added file containing model names and hyperaprams to run for all models inc FS

UQ_FS_fn.py

@@ -10,19 +10,26 @@ Created on Mon May 23 23:25:26 2022
 def fsgs(input_df
          , target
          , blind_test_df = pd.DataFrame()
+         , blind_test_target = pd.Series(dtype = 'int64')
          #, y_trueS = pd.Series()
          , estimator = LogisticRegression(**rs)
          , param_gridLd = {}
-         , cv_method = 10
+         , cv_method =  StratifiedKFold(n_splits = 10
+                                    , shuffle = True,**rs)
          , var_type = ['numerical'
                      , 'categorical'
                      , 'mixed']
          , fs_estimator = [LogisticRegression(**rs)]
-         , fs = RFECV(DecisionTreeClassifier(**rs) , cv = 10, scoring = 'matthews_corrcoef')
+         , fs = RFECV(DecisionTreeClassifier(**rs) 
+                      , cv =  StratifiedKFold(n_splits = 10
+                                    , shuffle = True,**rs)
+                      , scoring = 'matthews_corrcoef')
          ):
     '''
     returns
-    Dict containing results from FS and hyperparam tuning
+    Dict containing results from FS and hyperparam tuning for a given estiamtor
+    >>> ADD MORE <<<
+    optimised/selected based on mcc
     '''
     # Determine categorical and numerical features
     numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
@@ -68,11 +75,10 @@ def fsgs(input_df
     ############################################################################   
     # Create Pipeline object
     pipe = Pipeline([
-    #('pre', MinMaxScaler()),
+        ('pre', col_transform),
-    ('pre', col_transform),
+        ('fs', fs),
-     ('fs', fs),
+        #('clf',  LogisticRegression(**rs))])
-    #('clf',  LogisticRegression(**rs))])
+        ('clf', estimator)])
-     ('clf', estimator)])
     ############################################################################   
     # Define GridSearchCV
     gscv_fs = GridSearchCV(pipe
@@ -119,8 +125,8 @@ def fsgs(input_df
     #tp = gscv_fs.predict(X_bts)
     tp = gscv_fs.predict(blind_test_df)
 
-    print('\nMCC on Blind test:'     , round(matthews_corrcoef(y_bts, tp),2))
+    print('\nMCC on Blind test:'     , round(matthews_corrcoef(blind_test_target, tp),2))
-    print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
+    print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, tp),2))
     
     #=================
     # info extraction
@@ -191,9 +197,9 @@ def fsgs(input_df
     #bts_predict = gscv_fs.predict(X_bts)
     bts_predict = gscv_fs.predict(blind_test_df)
 
-    print('\nMCC on Blind test:'     , round(matthews_corrcoef(y_bts, bts_predict),2))
+    print('\nMCC on Blind test:'     , round(matthews_corrcoef(blind_test_target, bts_predict),2))
-    print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
+    print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
-    bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
+    bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
     
     # Diff b/w train and bts test scores
     train_test_diff = train_bscore - bts_mcc_score
@@ -213,12 +219,12 @@ def fsgs(input_df
     
     lr_btsD
     #lr_btsD['bts_mcc']       = bts_mcc_score
-    lr_btsD['bts_fscore']    = round(f1_score(y_bts, bts_predict),2)
+    lr_btsD['bts_fscore']    = round(f1_score(blind_test_target, bts_predict),2)
-    lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
+    lr_btsD['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
-    lr_btsD['bts_recall']    = round(recall_score(y_bts, bts_predict),2)
+    lr_btsD['bts_recall']    = round(recall_score(blind_test_target, bts_predict),2)
-    lr_btsD['bts_accuracy']  = round(accuracy_score(y_bts, bts_predict),2)
+    lr_btsD['bts_accuracy']  = round(accuracy_score(blind_test_target, bts_predict),2)
-    lr_btsD['bts_roc_auc']   = round(roc_auc_score(y_bts, bts_predict),2)
+    lr_btsD['bts_roc_auc']   = round(roc_auc_score(blind_test_target, bts_predict),2)
-    lr_btsD['bts_jaccard']   = round(jaccard_score(y_bts, bts_predict),2)
+    lr_btsD['bts_jaccard']   = round(jaccard_score(blind_test_target, bts_predict),2)
     lr_btsD
     
     #===========================
@@ -229,7 +235,7 @@ def fsgs(input_df
     fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
     all_featuresL = list(all_features)
     fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
-    fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
+    fs_res_array_rankf = str(list( gscv_fs.best_estimator_.named_steps['fs'].ranking_))
     sel_featuresf = list(sel_features)
     n_sf = int(n_sf)
     

UQ_FS_fn_CALL.py

@@ -6,9 +6,27 @@ Created on Tue May 24 08:11:05 2022
 @author: tanu
 """
 
+# my function
+#import fsgs from UQ_FS_fn
+
+fsgs(input_df = X
+     , target = y
+     , param_gridLd = param_grid_abc
+     , blind_test_df = X_bts
+     , blind_test_target = y_bts
+     , estimator = AdaBoostClassifier(**rs)
+     , var_type = 'mixed')
+
+ds_lrD = fsgs(input_df = X
+     , target = y
+     , param_gridLd = param_grid_lr
+     , blind_test_df = X_bts
+     , blind_test_target = y_bts
+     , estimator = LogisticRegression(**rs)
+     , var_type = 'mixed')
+
+
 
-import fsgs from 
-fsgs(X,y,param_gridLd=param_grid_abc, blind_test_df = X_bts, estimator=AdaBoostClassifier(**rs), var_type = 'mixed')
 
 
 
@@ -17,13 +35,13 @@ fsgs(X,y,param_gridLd=param_grid_abc, blind_test_df = X_bts, estimator=AdaBoostC
 # Write final output file
 # https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
 #========================================
-#output final dict as a json
+# #output final dict as a json
-outFile = 'LR_FS.json'
+# outFile = 'LR_FS.json'
-with open(outFile, 'w') as f:
+# with open(outFile, 'w') as f:
-    f.write(json.dumps(output_modelD,cls=NpEncoder))
+#     f.write(json.dumps(output_modelD,cls=NpEncoder))
     
-# read json
+# # read json
-file = 'LR_FS.json'
+# file = 'LR_FS.json'
-with open(file, 'r') as f:
+# with open(file, 'r') as f:
-    data = json.load(f)
+#     data = json.load(f)
 ##############################################################################

classification_names_params.py

@@ -1,266 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Mar 18 09:47:48 2022
-
-@author: tanu
-"""
-
-#%% Useful links
-# https://stackoverflow.com/questions/41844311/list-of-all-classification-algorithms
-# https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
-# https://github.com/davidsbatista/machine-learning-notebooks/blob/master/hyperparameter-across-models.ipynb
-# https://scikit-learn.org/stable/modules/svm.html#classification
-# https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/ # [params]
-# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html [ algo]
-# As a general rule of thumb, it is required to run baseline models on the dataset. I know H2O- AutoML and other AutoML packages do this. But I want to try using Scikit-learn Pipeline,
-    #  https://codereview.stackexchange.com/questions/256934/model-pipeline-to-run-multiple-classifiers-for-ml-classification
-# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html
-# QDA: https://www.geeksforgeeks.org/quadratic-discriminant-analysis/
-
-names = [
-    "Nearest Neighbors",
-    "Linear SVM",
-    "RBF SVM",
-    "Gaussian Process",
-    "Decision Tree",
-    "Random Forest",
-    "Neural Net",
-    "AdaBoost",
-    "Naive Bayes",
-    "QDA",
-]
-
-classifiers = [
-    KNeighborsClassifier(5),
-    SVC(kernel="linear", C=0.025),
-    SVC(gamma=2, C=1),
-    GaussianProcessClassifier(1.0 * RBF(1.0)),
-    DecisionTreeClassifier(max_depth=5),
-    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
-    MLPClassifier(alpha=1, max_iter=1000),
-    AdaBoostClassifier(),
-    GaussianNB(),
-    QuadraticDiscriminantAnalysis(),
-]
-
-# NOTE Logistic regression
-# The choice of the algorithm depends on the penalty chosen: Supported penalties by solver:
-#     ‘newton-cg’ - [‘l2’, ‘none’]
-#     ‘lbfgs’ - [‘l2’, ‘none’]
-#     ‘liblinear’ - [‘l1’, ‘l2’]
-#     ‘sag’ - [‘l2’, ‘none’]
-#     ‘saga’ - [‘elasticnet’, ‘l1’, ‘l2’, ‘none’]
-
-# SVR?
-# estimator=SVR(kernel='rbf')
-# param_grid={
-#             'C': [1.1, 5.4, 170, 1001],
-#             'epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14, 0.05, 8, 0.2, 3, 2, 7],
-#             'gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]
-#         }
-
-
-#%% Classification algorithms param grid
-#%% LogisticRegression()
-#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
-    gs_lr = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', LogisticRegression(**rs
-                                , **njobs))
-    ))
-    gs_lr_params = {
-        'clf__C'        : [0.0001, 0.001, 0.01, 0.1 ,1, 10, 100]
-        #'C': np.logspace(-4, 4, 50)
-        , 'clf__penalty': ['l1', 'l2', 'elasticnet', 'none']
-        , 'clf__solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
-        }      
-#%% DecisionTreeClassifier()
-
-    gs_dt = Pipeline((
-    ('pre'  , MinMaxScaler())
-    , ('clf', DecisionTreeClassifier(**rs
-                                     , **njobs))
-    ))
-    gs_dt_params = {
-            'clf__max_depth': [ 2,  4,  6, 8, 10]
-            , 'clf__criterion':['gini','entropy']
-            , "clf__max_features":["auto", None]
-            , "clf__max_leaf_nodes":[10,20,30,40]
-            }    
-#%% KNeighborsClassifier()
-#https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
-    gs_knn = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', KNeighborsClassifier(**rs
-                                  , **njobs))
-    ))
-    
-    gs_knn_params = {
-    'clf__n_neighbors': [5, 7, 11]
-    #, 'clf__n_neighbors': range(1, 21, 2)
-    ,'clf__metric'     : ['euclidean', 'manhattan', 'minkowski']
-    , 'clf__weights'    : ['uniform', 'distance']
-    }
-#%% RandomForestClassifier()
-
-    gs_rf = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', RandomForestClassifier(**rs
-                                    , **njobs
-                                    , bootstrap = True
-                                    , oob_score = True))
-    ))
-    gs_rf_params = {
-        'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
-        , 'clf__class_weight':['balanced','balanced_subsample']
-        , 'clf__n_estimators': [10, 100, 1000]
-        , 'clf__criterion': ['gini', 'entropy']
-        , 'clf__max_features': ['auto', 'sqrt']
-        , 'clf__min_samples_leaf': [2, 4, 8, 50]
-        , 'clf__min_samples_split': [10, 20]
-        }
-#%% XGBClassifier() # SPNT
-# https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
-# https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
-    gs_xgb = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', XGBClassifier(**rs
-                           , **njobs))
-    ))
-    
-    gs_xgb_params = {
-        'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
-        , 'clf__max_depth': [4, 6, 8, 10, 12, 16, 20]
-        , 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
-        , 'clf__max_features': ['auto', 'sqrt']
-        }  
-        
-#%% MLPClassifier()
-# https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
-    gs_mlp = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', MLPClassifier(**rs
-                           , **njobs
-                           , max_iter = 500))
-      ))
-
-    gs_mlp_params = {
-        'clf__hidden_layer_sizes': [(1), (2), (3)]
-        , 'clf__max_features': ['auto', 'sqrt']
-        , 'clf__min_samples_leaf': [2, 4, 8]
-        , 'clf__min_samples_split': [10, 20]
-        }
-#%% RidgeClassifier()
-# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html
-    gs_rc = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', RidgeClassifier(**rs
-                           , **njobs))
-      ))
-
-    gs_rc_params = {
-        'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
-        }
-
-#%% SVC()
-# https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
-    gs_svc = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', SVC(**rs
-                 , **njobs))
-      ))
-
-    gs_svc_params = {
-        'clf__kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} 
-       , 'clf__C'    : [50, 10, 1.0, 0.1, 0.01]
-       , 'clf__gamma': ['scale', 'auto'] }
-
-#%% BaggingClassifier()
-#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html
-    gs_bdt = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', BaggingClassifier(**rs
-                               , **njobs
-                               , bootstrap = True
-                               , oob_score = True))
-      ))
-
-    gs_bdt_params = {
-        'clf__n_estimators'    : [10, 100, 1000]
-       # If None, then the base estimator is a DecisionTreeClassifier.
-       , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()']# if none, DT is used
-       , 'clf__gamma': ['scale', 'auto'] }
-       
-#%% GradientBoostingClassifier()
-# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
-    gs_gb = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', GradientBoostingClassifier(**rs))
-      ))
-
-    gs_bdt_params = {
-        'clf__n_estimators'   : [10, 100, 200, 500, 1000]
-        , 'clf__n_estimators' : [10, 100, 1000]
-        , 'clf__learning_rate': [0.001, 0.01, 0.1]
-        , 'clf__subsample'    : [0.5, 0.7, 1.0]
-        , 'clf__max_depth'     : [3, 7, 9]
-        }
-#%% AdaBoostClassifier()
-#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier
-    gs_gb = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', AdaBoostClassifier(**rs))
-      ))
-
-    gs_bdt_params = {
-        'clf__n_estimators': [none, 1, 2]
-       , 'clf__base_estiamtor'  : ['None', 1*SVC(), 1*KNeighborsClassifier()]
-       #, 'clf___splitter'  :   ["best", "random"]
-        }
-#%% GaussianProcessClassifier()
-# https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
-    #GaussianProcessClassifier(1.0 * RBF(1.0)),
-    gs_gpc = Pipeline((
-    ('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
-    ,('clf', GaussianProcessClassifier(**rs))
-      ))
-
-    gs_gpc_params = {
-        'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
-        }
-
-#%% GaussianNB()
-# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
-    gs_gnb = Pipeline((
-    ('pre' , MinMaxScaler())
-    , ('pca', PCA() )# CHECK if it wants -1 to 1
-    ,('clf', GaussianNB(**rs))
-      ))
-
-    gs_gnb_params = {
-        'clf__priors': [None]
-        , 'clf__var_smoothing': np.logspace(0,-9, num=100)
-        }
-
-#%% QuadraticDiscriminantAnalysis()
-#https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
-    
-    gs_qda = Pipeline((
-    ('pre' , MinMaxScaler())
-    #, ('pca', PCA() )# CHECK if it wants -1 to 1
-    ,('clf', QuadraticDiscriminantAnalysis())
-      ))
-#%% BernoulliNB()
-# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html
-    gs_gnb = Pipeline((
-    ('pre' , MinMaxScaler())
-    ,('clf', BernoulliNB())
-      ))
-BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
-    gs_gnb_params = {
-        'clf__alpha': [0, 1]
-        , 'clf__binarize':['None', 0]
-        , 'clf__fit_prior': [True]
-        , 'clf__class_prior': ['None']
-        }

classification_params_FS.py

@@ -0,0 +1,480 @@
+########################################################################
+#======================
+# AdaBoostClassifier()
+#======================
+estimator =  AdaBoostClassifier(**rs)
+
+# Define pipleline with steps
+pipe_abc = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))    
+#    , ('clf',  AdaBoostClassifier(**rs))])
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_abc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+              
+    {
+#        'clf': [AdaBoostClassifier(**rs)],      
+         'clf__n_estimators': [1, 2, 5, 10]
+#       , 'clf__base_estimator'  : ['SVC']
+#       , 'clf__splitter'  :   ["best", "random"]
+        }
+]
+########################################################################     
+#======================
+# BaggingClassifier()
+#======================                  
+estimator = BaggingClassifier(**rs
+                          , **njobs
+                          , bootstrap = True
+                          , oob_score = True)
+
+# Define pipleline with steps
+pipe_bc = Pipeline([
+    
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_bc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    
+    {
+#        'clf': [BaggingClassifier(**rs, **njobs , bootstrap = True, oob_score = True)],
+    'clf__n_estimators'    : [10, 25, 50, 100, 150, 200, 500, 700, 1000]
+#       , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()'] # if none, DT is used
+        }
+]
+########################################################################
+#======================  
+# BernoulliNB ()
+#======================  
+# Define estimator
+estimator =  BernoulliNB()
+
+# Define pipleline with steps
+pipe_bnb = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_bnb = [
+    {'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]    
+     },
+    
+    {
+#        'clf': [BernoulliNB()],        
+         'clf__alpha': [1, 0]
+        , 'clf__binarize':[None, 0]
+        , 'clf__fit_prior': [True]
+        , 'clf__class_prior': [None]
+        }
+]
+########################################################################
+#===========================
+# DecisionTreeClassifier()
+#=========================== 
+ 
+# Define estimator
+estimator =  DecisionTreeClassifier(**rs)
+
+# Define pipleline with steps
+pipe_dt = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))    
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_dt = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },    
+    
+    {
+#        'clf': [DecisionTreeClassifier(**rs)],
+         'clf__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
+        , 'clf__class_weight':['balanced']
+        , 'clf__criterion': ['gini', 'entropy', 'log_loss']
+        , 'clf__max_features': [None, 'sqrt', 'log2']
+        , 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__min_samples_split': [2, 5, 15, 20]
+        }
+]
+
+#########################################################################
+#==============================
+# GradientBoostingClassifier()
+#==============================
+# Define estimator
+estimator =  GradientBoostingClassifier(**rs)
+
+# Define pipleline with steps
+pipe_gbc = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_gbc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    {
+#        'clf': [GradientBoostingClassifier(**rs)],
+         'clf__n_estimators'   : [10, 100, 200, 500, 1000]
+        , 'clf__n_estimators' : [10, 100, 1000]
+        , 'clf__learning_rate': [0.001, 0.01, 0.1]
+        , 'clf__subsample'    : [0.5, 0.7, 1.0]
+        , 'clf__max_depth'     : [3, 7, 9]
+        
+        }
+]
+
+#########################################################################
+#=========================== 
+# GaussianNB ()
+#=========================== 
+# Define estimator
+estimator =  GaussianNB()
+
+# Define pipleline with steps
+pipe_gnb = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)
+    ])
+
+
+# Define hyperparmeter space to search for
+param_grid_gnb = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    {
+#        'clf': [GaussianNB()],       
+         'clf__priors': [None]
+        , 'clf__var_smoothing': np.logspace(0,-9, num=100)
+        }
+]
+
+#########################################################################
+#=========================== 
+# GaussianProcessClassifier()
+#=========================== 
+# Define estimator
+estimator =  GaussianProcessClassifier(**rs)
+
+# Define pipleline with steps
+pipe_gbc = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_gbc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+         
+    {
+#        'clf': [GaussianProcessClassifier(**rs)], 
+         'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
+        }
+]
+
+#########################################################################
+#=========================== 
+# KNeighborsClassifier ()
+#=========================== 
+# Define estimator
+estimator =  KNeighborsClassifier(**njobs)
+
+# Define pipleline with steps
+pipe_knn = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))    
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_knn = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+             
+    {
+#        'clf': [KNeighborsClassifier(**njobs)],
+        'clf__n_neighbors': range(21, 51, 2)
+        #, 'clf__n_neighbors': [5, 7, 11]
+        , 'clf__metric'     : ['euclidean', 'manhattan', 'minkowski']
+        , 'clf__weights'    : ['uniform', 'distance']
+
+        }
+]
+#########################################################################
+#=========================== 
+# LogisticRegression ()
+#=========================== 
+# Define estimator
+estimator =  LogisticRegression(**rs)
+
+# Define pipleline with steps
+pipe_lr = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))       
+    , ('clf',  estimator)])
+
+# Define hyperparmeter space to search for
+param_grid_lr = [
+    
+    {'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [rskf_cv]
+     },
+    
+    {
+#       'clf': [LogisticRegression(**rs)],
+        'clf__C': np.logspace(0, 4, 10),
+        'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
+        'clf__max_iter': list(range(100,800,100)),
+        'clf__solver': ['saga']
+    },
+    {
+#        'clf': [LogisticRegression(**rs)],
+        'clf__C': np.logspace(0, 4, 10),
+        'clf__penalty': ['l2', 'none'],
+        'clf__max_iter': list(range(100,800,100)),
+        'clf__solver': ['newton-cg', 'lbfgs', 'sag']
+    }, 
+    {
+#        'clf': [LogisticRegression(**rs)],
+        'clf__C': np.logspace(0, 4, 10),
+        'clf__penalty': ['l1', 'l2'],
+        'clf__max_iter': list(range(100,800,100)),
+        'clf__solver': ['liblinear']
+    }
+
+]
+#########################################################################
+#================== 
+# MLPClassifier()
+#================== 
+# Define estimator
+estimator =  MLPClassifier(**rs)
+
+# Define pipleline with steps
+pipe_mlp = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))        
+    , ('clf',  estimator)
+    ])
+
+param_grid_mlp = [    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    
+    {
+#        'clf': [MLPClassifier(**rs, max_iter = 1000)],
+         'clf__max_iter': [1000, 2000]
+        , 'clf__hidden_layer_sizes': [(1), (2), (3), (5), (10)]
+        , 'clf__solver': ['lbfgs', 'sgd', 'adam']
+        , 'clf__learning_rate': ['constant', 'invscaling', 'adaptive']
+        #, 'clf__learning_rate': ['constant']
+        
+        }
+]
+
+#########################################################################
+#==================================
+# QuadraticDiscriminantAnalysis()
+#==================================
+# Define estimator
+estimator =  QuadraticDiscriminantAnalysis(**rs)
+
+# Define pipleline with steps
+pipe_qda = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))    
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_qda = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    
+    {
+#        'clf': [QuadraticDiscriminantAnalysis()],
+        'clf__priors': [None]
+
+        }
+]
+
+#########################################################################
+#====================
+# RidgeClassifier()
+#====================
+
+# Define estimator
+estimator =  RidgeClassifier(**rs)
+
+# Define pipleline with steps
+pipe_abc = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
+    , ('clf',  estimator)
+    ])
+
+param_grid_rc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    
+    {
+     #'clf' : [RidgeClassifier(**rs)],
+      'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
+     }
+]
+#######################################################################
+#=========================== 
+# RandomForestClassifier()
+#=========================== 
+# Define estimator
+estimator =  [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
+
+# Define pipleline with steps
+pipe_rf = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_rf = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+           
+    {
+#        'clf': [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)],
+        'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
+        , 'clf__class_weight':['balanced','balanced_subsample']
+        , 'clf__n_estimators': [10, 25, 50, 100, 200, 300] # go upto a 100
+        , 'clf__criterion': ['gini', 'entropy', 'log_loss']
+        , 'clf__max_features': ['sqrt', 'log2', None] #deafult is sqrt
+        , 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__min_samples_split': [2, 5, 15, 20]
+        }
+]
+#######################################################################
+#========
+# SVC()
+#========
+
+estimator =  SVC(**rs)
+
+# Define pipleline with steps
+pipe_svc = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
+    , ('clf',  estimator)
+    ])
+
+# Define hyperparmeter space to search for
+param_grid_svc = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+             
+    {
+#        'clf': [SVC(**rs)],    
+        'clf__kernel': ['poly', 'rbf', 'sigmoid']
+        #, 'clf__kernel': ['linear']
+       , 'clf__C'    : [50, 10, 1.0, 0.1, 0.01]
+       , 'clf__gamma': ['scale', 'auto'] 
+        
+        }
+]
+
+#######################################################################
+#================= 
+# XGBClassifier ()
+#================= 
+
+# Define estimator
+#https://www.datatechnotes.com/2019/07/classification-example-with.html
+# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
+#        colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
+#        max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
+#        n_estimators=100, n_jobs=1, nthread=None,
+#        objective='multi:softprob', random_state=0, reg_alpha=0,
+#        reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
+#        subsample=1, verbosity=1)
+estimator =  XGBClassifier(**rs, **njobs, verbose = 3)
+
+# Define pipleline with steps
+pipe_xgb = Pipeline([
+    ('pre', MinMaxScaler())
+    , ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
+#    , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
+    , ('clf',  estimator)
+    ])
+
+param_grid_xgb = [
+    {
+    'fs__min_features_to_select' : [1,2]
+#     , 'fs__cv': [cv]
+     },
+    {
+#        'clf': [XGBClassifier(**rs , **njobs, verbose = 3)],
+         'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
+        , 'clf__max_depth'   : [4, 6, 8, 10, 12, 16, 20]
+        , 'clf__n_estimators': [10, 25, 50, 100, 200, 300]
+        #, 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
+        #, 'clf__max_features': ['auto', 'sqrt']
+        }
+]
+
+#######################################################################
+

uq_ml_models_FS/fs_UQ_LR.py

@@ -16,7 +16,6 @@ Created on Tue Mar 15 11:09:50 2022
 cv = skf_cv
 
 # LogisticRegression: Feature Selelction + GridSearch CV + Pipeline
-
 ###############################################################################
 # Define estimator
 estimator =  LogisticRegression(**rs)

uq_ml_models_FS/fs_UQ_RF.py

@@ -8,7 +8,7 @@ Created on Wed May 18 06:03:24 2022
 #cv = rskf_cv
 cv = skf_cv
 
-# AdaBoostClassifier: Feature Selelction + GridSearch CV + Pipeline
+# RandomForestClassifier: Feature Selelction + GridSearch CV + Pipeline
 ###############################################################################
 # Define estimator
 estimator =  [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
@@ -190,4 +190,4 @@ output_modelD
 #     json.dump(output_modelD, f)
 # #
 # with open(file, 'r') as f:
-#     data = json.load(f)
+#     data = json.load(f)