70 lines
1.9 KiB
Python
Executable File
70 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2017 Mycroft AI Inc.
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import json
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from prettyparse import create_parser
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from precise.network_runner import Listener
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from precise.params import inject_params
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from precise.scripts.test import show_stats
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from precise.train_data import TrainData
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usage = '''
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Evaluate a list of models on a dataset
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:-t --use-train
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Evaluate training data instead of test data
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:-o --output str stats.json
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Output json file
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...
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'''
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def main():
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parser = create_parser(usage)
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parser.add_argument('models', nargs='*', help='List of model filenames')
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args = TrainData.parse_args(parser)
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data = TrainData.from_both(args.db_file, args.db_folder, args.data_dir)
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filenames = sum(data.train_files if args.use_train else data.test_files, [])
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print('Data:', data)
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stats = {}
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for model_name in args.models:
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inject_params(model_name)
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train, test = data.load(args.use_train, not args.use_train)
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inputs, targets = train if args.use_train else test
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predictions = Listener.find_runner(model_name)(model_name).predict(inputs)
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true_pos, true_neg = [], []
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false_pos, false_neg = [], []
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for name, target, prediction in zip(filenames, targets, predictions):
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{
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(True, False): false_pos,
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(True, True): true_pos,
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(False, True): false_neg,
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(False, False): true_neg
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}[prediction[0] > 0.5, target[0] > 0.5].append(name)
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print('----', model_name, '----')
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show_stats(false_pos, false_neg, true_pos, true_neg, False)
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stats[model_name] = {
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'true_pos': len(true_pos),
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'true_neg': len(true_neg),
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'false_pos': len(false_pos),
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'false_neg': len(false_neg),
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}
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print('Writing to:', args.output)
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with open(args.output, 'w') as f:
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json.dump(stats, f)
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if __name__ == '__main__':
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main()
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