forked from moreo/QuaPy
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import argparse
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import quapy as qp
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from data import ResultSubmission
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import constants
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import os
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import pickle
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from tqdm import tqdm
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from data import gen_load_samples_T1
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from glob import glob
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import constants
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"""
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LeQua2022 prediction script
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"""
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def main(args):
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# check the number of samples
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nsamples = len(glob(os.path.join(args.samples, '*.txt')))
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if nsamples not in {constants.DEV_SAMPLES, constants.TEST_SAMPLES}:
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print(f'Warning: The number of samples does neither coincide with the expected number of '
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f'dev samples ({constants.DEV_SAMPLES}) nor with the expected number of '
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f'test samples ({constants.TEST_SAMPLES}).')
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# load pickled model
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model = pickle.load(open(args.model, 'rb'))
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# predictions
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predictions = ResultSubmission()
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for sampleid, sample in tqdm(gen_load_samples_T1(args.samples, args.nf), desc='predicting', total=nsamples):
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predictions.add(sampleid, model.quantify(sample))
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# saving
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qp.util.create_parent_dir(args.output)
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predictions.dump(args.output)
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if __name__=='__main__':
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parser = argparse.ArgumentParser(description='LeQua2022 prediction script')
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parser.add_argument('model', metavar='MODEL-PATH', type=str,
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help='Path of saved model')
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parser.add_argument('samples', metavar='SAMPLES-PATH', type=str,
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help='Path to the directory containing the samples')
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parser.add_argument('output', metavar='PREDICTIONS-PATH', type=str,
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help='Path where to store the predictions file')
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parser.add_argument('nf', metavar='NUM-FEATURES', type=int,
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help='Number of features seen during training')
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args = parser.parse_args()
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if not os.path.exists(args.samples):
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raise FileNotFoundError(f'path {args.samples} does not exist')
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if not os.path.isdir(args.samples):
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raise ValueError(f'path {args.samples} is not a valid directory')
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main(args)
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