Source code for deeppavlov.dataset_iterators.file_paths_iterator

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from logging import getLogger
from pathlib import Path
from typing import Tuple, Iterator, Optional, Dict, List, Union

import numpy as np

from deeppavlov.core.common.registry import register
from import DataLearningIterator
from import chunk_generator

log = getLogger(__name__)

[docs]@register('file_paths_iterator') class FilePathsIterator(DataLearningIterator): """Dataset iterator for datasets like 1 Billion Word Benchmark. It gets lists of file paths from the data dictionary and returns lines from each file. Args: data: dict with keys ``'train'``, ``'valid'`` and ``'test'`` and values seed: random seed for data shuffling shuffle: whether to shuffle data during batching """ def __init__(self, data: Dict[str, List[Union[str, Path]]], seed: Optional[int] = None, shuffle: bool = True, *args, **kwargs) -> None: self.seed = seed self.np_random = np.random.RandomState(seed) super().__init__(data, seed, shuffle, *args, **kwargs) def _shard_generator(self, shards: List[Union[str, Path]], shuffle: bool = False) -> List[str]: shards_to_choose = list(shards) if shuffle: self.np_random.shuffle(shards_to_choose) for shard in shards_to_choose:'Loaded shard from {shard}') with open(shard, encoding='utf-8') as f: lines = f.readlines() if shuffle: self.np_random.shuffle(lines) yield lines def gen_batches(self, batch_size: int, data_type: str = 'train', shuffle: Optional[bool] = None) \ -> Iterator[Tuple[str, str]]: if shuffle is None: shuffle = self.shuffle tgt_data =[data_type] shard_generator = self._shard_generator(tgt_data, shuffle=shuffle) for shard in shard_generator: if not (batch_size): bs = len(shard) lines_generator = chunk_generator(shard, bs) for lines in lines_generator: yield (lines, [None] * len(lines))