diff --git a/UQ_FS_eg.py b/UQ_FS_eg.py
new file mode 100644
index 0000000..80fa6a3
--- /dev/null
+++ b/UQ_FS_eg.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 21 02:52:36 2022
+
+@author: tanu
+"""
+# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.datasets import make_classification
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import GridSearchCV
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import SelectKBest, mutual_info_classif
+#pd.options.plotting.backend = "plotly"
+X_eg, y_eg = make_classification(n_samples=1000,
+                           n_features=30,
+                           n_informative=5,
+                           n_redundant=5,
+                           n_classes=2,
+                           random_state=123)
+
+pipe = Pipeline([('scaler', StandardScaler()),
+                 ('selector', SelectKBest(mutual_info_classif, k=9)),
+                 ('classifier', LogisticRegression())])
+
+search_space = [{'selector__k': [5, 6, 7, 10]},
+                {'classifier': [LogisticRegression()],
+                 'classifier__C': [0.01,1.0],
+                 'classifier__solver': ['saga', 'lbfgs']},
+                {'classifier': [RandomForestClassifier(n_estimators=100)],
+                 'classifier__max_depth': [5, 10, None]},
+                {'classifier': [KNeighborsClassifier()],
+                 'classifier__n_neighbors': [3, 7, 11],
+                 'classifier__weights': ['uniform', 'distance']}]
+
+
+
+clf = GridSearchCV(pipe, search_space, cv=10, verbose=0)
+
+clf2 = clf.fit(X_eg, y_eg)
+clf2._check_feature_names
+clf2.best_estimator_.named_steps['selector'].n_features_in_
+
+clf2.best_estimator_ #n of best features
+clf2.best_params_ 
+clf2.best_estimator_.get_params
+clf2.get_feature_names()
+
+
+
+clf3 = clf2.best_estimator_ # 
+clf3._final_estimator
+clf3._final_estimator.C
+clf3._final_estimator.solver
+
+
+fs_bmod = clf2.best_estimator_
+print('\nbest model with feature selection:', fs_bmod)
+
+
diff --git a/UQ_LR_FS_p2.py b/UQ_LR_FS_p2.py
new file mode 100644
index 0000000..05ea68a
--- /dev/null
+++ b/UQ_LR_FS_p2.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May 16 05:59:12 2022
+
+@author: tanu
+"""
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 15 11:09:50 2022
+
+@author: tanu
+"""
+#%% Import libraries, data, and scoring func: UQ_pnca_ML.py
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
+
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+        #feature = RFECV()
+    ):
+        """
+        A Custom BaseEstimator that can switch between classifiers.
+        :param estimator: sklearn object - The classifier
+        """ 
+        self.estimator = estimator
+    
+    def fit(self, X, y=None, **kwargs):
+        self.estimator.fit(X, y)
+        return self
+
+    def predict(self, X, y=None):
+        return self.estimator.predict(X)
+    
+    def predict_proba(self, X):
+        return self.estimator.predict_proba(X)
+    
+    def score(self, X, y):
+        return self.estimator.score(X, y)
+    
+parameters = [
+    # {'feature__fs__estimator': LogisticRegression(**rs)
+    #  , 'feature__fs__cv': [10]
+    #  , 'feature__fs__scoring': ['matthews_corrcoef']
+    #  },
+    
+    {
+        'clf__estimator': [LogisticRegression(**rs)],
+        'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        #'clf__estimator__C': np.logspace(0, 4, 10),
+        'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['saga']
+    }#,
+    # {
+    #     'clf__estimator': [MODEL2(**rs)],
+    #     #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+    #     'clf__estimator__C': np.logspace(0, 4, 10),
+    #     'clf__estimator__penalty': ['l2', 'none'],
+    #     'clf__estimator__max_iter': list(range(100,800,100)),
+    #     'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
+    # }, 
+]    
+#%% Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler())
+#   , ('fs',  RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
+   , ('selector', SelectKBest(mutual_info_classif, k=6))
+   , ('clf', ClfSwitcher())
+])
+
+#%% Grid search i.e hyperparameter tuning and refitting on mcc
+mod_fs = GridSearchCV(pipeline
+                    , parameters
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+#%% Fit 
+mod_fs_fit = mod_fs.fit(X, y)
+mod_fs_fbm = mod_fs_fit.best_params_
+mod_fs_fbmr = mod_fs_fit.cv_results_
+mod_fs_fbs = mod_fs_fit.best_score_
+print('Best model:\n', mod_fs_fbm)
+print('Best models score:\n', mod_fs_fbs, ':' , round(mod_fs_fbs, 2))
+
+#print('\nMean test score from fit results:', round(mean(mod_fs_fbmr['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(mod_fs_fbmr['mean_test_mcc']),2))
+
+###############################################################################
+#%% Blind test
+######################################
+#         Blind test
+######################################
+test_predict = mod_fs_fit.predict(X_bts)
+print(test_predict)
+print('\nMCC on Blind test:'     , round(matthews_corrcoef(y_bts, test_predict),2))
+print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, test_predict),2))