def MFE(self, X_split, y_split, model):
if model == 'SVM':
X_split_scaled = standard_scale(X_split)
Model = SVM()
Model.fit(X_split_scaled[0], y_split[0])
y_hat = Model.predict(X_split_scaled[2])
elif model == 'RF':
Model = RF()
Model.fit(np.concatenate([X_split[0], X_split[1]]),
np.concatenate([y_split[0], y_split[1]]))
y_hat = Model.predict(X_split[2])
elif model == 'FNN':
X_split_scaled = standard_scale(X_split)
Model = FNN(model)
Model.fit(X_split_scaled[0],
y_split[0],
validation_data=[X_split_scaled[1], y_split[1]],
epochs=self.MAX_EPOCH,
batch_size=self.BATCH_SIZE,
callbacks=[self.es])
y_hat = Model.predict_classes(X_split_scaled[2])
else:
print('model undefined')
return self.evaluate(y_split[2], y_hat)
