Upload app.py

app.py

@@ -51,21 +51,29 @@ def get_metrics(model, X, y):
         """
     }
 
-
-def plot_confusion_matrices(model1, model2, X, y, labels=["Base Model", "Best Model"]):
+def plot_confusion_matrices(model1, model2, X, y, labels=["Logistic Regression", "Gradient Boosting"], class_names=None, value_fontsize=28, value_color='red'):
     fig, axes = plt.subplots(1, 2, figsize=(16, 8))
-    #fig, axes = plt.subplots(1, 2, figsize=(50, 25))
 
     for i, model in enumerate([model1, model2]):
         y_pred = model.predict(X)
         cm = confusion_matrix(y, y_pred)
-        disp = ConfusionMatrixDisplay(confusion_matrix=cm)
+
+        # Plot with default text
+        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)
         disp.plot(ax=axes[i], cmap='Blues', colorbar=False)
-        # Manually annotate the matrix with larger font
+
+        # Remove default text artists
+        for artist in axes[i].texts:
+            artist.set_visible(False)
+
+        # Manually annotate with custom font and color
         for (j, k), val in np.ndenumerate(cm):
-            axes[i].text(k, j, f"{val}", ha='center', va='center', fontsize=28, color='red')
-        axes[i].set_title(f"{labels[i]} Confusion Matrix", fontsize=30)
-        axes[i].tick_params(axis='both', labelsize=36)
+            axes[i].text(k, j, f"{val}", ha='center', va='center', fontsize=value_fontsize, color=value_color)
+
+        axes[i].set_title(f"{labels[i]}", fontsize=30)
+        axes[i].tick_params(axis='both', labelsize=24)
+        axes[i].set_xlabel("Predicted Default", fontsize=24)
+        axes[i].set_ylabel("Actual Default", fontsize=24)
 
     plt.tight_layout()
     return plt.gcf()
@@ -84,8 +92,8 @@ def evaluate(file):
 
     # Combined ROC Curve
     plt.figure()
-    plt.plot(base['fpr'], base['tpr'], label=f"Base Model (AUC={auc(base['fpr'], base['tpr']):.2f})", linestyle='--')
-    plt.plot(best['fpr'], best['tpr'], label=f"Best Model (AUC={auc(best['fpr'], best['tpr']):.2f})", linestyle='-')
+    plt.plot(base['fpr'], base['tpr'], label=f"Logistic Regression (AUC={auc(base['fpr'], base['tpr']):.2f})", linestyle='--')
+    plt.plot(best['fpr'], best['tpr'], label=f"Gradient Boosting (AUC={auc(best['fpr'], best['tpr']):.2f})", linestyle='-')
     plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)
     plt.xlabel("False Positive Rate")
     plt.ylabel("True Positive Rate")
@@ -95,8 +103,8 @@ def evaluate(file):
 
     # Combined PR Curve
     plt.figure()
-    plt.plot(base['recall_vals'], base['precision_vals'], label=f"Base Model (AUC={auc(base['recall_vals'], base['precision_vals']):.2f})", linestyle='--')
-    plt.plot(best['recall_vals'], best['precision_vals'], label=f"Best Model (AUC={auc(best['recall_vals'], best['precision_vals']):.2f})", linestyle='-')
+    plt.plot(base['recall_vals'], base['precision_vals'], label=f"Logistic Regression (AUC={auc(base['recall_vals'], base['precision_vals']):.2f})", linestyle='--')
+    plt.plot(best['recall_vals'], best['precision_vals'], label=f"Gradient Boosting (AUC={auc(best['recall_vals'], best['precision_vals']):.2f})", linestyle='-')
     plt.xlabel("Recall")
     plt.ylabel("Precision")
     plt.title("Combined Precision-Recall Curve")
@@ -104,9 +112,9 @@ def evaluate(file):
     pr_fig = plt.gcf()
 
     # Confusion Matrices
-    cm_fig = plot_confusion_matrices(base_model, best_model, X, y)
+    cm_fig = plot_confusion_matrices(base_model, best_model, X, y, class_names=["Negative", "Positive"], value_fontsize=32, value_color='red')
 
-    combined_metrics = f"📊 Base Model:\n{base['metrics']}\n\n📊 Best Model:\n{best['metrics']}"
+    combined_metrics = f"📊 Base Model:\n(Logistic Regression){base['metrics']}\n\n📊 Best Model:\n(Gradient Boosting){best['metrics']}"
 
     return combined_metrics, roc_fig, pr_fig, cm_fig