comparison to_categorical.py @ 35:eeaf989f1024 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author bgruening
date Tue, 13 Apr 2021 18:09:01 +0000
parents
children e76f6dfea5c9
comparison
equal deleted inserted replaced
34:2d032cff49eb 35:eeaf989f1024
1 import argparse
2 import json
3 import warnings
4
5 import numpy as np
6 import pandas as pd
7 from keras.utils import to_categorical
8
9
10 def main(inputs, infile, outfile, num_classes=None):
11 """
12 Parameter
13 ---------
14 input : str
15 File path to galaxy tool parameter
16
17 infile : str
18 File paths of input vector
19
20 outfile : str
21 File path to output matrix
22
23 num_classes : str
24 Total number of classes. If None, this would be inferred as the (largest number in y) + 1
25
26 """
27 warnings.simplefilter("ignore")
28
29 with open(inputs, "r") as param_handler:
30 params = json.load(param_handler)
31
32 input_header = params["header0"]
33 header = "infer" if input_header else None
34
35 input_vector = pd.read_csv(infile, sep="\t", header=header)
36
37 output_matrix = to_categorical(input_vector, num_classes=num_classes)
38
39 np.savetxt(outfile, output_matrix, fmt="%d", delimiter="\t")
40
41
42 if __name__ == "__main__":
43 aparser = argparse.ArgumentParser()
44 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
45 aparser.add_argument("-y", "--infile", dest="infile")
46 aparser.add_argument("-n", "--num_classes", dest="num_classes", type=int, default=None)
47 aparser.add_argument("-o", "--outfile", dest="outfile")
48 args = aparser.parse_args()
49
50 main(args.inputs, args.infile, args.outfile, args.num_classes)