runCancerDAE2.py

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 DAEclass import DAE
import os
from keras import backend as K


if __name__ == '__main__':
    data_dir_list = []
    result_dir_list = []
    data_path = r"data/cancer4"
    result_path = r"result/cancer4"
    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/kidney', 'data/cancer/lung', 'data/cancer/liver']
    #result_dir_list=['result/cancer/breast', 'result/cancer/kidney', 'result/cancer/lung', 'result/cancer/liver']

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

        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)

        noise_factor = 0.1

        encoding1_dim1 = 1000
        encoding2_dim1 = 100
        middle_dim1 = 4
        dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]
        ae1 = DAE(omics1, dims1, noise_factor)
        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 = DAE(omics2, dims2,noise_factor)
        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 = DAE(omics3, dims3,noise_factor)
        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("{}/DAE_FAETC_EM.txt".format(resultpath)):
            os.mknod("{}/DAE_FAETC_EM.txt".format(resultpath))
        np.savetxt("{}/DAE_FAETC_EM.txt".format(resultpath), encoded_factors)
        K.clear_session()