Boxplots for cv metrics generated

model_selection/cv_metrics_distr.py

@@ -26,14 +26,23 @@ if __name__ == "__main__":
             else:
                 model_names = model_names_simple
             # Create figure for current sheet, one row per metric
-            fig, axes = plt.subplots(len(metric_names), 1, figsize=(15, 8 * len(metric_names)))
+            fig, axes = plt.subplots(len(metric_names), 1, figsize=(10, 10 * len(metric_names)))
             for metric_id, metric_name in enumerate(metric_names):
                 # Get the axis for the current metric
                 ax = axes[metric_id]
+                # List to store the metric array for each model
+                metric_data = []
                 for model_name in model_names:
+                    # Construct the row name
                     row_name = f'{model_name}_{metric_name}'
-                    # Collect data for the current model's metric
                     metric_row = df.loc[df['Unnamed: 0'] == row_name].iloc[0, 1:].values
-                    if group == 'pre' and method == '_ORIG' and metric_id == 0 and model_name == 'DT':
-                        print(metric_row)
+                    metric_data.append(metric_row)
+                # Plot boxplot for the current metric across all models
+                ax.boxplot(metric_data, labels=model_names)
+                ax.set_title(f'{metric_name} for {group}{method}')
+            plt.tight_layout()
+            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")
     # --------------------------------------------------------------------------------------------------------
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