Mercurial > repos > bgruening > model_prediction

annotate label_encoder.py @ 11:60c726f5cc5e draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Sat, 01 May 2021 00:55:20 +0000
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children 3bb1b688b0e4
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60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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1 import argparse
60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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2 import json
60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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3 import warnings
60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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5 import numpy as np
60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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6 import pandas as pd
60c726f5cc5e "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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7 from sklearn.preprocessing import LabelEncoder
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10 def main(inputs, infile, outfile):
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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 vector
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23 """
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24 warnings.simplefilter('ignore')
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26 with open(inputs, 'r') as param_handler:
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27 params = json.load(param_handler)
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29 input_header = params['header0']
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30 header = 'infer' if input_header else None
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32 input_vector = pd.read_csv(infile, sep='\t', header=header)
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34 le = LabelEncoder()
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36 output_vector = le.fit_transform(input_vector)
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38 np.savetxt(outfile, output_vector, fmt="%d", delimiter='\t')
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41 if __name__ == '__main__':
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42 aparser = argparse.ArgumentParser()
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43 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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44 aparser.add_argument("-y", "--infile", dest="infile")
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45 aparser.add_argument("-o", "--outfile", dest="outfile")
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46 args = aparser.parse_args()
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48 main(args.inputs, args.infile, args.outfile)