96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Calculate statistics of feature files."""
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import argparse
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import logging
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from pathlib import Path
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import jsonlines
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from tqdm import tqdm
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from parakeet.datasets.data_table import DataTable
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def main():
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"""Run preprocessing process."""
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parser = argparse.ArgumentParser(
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description="Compute mean and variance of dumped raw features.")
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parser.add_argument(
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"--metadata", type=str, help="json file with id and file paths ")
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parser.add_argument(
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"--field-name",
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type=str,
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help="name of the field to compute statistics for.")
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parser.add_argument(
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"--output",
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type=str,
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help="path to save statistics. if not provided, "
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"stats will be saved in the above root directory with name stats.npy")
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parser.add_argument(
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"--verbose",
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type=int,
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default=1,
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help="logging level. higher is more logging. (default=1)")
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args = parser.parse_args()
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# set logger
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if args.verbose > 1:
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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elif args.verbose > 0:
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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else:
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logging.basicConfig(
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level=logging.WARN,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
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)
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logging.warning('Skip DEBUG/INFO messages')
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# check directory existence
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if args.output is None:
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args.output = Path(
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args.metadata).parent.with_name(args.field_name + "_stats.npy")
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else:
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args.output = Path(args.output)
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args.output.parent.mkdir(parents=True, exist_ok=True)
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with jsonlines.open(args.metadata, 'r') as reader:
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metadata = list(reader)
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dataset = DataTable(
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metadata,
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fields=[args.field_name],
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converters={args.field_name: np.load}, )
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logging.info(f"The number of files = {len(dataset)}.")
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# calculate statistics
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scaler = StandardScaler()
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for datum in tqdm(dataset):
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# StandardScalar supports (*, num_features) by default
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scaler.partial_fit(datum[args.field_name])
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stats = np.stack([scaler.mean_, scaler.scale_], axis=0)
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np.save(str(args.output), stats.astype(np.float32), allow_pickle=False)
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if __name__ == "__main__":
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main()
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