added classification_params.py i.e hyperparams for each model without FS

classification_params.py

@@ -0,0 +1,479 @@
+########################################################################
+#======================
+# 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__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_rc = 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']
+        }
+]
+
+#######################################################################
+