Mercurial > repos > bgruening > sklearn_feature_selection

annotate to_categorical.py @ 33:e918f33f7d4f draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8850f42c2c3763e614f7454c9c006f3d2ff572c0
author bgruening
date Fri, 27 May 2022 11:29:05 +0000
parents 5773e98921fc
children
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93f3b307485f "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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1 import argparse
93f3b307485f "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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2 import json
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3 import warnings
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5 import numpy as np
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6 import pandas as pd
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7 from keras.utils import to_categorical
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10 def main(inputs, infile, outfile, num_classes=None):
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11 """
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12 Parameter
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13 ---------
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14 input : str
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15 File path to galaxy tool parameter
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17 infile : str
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18 File paths of input vector
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20 outfile : str
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21 File path to output matrix
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23 num_classes : str
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24 Total number of classes. If None, this would be inferred as the (largest number in y) + 1
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26 """
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27 warnings.simplefilter("ignore")
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29 with open(inputs, "r") as param_handler:
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30 params = json.load(param_handler)
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32 input_header = params["header0"]
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33 header = "infer" if input_header else None
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35 input_vector = pd.read_csv(infile, sep="\t", header=header)
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37 output_matrix = to_categorical(input_vector, num_classes=num_classes)
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39 np.savetxt(outfile, output_matrix, fmt="%d", delimiter="\t")
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42 if __name__ == "__main__":
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43 aparser = argparse.ArgumentParser()
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44 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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45 aparser.add_argument("-y", "--infile", dest="infile")
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46 aparser.add_argument(
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47 "-n", "--num_classes", dest="num_classes", type=int, default=None
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48 )
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49 aparser.add_argument("-o", "--outfile", dest="outfile")
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50 args = aparser.parse_args()
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52 main(args.inputs, args.infile, args.outfile, args.num_classes)