added manual print statements

streamline/modeling/basemodel.py

@@ -88,6 +88,7 @@ def optimize(self, x_train, y_train, n_trails, timeout, feature_names=None):
 
         if not self.is_single:
             optuna.logging.set_verbosity(optuna.logging.WARNING)
+            logging.info("Running Optuna Hyperparameter Optimization")
             self.study = optuna.create_study(direction=self.metric_direction, sampler=self.sampler)
             if self.model_name in ["Extreme Gradient Boosting", "Light Gradient Boosting"]:
                 pos_inst = sum(y_train)
@@ -124,6 +125,8 @@ def optimize(self, x_train, y_train, n_trails, timeout, feature_names=None):
             self.params = best_trial.params
             self.model = copy.deepcopy(self.model).set_params(**best_trial.params)
         else:
+            logging.info("Only one set of hyperparameters")
+            logging.info("Skipping Optuna Hyperparameter Optimization")
             self.params = copy.deepcopy(self.param_grid)
             for key, value in self.param_grid.items():
                 self.params[key] = value[0]

streamline/models/learning_based.py

@@ -91,6 +91,8 @@ def __init__(self, cv_folds=3, scoring_metric='balanced_accuracy',
         if len(self.param_grid['learning_iterations']) == 1 and len(self.param_grid['N']) == 1 and \
             len(self.param_grid['nu']) == 1:
             self.param_grid['rule_compaction'] = ['QRF', ]
+        logging.info("Printing Rule Compaction Parameters")
+        logging.info(str(self.param_grid['rule_compaction']))
         self.param_grid['expert_knowledge'] = expert_knowledge
         self.param_grid['random_state'] = [random_state, ]
         self.small_name = "ExSTraCS"