HKP_ML_DL/Autoencoder_for LightGBM.py at master · stepchoi/HKP_ML_DL · GitHub

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import numpy as np
import pandas as pd
from dateutil.relativedelta import relativedelta
from tqdm import tqdm

from keras.layers import Dense
from keras.models import Sequential, Model
from keras.utils.vis_utils import plot_model


def AE_fitting(training_x, reduced_dens):

    model = Sequential()
    if reduced_dens > 700:
        second_layer = 2400
    elif reduced_dens < 600:
        second_layer = 1800
    else:
        second_layer = 2000

    model.add(Dense(units=second_layer, activation='tanh', name='en1', input_shape=[3169]))
    model.add(Dense(units=reduced_dens, activation='tanh', name='en2'))
    model.add(Dense(units=second_layer, activation='tanh', name='de1'))
    model.add(Dense(units=3169, name='de2'))

    model.summary()

    # extract compressed feature
    model.compile(optimizer='adam', loss='mae')

    model.fit(training_x, training_x, batch_size=2000, epochs=50)
    feature_model = Model(inputs=model.input, outputs=model.get_layer(name='en2').output)

    return feature_model

def AE_predict(x, feature_model):

    compressed_x = feature_model.predict(x)
    print('feature shape=', compressed_x.shape)

    return compressed_x


if __name__ == "__main__":

    training_x = pd.read_csv('trainingset0.csv', index_col=0)

    feature_model = AE_fitting(training_x, 508)
    training_compressed_x = AE_predict(training_x, feature_model)
    print(training_compressed_x.shape)