[8af014]: / source / arguments.py

Download this file

124 lines (111 with data), 2.8 kB

  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
import argparse
import os
def argparser():
parser = argparse.ArgumentParser()
# for model
parser.add_argument(
'--seq_window_lengths',
type=int,
nargs='+',
help='Space seperated list of motif filter lengths. (ex, --window_lengths 4 8 12)'
)
parser.add_argument(
'--smi_window_lengths',
type=int,
nargs='+',
help='Space seperated list of motif filter lengths. (ex, --window_lengths 4 8 12)'
)
parser.add_argument(
'--num_windows',
type=int,
nargs='+',
help='Space seperated list of the number of motif filters corresponding to length list. (ex, --num_windows 100 200 100)'
)
parser.add_argument(
'--num_hidden',
type=int,
default=0,
help='Number of neurons in hidden layer.'
)
parser.add_argument(
'--num_classes',
type=int,
default=0,
help='Number of classes (families).'
)
parser.add_argument(
'--max_seq_len',
type=int,
default=0,
help='Length of input sequences.'
)
parser.add_argument(
'--max_smi_len',
type=int,
default=0,
help='Length of input sequences.'
)
# for learning
parser.add_argument(
'--learning_rate',
type=float,
default=0.001,
help='Initial learning rate.'
)
parser.add_argument(
'--num_epoch',
type=int,
default=100,
help='Number of epochs to train.'
)
parser.add_argument(
'--batch_size',
type=int,
default=256,
help='Batch size. Must divide evenly into the dataset sizes.'
)
parser.add_argument(
'--dataset_path',
type=str,
default='/data/kiba/',
help='Directory for input data.'
)
parser.add_argument(
'--problem_type',
type=int,
default=1,
help='Type of the prediction problem (1-4)'
)
parser.add_argument(
'--binary_th',
type=float,
default=0.0,
help='Threshold to split data into binary classes'
)
parser.add_argument(
'--is_log',
type=int,
default=0,
help='use log transformation for Y'
)
parser.add_argument(
'--checkpoint_path',
type=str,
default='',
help='Path to write checkpoint file.'
)
parser.add_argument(
'--log_dir',
type=str,
default='/tmp',
help='Directory for log data.'
)
FLAGS, unparsed = parser.parse_known_args()
# check validity
#assert( len(FLAGS.window_lengths) == len(FLAGS.num_windows) )
return FLAGS
def logging(msg, FLAGS):
fpath = os.path.join( FLAGS.log_dir, "log.txt" )
with open( fpath, "a" ) as fw:
fw.write("%s\n" % msg)
#print(msg)