geneMatrix/make_hfile.py at master · shahryary/geneMatrix · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
#!/usr/bin/env python

# validating interacts in memory based on CPU/GPU
import os
import time
import pandas as pd
import traceback
import numpy as np
import sys
from keras.models import load_model
from itertools import chain

np.set_printoptions(suppress=True)


def matrix_fasta_regions(tmp_one, tmp_two):
    arr_matrix = np.zeros((4, 5000))  # ACGT
    fp = open(chr_dir + str(tmp_one) + "_" + str(tmp_two) + ".txt")
    for i, line in enumerate(fp):
        if i == 1:
            line_character_count = 0
            for c in line:
                # print c
                if c == "A":
                    arr_matrix[0][line_character_count] = 1
                if c == "C":
                    arr_matrix[1][line_character_count] = 1
                if c == "G":
                    arr_matrix[2][line_character_count] = 1
                if c == "T":
                    arr_matrix[3][line_character_count] = 1
                line_character_count += 1
    fp.close()
    arr_matrix = arr_matrix.astype('int')
    return arr_matrix


def prediction(test1, test2):
    pred_result = []
    pred_labels = model.predict([test1, test2])
    for item in zip(pred_labels):
        if np.round(item) == 1:
            result = 1
        else:
            result = 0
        pred_result.append([result, "%.8f" % item[0][0]])
    return pred_result


def ext_first(lst):
    return [item[1] for item in lst]


def ext_second(lst):
    return [item[0] for item in lst]


def run_prediction(csvfile, chrm):
    num_lines = sum(1 for line in open(csvfile))
    chunk_size = 100000
    first_record = True
    tmp_s1 = []
    tmp_e1 = []
    count = 0
    file_number = 0
    try:
        for chunk in pd.read_csv(csvfile, sep="\t", header=None, skiprows=1,
                                 chunksize=chunk_size):
            start_time = time.time()
            test1 = []
            test2 = []
            A = []
            B = []
            main_predict=[]
            tmp_records = []
            regions = np.array(chunk)
            for item in regions:
                if first_record:
                    tmp_s1 = item[1]
                    tmp_e1 = item[2]
                    test1 = matrix_fasta_regions(tmp_s1, tmp_e1)
                    first_record = False

                if tmp_s1 != item[1] and tmp_e1 != item[2]:
                    test1 = matrix_fasta_regions(item[1], item[2])
                    tmp_s1 = item[1]
                    tmp_e1 = item[2]

                test2 = matrix_fasta_regions(item[3], item[4])
                A.append(test1)
                B.append(test2)
                count += 1
                tmp_records.append([chrm, tmp_s1, tmp_e1, item[3], item[4]])

            A = np.array(A).transpose(0, 2, 1)
            B = np.array(B).transpose(0, 2, 1)
            print("\n--- Computing predictions ... ---")
            result = prediction(A, B)
            main_predict.append(result)
            first_record = True
            print("--- Processed: ", count, "out of: ", num_lines)
            main_predict = list(chain.from_iterable(main_predict))
            dataframe = pd.DataFrame(tmp_records, columns=['chr', 's1', 'e1', 's2', 'e2'])
            dataframe['prob'] = ext_first(main_predict)
            dataframe['interacted'] = ext_second(main_predict)
            dataframe = dataframe.reset_index(drop=True)
            print("--- Writing into file ... ---- ")
            file_number += 1
            dataframe.to_csv(output + folder_name + "/part-" + str(file_number) + "-" + str(count) + "-" + folder_name +
                             ".csv", sep='\t', index=False, encoding='utf-8')
            print("--- %s seconds ---" % (time.time() - start_time))
            print(15*"-")

    except Exception as e:
        print("type error: " + str(e))
        print(traceback.format_exc())


hdf_file = "/mnt/intStorage/deeplearning/myDP/logs/fulltrain/"
chr_dir = "/home/yadi/DP/geneMatrix/chr1/"
output = "/mnt/intStorage/output/"

model = load_model(hdf_file+'weights-improvement-15.hdf5')

csv_file = sys.argv[1]
folder_name = os.path.basename(os.path.splitext(csv_file)[0])

create_dir = output + folder_name
if not os.path.exists(create_dir):
    try:
        os.mkdir(create_dir)
    except OSError:
        print("Creation of the directory failed")

run_prediction(csv_file, chrm="1")