def getReducedFeats(dataX, dataY, ifeat):
X = []
selectedFeats = []
if ifeat in [3, 5]:
regressor = SVC(kernel="linear")
rfe = RFE(regressor, ifeat)
X = rfe.fit_transform(dataX, dataY)
for i in np.argwhere(rfe._get_support_mask()):
selectedFeats.extend(i+1)
elif ifeat == 'all':
X = dataX
selectedFeats = 'all'
elif ifeat == 'pca':
pca = PCA(n_components=0.9)
X = pca.fit_transform(dataX)
selectedFeats = 'pca'
return X, selectedFeats
