Source code for

# 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 random import Random
from typing import List, Generator, Tuple, Any, Optional

from deeppavlov.core.common.registry import register

logger = getLogger(__name__)

[docs]@register('data_fitting_iterator') class DataFittingIterator: """Dataset iterator for fitting estimator models, like vocabs, kNN, vectorizers. Data is passed as a list of strings(documents). Generate batches (for large datasets). Args: data: list of documents doc_ids: provided document ids seed: random seed for data shuffling shuffle: whether to shuffle data during batching Attributes: shuffle: whether to shuffle data during batching random: instance of :class:`Random` initialized with a seed data: list of documents doc_ids: provided by a user ids or generated automatically ids """ def __init__(self, data: List[str], doc_ids: List[Any] = None, seed: int = None, shuffle: bool = True, *args, **kwargs) -> None: self.shuffle = shuffle self.random = Random(seed) = data self.doc_ids = doc_ids or self.get_doc_ids() def get_doc_ids(self): """Generate doc ids. Returns: doc ids """ return list(range(len( def get_doc_content(self, doc_id: Any) -> Optional[str]: """Get doc content by id. Args: doc_id: an id for a doc which content should be extracted Returns: doc content as a string if id exists or raise an error """ return[doc_id] def gen_batches(self, batch_size: int, shuffle: bool = None) \ -> Generator[Tuple[List[str], List[int]], Any, None]: """Gen batches of documents. Args: batch_size: a number of samples in a single batch shuffle: whether to shuffle data during batching Yields: generated tuple of documents and their ids """ if shuffle is None: shuffle = self.shuffle if shuffle: _doc_ids = self.random.sample(self.doc_ids, len(self.doc_ids)) else: _doc_ids = self.doc_ids if batch_size > 0: batches = [_doc_ids[i:i + batch_size] for i in range(0, len(_doc_ids), batch_size)] else: batches = [_doc_ids] # DEBUG # len_batches = len(batches) for i, doc_ids in enumerate(batches): # DEBUG # # "Processing batch # {} of {} ({} documents)".format(i, len_batches, len(doc_index))) docs = [self.get_doc_content(doc_id) for doc_id in doc_ids] yield docs, doc_ids def get_instances(self): """Get all data""" doc_ids = list(self.doc_ids) docs = [self.get_doc_content(doc_id) for doc_id in doc_ids] return docs, doc_ids