Mercurial > repos > bgruening > sklearn_train_test_eval
view to_categorical.py @ 12:443471b0a27a draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d970dae980fbe349414fc0889f719d875d999c5b"
author | bgruening |
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date | Fri, 27 Aug 2021 09:42:06 +0000 |
parents | caf7d2b71a48 |
children |
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import argparse import json import warnings import numpy as np import pandas as pd from keras.utils import to_categorical def main(inputs, infile, outfile, num_classes=None): """ Parameter --------- input : str File path to galaxy tool parameter infile : str File paths of input vector outfile : str File path to output matrix num_classes : str Total number of classes. If None, this would be inferred as the (largest number in y) + 1 """ 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) output_matrix = to_categorical(input_vector, num_classes=num_classes) np.savetxt(outfile, output_matrix, 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( "-n", "--num_classes", dest="num_classes", type=int, default=None ) aparser.add_argument("-o", "--outfile", dest="outfile") args = aparser.parse_args() main(args.inputs, args.infile, args.outfile, args.num_classes)