diff --git a/classification_params.py b/classification_params.py new file mode 100644 index 0000000..0991e25 --- /dev/null +++ b/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'] + } +] + +####################################################################### +