File size: 2,860 Bytes
ab6c03c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import argparse
import csv
import os
import h5py
import numpy as np
import random
from process_pretrain_data import get_kmer_sequence
from multiprocessing import Pool


def generate_example(X, Y, kmer, index):
    # assert X.shape[0] == Y.shape[0]
    lines = []
    for j in range(len(X)):
        if j % 1000 == 0:
            print("%s : %s" % (index, j))

        label = list(np.zeros(200,dtype=int)) + list(np.where(Y[j]==1)[1]) + list(np.zeros(201-kmer,dtype=int))

        sequence = get_kmer_sequence(X[j].decode("utf-8"), kmer)
        lines.append([sequence, label])
    
    return lines


def Process(args):
    filename = args.file_path
    h5 = h5py.File(filename, "r")
    num_chunks = len(h5.keys())//2
    keys = list(h5.keys())[:num_chunks]


    X = []

    for i, key in enumerate(keys):
        x_key = key
        y_key = x_key.replace("X","Y")

        X_l = h5[x_key]
        Y_l = h5[y_key][0]

        X.extend(X_l)
        
        if i == 0:
            Y = Y_l
        else:
            Y = np.concatenate([Y, Y_l], axis=0)

        print("%d : %d, %d, %s" % (i, len(X), Y.shape[0], str(key)))
    
    print(len(X))
    print(len(Y))
    
    n_proc = int(args.n_process)
    print("number of processes for converting feature: " + str(n_proc))
    p = Pool(n_proc)
    indexes = [0]
    len_slice = int(len(X)/n_proc)
    for i in range(1, n_proc+1):
        if i != n_proc:
            indexes.append(len_slice*(i))
        else:
            indexes.append(len(X))
    
    results = []
    
    for i in range(n_proc):
        results.append(p.apply_async(generate_example, args=(X[indexes[i]:indexes[i+1]], Y[indexes[i]:indexes[i+1]], args.kmer, i)))
        print(str(i+1) + ' processor started !')
    
    p.close()
    p.join()

    lines = []
    for result in results:
        lines.extend(result.get())

    
    path = "/".join(args.file_path.split('/')[:-1]) + "/" + str(args.kmer) + "/train.txt"
    print(path)
    file = open(path, "w")
    for line in lines:
        for k, word in enumerate(line[0]):
            file.write(str(word) + " " + str(line[1][k]) + "\n")
        file.write("\n")

    




        

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--kmer",
        default=1,
        type=int,
        help="K-mer",
    )
    parser.add_argument(
        "--n_process",
        default=24,
        type=int,
        help="Number of processes for data processing",
    )
    parser.add_argument(
        "--file_path",
        default=None,
        type=str,
        help="The path of the file to be processed",
    )
    parser.add_argument(
        "--output_path",
        default=None,
        type=str,
        help="The path of the processed data",
    )
    args = parser.parse_args()

    Process(args)
    

    


if __name__ == "__main__":
    main()