######################################################################## #====================== # 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'] } ] #######################################################################