from keras.layers import Input, Dense

from keras.models import Model

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

from sklearn.cluster import k_means

from sklearn.metrics import silhouette_score, davies_bouldin_score

from sklearn.preprocessing import normalize

import time

from sklearn import metrics

from myUtils import *

from SVAEclass import VAE

import os

from keras import backend as K

def get_EM(datapath,resultpath):

    omics1 = np.loadtxt('{}/log_exp_omics.txt'.format(datapath))

    omics1 = np.transpose(omics1)

    omics1 = normalize(omics1, axis=0, norm='max')

    print(omics1.shape)

    omics2 = np.loadtxt('{}/log_mirna_omics.txt'.format(datapath))

    omics2 = np.transpose(omics2)

    omics2 = normalize(omics2, axis=0, norm='max')

    print(omics2.shape)

    omics3 = np.loadtxt('{}/methy_omics.txt'.format(datapath))

    omics3 = np.transpose(omics3)

    omics3 = normalize(omics3, axis=0, norm='max')

    print(omics3.shape)

    omics = np.concatenate((omics1, omics2, omics3), axis=1)

    print(omics.shape)

    encoding1_dim1 = 1000

    encoding2_dim1 = 100

    middle_dim1 = 4

    dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]

    ae1 = VAE(omics1, dims1)

    ae1.train()

    ae1.autoencoder.summary()

    encoded_factor1 = ae1.predict(omics1)

    encoding1_dim2 = 500

    encoding2_dim2 = 50

    middle_dim2 = 2

    dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]

    ae2 = VAE(omics2, dims2)

    ae2.train()

    ae2.autoencoder.summary()

    encoded_factor2 = ae2.predict(omics2)

    encoding1_dim3 = 1000

    encoding2_dim3 = 100

    middle_dim3 = 4

    dims3 = [encoding1_dim3, encoding2_dim3, middle_dim3]

    ae3 = VAE(omics3, dims3)

    ae3.autoencoder.summary()

    ae3.train()

    encoded_factor3 = ae3.predict(omics3)

    encoded_factors = np.concatenate((encoded_factor1, encoded_factor2, encoded_factor3), axis=1)

    if not os.path.exists("{}/SVAE_FAETC_EM.txt".format(resultpath)):

        os.mknod("{}/SVAE_FAETC_EM.txt".format(resultpath))

    np.savetxt("{}/SVAE_FAETC_EM.txt".format(resultpath), encoded_factors)

    K.clear_session()

if __name__ == '__main__':

    data_dir_list = []

    result_dir_list = []

    data_path = r"data/cancer"

    result_path = r"result/cancer"

    dir_or_files = os.listdir(data_path)

    for dir_file in dir_or_files:

        # 获取目录或者文件的路径

        data_dir_file_path = os.path.join(data_path, dir_file)

        result_dir_file_path = os.path.join(result_path, dir_file)

        # 判断该路径为文件还是路径

        if os.path.isdir(data_dir_file_path):

            data_dir_list.append(data_dir_file_path)

            if not os.path.exists(result_dir_file_path):

                os.makedirs(result_dir_file_path)

            result_dir_list.append(result_dir_file_path)

    #print(data_dir_list)

    #print(result_dir_list)

    # data_dir_list=['data/cancer/breast', 'data/cancer/gbm', 'data/cancer/ovarian', 'data/cancer/sarcoma', 'data/cancer/lung', 'data/cancer/liver']

    # result_dir_list=['result/cancer/breast', 'result/cancer/gbm', 'result/cancer/ovarian', 'result/cancer/sarcoma', 'result/cancer/lung', 'result/cancer/liver']

    for datapath,resultpath in zip(data_dir_list,result_dir_list):

        get_EM(datapath, resultpath)

    # datapath='data/cancer/gbm'

    # resultpath='result/cancer/gbm'

    get_EM(datapath, resultpath)