def experiment2(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train, 1000) keep = f(over_sampled_train[keep]) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment3_1(train, test, variance): over_sampled_train = SMOTEOverSampling(train) keep = lowVarianceElimination(over_sampled_train, variance) keep = univariateFSelect(over_sampled_train[keep], 1000) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment8(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train) keep = f(over_sampled_train[keep]) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return feedForwardNN(train, test)
def experiment10(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train) keep = f(over_sampled_train[keep]) train = over_sampled_train[keep] test = test[keep] return svm(train, test)
def majority_vote_exp_1(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train) keep = f(over_sampled_train[keep]) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return randomForest_neuralNet_svm(train, test)
def univariate_function_exp_SM_UFS_ST_SVM(train, test, score_function): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train, score_func=score_function) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment16(train, test, f): keep = univariateFSelect(train) keep = f(train[keep]) train = Standardization(train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment20(train, test, f): keep = univariateFSelect(train) keep = f(train[keep]) train = Standardization(train[keep]) test = Standardization(test[keep]) return feedForwardNN(train, test)
def experiment12(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = univariateFSelect(over_sampled_train) keep = f(over_sampled_train[keep]) return randomForest(over_sampled_train[keep], test[keep])