diff --git a/scripts/ml/ml_functions/SplitTTS.py b/scripts/ml/ml_functions/SplitTTS.py
index 64035ab..ad417e4 100644
--- a/scripts/ml/ml_functions/SplitTTS.py
+++ b/scripts/ml/ml_functions/SplitTTS.py
@@ -47,7 +47,7 @@ njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number
 # NOTE: split_type 'none_with_bts' and 'none_only': WORK on complete data ONLY irrespective of data_type
 def split_tts(ml_input_data
               , data_type      = ['actual', 'complete']
-              , split_type     = ['70_30', '80_20', 'sl', 'none_with_bts', 'none_only', 'reverse']
+              , split_type     = ['70_30', '80_20', 'sl', 'rt', 'none_bts', 'none']
               , oversampling   = True
               , dst_colname    = 'dst'# determine how to subset the actual vs reverse data
               , target_colname = 'dst_mode'
@@ -116,19 +116,7 @@ def split_tts(ml_input_data
     if split_type == 'sl':
         tts_test_size = 1/np.sqrt(x_ncols)
         train_sl = 1 - tts_test_size # for reference
-        
-    if split_type == 'none_with_bts': # always on complete data
-        temp_df_train = ml_input_data[ml_input_data[dst_colname].notna()]
-        X = temp_df_train.drop(cols_to_dropL, axis = 1)
-        y = temp_df_train[target_colname]
-        
-        temp_df_bts = ml_input_data[ml_input_data[dst_colname].isna()]
-        X_bts       = temp_df_bts.drop(cols_to_dropL, axis = 1)
-        y_bts       = temp_df_bts[target_colname]
-        
-        n_test_data_size = len(X) + len(X_bts)
-        test_data_shape = X_bts.shape
-
+    
     if split_type == 'rt': # always on complete data
         temp_df_train = ml_input_data[ml_input_data[dst_colname].isna()]
         X = temp_df_train.drop(cols_to_dropL, axis = 1)
@@ -138,10 +126,22 @@ def split_tts(ml_input_data
         X_bts       = temp_df_bts.drop(cols_to_dropL, axis = 1)
         y_bts       = temp_df_bts[target_colname]
         
+        n_test_data_size = len(X) + len(X_bts)
+        test_data_shape = X_bts.shape    
+        
+    if split_type == 'none_bts': # always on complete data
+        temp_df_train = ml_input_data[ml_input_data[dst_colname].notna()]
+        X = temp_df_train.drop(cols_to_dropL, axis = 1)
+        y = temp_df_train[target_colname]
+        
+        temp_df_bts = ml_input_data[ml_input_data[dst_colname].isna()]
+        X_bts       = temp_df_bts.drop(cols_to_dropL, axis = 1)
+        y_bts       = temp_df_bts[target_colname]
+        
         n_test_data_size = len(X) + len(X_bts)
         test_data_shape = X_bts.shape
                 
-    if split_type == 'none_only':
+    if split_type == 'none': # always on complete data
         temp_df_train = ml_input_data.copy() # always complete
         X = temp_df_train.drop(cols_to_dropL, axis = 1)
         y = temp_df_train[target_colname]
@@ -163,10 +163,10 @@ def split_tts(ml_input_data
     yc1 = Counter(y)
     yc1_ratio = yc1[0]/yc1[1]
     
-    if split_type in ['none_only']:
+    if split_type in ['none']:
         outDict.update({'X'       : X
-                , 'y'             : y
-                })
+                        , 'y'     : y })
+        
         yc2              = "NO Blind test data"
         yc2_ratio        = "NO Blind test data"
         n_test_data_size = "NO Blind test data"
diff --git a/scripts/ml/ml_iterator.py b/scripts/ml/ml_iterator.py
index 805126f..6daf527 100755
--- a/scripts/ml/ml_iterator.py
+++ b/scripts/ml/ml_iterator.py
@@ -41,9 +41,9 @@ gene_model_paramD = {'data_combined_model'       : False
 ###############################################################################
 #ml_genes = ["pncA", "embB", "katG", "rpoB", "gid"]
 
-ml_gene_drugD = {#'pncA'   : 'pyrazinamide'
-                 # 'embB' : 'ethambutol'
-                 'katG' : 'isoniazid'
+ml_gene_drugD = {'pncA'   : 'pyrazinamide'
+                 , 'embB' : 'ethambutol'
+                 , 'katG' : 'isoniazid'
                  , 'rpoB' : 'rifampicin'
                  , 'gid'  : 'streptomycin'
                  }