diff --git a/TrainerClass.py b/TrainerClass.py
index 8518bce..e5dfc28 100644
--- a/TrainerClass.py
+++ b/TrainerClass.py
@@ -121,7 +121,7 @@ class eNoseTrainer:
 
         return model
 
-    def train_and_score_model_keras(self, X_train, X_test, y_train, y_test, seed, label):
+    def train_and_score_model_keras(self, X_train, X_test, Y_train, Y_test, seed, label):
 #        set_random_seed(seed)
         ntrials = 6
         tuner = RandomSearch(
@@ -140,7 +140,7 @@ class eNoseTrainer:
         os.makedirs(search_dir, exist_ok=True)
         search_callback = TensorBoard(log_dir=search_dir)
         early_stopping_search = EarlyStopping(monitor='val_loss', patience=13, min_delta=0.005, start_from_epoch=7, restore_best_weights=True)
-        tuner.search(X_train, y_train, epochs=150, batch_size=10, validation_data=(X_test, y_test), callbacks=[early_stopping_search, search_callback])
+        tuner.search(X_train, Y_train, epochs=150, batch_size=10, validation_data=(X_test, Y_test), callbacks=[early_stopping_search, search_callback])
         best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
 
         self.trained += 1
@@ -172,23 +172,23 @@ class eNoseTrainer:
         else:
             return {}
 
-    def train_and_score_model(self, model, X_train, X_test, y_train, y_test):
+    def train_and_score_model(self, model, X_train, X_test, Y_train, Y_test):
         param_dist = self.get_tunable_params(model)
 
         cv = StratifiedShuffleSplit(n_splits=int(1/(2*self.ratio))+1, test_size=self.ratio, random_state=get_seed())
         grid_search = GridSearchCV(estimator=model, param_grid=param_dist, scoring='neg_mean_squared_error', cv=cv, verbose=10, n_jobs=-1)
 
-        grid_search.fit(X_train, y_train)
+        grid_search.fit(X_train, Y_train)
 
         optimized_model = grid_search.best_estimator_
         model_params = grid_search.best_params_
 
         y_aux = optimized_model.predict(X_train)
-        tmse = mean_squared_error(y_train, y_aux)
+        tmse = mean_squared_error(Y_train, y_aux)
 
         y_pred = optimized_model.predict(X_test)
-        mse = mean_squared_error(y_test, y_pred)
-        mae = mean_absolute_error(y_test, y_pred)
+        mse = mean_squared_error(Y_test, y_pred)
+        mae = mean_absolute_error(Y_test, y_pred)
         rmse = np.sqrt(mse)
 
         return tmse, mse, mae, rmse, optimized_model, model_params
@@ -205,9 +205,14 @@ class eNoseTrainer:
 
         node = os.uname()[1]
         X_xboost, Y_xboost, G_xboost = self.loader.load_dataset_xboost()
+        self.logger.debug(f"X_xboost: {X_xboost.shape}")
+        self.logger.debug(f"Y_xboost: {Y_xboost.shape}")
+        self.logger.debug(f"G_xboost: {G_xboost.shape}")
 
         discretizer = KBinsDiscretizer(n_bins=50*Y_xboost.shape[1], encode='ordinal', strategy='uniform')
-        Y_discrete = discretizer.fit_transform(Y_xboost)
+        discretizer.fit(Y_xboost)
+        Y_discrete = discretizer.transform(Y_xboost)
+        self.logger.debug(f"Y_discrete: {Y_discrete.shape}")
 
         gss = StratifiedGroupKFold(n_splits=int(1/self.ratio), shuffle=True, random_state=get_seed())
 
@@ -215,8 +220,12 @@ class eNoseTrainer:
         os.makedirs('{}/{}/{}'.format(self.name, self.target, dataset), exist_ok=True)
 
         for i, (train_index, test_index) in enumerate(gss.split(X_xboost, Y_discrete, G_xboost)):
-            X_train, X_test = X_xboost[train_index], Y_xboost[test_index]
-            y_train, y_test = Y_xboost[train_index], Y_xboost[test_index]
+            X_train, X_test = X_xboost[train_index], X_xboost[test_index]
+            Y_train, Y_test = Y_xboost[train_index], Y_xboost[test_index]
+            self.logger.debug(f"X_train: {X_train.shape}")
+            self.logger.debug(f"X_test: {X_test.shape}")
+            self.logger.debug(f"Y_train: {Y_train.shape}")
+            self.logger.debug(f"Y_test: {Y_test.shape}")
 
 
             for model in self.get_model_train():
@@ -229,7 +238,7 @@ class eNoseTrainer:
 
                 model_file = '{}/{}/{}/{}'.format(self.name, self.target, dataset, model_id )
 
-                tmse, mse, mae, rmse, optimized_model, model_params = self.train_and_score_model(model, X_train, X_test, y_train, y_test)
+                tmse, mse, mae, rmse, optimized_model, model_params = self.train_and_score_model(model, X_train, X_test, Y_train, Y_test)
 
                 ts = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
                 joblib.dump(optimized_model, model_file)
@@ -239,8 +248,8 @@ class eNoseTrainer:
                                     "Dataset": dataset,
                                     "Samples": Y_xboost.shape[0],
                                     "Target": self.target,
-                                    "Train Size": y_train.shape[0],
-                                    "Train Ratio": y_train.shape[0]/Y_xboost.shape[0],
+                                    "Train Size": Y_train.shape[0],
+                                    "Train Ratio": Y_train.shape[0]/Y_xboost.shape[0],
                                     "Ratio": self.ratio,
                                     "Model": model_id,
                                     "Params": json.dumps(model_params),
@@ -258,7 +267,7 @@ class eNoseTrainer:
         #     model_file = '{}/{}/DNN_{}'.format(self.name, label, seed )
         #     model_label = "{}".format(label)
         #
-        #     accuracy, specificity, recall, f1, roc_auc, optimized_model, parms = self.train_and_score_model_keras(X_train, X_test, y_train, y_test, seed, model_label)
+        #     accuracy, specificity, recall, f1, roc_auc, optimized_model, parms = self.train_and_score_model_keras(X_train, X_test, Y_train, Y_test, seed, model_label)
         #     ts = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
         #
         #     newrow = pd.DataFrame( [{"node": node,