.658287 0.563588 0.174011\n", + "1 0.139356 3.151628 0.987434 0.744207\n", + "2 1.347192 -0.735051 -1.078814 0.459109\n", + "3 1.347192 0.071618 0.404646 0.744207\n", + "4 -0.464562 0.071618 -0.284103 -1.821676" ] }, "execution_count": 118, @@ -4210,11 +17753,18 @@ "X_global = vars_numeriques_scaled.merge(\n", " variables_categorielles_ohe, left_index=True, right_index=True\n", ")\n", - "\n", "# Réorganisation des données\n", "X = X_global.to_numpy()\n", - "Y = data_retraitee[\"sinistré\"]\n", - "\n", + "Y = np.array(data_model[\"SINISTRE\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "177643ec", + "metadata": {}, + "outputs": [], + "source": [ "# Sampling en 80% train et 20% test\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", @@ -4231,7 +17781,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 121, "id": "cb60fe19", "metadata": {}, "outputs": [], @@ -4241,14 +17791,17 @@ " \"n_estimators\": [100, 200, 250],\n", " \"learning_rate\": [0.5, 0.7, 0.9],\n", "}\n", - "scoring = 'recall'\n", + "\n", "# Nombre de folds pour la validation croisée\n", - "num_folds = 5" + "num_folds = 5\n", + "\n", + "# Initialisation du modèle GradientBoostingClassifier\n", + "gb = GradientBoostingClassifier(random_state=42)" ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 122, "id": "b976720e", "metadata": {}, "outputs": [ @@ -4256,7 +17809,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Meilleurs hyperparamètres : {'learning_rate': 0.5, 'n_estimators': 100}\n" + "Meilleurs hyperparamètres : {'learning_rate': 0.9, 'n_estimators': 250}\n" ] } ], @@ -4271,7 +17824,7 @@ " cv=StratifiedKFold(\n", " n_splits=num_folds, shuffle=True, random_state=42\n", " ), # Validation croisée avec 5 folds\n", - " scoring=scoring, # Métrique d'évaluation (moins c'est mieux)\n", + " scoring='recall', # Métrique d'évaluation (moins c'est mieux)\n", " n_jobs=-1, # Utiliser tous les cœurs du processeur\n", ")\n", "\n", @@ -4280,12 +17833,12 @@ "\n", "# Afficher les meilleurs hyperparamètres\n", "best_params = grid_search.best_params_\n", - "print(\"Meilleurs hyperparamètres : \", best_params)\n" + "print(\"Meilleurs hyperparamètres : \", best_params)" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 123, "id": "0a35a4bf", "metadata": {}, "outputs": [], @@ -4296,7 +17849,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 124, "id": "e12177a8", "metadata": {}, "outputs": [ @@ -4304,52 +17857,35236 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE pour le fold 1: 1.0\n", - "RMSE pour le fold 2: 1.0\n", - "RMSE pour le fold 3: 1.0\n", - "RMSE pour le fold 4: 1.0\n", - "RMSE pour le fold 5: 1.0\n", + "Recall pour le fold 1: 0.015151515151515152\n", + "Recall pour le fold 2: 0.022727272727272728\n", + "Recall pour le fold 3: 0.030303030303030304\n", + "Recall pour le fold 4: 0.007633587786259542\n", + "Recall pour le fold 5: 0.022727272727272728\n", "\n", "\n", - "MSE pour le fold 1: 1.0\n", - "MSE pour le fold 2: 1.0\n", - "MSE pour le fold 3: 1.0\n", - "MSE pour le fold 4: 1.0\n", - "MSE pour le fold 5: 1.0\n", + "Accuracy pour le fold 1: 0.9293239683933274\n", + "Accuracy pour le fold 2: 0.9218612818261633\n", + "Accuracy pour le fold 3: 0.9341527655838455\n", + "Accuracy pour le fold 4: 0.9336846728151076\n", + "Accuracy pour le fold 5: 0.9358805445761967\n", "\n", "\n", - "MAE pour le fold 1: 1.0\n", - "MAE pour le fold 2: 1.0\n", - "MAE pour le fold 3: 1.0\n", - "MAE pour le fold 4: 1.0\n", - "MAE pour le fold 5: 1.0\n" + "Precision pour le fold 1: 0.06060606060606061\n", + "Precision pour le fold 2: 0.057692307692307696\n", + "Precision pour le fold 3: 0.15384615384615385\n", + "Precision pour le fold 4: 0.045454545454545456\n", + "Precision pour le fold 5: 0.15\n" ] } ], "source": [ - "# Cross validation\n", - "# RMSE de chaque fold\n", - "rmse_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring=scoring)\n", + "# Recall de chaque fold\n", + "recall_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='recall')\n", "\n", "# Afficher les scores pour chaque fold\n", - "for i, score in enumerate(rmse_scores):\n", - " print(f\"RMSE pour le fold {i + 1}: {score}\")\n", + "for i, score in enumerate(recall_scores):\n", + " print(f\"Recall pour le fold {i + 1}: {score}\")\n", "\n", - "# MSE de chaque fold\n", - "mse_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring=scoring)\n", + "# Accuracy de chaque fold\n", + "accuracy_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='accuracy')\n", "\n", "# Afficher les scores pour chaque fold\n", "print(\"\\n\")\n", - "for i, score in enumerate(mse_scores):\n", - " print(f\"MSE pour le fold {i + 1}: {score}\")\n", + "for i, score in enumerate(accuracy_scores):\n", + " print(f\"Accuracy pour le fold {i + 1}: {score}\")\n", "\n", - "# MAE de chaque fold\n", - "mae_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring=scoring)\n", + "# Precision de chaque fold\n", + "precision_scores = cross_val_score(best_gbc, X_train, y_train, cv=num_folds, scoring='precision')\n", "\n", "# Afficher les scores pour chaque fold\n", "print(\"\\n\")\n", - "for i, score in enumerate(mae_scores):\n", - " print(f\"MAE pour le fold {i + 1}: {score}\")\n" + "for i, score in enumerate(precision_scores):\n", + " print(f\"Precision pour le fold {i + 1}: {score}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "5683862c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exactitude : 0.024242424242424242\n" + ] + } + ], + "source": [ + "# Entraîner le modèle final sur la base entière\n", + "best_gbc.fit(X_train, y_train)\n", + "\n", + "# Faire des prédictions sur l'ensemble de test\n", + "y_pred = best_gbc.predict(X_test)\n", + "\n", + "# Calculer le recall des prédictions\n", + "recall = metrics.recall_score(y_test, y_pred)\n", + "print(f\"Exactitude : {recall}\")" + ] + }, + { + "cell_type": "markdown", + "id": "525aef5a", + "metadata": {}, + "source": [ + "### Fitting avec *Oversampling*" + ] + }, + { + "cell_type": "markdown", + "id": "76077b9d", + "metadata": {}, + "source": [ + "#### *Oversampling*" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "764406bd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "bingroup": "x", + "hovertemplate": "SINISTRE=%{x}
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+ }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Distribution de la variable Y_train_resampled" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "SINISTRE" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Observation de la distribution sur Y_train\n", + "df = pd.DataFrame(y_train_resampled, columns=[\"SINISTRE\"])\n", + "fig = px.histogram(\n", + " df, x=\"SINISTRE\", title=\"Distribution de la variable Y_train_resampled\"\n", + ")\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "dd1eb242", + "metadata": {}, + "source": [ + "#### Fitting sur le nouvel échantillon" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "7f2b0273", + "metadata": {}, + "outputs": [], + "source": [ + "# Définir la grille d'hyperparamètres à rechercher\n", + "param_grid = {\n", + " \"n_estimators\": [475, 500, 525],\n", + " \"learning_rate\": [0, 7, 0.9, 1.5],\n", + "}\n", + "\n", + "# Nombre de folds pour la validation croisée\n", + "num_folds = 5\n", + "\n", + "# Initialisation du modèle GradientBoostingClassifier\n", + "gb = GradientBoostingClassifier(random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "1d02b400", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleurs hyperparamètres : {'learning_rate': 0.9, 'n_estimators': 500}\n" + ] + } + ], + "source": [ + "# Création de l'objet GridSearchCV pour la recherche sur grille avec validation croisée\n", + "grid_search = GridSearchCV(\n", + " estimator=gb,\n", + " param_grid=param_grid,\n", + " cv=StratifiedKFold(\n", + " n_splits=num_folds, shuffle=True, random_state=42\n", + " ), # Validation croisée stratifiée avec 5 plis\n", + " scoring=\"recall\", # Métrique d'évaluation\n", + " n_jobs=-1, # Utiliser tous les cœurs du processeur\n", + ")\n", + "\n", + "# Exécution de la recherche sur grille\n", + "grid_search.fit(X_train_resampled, y_train_resampled)\n", + "\n", + "# Afficher les meilleurs hyperparamètres\n", + "best_params = grid_search.best_params_\n", + "print(\"Meilleurs hyperparamètres : \", best_params)\n", + "\n", + "# Initialiser un modèle avec les meilleurs hyperparamètres\n", + "best_gbc = GradientBoostingClassifier(random_state=42, **best_params)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "28956389", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recall pour le fold 1: 0.9948741845293569\n", + "Recall pour le fold 2: 0.9972041006523765\n", + "Recall pour le fold 3: 0.9972041006523765\n", + "Recall pour le fold 4: 0.9981351981351981\n", + "Recall pour le fold 5: 0.9958061509785647\n", + "\n", + "\n", + "Accuracy pour le fold 1: 0.945479962721342\n", + "Accuracy pour le fold 2: 0.9452469711090401\n", + "Accuracy pour le fold 3: 0.9391891891891891\n", + "Accuracy pour le fold 4: 0.9461663947797716\n", + "Accuracy pour le fold 5: 0.9370776042880448\n", + "\n", + "\n", + "Precision pour le fold 1: 0.9054283290924512\n", + "Precision pour le fold 2: 0.9033347403967918\n", + "Precision pour le fold 3: 0.8935281837160751\n", + "Precision pour le fold 4: 0.9041385135135135\n", + "Precision pour le fold 5: 0.8911592994161801\n" + ] + } + ], + "source": [ + "# Zoom sur la CV\n", + "# Recall de chaque fold\n", + "recall_scores = cross_val_score(\n", + " best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"recall\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "for i, score in enumerate(recall_scores):\n", + " print(f\"Recall pour le fold {i + 1}: {score}\")\n", + "\n", + "# Accuracy de chaque fold\n", + "accuracy_scores = cross_val_score(\n", + " best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"accuracy\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "print(\"\\n\")\n", + "for i, score in enumerate(accuracy_scores):\n", + " print(f\"Accuracy pour le fold {i + 1}: {score}\")\n", + "\n", + "# Precision de chaque fold\n", + "precision_scores = cross_val_score(\n", + " best_gbc, X_train_resampled, y_train_resampled, cv=num_folds, scoring=\"precision\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "print(\"\\n\")\n", + "for i, score in enumerate(precision_scores):\n", + " print(f\"Precision pour le fold {i + 1}: {score}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "5ab7fedd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingClassifier(learning_rate=0.9, n_estimators=500, random_state=42)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingClassifier(learning_rate=0.9, n_estimators=500, random_state=42)" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Entraîner le modèle final sur toute la base\n", + "best_gbc.fit(X_train_resampled, y_train_resampled)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "7df7f223", + "metadata": {}, + "outputs": [], + "source": [ + "# Faire des prédictions sur l'ensemble de test\n", + "y_pred = best_gbc.predict(X_test)" ] }, { @@ -4372,11 +53109,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 134, "id": "5d9ef448", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Matrice de confusion\n", + "confusion_matrix = metrics.confusion_matrix(y_test, y_pred)\n", + "cm_display = metrics.ConfusionMatrixDisplay(\n", + " confusion_matrix=confusion_matrix, display_labels=[False, True]\n", + ")\n", + "\n", + "cm_display.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "037188fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recall : 0.1696969696969697\n" + ] + } + ], + "source": [ + "# Calculer le recall des prédictions\n", + "recall = metrics.recall_score(y_test, y_pred)\n", + "print(f\"Recall : {recall}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "b9d20ebf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exactitude : 0.8599016853932584\n" + ] + } + ], + "source": [ + "# Calculer l'accuracy des prédictions\n", + "acc = metrics.accuracy_score(y_test, y_pred)\n", + "print(f\"Exactitude : {acc}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "7f867d05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision : 0.09655172413793103\n" + ] + } + ], + "source": [ + "# Calculer le precision des prédictions\n", + "precision = metrics.precision_score(y_test, y_pred)\n", + "print(f\"Precision : {precision}\")" + ] } ], "metadata": {