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 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 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 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 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 experiment6(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(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 neuralNet_epoch_exp_SM_LV_ST_NN(train, test, epochs):
over_sampled_train = SMOTEOverSampling(train)
keep = lowVarianceElimination(over_sampled_train, 0.8)
train = Standardization(over_sampled_train[keep])
test = Standardization(test[keep])
return feedForwardNN(train, test, epochs=epochs)
def random_forest_depth_exp_SM_LV_ST_RF(train, test, max_depth):
over_sampled_train = SMOTEOverSampling(train)
keep = lowVarianceElimination(over_sampled_train, 0.8)
train = Standardization(over_sampled_train[keep])
test = Standardization(test[keep])
return randomForest(train, test, max_depth=max_depth)
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 experiment13(train, test, f):
over_sampled_train = SMOTEOverSampling(train)
keep = f(over_sampled_train)
train = over_sampled_train[keep]
test = test[keep]
return feedForwardNN(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)
def experiment12_1(train, test, f):
over_sampled_train = SMOTEOverSampling(train)
keep = decisionTreeFSelect(over_sampled_train)
keep = f(over_sampled_train[keep])
return randomForest(over_sampled_train[keep], test[keep])
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])
def experiment11(train, test, f):
over_sampled_train = SMOTEOverSampling(train)
keep = f(over_sampled_train)
return randomForest(over_sampled_train[keep], test[keep])