diff --git a/scripts/ml/ml_functions/MultClfs_SIMPLE.py b/scripts/ml/ml_functions/MultClfs_SIMPLE.py
index b646da2..7e0fdba 100644
--- a/scripts/ml/ml_functions/MultClfs_SIMPLE.py
+++ b/scripts/ml/ml_functions/MultClfs_SIMPLE.py
@@ -144,10 +144,9 @@ scoreBT_mapD = {'bts_mcc'          : 'MCC'
 ############################
 # Multiple Classification - Model Pipeline
 def MultModelsCl(input_df, target
-                       #, skf_cv
                        , sel_cv
-                       #, blind_test_df
-                       #, blind_test_target
+                       , blind_test_df
+                       , blind_test_target
                        , tts_split_type 
 
                        , resampling_type = 'none' # default
@@ -230,37 +229,37 @@ def MultModelsCl(input_df, target
     #======================================================
     # Specify multiple Classification Models  
     #======================================================
-    models = [('AdaBoost Classifier'     , AdaBoostClassifier(**rs) )
-               # , ('Bagging Classifier'        , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
-               # , ('Decision Tree'             , DecisionTreeClassifier(**rs) ) 
-               #  , ('Extra Tree'                , ExtraTreeClassifier(**rs) )
-               #  , ('Extra Trees'               , ExtraTreesClassifier(**rs) ) 
-               #  , ('Gradient Boosting'         , GradientBoostingClassifier(**rs) )
-               #  , ('Gaussian NB'               , GaussianNB() )
-               #  , ('Gaussian Process'          , GaussianProcessClassifier(**rs) )
-               #  , ('K-Nearest Neighbors'       , KNeighborsClassifier() ) 
+    models = [('AdaBoost Classifier'            , AdaBoostClassifier(**rs) )
+                 , ('Bagging Classifier'        , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
+                 , ('Decision Tree'             , DecisionTreeClassifier(**rs) ) 
+                 , ('Extra Tree'                , ExtraTreeClassifier(**rs) )
+                 , ('Extra Trees'               , ExtraTreesClassifier(**rs) ) 
+                 , ('Gradient Boosting'         , GradientBoostingClassifier(**rs) )
+                 , ('Gaussian NB'               , GaussianNB() )
+                 , ('Gaussian Process'          , GaussianProcessClassifier(**rs) )
+                 , ('K-Nearest Neighbors'       , KNeighborsClassifier() ) 
                  , ('LDA'                       , LinearDiscriminantAnalysis() )
                  , ('Logistic Regression'       , LogisticRegression(**rs) )
-               # , ('Logistic RegressionCV'     , LogisticRegressionCV(cv = 3, **rs))
-               # , ('MLP'                       , MLPClassifier(max_iter = 500, **rs) ) 
-                #, ('Multinomial'               , MultinomialNB() )
-                # , ('Naive Bayes'               , BernoulliNB() )
-                # , ('Passive Aggresive'         , PassiveAggressiveClassifier(**rs, **njobs) )
-                # , ('QDA'                       , QuadraticDiscriminantAnalysis() )
+                 , ('Logistic RegressionCV'     , LogisticRegressionCV(cv = 3, **rs))
+                 , ('MLP'                       , MLPClassifier(max_iter = 500, **rs) ) 
+                 , ('Multinomial'               , MultinomialNB() )
+                 , ('Naive Bayes'               , BernoulliNB() )
+                 , ('Passive Aggresive'         , PassiveAggressiveClassifier(**rs, **njobs) )
+                 , ('QDA'                       , QuadraticDiscriminantAnalysis() )
                 # , ('Random Forest'             , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) ) 
-                # # , ('Random Forest2'            , RandomForestClassifier(min_samples_leaf = 5
-                #                                                         , n_estimators     = 1000
-                #                                                         , bootstrap        = True
-                #                                                         , oob_score        = True
-                #                                                         , **njobs
-                #                                                         , **rs
-                #                                                         , max_features     = 'auto') ) 
-             #     , ('Ridge Classifier'          , RidgeClassifier(**rs)  )
-             #     , ('Ridge ClassifierCV'        , RidgeClassifierCV(cv = 3) )          
-             #     , ('SVC'                       , SVC(**rs) ) 
-             #     , ('Stochastic GDescent'       , SGDClassifier(**rs, **njobs) )
+                 , ('Random Forest2'            , RandomForestClassifier(min_samples_leaf = 5
+                                                                         , n_estimators     = 1000
+                                                                         , bootstrap        = True
+                                                                         , oob_score        = True
+                                                                         , **njobs
+                                                                         , **rs
+                                                                         , max_features     = 'auto') ) 
+                  , ('Ridge Classifier'          , RidgeClassifier(**rs)  )
+                  , ('Ridge ClassifierCV'        , RidgeClassifierCV(cv = 3) )          
+                  , ('SVC'                       , SVC(**rs) ) 
+                  , ('Stochastic GDescent'       , SGDClassifier(**rs, **njobs) )
                   , ('XGBoost'                   , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
-             # 
+              
              ]
                 
     mm_skf_scoresD = {}
diff --git a/scripts/ml/ml_functions/test_func_singlegene.py b/scripts/ml/ml_functions/test_func_singlegene.py
index 5d06db8..6abccb4 100644
--- a/scripts/ml/ml_functions/test_func_singlegene.py
+++ b/scripts/ml/ml_functions/test_func_singlegene.py
@@ -45,10 +45,13 @@ spl_type = '70_30'
 #spl_type = '80_20'
 #spl_type = 'sl'
 
+#data_type  = "actual"
+data_type = "complete"
+
 df2 = split_tts(df
-          , data_type = 'actual'
+          , data_type = data_type
           , split_type = spl_type
-          , oversampling = False
+          , oversampling = True
           , dst_colname = 'dst'
           , target_colname = 'dst_mode'
           , include_gene_name = True
@@ -67,8 +70,8 @@ Counter(df2['y'])
 Counter(df2['y_bts'])
 
 
-fooD = MultModelsCl(input_df = df2['X']
-                , target = df2['y']
+fooD = MultModelsCl(input_df = df2['X_ros']
+                , target = df2['y_ros']
                 , sel_cv = skf_cv
                 , run_blind_test = True
                 , blind_test_df =  df2['X_bts']
@@ -83,7 +86,7 @@ fooD = MultModelsCl(input_df = df2['X']
 for k, v in fooD.items():
     print('\nModel:', k
           , '\nTRAIN MCC:', fooD[k]['test_mcc']
-          ,  '\nBTS MCC:' , fooD[k]['bts_mcc']
+          , '\nBTS MCC:' , fooD[k]['bts_mcc']
           , '\nDIFF:',fooD[k]['bts_mcc'] - fooD[k]['test_mcc'] )
     
 #%% CHECK SCALING
diff --git a/scripts/ml/ml_iterator.py b/scripts/ml/ml_iterator.py
index de8e62a..a633537 100755
--- a/scripts/ml/ml_iterator.py
+++ b/scripts/ml/ml_iterator.py
@@ -25,7 +25,7 @@ from GetMLData import *
 from SplitTTS import *
 
 # param dict for getmldata()
-combined_model_paramD = {'data_combined_model'   : False
+gene_model_paramD = {'data_combined_model'   : False
                     , 'use_or'                   : False
                     , 'omit_all_genomic_features': False
                     , 'write_maskfile'           : False
@@ -48,7 +48,7 @@ for gene, drug in ml_gene_drugD.items():
            , '\nDrug:', drug)
     gene_low = gene.lower()
     gene_dataD[gene_low] = getmldata(gene, drug
-                                     , **combined_model_paramD)
+                                     , **gene_model_paramD)
 
     for split_type in split_types:
         for data_type in split_data_types: