def experiment4(train, test, variance): over_sampled_train = SMOTEOverSampling(train) keep = lowVarianceElimination(over_sampled_train, variance) keep = lassoFSelect(over_sampled_train[keep]) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment2_1(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = decisionTreeFSelect(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_2(train, test, variance): over_sampled_train = SMOTEOverSampling(train) keep = lowVarianceElimination(over_sampled_train, variance) keep = decisionTreeFSelect(over_sampled_train[keep], 1000) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment3_0_1(train, test, k): over_sampled_train = SMOTEOverSampling(train) keep = lowVarianceElimination(over_sampled_train, 0.8) keep = univariateFSelect(over_sampled_train[keep], k) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment10_1(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = decisionTreeFSelect(over_sampled_train) keep = f(over_sampled_train[keep]) train = over_sampled_train[keep] test = test[keep] return svm(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 pca_n_components_exp(train, test, components): over_sampled_train = SMOTEOverSampling(train) keep = lowVarianceElimination(over_sampled_train, 0.8) train = _PCA(over_sampled_train[keep], components) test = _PCA(test[keep], components) return 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 experiment15(train, test, f): keep = f(train) train = Standardization(train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment16_1(train, test, f): keep = decisionTreeFSelect(train) keep = f(train[keep]) train = Standardization(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 experiment1(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = f(over_sampled_train) train = Standardization(over_sampled_train[keep]) test = Standardization(test[keep]) return svm(train, test)
def experiment9(train, test, f): over_sampled_train = SMOTEOverSampling(train) keep = f(over_sampled_train) train = over_sampled_train[keep] test = test[keep] return svm(train, test)