From 5408ce677b52dd13d9bd4b5c9438a5bd83ea8a05 Mon Sep 17 00:00:00 2001
From: arevelle-ensae <alexis.revelle@ensae.fr>
Date: Mon, 18 Mar 2024 09:10:28 +0000
Subject: [PATCH] add calibration curve

---
 Sport/Modelization/2_Modelization_sport.ipynb | 30 +++++++++++++++++--
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/Sport/Modelization/2_Modelization_sport.ipynb b/Sport/Modelization/2_Modelization_sport.ipynb
index b3ff399..a531471 100644
--- a/Sport/Modelization/2_Modelization_sport.ipynb
+++ b/Sport/Modelization/2_Modelization_sport.ipynb
@@ -324,10 +324,24 @@
     "    plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n",
     "    plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n",
     "    plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
-    "    plt.xlabel('Taux de faux positifs (FPR)')\n",
-    "    plt.ylabel('Taux de vrais positifs (TPR)')\n",
-    "    plt.title('Courbe ROC : modèle logistique')\n",
+    "    plt.xlabel(\"False Positive Rate\")\n",
+    "    plt.ylabel(\"True Positive Rate\")\n",
+    "    plt.title(\"ROC Curve\", size=18)\n",
     "    plt.legend(loc=\"lower right\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def draw_calibration_curve(X_test, y_test):\n",
+    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n",
+    "    frac_pos, mean_pred = calibration_curve(y_test, y_probs_bs, n_bins=10)\n",
+    "\n",
+    "    # Plot the calibration curve\n",
+    "    plt.plot(mean_pred, frac_pos, 's-', label='Logistic Regression')\n",
+    "    plt.plot([0, 1], [0, 1], 'k--', label='Perfectly calibrated')\n",
+    "    plt.xlabel('Mean predicted value')\n",
+    "    plt.ylabel('Fraction of positive predictions')\n",
+    "    plt.title(\"Calibration Curve\")\n",
+    "    plt.legend()\n",
     "    plt.show()"
    ]
   },
@@ -1552,6 +1566,16 @@
     "draw_prob_distribution(X_test)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7ee0972-79ac-481e-a370-d71b085a3c27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "draw_calibration_curve(X_test, y_test)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb",