Paths updated model_selection

model_selection/cv_metric_gen.py

@@ -272,15 +272,14 @@ if __name__ == "__main__":
                 # 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)
-
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df
             # Adjust layout and save figure
             plt.tight_layout()
-            plt.savefig(f'./output_cv_metrics/curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
+            plt.savefig(f'./output/cv_metrics/curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
             plt.close(fig)
     # Write results to Excel file
-    with pd.ExcelWriter('./output_cv_metrics/metrics.xlsx') as writer:
+    with pd.ExcelWriter('./output./cv_metrics/metrics.xlsx') as writer:
         for sheet_name, data in scores_sheets.items():
             data.to_excel(writer, sheet_name=sheet_name)
     print("Successful cv metric generation for tuned models")

model_selection/cv_metrics_distr.py

@@ -44,7 +44,7 @@ if __name__ == "__main__":
                 if metric_name in ['F1', 'PREC', 'REC', 'ACC', 'AUROC', 'AUPRC']:
                     ax.set_ylim(0, 1)
             plt.tight_layout()
-            fig.savefig(f'./output_cv_metrics/distributions/{group}{method}.svg', format='svg', dpi=600)
+            fig.savefig(f'./output/cv_metrics/distributions/{group}{method}.svg', format='svg', dpi=600)
             plt.close(fig)  # Close the figure to free up memory
 
     print("Succesful distribution plots generation")

model_selection/hyperparam_tuning.py

@@ -159,7 +159,7 @@ if __name__ == "__main__":
             sheets_dict[sheet_name] = hyperparam_df
 
     # Write results to Excel file
-    with pd.ExcelWriter('./output_hyperparam/hyperparamers.xlsx') as writer:
+    with pd.ExcelWriter('./output/hyperparam/hyperparamers.xlsx') as writer:
         for sheet_name, data in sheets_dict.items():
             data.to_excel(writer, sheet_name=sheet_name)
 

model_selection/test_models.py

@@ -77,7 +77,7 @@ def read_data():
 def get_tuned_models(group_str, method_str):
 
     # Read sheet corresponding to group and method with tuned models and their hyperparam
-    tuned_models_df = pd.read_excel("./output_hyperparam/hyperparamers.xlsx",sheet_name=f"{group_str}_{method_str}")
+    tuned_models_df = pd.read_excel("./output/hyperparam/hyperparamers.xlsx",sheet_name=f"{group_str}_{method_str}")
     # Mapping from model abbreviations to sklearn model classes
     model_mapping = {
         'DT': DecisionTreeClassifier,
@@ -274,13 +274,13 @@ if __name__ == "__main__":
                 # ----------------------------------------------------------
             # Adjust layout and save/show figure
             plt.tight_layout()
-            plt.savefig(f'./output_test/plots/{group}_{method_names[j]}.svg', format='svg', dpi=500)
+            plt.savefig(f'./output/testing/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]}"
             scores_sheets[sheet_name] = scores_df
     # Write results to Excel file
-    with pd.ExcelWriter('./output_test/testing_tuned_models.xlsx') as writer:
+    with pd.ExcelWriter('./output/testing/testing_tuned_models.xlsx') as writer:
         for sheet_name, data in scores_sheets.items():
             data.to_excel(writer, sheet_name=sheet_name)
     print("Successful evaluation with test dataset")