identify.py

import json
import os

import pandas as pd
import tensorflow as tf
from tqdm import tqdm


def Identify(modelDir, inOutFiles):
    print('### Run DeepFavored ...')
    with open(modelDir + '/hyperparams.json') as f:
        confArgs = json.load(f)
    features = confArgs['trainDataHyparams']['componentStats']
    with tf.Graph().as_default() as g:  # use tf.Graph().as_default() to separate differnt Scope
        with tf.compat.v1.Session() as sess:
            saver = tf.compat.v1.train.import_meta_graph(modelDir + '/model/model.meta')  # , clear_devices=True)
            saver.restore(sess, modelDir + '/model/model')
            graph = tf.compat.v1.get_default_graph()

            X_tensor = graph.get_tensor_by_name("X:0")
            H_tensor = graph.get_tensor_by_name("Y1_pred:0")
            O_tensor = graph.get_tensor_by_name("Y2_pred:0")

            for inFile, outFile in tqdm(inOutFiles):
                df = pd.read_csv(inFile, sep='\t')
                df.dropna(inplace=True)
                df_features = df[features]
                X = df_features.values
                positions = df.POS.values
                H, O = sess.run([H_tensor, O_tensor], feed_dict={X_tensor: X})
                rows = []
                for pos, h, o in list(zip(positions, H, O)):
                    h, o = h[0], o[0]
                    DFscore = h * o
                    coor, h, o, DFscore = int(pos), round(h, 3), round(o, 3), round(DFscore, 3)
                    rows.append((pos, h, o, DFscore))

                rows = sorted(rows, key=lambda x: x[0])
                if outFile[-7:] != '.df.out':
                    outFile += '.df.out'
                with open(outFile, 'w') as f:
                    f.write('POS\tDF_H\tDF_O\tDF\n')
                    for row in rows:
                        row = [str(i) for i in row]
                        f.write('\t'.join(row) + '\n')


def IdentifyWithArgs(args):
    inOutFiles = []
    outDir = args.outDir
    if outDir is None:
        if os.path.isfile(args.input):
            outDir = args.input.split('/')[0:-1]
        else:
            outDir = args.input
    os.makedirs(outDir, exist_ok=True)
    if os.path.isfile(args.input):
        fileName = args.input.split('/')[-1]
        inOutFiles.append((args.input, outDir+'/'+fileName))
    else:
        for fileName in os.listdir(args.input):
            inFile = args.input + '/' + fileName
            outFile = outDir + '/' + fileName
            inOutFiles.append((inFile, outFile))
    Identify(args.modelDir, inOutFiles)