Fixed bug in hparam tuning for XGBoost + added additional metrics to XGBoost reports

protac_degradation_predictor/optuna_utils_xgboost.py

@@ -90,7 +90,7 @@ def train_and_evaluate_xgboost(
     )
 
     # Evaluate model
-    val_pred = model.inplace_predict(dval)
+    val_pred = model.predict(dval)
     val_pred_binary = (val_pred > 0.5).astype(int)
     metrics = {
         'val_acc': accuracy_score(y_val, val_pred_binary),
@@ -102,7 +102,7 @@ def train_and_evaluate_xgboost(
     preds = {'val_pred': val_pred}
 
     if test_df is not None:
-        test_pred = model.inplace_predict(dtest)
+        test_pred = model.predict(dtest)
         test_pred_binary = (test_pred > 0.5).astype(int)        
         metrics.update({
             'test_acc': accuracy_score(y_test, test_pred_binary),
@@ -335,6 +335,7 @@ def xgboost_hyperparameter_tuning_and_training(
 
     # Get the majority vote for the test predictions
     majority_vote_metrics = get_majority_vote_metrics(test_preds, test_df, active_label)
+    majority_vote_metrics.update(get_dataframe_stats(train_val_df, test_df=test_df, active_label=active_label))
     majority_vote_report = pd.DataFrame([majority_vote_metrics])
     majority_vote_report['model_type'] = 'XGBoost'