sklearn_discriminant_classifier: to_categorical.py comparison

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"

comparison

equal deleted inserted replaced

-:2d032cff49eb
+:eeaf989f1024
+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)

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