def build_binary_classifiers(path_g1_sg2m, path_g1s_g2m):
"""
Build the stacked neural network with single output neuron for binary classification to G1 vs. S+G2M and
G1+S vs. G2M phases, and evaluate its performance
:param path_g1_sg2m: Path to the labeled dataset in two labels : G1 and SG2M
:param path_g1s_g2m: Path to the labeled dataset in two labels : G1S and G2M
:return: Accuracy of classification of each model.
"""
############### Ordinal Classifier #################
dr1 = DatasetReader(path_g1_sg2m)
dr2 = DatasetReader(path_g1s_g2m)
binary_train1 = dr1.load_data()
binary_train2 = dr2.load_data()
oc1 = OrdinalClassifier(binary_train1[0], binary_train1[1])
oc2 = OrdinalClassifier(binary_train2[0], binary_train2[1])
r1 = oc1.classify()
r2 = oc2.classify()
return r1, r2
def build_stacked_ae(path):
"""
Build the stacked auto-encoder neural network, and evaluate its performance
:param path: Path to the genetic dataset
:return: Accuracy of classification of cell cycle phase.
"""
############### Stacked Auto-Encoders ##############
dr = DatasetReader(path)
train = dr.load_data()
ae = StackedAutoencoder(train[0], train[1], train[2], 3)
ae.create_autoencoder()
result = ae.evaluate_autoencoder()
return result[1] * 100
print("Accuracy: %.2f%%" % (result[1] * 100))
