Ready to test new integration with DT

model_selection/cv_metric_gen.py

@@ -175,8 +175,8 @@ if __name__ == "__main__":
     # Metric generation through cv for tuned models3
     # --------------------------------------------------------------------------------------------------------
     scores_sheets = {} # To store score dfs as sheets in the same excel file
-    for i, group in enumerate(['pre', 'post']): # 'post'
-        for j, method in enumerate(['', 'over_', 'under_']): 
+    for i, group in enumerate(['pre']):
+        for j, method in enumerate(['']): 
             # Get train dataset based on group and method
             X_train = data_dic['X_train_' + method + group]
             y_train = data_dic['y_train_' + method + group]
@@ -184,18 +184,15 @@ if __name__ == "__main__":
             models = get_tuned_models(group, method_names[j])
             # Scores df -> one column per cv split, one row for each model-metric
             scores_df = pd.DataFrame(columns=range(1,11), index=[f"{model_name}_{metric_name}" for model_name in models.keys() for metric_name in scorings.keys()])
-            # Create a figure for all models in this group-method
+            # Create a figure with 2 subplots (roc and pr curves) for each model in this group-method
             fig, axes = plt.subplots(len(models), 2, figsize=(10, 8 * len(models)))
             if len(models) == 1:  # Adjustment if there's only one model (axes indexing issue)
                 axes = [axes]
             # Metric generation for each model
             for model_idx, (model_name, model) in enumerate(models.items()):
                 print(f"{group}-{method_names[j]}-{model_name}")
-                # # Retrieve cv scores for our metrics of interest
-                # scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
-                # # Save results of each fold
-                # for metric_name in scorings.keys():
-                #     scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
+                if model_name == 'DT':
+                    # Curve generation setup
                     mean_fpr = np.linspace(0, 1, 100)
                     tprs, aucs = [], []
                     mean_recall = np.linspace(0, 1, 100)
@@ -203,17 +200,24 @@ if __name__ == "__main__":
                     cmap = plt.get_cmap('tab10')  # Colormap
                     # Initialize storage for scores for each fold
                     fold_scores = {metric_name: [] for metric_name in scorings.keys()}
-                # Loop through each fold in the cross-validation
+                    # Manually loop through each fold in the cross-validation
                     for fold_idx, (train_idx, test_idx) in enumerate(cv.split(X_train, y_train)):
                         X_train_fold, X_test_fold = X_train[train_idx], X_train[test_idx]
                         y_train_fold, y_test_fold = y_train[train_idx], y_train[test_idx]
                         # Fit the model on the training data
                         model.fit(X_train_fold, y_train_fold)
+                        # --------------------- SCORINGS ---------------------------
                         # Predict on the test data
+                        # Check if the model has a decision_function method
                         if hasattr(model, "decision_function"):
+                            # Use decision_function to get the continuous scores for each test sample
                             y_score = model.decision_function(X_test_fold)
                         else:
-                        y_score = model.predict_proba(X_test_fold)[:, 1]  # Use probability of positive class
+                            # If decision_function is not available, use predict_proba to get probabilities
+                            # predict_proba returns an array with probabilities for all classes
+                            # [:, 1] extracts the probability for the positive class (class 1)
+                            y_score = model.predict_proba(X_test_fold)[:, 1] 
+                        # Get the predicted class labels for the test data
                         y_pred = model.predict(X_test_fold)
                         # Calculate and store the scores for each metric
                         for metric_name, scorer in scorings.items():
@@ -222,6 +226,7 @@ if __name__ == "__main__":
                             else:
                                 score = scorer._score_func(y_test_fold, y_pred)
                             fold_scores[metric_name].append(score)
+                        # --------------------- END SCORINGS ---------------------------
                     # --------------------- CURVES ---------------------------
                         # Generate ROC curve for the fold
                         roc_display = RocCurveDisplay.from_estimator(model, X_test_fold, y_test_fold,
@@ -238,9 +243,6 @@ if __name__ == "__main__":
                         interp_precision = np.interp(mean_recall, pr_display.recall[::-1], pr_display.precision[::-1])
                         precisions.append(interp_precision)
                         pr_aucs.append(pr_display.average_precision)
-                # Store the fold scores in the dataframe
-                for metric_name, scores in fold_scores.items():
-                    scores_df.loc[f"{model_name}_{metric_name}"] = np.around(scores, 4)
                 # Plot diagonal line for random guessing in ROC curve
                 axes[model_idx][0].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8, label='Random guessing')
                 # Compute mean ROC curve
@@ -261,6 +263,10 @@ if __name__ == "__main__":
                 # Set Precision-Recall plot limits and title
                 axes[model_idx][1].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"Precision-Recall Curve - {model_name} ({group}-{method_names[j]})")
                 axes[model_idx][1].legend(loc="lower right")
+                # --------------------- END CURVES ---------------------------
+                # Store the fold scores in the dataframe
+                for metric_name, scores in fold_scores.items():
+                    scores_df.loc[f"{model_name}_{metric_name}"] = np.around(scores, 4)
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df
@@ -273,3 +279,4 @@ if __name__ == "__main__":
         for sheet_name, data in scores_sheets.items():
             data.to_excel(writer, sheet_name=sheet_name)
     print("Successful cv metric generation for tuned models")
+    # --------------------------------------------------------------------------------------------------------
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