diff --git a/UQ_yc_RunAllClfs.py b/UQ_yc_RunAllClfs.py
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+++ b/UQ_yc_RunAllClfs.py
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+import pandas as pd
+import numpy as np
+import scipy as sp
+import time
+import sys
+import os
+import re
+import argparse
+from math import sqrt
+from scipy import stats
+import joblib
+# Alogorithm
+from xgboost.sklearn import XGBClassifier
+from sklearn import svm
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neural_network import MLPRegressor
+from sklearn.utils import all_estimators
+# Pre-processing
+from sklearn import preprocessing
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import make_classification
+from sklearn.pipeline import Pipeline, make_pipeline
+from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
+# Metric
+from sklearn.metrics import mean_squared_error, make_scorer, roc_auc_score, f1_score, matthews_corrcoef, accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report
+###############################################################################
+# TT imports
+from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
+from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
+from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
+from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
+from copy import deepcopy
+from sklearn import linear_model
+from sklearn import datasets
+from collections import Counter
+
+from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
+from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
+
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.gaussian_process import GaussianProcessClassifier, kernels
+from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
+
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
+from sklearn.neural_network import MLPClassifier
+
+from sklearn.svm import SVC
+from xgboost import XGBClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+
+from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_transformer
+
+from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
+from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
+
+from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
+from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
+
+from sklearn.pipeline import Pipeline, make_pipeline
+
+from sklearn.feature_selection import RFE, RFECV
+
+import itertools
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from statistics import mean, stdev, median, mode
+
+from imblearn.over_sampling import RandomOverSampler
+from imblearn.under_sampling import RandomUnderSampler
+from imblearn.over_sampling import SMOTE
+from sklearn.datasets import make_classification
+from imblearn.combine import SMOTEENN
+from imblearn.combine import SMOTETomek
+
+from imblearn.over_sampling import SMOTENC
+from imblearn.under_sampling import EditedNearestNeighbours
+from imblearn.under_sampling import RepeatedEditedNearestNeighbours
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.base import BaseEstimator
+from sklearn.impute import KNNImputer as KNN
+import json
+##############################################################################
+
+# other vars
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+
+scoring_fn =  ({'accuracy'      : make_scorer(accuracy_score)
+                 , 'fscore'     : make_scorer(f1_score)
+                 , 'mcc'        : make_scorer(matthews_corrcoef)
+                 , 'precision'  : make_scorer(precision_score)
+                 , 'recall'     : make_scorer(recall_score)
+                 , 'roc_auc'    : make_scorer(roc_auc_score)
+                 , 'jcc'        : make_scorer(jaccard_score)
+            }) 
+  
+skf_cv = StratifiedKFold(n_splits = 10
+                          #, shuffle = False, random_state= None)
+                           , shuffle = True,**rs)
+
+rskf_cv = RepeatedStratifiedKFold(n_splits = 10
+                                  , n_repeats = 3
+                                  , **rs)
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
+#%% YC 
+#def run_all_ML(input_pd, target_label, bts_input, bts_target, var_type):
+def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, preprocess = True, var_type = 'numerical'):
+
+    #y = input_pd[target_label]
+    #X = input_pd.drop(target_label,axis=1)
+    y = target_label
+    X = input_pd
+    
+    # Determine categorical and numerical features
+    numerical_ix = input_pd.select_dtypes(include=['int64', 'float64']).columns
+    numerical_ix
+    categorical_ix = input_pd.select_dtypes(include=['object', 'bool']).columns
+    categorical_ix    
+
+    # Determine preprocessing steps ~ var_type
+    if var_type == 'numerical':
+        t = [('num', MinMaxScaler(), numerical_ix)]
+
+    if var_type == 'categorical':
+        t = [('cat', OneHotEncoder(), categorical_ix)]
+    
+    if var_type == 'mixed':
+        t = [('num', MinMaxScaler(), numerical_ix)
+              , ('cat', OneHotEncoder(), categorical_ix)]
+        
+    col_transform = ColumnTransformer(transformers = t
+                                       , remainder='passthrough')
+    result_pd     = pd.DataFrame()
+    result_bts_pd = pd.DataFrame()
+    #results_btsD = {}
+    results_all = {}
+    
+    for name, algorithm in all_estimators(type_filter="classifier"):
+        try:
+            estmator = algorithm()
+            temp_pd = pd.DataFrame()
+            temp_cm = pd.DataFrame()
+
+            # # orig
+            # pipe = Pipeline([
+            #     ("model"    , algorithm())
+            # ])
+            
+            # turn on and off preprocessing
+            if preprocess == True:
+                pipe = Pipeline([
+                    ('prep'     , col_transform),
+                    ("model"    , algorithm()) 
+                    ])
+            else:   
+                pipe = Pipeline([
+                    ("model"    , algorithm())
+                ])
+                
+            # cross val scores
+            y_pred   = cross_val_predict(pipe, X, y, cv = 10, **njobs)
+# CHANGE to cross_validate: ONLY THEN CAN YOU TRUST
+            # y_pred   = cross_validate(pipe, X, y
+            #                           , cv = 10
+            #                           , scoring = scoring_fn
+            #                           , **njobs)
+
+            _mcc     = round(matthews_corrcoef(y_pred, y), 3)
+            _bacc    = round(balanced_accuracy_score(y_pred, y), 3)
+            _f1      = round(f1_score(y_pred, y), 3)
+            _roc_auc = round(roc_auc_score(y_pred, y), 3)
+            _tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel()
+            
+            # result_pd = result_pd.append(pd.DataFrame(np.column_stack([name
+            #                                                            , _tp, _tn
+            #                                                            , _fp , _fn
+            #                                                            , _roc_auc
+            #                                                            , _mcc
+            #                                                            , _bacc, _f1]),\
+            #                                           columns=['estimator', 'TP', 'TN', 'FP', 'FN',
+            #                                                    'roc_auc', 'matthew', 'bacc', 'f1']),\
+            #                              ignore_index=True)
+            
+            result_pd = result_pd.append(pd.DataFrame(np.column_stack([name
+                                                                       , _mcc
+                                                                       , _roc_auc
+                                                                       , _bacc, _f1
+                                                                       , _tp, _tn
+                                                                       , _fp , _fn]),\
+                                                      columns=['estimator', 'matthew', 'roc_auc', 'bacc', 'f1',\
+                                                               'TP', 'TN', 'FP', 'FN']),\
+                                         ignore_index=True)            
+            #=========================
+            # Blind test: BTS results
+            #=========================
+            #Build the final results with all scores for a feature selected model
+            pipe.fit(input_pd, target_label)
+            bts_predict = pipe.predict(blind_test_input_df)
+
+            bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
+            print('\nMCC on Blind test:'     , bts_mcc_score)
+            #print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
+            
+            _mccBTS     = round(matthews_corrcoef(bts_predict, blind_test_target), 3)
+            _baccBTS    = round(balanced_accuracy_score(bts_predict, blind_test_target), 3)
+            _f1BTS      = round(f1_score(bts_predict, blind_test_target), 3)
+            _roc_aucBTS = round(roc_auc_score(bts_predict, blind_test_target), 3)
+            _tnBTS, _fpBTS, _fnBTS, _tpBTS = confusion_matrix(bts_predict, blind_test_target).ravel()
+            
+            # result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
+            #                                                                 , _tpBTS, _tnBTS
+            #                                                                 , _fpBTS, _fnBTS
+            #                                                                 , _roc_aucBTS
+            #                                                                 , _mccBTS
+            #                                                                 , _baccBTS, _f1BTS]),\
+            #                                           columns=['estimator', 'TP', 'TN', 'FP', 'FN',
+            #                                                     'roc_auc', 'matthew', 'bacc', 'f1']),\
+            #                               ignore_index=True)
+            result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
+                                                                            , _mccBTS
+                                                                            , _roc_aucBTS
+                                                                            , _baccBTS, _f1BTS
+                                                                            , _tpBTS, _tnBTS
+                                                                            , _fpBTS, _fnBTS]),\
+                                                      columns=['estimator','matthew', 'roc_auc', 'bacc', 'f1',\
+                                                               'TP', 'TN', 'FP', 'FN']),\
+                                          ignore_index=True)            
+      
+            
+            results_all['CrossValResultsDF']   = result_pd
+            results_all['BlindTestResultsDF']  = result_bts_pd
+
+        except Exception as e:
+            print("XXXGot an error while running {}".format(name))
+            print(e)
+            
+            
+    #return(result_pd)    
+    return(results_all)
+    
+
+#%% CALL function
+#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+# Baseline_data
+
+YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
+CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
+BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# from sklearn.utils import all_estimators
+# for name, algorithm in all_estimators(type_filter="classifier"):
+#     clf = algorithm()
+#     print('Name:', name, '\nAlgo:', clf)
+
+# Random Oversampling
+YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
+CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
+BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
+BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# Random Undersampling
+YC_resD_rus = run_all_ML(input_pd=X_rus, target_label=y_rus, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
+CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
+BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
+BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# Random Oversampling+Undersampling
+YC_resD_rouC = run_all_ML(input_pd=X_rouC, target_label=y_rouC, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
+CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
+BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
+BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# SMOTE NC
+YC_resD_smnc = run_all_ML(input_pd=X_smnc, target_label=y_smnc, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
+CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
+BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
+BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
+##############################################################################
+#============================================
+# BASELINE models with dissected featues
+#============================================
+# Genomics
+yC_gf = run_all_ML(input_pd=X[X_genomicFN], target_label=y, blind_test_input_df=X_bts[X_genomicFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_gfCT_baseline= yC_gf['CrossValResultsDF']
+yc_gfCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_gfBT_baseline = yC_gf['BlindTestResultsDF']
+yc_gfBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# Evolutionary
+yC_ev = run_all_ML(input_pd=X[X_evolFN], target_label=y, blind_test_input_df=X_bts[X_evolFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_evCT_baseline= yC_ev['CrossValResultsDF']
+yc_evCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_evBT_baseline = yC_ev['BlindTestResultsDF']
+yc_evBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# strucF:All
+yC_sfall = run_all_ML(input_pd=X[X_strFN], target_label=y, blind_test_input_df=X_bts[X_strFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_sfallCT_baseline= yC_sfall['CrossValResultsDF']
+yc_sfallCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_sfallBT_baseline = yC_sfall['BlindTestResultsDF']
+yc_sfallBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# strucF:Common ONLY
+yC_sfco= run_all_ML(input_pd=X[common_cols_stabiltyN], target_label=y
+                   , blind_test_input_df=X_bts[common_cols_stabiltyN]
+                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_sfcoCT_baseline= yC_sfco['CrossValResultsDF']
+yc_sfcoCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_sfcoBT_baseline = yC_sfco['BlindTestResultsDF']
+yc_sfcoBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# strucF:common_stability + foldX_cols i.e interaction
+yC_fxss= run_all_ML(input_pd=X[common_cols_stabiltyN+foldX_cols], target_label=y
+                   , blind_test_input_df=X_bts[common_cols_stabiltyN+foldX_cols]
+                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_fxssCT_baseline= yC_fxss['CrossValResultsDF']
+yc_fxssCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_fxssBT_baseline = yC_fxss['BlindTestResultsDF']
+yc_fxssBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+# categorical
+yC_cat= run_all_ML(input_pd=X[categorical_FN], target_label=y
+                   , blind_test_input_df=X_bts[categorical_FN]
+                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
+yc_catCT_baseline= yC_cat['CrossValResultsDF']
+yc_catCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+yc_catBT_baseline = yC_cat['BlindTestResultsDF']
+yc_catBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
+
+