added pratice and feature selection scripts for LR and hyperparam for all classification models as separate scripts in uq_ml_models

UQ_LR_FS.py

@@ -0,0 +1,299 @@
+#!/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 libs
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import GridSearchCV
+from sklearn import datasets
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.svm import SVC
+
+from sklearn.base import BaseEstimator
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+from xgboost import XGBClassifier
+#####################
+from sklearn.feature_selection import RFE
+from sklearn.feature_selection import RFECV
+from sklearn.linear_model import LogisticRegression
+from sklearn.feature_selection import SelectFromModel
+from sklearn.feature_selection import SequentialFeatureSelector
+
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+#%%
+
+y.to_frame().value_counts().plot(kind = 'bar')
+blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
+
+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)
+                 ,  'jaccard'   : make_scorer(jaccard_score)
+            })    
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
+
+#%% Logistic Regression + hyperparam + FS: BaseEstimator: ClfSwitcher()
+model_lr = LogisticRegression(**rs)
+model_rfecv = RFECV(estimator = model_lr
+                    , cv = skf_cv
+                    #, cv = 10
+                    , scoring = 'matthews_corrcoef'
+                    )
+
+model_rfecv = SequentialFeatureSelector(estimator = model_lr
+                                          , n_features_to_select = 'auto'
+                                          , tol = None
+#                                         , cv = 10
+                                          , cv = skf_cv
+#                                          , direction ='backward'
+                                          , direction ='forward'
+                                          , **njobs)
+
+# param_grid = [
+#       { 'C': np.logspace(0, 4, 10),
+#          'penalty': ['l1', 'l2'],
+#          'max_iter': [100],
+#          'solver': ['saga']
+#          }#,
+#      # { 'C': [1],
+#      #    'penalty': ['l1'],
+#      #    'max_iter': [100],
+#      #    'solver': ['saga']
+#      #    }
+# ]    
+
+param_grid2 = [
+    {
+        #'clf__estimator': [LogisticRegression(**rs)],
+        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'C': np.logspace(0, 4, 10),
+        'penalty': ['none', 'l1', 'l2', 'elasticnet'],
+        'max_iter': list(range(100,800,100)),
+        'solver': ['saga']
+    },
+    {
+        #'clf__estimator': [LogisticRegression(**rs)],
+        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'C': np.logspace(0, 4, 10),
+        'penalty': ['l2', 'none'],
+        'max_iter': list(range(100,800,100)),
+        'solver': ['newton-cg', 'lbfgs', 'sag']
+    }, 
+    {
+        #'clf__estimator': [LogisticRegression(**rs)],
+        #'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'C': np.logspace(0, 4, 10),
+        'penalty': ['l1', 'l2'],
+        'max_iter': list(range(100,800,100)),
+        'solver': ['liblinear']
+    }
+
+]    
+
+#-------------------------------------------------------------------------------
+# Grid search CV + FS
+gscv_lr = GridSearchCV(model_lr
+                    , param_grid2
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , return_train_score = False
+                    , verbose = 3
+                    , **njobs)
+
+#------------------------------------------------------------------------------
+# Create pipeline
+pipeline = Pipeline([('pre', MinMaxScaler())
+                     #, ('feature_selection', sfs_selector)
+                     , ('feature_selection', model_rfecv )
+                     , ('clf', gscv_lr)])
+  
+# Fit
+lr_fs = pipeline.fit(X,y)
+
+pipeline.predict(X_bts)
+lr_fs.predict(X_bts)
+
+test_predict = pipeline.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+#y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_bts, test_predict))
+print(matthews_corrcoef(y_bts, test_predict))
+
+###############################################################################
+#####################
+# Feature selection: AFTER model selection
+# https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172
+
+###############################################################################
+
+######################################
+# Blind test
+######################################
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )) 
+
+#test_predict = gscv_lr_fit.predict(X_bts)
+test_predict =  pipeline.predict(X_bts)
+test_predict_fs = sfs_selector.predict(X_bts)
+
+print(test_predict)
+
+print(accuracy_score(y_bts, test_predict))
+print(matthews_corrcoef(y_bts, test_predict))
+
+# create a dict with all scores
+lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
+               'bts_fscore':None
+               , 'bts_mcc':None
+               , 'bts_precision':None
+               , 'bts_recall':None
+               , 'bts_accuracy':None
+               , 'bts_roc_auc':None
+               , 'bts_jaccard':None }
+lr_bts_dict
+lr_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+lr_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+lr_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+lr_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+lr_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+lr_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+lr_bts_dict
+
+# Create a df from dict with all scores
+lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
+lr_bts_df.columns = ['Logistic_Regression']
+print(lr_bts_df)
+
+# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
+# d2
+# def Merge(dict1, dict2):
+#     res = {**dict1, **dict2}
+#     return res
+# d3 = Merge(d2, lr_bts_dict)
+# d3
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_lr_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+lr_bts_df.columns
+lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
+lr_output
+
+# Format the combined df
+# Drop the best_model_params row from lr_output
+lr_df = lr_output.drop([0], axis = 0)
+lr_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+# FIXME: confusion matrix
+
+print(confusion_matrix(y_bts, test_predict))
+#%% Feature selection
+
+#####################
+# Feature selection: AFTER model selection?
+# ADD that within the loop
+# https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172
+#####################
+from sklearn.feature_selection import RFE
+from sklearn.linear_model import LogisticRegression
+from sklearn.feature_selection import SelectFromModel
+from sklearn.feature_selection import SequentialFeatureSelector
+
+# RFE: ~ model coef or feature_importance
+rfe_selector = RFE(estimator = LogisticRegression(**rs
+                                                  , penalty='l1'
+                                                  , solver='saga'
+                                                  , max_iter = 100
+                                                  , C= 1.0)
+                   , n_features_to_select = None # median by default
+                   , step = 1)
+rfe_selector.fit(X, y)
+rfe_fs = X.columns[rfe_selector.get_support()]
+print('\nFeatures selected from Recursive Feature Elimination:', len(rfe_fs)
+      , '\nThese are:', rfe_fs)
+
+# SFM: ~ model coef or feature_importance
+sfm_selector = SelectFromModel(estimator = LogisticRegression(**rs
+                                                  , penalty='l1'
+                                                  , solver='saga'
+                                                  , max_iter = 100
+                                                  , C= 1.0)
+                               , threshold = "median"
+                               , max_features = None ) # median by default
+sfm_selector.fit(X, y)
+sfm_fs = X.columns[sfm_selector.get_support()]
+
+print('\nFeatures selected from Select From Model:', len(sfm_fs)
+      , '\nThese are:', sfm_fs)
+
+# SFS:ML CV
+sfs_selector = SequentialFeatureSelector(estimator = LogisticRegression(**rs
+                                                  , penalty='l1'
+                                                  , solver='saga'
+                                                  , max_iter = 100
+                                                  , C = 1.0)
+                                         , n_features_to_select = 'auto'
+                                         , tol = None
+                                         , cv = 10
+                                         #, cv = skf_cv
+#                                         , direction ='backward'
+                                         , direction ='forward'
+
+                                         , **njobs)
+sfs_selector.fit(X, y)
+sfsb_fs = X.columns[sfs_selector.get_support()]
+
+print('\nFeatures selected from Sequential Feature Selector (Greedy):', len(sfsb_fs)
+      , '\nThese are:', sfsb_fs)
+
+#Features selected from Sequential Feature Selector (Greedy, Backward): 7 [CV = SKF_CV]
+#These are: Index(['ligand_distance', 'duet_stability_change', 'ddg_foldx', 'deepddg',
+#      'contacts', 'rd_values', 'snap2_score']
+
+#Features selected from Sequential Feature Selector (Greedy, Backward): 7 [CV=10]
+#These are: Index(['ligand_distance', 'deepddg', 'contacts', 'rsa', 'kd_values',
+#       'rd_values', 'maf']
+
+#-----
+# Features selected from Sequential Feature Selector (Greedy, Forward): 6 [CV = SKF_CV]
+# These are: Index(['ligand_distance', 'ddg_dynamut2', 'rsa', 'kd_values', 'rd_values', 'maf']
+
+# Features selected from Sequential Feature Selector (Greedy, Forward): 6 [CV  = 10]
+#These are: Index(['duet_stability_change', 'deepddg', 'ddg_dynamut2', 'rsa', 'kd_values', 'maf']
+###############################################################################

UQ_LR_p1.py

@@ -0,0 +1,211 @@
+#!/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 libs
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import GridSearchCV
+from sklearn import datasets
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.svm import SVC
+
+from sklearn.base import BaseEstimator
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+from xgboost import XGBClassifier
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+#%% Get train-test split and scoring functions
+# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
+#                                                     , num_df_wtgt['mutation_class']
+#                                                     , test_size    = 0.33
+#                                                     , random_state = 2
+#                                                     , shuffle      = True
+#                                                     , stratify     = num_df_wtgt['mutation_class'])
+
+y.to_frame().value_counts().plot(kind = 'bar')
+blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
+
+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)
+                 ,  'jaccard'   : make_scorer(jaccard_score)
+            })    
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
+
+#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        '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': [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': ['l2', 'none'],
+    #     'clf__estimator__max_iter': list(range(100,800,100)),
+    #     'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
+    # }, 
+    # {
+    #     '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': ['l1', 'l2'],
+    #     'clf__estimator__max_iter': list(range(100,800,100)),
+    #     'clf__estimator__solver': ['liblinear']
+    # }
+
+]    
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_lr = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_lr_fit = gscv_lr.fit(X, y)
+gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
+gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
+
+print('Best model:\n', gscv_lr_fit_be_mod)
+print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
+
+#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+
+###############################################################################
+
+
+######################################
+# Blind test
+######################################
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )) 
+
+test_predict = gscv_lr_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_bts, test_predict))
+print(matthews_corrcoef(y_bts, test_predict))
+
+# create a dict with all scores
+lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
+               'bts_fscore':None
+               , 'bts_mcc':None
+               , 'bts_precision':None
+               , 'bts_recall':None
+               , 'bts_accuracy':None
+               , 'bts_roc_auc':None
+               , 'bts_jaccard':None }
+lr_bts_dict
+lr_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+lr_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+lr_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+lr_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+lr_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+lr_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+lr_bts_dict
+
+# Create a df from dict with all scores
+lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
+lr_bts_df.columns = ['Logistic_Regression']
+print(lr_bts_df)
+
+# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
+# d2
+# def Merge(dict1, dict2):
+#     res = {**dict1, **dict2}
+#     return res
+# d3 = Merge(d2, lr_bts_dict)
+# d3
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_lr_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+lr_bts_df.columns
+lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
+lr_output
+
+# Format the combined df
+# Drop the best_model_params row from lr_output
+lr_df = lr_output.drop([0], axis = 0)
+lr_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+# FIXME: confusion matrix
+print(confusion_matrix(y_bts, test_predict))
+
+cm = confusion_matrix(y_bts, test_predict)

uq_ml_models/UQ_ABC.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [AdaBoostClassifier(**rs)]      
+       , 'clf__estimator__n_estimators': [none, 1, 2]
+       , 'clf__estimator__base_estiamtor'  : ['None', 1*SVC(), 1*KNeighborsClassifier()]
+       #, 'clf__estimator___splitter'  :   ["best", "random"]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_abc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_abc_fit  = gscv_abc.fit(X, y)
+
+gscv_abc_fit_be_mod = gscv_abc_fit.best_params_
+gscv_abc_fit_be_res = gscv_abc_fit.cv_results_
+
+print('Best model:\n', gscv_abc_fit_be_mod)
+print('Best models score:\n', gscv_abc_fit.best_score_, ':' , round(gscv_abc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_abc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_abc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_abc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+abc_bts_dict = {#'best_model': list(gscv_abc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+abc_bts_dict
+abc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+abc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+abc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+abc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+abc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+abc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+abc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+abc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(abc_bts_dict, orient = 'index', columns = 'best_model')
+
+abc_bts_df = pd.DataFrame.from_dict(abc_bts_dict,orient = 'index')
+abc_bts_df.columns = ['Logistic_Regression']
+print(abc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_abc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+abc_bts_df.columns
+abc_output = pd.concat([model_params_df, abc_bts_df], axis = 0)
+abc_output
+
+# Format the combined df
+# Drop the best_model_params row from abc_output
+abc_df = abc_output.drop([0], axis = 0)
+abc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_BC.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [BaggingClassifier(**rs
+                                                  , **njobs
+                                                  , bootstrap = True
+                                                  , oob_score = True)],
+       , 'clf__estimator__n_estimators'    : [10, 100, 1000]
+       # If None, then the base estimator is a DecisionTreeClassifier.
+       , 'clf__estimator__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()']# if none, DT is used
+       , 'clf__estimator__gamma': ['scale', 'auto'] 
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_bc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_bc_fit  = gscv_bc.fit(X, y)
+
+gscv_bc_fit_be_mod = gscv_bc_fit.best_params_
+gscv_bc_fit_be_res = gscv_bc_fit.cv_results_
+
+print('Best model:\n', gscv_bc_fit_be_mod)
+print('Best models score:\n', gscv_bc_fit.best_score_, ':' , round(gscv_bc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_bc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_bc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_bc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+bc_bts_dict = {#'best_model': list(gscv_bc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+bc_bts_dict
+bc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+bc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+bc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+bc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+bc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+bc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+bc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+bc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(bc_bts_dict, orient = 'index', columns = 'best_model')
+
+bc_bts_df = pd.DataFrame.from_dict(bc_bts_dict,orient = 'index')
+bc_bts_df.columns = ['Logistic_Regression']
+print(bc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_bc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+bc_bts_df.columns
+bc_output = pd.concat([model_params_df, bc_bts_df], axis = 0)
+bc_output
+
+# Format the combined df
+# Drop the best_model_params row from bc_output
+bc_df = bc_output.drop([0], axis = 0)
+bc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_BNB.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [BernoulliNB()]        
+        , 'clf__estimator__alpha': [0, 1]
+        , 'clf__estimator__binarize':['None', 0]
+        , 'clf__estimator__fit_prior': [True]
+        , 'clf__estimator__class_prior': ['None']
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_bnb = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_bnb_fit  = gscv_bnb.fit(X, y)
+
+gscv_bnb_fit_be_mod = gscv_bnb_fit.best_params_
+gscv_bnb_fit_be_res = gscv_bnb_fit.cv_results_
+
+print('Best model:\n', gscv_bnb_fit_be_mod)
+print('Best models score:\n', gscv_bnb_fit.best_score_, ':' , round(gscv_bnb_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_bnb_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_bnb_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_bnb_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+bnb_bts_dict = {#'best_model': list(gscv_bnb_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+bnb_bts_dict
+bnb_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+bnb_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+bnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+bnb_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+bnb_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+bnb_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+bnb_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+bnb_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(bnb_bts_dict, orient = 'index', columns = 'best_model')
+
+bnb_bts_df = pd.DataFrame.from_dict(bnb_bts_dict,orient = 'index')
+bnb_bts_df.columns = ['Logistic_Regression']
+print(bnb_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_bnb_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+bnb_bts_df.columns
+bnb_output = pd.concat([model_params_df, bnb_bts_df], axis = 0)
+bnb_output
+
+# Format the combined df
+# Drop the best_model_params row from bnb_output
+bnb_df = bnb_output.drop([0], axis = 0)
+bnb_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_DT.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [DecisionTreeClassifier(**rs
+                                                  , **njobs)]
+        , 'clf__estimator__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
+        , 'clf__estimator__class_weight':['balanced','balanced_subsample']
+        , 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
+        , 'clf__estimator__max_features': [None, 'sqrt', 'log2']
+        , 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__estimator__min_samples_split': [2, 5, 15, 20]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_dt = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_dt_fit  = gscv_dt.fit(X, y)
+
+gscv_dt_fit_be_mod = gscv_dt_fit.best_params_
+gscv_dt_fit_be_res = gscv_dt_fit.cv_results_
+
+print('Best model:\n', gscv_dt_fit_be_mod)
+print('Best models score:\n', gscv_dt_fit.best_score_, ':' , round(gscv_dt_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_dt_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_dt_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_dt_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+dt_bts_dict = {#'best_model': list(gscv_dt_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+dt_bts_dict
+dt_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+dt_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+dt_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+dt_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+dt_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+dt_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+dt_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+dt_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(dt_bts_dict, orient = 'index', columns = 'best_model')
+
+dt_bts_df = pd.DataFrame.from_dict(dt_bts_dict,orient = 'index')
+dt_bts_df.columns = ['Logistic_Regression']
+print(dt_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_dt_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+dt_bts_df.columns
+dt_output = pd.concat([model_params_df, dt_bts_df], axis = 0)
+dt_output
+
+# Format the combined df
+# Drop the best_model_params row from dt_output
+dt_df = dt_output.drop([0], axis = 0)
+dt_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_GBC.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [GradientBoostingClassifier(**rs)]
+        , 'clf__estimator__n_estimators'   : [10, 100, 200, 500, 1000]
+        , 'clf__estimator__n_estimators' : [10, 100, 1000]
+        , 'clf__estimator__learning_rate': [0.001, 0.01, 0.1]
+        , 'clf__estimator__subsample'    : [0.5, 0.7, 1.0]
+        , 'clf__estimator__max_depth'     : [3, 7, 9]
+        
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_gbc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_gbc_fit  = gscv_gbc.fit(X, y)
+
+gscv_gbc_fit_be_mod = gscv_gbc_fit.best_params_
+gscv_gbc_fit_be_res = gscv_gbc_fit.cv_results_
+
+print('Best model:\n', gscv_gbc_fit_be_mod)
+print('Best models score:\n', gscv_gbc_fit.best_score_, ':' , round(gscv_gbc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_gbc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_gbc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_gbc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+gbc_bts_dict = {#'best_model': list(gscv_gbc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+gbc_bts_dict
+gbc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+gbc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+gbc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+gbc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+gbc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+gbc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+gbc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+gbc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(gbc_bts_dict, orient = 'index', columns = 'best_model')
+
+gbc_bts_df = pd.DataFrame.from_dict(gbc_bts_dict,orient = 'index')
+gbc_bts_df.columns = ['Logistic_Regression']
+print(gbc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_gbc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+gbc_bts_df.columns
+gbc_output = pd.concat([model_params_df, gbc_bts_df], axis = 0)
+gbc_output
+
+# Format the combined df
+# Drop the best_model_params row from gbc_output
+gbc_df = gbc_output.drop([0], axis = 0)
+gbc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_GNB.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [GaussianNB(**rs)]        
+        , 'clf__estimator__priors': [None]
+        , 'clf__estimator__var_smoothing': np.logspace(0,-9, num=100)
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_gnb = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_gnb_fit  = gscv_gnb.fit(X, y)
+
+gscv_gnb_fit_be_mod = gscv_gnb_fit.best_params_
+gscv_gnb_fit_be_res = gscv_gnb_fit.cv_results_
+
+print('Best model:\n', gscv_gnb_fit_be_mod)
+print('Best models score:\n', gscv_gnb_fit.best_score_, ':' , round(gscv_gnb_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_gnb_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_gnb_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_gnb_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+gnb_bts_dict = {#'best_model': list(gscv_gnb_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+gnb_bts_dict
+gnb_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+gnb_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+gnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+gnb_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+gnb_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+gnb_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+gnb_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+gnb_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(gnb_bts_dict, orient = 'index', columns = 'best_model')
+
+gnb_bts_df = pd.DataFrame.from_dict(gnb_bts_dict,orient = 'index')
+gnb_bts_df.columns = ['Logistic_Regression']
+print(gnb_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_gnb_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+gnb_bts_df.columns
+gnb_output = pd.concat([model_params_df, gnb_bts_df], axis = 0)
+gnb_output
+
+# Format the combined df
+# Drop the best_model_params row from gnb_output
+gnb_df = gnb_output.drop([0], axis = 0)
+gnb_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_GPC.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [GaussianProcessClassifier(**rs)]
+        
+        , 'clf__estimator__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_gpc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_gpc_fit  = gscv_gpc.fit(X, y)
+
+gscv_gpc_fit_be_mod = gscv_gpc_fit.best_params_
+gscv_gpc_fit_be_res = gscv_gpc_fit.cv_results_
+
+print('Best model:\n', gscv_gpc_fit_be_mod)
+print('Best models score:\n', gscv_gpc_fit.best_score_, ':' , round(gscv_gpc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_gpc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_gpc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_gpc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+gpc_bts_dict = {#'best_model': list(gscv_gpc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+gpc_bts_dict
+gpc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+gpc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+gpc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+gpc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+gpc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+gpc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+gpc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+gpc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(gpc_bts_dict, orient = 'index', columns = 'best_model')
+
+gpc_bts_df = pd.DataFrame.from_dict(gpc_bts_dict,orient = 'index')
+gpc_bts_df.columns = ['Logistic_Regression']
+print(gpc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_gpc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+gpc_bts_df.columns
+gpc_output = pd.concat([model_params_df, gpc_bts_df], axis = 0)
+gpc_output
+
+# Format the combined df
+# Drop the best_model_params row from gpc_output
+gpc_df = gpc_output.drop([0], axis = 0)
+gpc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_KNN.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [KNeighborsClassifier(**rs
+                                                  , **njobs]
+        #, 'clf__estimator__n_neighbors': range(1, 21, 2)
+        , 'clf__estimator__n_neighbors': [5, 7, 11]
+        , 'clf__estimator__metric'     : ['euclidean', 'manhattan', 'minkowski']
+        , 'clf__estimator__weights'    : ['uniform', 'distance']
+
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_knn = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_knn_fit  = gscv_knn.fit(X, y)
+
+gscv_knn_fit_be_mod = gscv_knn_fit.best_params_
+gscv_knn_fit_be_res = gscv_knn_fit.cv_results_
+
+print('Best model:\n', gscv_knn_fit_be_mod)
+print('Best models score:\n', gscv_knn_fit.best_score_, ':' , round(gscv_knn_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_knn_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_knn_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_knn_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+knn_bts_dict = {#'best_model': list(gscv_knn_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+knn_bts_dict
+knn_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+knn_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+knn_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+knn_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+knn_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+knn_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+knn_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+knn_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(knn_bts_dict, orient = 'index', columns = 'best_model')
+
+knn_bts_df = pd.DataFrame.from_dict(knn_bts_dict,orient = 'index')
+knn_bts_df.columns = ['Logistic_Regression']
+print(knn_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_knn_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+knn_bts_df.columns
+knn_output = pd.concat([model_params_df, knn_bts_df], axis = 0)
+knn_output
+
+# Format the combined df
+# Drop the best_model_params row from knn_output
+knn_df = knn_output.drop([0], axis = 0)
+knn_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_LR.py

@@ -0,0 +1,207 @@
+#!/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 libs
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import GridSearchCV
+from sklearn import datasets
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.svm import SVC
+
+from sklearn.base import BaseEstimator
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+from xgboost import XGBClassifier
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+#%% Get train-test split and scoring functions
+# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
+#                                                     , num_df_wtgt['mutation_class']
+#                                                     , test_size    = 0.33
+#                                                     , random_state = 2
+#                                                     , shuffle      = True
+#                                                     , stratify     = num_df_wtgt['mutation_class'])
+
+y.to_frame().value_counts().plot(kind = 'bar')
+blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
+
+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)
+                 ,  'jaccard'   : make_scorer(jaccard_score)
+            })    
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
+
+#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        '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': [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': ['l2', 'none'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
+    }, 
+    {
+        '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': ['l1', 'l2'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['liblinear']
+    }
+
+]    
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_lr = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_lr_fit = gscv_lr.fit(X, y)
+gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
+gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
+
+print('Best model:\n', gscv_lr_fit_be_mod)
+print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
+
+#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+
+
+######################################
+# Blind test
+######################################
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )) 
+
+test_predict = gscv_lr_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_bts, test_predict))
+print(matthews_corrcoef(y_bts, test_predict))
+
+# create a dict with all scores
+lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
+               'bts_fscore':None
+               , 'bts_mcc':None
+               , 'bts_precision':None
+               , 'bts_recall':None
+               , 'bts_accuracy':None
+               , 'bts_roc_auc':None
+               , 'bts_jaccard':None }
+lr_bts_dict
+lr_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+lr_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+lr_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+lr_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+lr_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+lr_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+lr_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(lr_bts_dict, orient = 'index', columns = 'best_model')
+
+lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
+lr_bts_df.columns = ['Logistic_Regression']
+print(lr_bts_df)
+
+# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
+# d2
+# def Merge(dict1, dict2):
+#     res = {**dict1, **dict2}
+#     return res
+# d3 = Merge(d2, lr_bts_dict)
+# d3
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_lr_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+lr_bts_df.columns
+lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
+lr_output
+
+# Format the combined df
+# Drop the best_model_params row from lr_output
+lr_df = lr_output.drop([0], axis = 0)
+lr_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################

uq_ml_models/UQ_MLP.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [MLPClassifier(**rs
+                                        , **njobs
+                                        , max_iter = 500)],
+        , 'clf__estimator__hidden_layer_sizes': [(1), (2), (3)]
+        , 'clf__estimator__max_features': ['auto', 'sqrt']
+        , 'clf__estimator__min_samples_leaf': [2, 4, 8]
+        , 'clf__estimator__min_samples_split': [10, 20]
+        
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_mlp = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_mlp_fit  = gscv_mlp.fit(X, y)
+
+gscv_mlp_fit_be_mod = gscv_mlp_fit.best_params_
+gscv_mlp_fit_be_res = gscv_mlp_fit.cv_results_
+
+print('Best model:\n', gscv_mlp_fit_be_mod)
+print('Best models score:\n', gscv_mlp_fit.best_score_, ':' , round(gscv_mlp_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_mlp_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_mlp_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_mlp_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+mlp_bts_dict = {#'best_model': list(gscv_mlp_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+mlp_bts_dict
+mlp_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+mlp_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+mlp_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+mlp_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+mlp_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+mlp_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+mlp_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+mlp_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(mlp_bts_dict, orient = 'index', columns = 'best_model')
+
+mlp_bts_df = pd.DataFrame.from_dict(mlp_bts_dict,orient = 'index')
+mlp_bts_df.columns = ['Logistic_Regression']
+print(mlp_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_mlp_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+mlp_bts_df.columns
+mlp_output = pd.concat([model_params_df, mlp_bts_df], axis = 0)
+mlp_output
+
+# Format the combined df
+# Drop the best_model_params row from mlp_output
+mlp_df = mlp_output.drop([0], axis = 0)
+mlp_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_QDA.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [QuadraticDiscriminantAnalysis()]        
+
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_qda = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_qda_fit  = gscv_qda.fit(X, y)
+
+gscv_qda_fit_be_mod = gscv_qda_fit.best_params_
+gscv_qda_fit_be_res = gscv_qda_fit.cv_results_
+
+print('Best model:\n', gscv_qda_fit_be_mod)
+print('Best models score:\n', gscv_qda_fit.best_score_, ':' , round(gscv_qda_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_qda_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_qda_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_qda_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+qda_bts_dict = {#'best_model': list(gscv_qda_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+qda_bts_dict
+qda_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+qda_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+qda_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+qda_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+qda_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+qda_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+qda_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+qda_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(qda_bts_dict, orient = 'index', columns = 'best_model')
+
+qda_bts_df = pd.DataFrame.from_dict(qda_bts_dict,orient = 'index')
+qda_bts_df.columns = ['Logistic_Regression']
+print(qda_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_qda_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+qda_bts_df.columns
+qda_output = pd.concat([model_params_df, qda_bts_df], axis = 0)
+qda_output
+
+# Format the combined df
+# Drop the best_model_params row from qda_output
+qda_df = qda_output.drop([0], axis = 0)
+qda_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_RC.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [RidgeClassifier(**rs
+                                                  , **njobs)],
+        , 'clf__estimator__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_rc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_rc_fit  = gscv_rc.fit(X, y)
+
+gscv_rc_fit_be_mod = gscv_rc_fit.best_params_
+gscv_rc_fit_be_res = gscv_rc_fit.cv_results_
+
+print('Best model:\n', gscv_rc_fit_be_mod)
+print('Best models score:\n', gscv_rc_fit.best_score_, ':' , round(gscv_rc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_rc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_rc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_rc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+rc_bts_dict = {#'best_model': list(gscv_rc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+rc_bts_dict
+rc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+rc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+rc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+rc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+rc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+rc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+rc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+rc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(rc_bts_dict, orient = 'index', columns = 'best_model')
+
+rc_bts_df = pd.DataFrame.from_dict(rc_bts_dict,orient = 'index')
+rc_bts_df.columns = ['Logistic_Regression']
+print(rc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_rc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+rc_bts_df.columns
+rc_output = pd.concat([model_params_df, rc_bts_df], axis = 0)
+rc_output
+
+# Format the combined df
+# Drop the best_model_params row from rc_output
+rc_df = rc_output.drop([0], axis = 0)
+rc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_RF.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [RandomForestClassifier(**rs
+                                                  , **njobs
+                                                  , bootstrap = True
+                                                  , oob_score = True)],
+        'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
+        , 'clf__estimator__class_weight':['balanced','balanced_subsample']
+        , 'clf__estimator__n_estimators': [10, 25, 50, 100]
+        , 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
+        , 'clf__estimator__max_features': ['sqrt', 'log2', None] #deafult is sqrt
+        , 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__estimator__min_samples_split': [2, 5, 15, 20]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_rf = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_rf_fit  = gscv_rf.fit(X, y)
+
+gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
+gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
+
+print('Best model:\n', gscv_rf_fit_be_mod)
+print('Best models score:\n', gscv_rf_fit.best_score_, ':' , round(gscv_rf_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_rf_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+rf_bts_dict
+rf_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+rf_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+rf_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+rf_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+rf_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+rf_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+rf_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')
+
+rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
+rf_bts_df.columns = ['Logistic_Regression']
+print(rf_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_rf_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+rf_bts_df.columns
+rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
+rf_output
+
+# Format the combined df
+# Drop the best_model_params row from rf_output
+rf_df = rf_output.drop([0], axis = 0)
+rf_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_SVC.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [SVC(**rs
+                                , **njobs)],      
+       , 'clf__estimator__kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} 
+       , 'clf__estimator__C'    : [50, 10, 1.0, 0.1, 0.01]
+       , 'clf__estimator__gamma': ['scale', 'auto'] 
+        
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_svc = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_svc_fit  = gscv_svc.fit(X, y)
+
+gscv_svc_fit_be_mod = gscv_svc_fit.best_params_
+gscv_svc_fit_be_res = gscv_svc_fit.cv_results_
+
+print('Best model:\n', gscv_svc_fit_be_mod)
+print('Best models score:\n', gscv_svc_fit.best_score_, ':' , round(gscv_svc_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_svc_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_svc_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_svc_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+svc_bts_dict = {#'best_model': list(gscv_svc_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+svc_bts_dict
+svc_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+svc_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+svc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+svc_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+svc_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+svc_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+svc_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+svc_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(svc_bts_dict, orient = 'index', columns = 'best_model')
+
+svc_bts_df = pd.DataFrame.from_dict(svc_bts_dict,orient = 'index')
+svc_bts_df.columns = ['Logistic_Regression']
+print(svc_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_svc_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+svc_bts_df.columns
+svc_output = pd.concat([model_params_df, svc_bts_df], axis = 0)
+svc_output
+
+# Format the combined df
+# Drop the best_model_params row from svc_output
+svc_df = svc_output.drop([0], axis = 0)
+svc_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

uq_ml_models/UQ_XGB.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        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 = [
+    {
+        'clf__estimator': [XGBClassifier(**rs
+                                                  , **njobs]      
+        , 'clf__estimator__learning_rate': [0.01, 0.05, 0.1, 0.2]
+        , 'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20]
+        , 'clf__estimator__min_samples_leaf': [4, 8, 12, 16, 20]
+        , 'clf__estimator__max_features': ['auto', 'sqrt']
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_xgb = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_xgb_fit  = gscv_xgb.fit(X, y)
+
+gscv_xgb_fit_be_mod = gscv_xgb_fit.best_params_
+gscv_xgb_fit_be_res = gscv_xgb_fit.cv_results_
+
+print('Best model:\n', gscv_xgb_fit_be_mod)
+print('Best models score:\n', gscv_xgb_fit.best_score_, ':' , round(gscv_xgb_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_xgb_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_xgb_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_xgb_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+xgb_bts_dict = {#'best_model': list(gscv_xgb_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+xgb_bts_dict
+xgb_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+xgb_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+xgb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+xgb_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+xgb_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+xgb_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+xgb_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+xgb_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(xgb_bts_dict, orient = 'index', columns = 'best_model')
+
+xgb_bts_df = pd.DataFrame.from_dict(xgb_bts_dict,orient = 'index')
+xgb_bts_df.columns = ['Logistic_Regression']
+print(xgb_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_xgb_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+xgb_bts_df.columns
+xgb_output = pd.concat([model_params_df, xgb_bts_df], axis = 0)
+xgb_output
+
+# Format the combined df
+# Drop the best_model_params row from xgb_output
+xgb_df = xgb_output.drop([0], axis = 0)
+xgb_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+