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 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 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 experiment20_1(train, test, f):
keep = decisionTreeFSelect(train)
keep = f(train[keep])
train = Standardization(train[keep])
test = Standardization(test[keep])
return feedForwardNN(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])