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)
