Mercurial > repos > bgruening > sklearn_build_pipeline
view label_encoder.py @ 24:b1eda492f063 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author | bgruening |
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date | Thu, 11 Aug 2022 09:20:25 +0000 |
parents | 0c74d3164ac8 |
children | 118e230e85ce |
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import argparse import json import warnings import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder def main(inputs, infile, outfile): """ Parameter --------- input : str File path to galaxy tool parameter infile : str File paths of input vector outfile : str File path to output vector """ warnings.simplefilter('ignore') with open(inputs, 'r') as param_handler: params = json.load(param_handler) input_header = params['header0'] header = 'infer' if input_header else None input_vector = pd.read_csv(infile, sep='\t', header=header) le = LabelEncoder() output_vector = le.fit_transform(input_vector) np.savetxt(outfile, output_vector, fmt="%d", delimiter='\t') if __name__ == '__main__': aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-y", "--infile", dest="infile") aparser.add_argument("-o", "--outfile", dest="outfile") args = aparser.parse_args() main(args.inputs, args.infile, args.outfile)