Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate label_encoder.py @ 45:838ca001438d draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5eca9041ce0154eded5aec07195502d5eb3cdd4f
author bgruening
date Fri, 03 Nov 2023 23:25:01 +0000
parents 06d772036a62
children
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73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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1 import argparse
73e7f1c76ece "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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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 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)