regenerated plots and modified code to include CM as well

model_selection/test_models.py

@@ -17,6 +17,7 @@ from sklearn.tree import DecisionTreeClassifier
 from sklearn.metrics import RocCurveDisplay, roc_curve
 from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve
 import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
 # --------------------------------------------------------------------------------------------------------
 
 # Reading test data
@@ -250,7 +251,7 @@ if __name__ == "__main__":
             # Scores df
             scores_df = pd.DataFrame(index=models.keys(), columns=scorings.keys())
             # Create a figure for all models in this group-method
-            fig, axes = plt.subplots(len(models), 2, figsize=(8, 8 * len(models)))
+            fig, axes = plt.subplots(len(models), 3, figsize=(10, 8 * len(models)))
             if len(models) == 1:  # Adjustment if there's only one model (axes indexing issue)
                 axes = [axes]
             # Evaluate each model
@@ -268,15 +269,21 @@ if __name__ == "__main__":
                 # Calculate precision-recall curve
                 precision, recall, _ = precision_recall_curve(y_test, y_score, pos_label=model.classes_[1])
                 pr_display = PrecisionRecallDisplay(precision=precision, recall=recall).plot(ax=axes[model_idx][1])
+                # Get confusion matrix plot
+                y_pred = model.predict(X_test)
+                cm = confusion_matrix(y_test, y_pred)
+                ConfusionMatrixDisplay(cm).plot(ax=axes[model_idx[2]])
+                # Give name to plots
                 axes[model_idx][0].set_title(f'ROC Curve for {model_name}')
                 axes[model_idx][1].set_title(f'PR Curve for {model_name}')
+                axes[model_idx][2].set_title(f'CM for {model_name}')
                 # Evaluate at each of the scores of interest
                 for score_name, scorer in scorings.items():
                     score_value = scorer(model, X_test, y_test)
                     scores_df.at[model_name, score_name] = score_value
             # Adjust layout and save/show figure
             plt.tight_layout()
-            plt.savefig(f'./test_results/roc_pr_curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
+            plt.savefig(f'./test_results/aux_plots/{group}_{method_names[j]}.svg', format='svg', dpi=500)
             plt.close(fig)
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"