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 random import Random
from typing import List, Dict, Tuple, Any, Iterator

from deeppavlov.core.common.registry import register

[docs]@register('data_learning_iterator') class DataLearningIterator: """Dataset iterator for learning models, e. g. neural networks. Args: data: list of (x, y) pairs for every data type in ``'train'``, ``'valid'`` and ``'test'`` seed: random seed for data shuffling shuffle: whether to shuffle data during batching Attributes: shuffle: whether to shuffle data during batching random: instance of ``Random`` initialized with a seed """ def split(self, *args, **kwargs): """ Manipulate self.train, self.valid, and self.test into their final form. """ pass def preprocess(self, data: List[Tuple[Any, Any]], *args, **kwargs) -> List[Tuple[Any, Any]]: """ Transform the data for a specific data type (e.g. ``'train'``). """ return data def __init__(self, data: Dict[str, List[Tuple[Any, Any]]], seed: int = None, shuffle: bool = True, *args, **kwargs) -> None: self.shuffle = shuffle self.random = Random(seed) self.train = self.preprocess(data.get('train', []), *args, **kwargs) self.valid = self.preprocess(data.get('valid', []), *args, **kwargs) self.test = self.preprocess(data.get('test', []), *args, **kwargs) self.split(*args, **kwargs) = { 'train': self.train, 'valid': self.valid, 'test': self.test, 'all': self.train + self.test + self.valid } def gen_batches(self, batch_size: int, data_type: str = 'train', shuffle: bool = None) -> Iterator[Tuple[tuple, tuple]]: """Generate batches of inputs and expected output to train neural networks Args: batch_size: number of samples in batch data_type: can be either 'train', 'test', or 'valid' shuffle: whether to shuffle dataset before batching Yields: a tuple of a batch of inputs and a batch of expected outputs """ if shuffle is None: shuffle = self.shuffle data =[data_type] data_len = len(data) if data_len == 0: return order = list(range(data_len)) if shuffle: self.random.shuffle(order) if batch_size < 0: batch_size = data_len for i in range((data_len - 1) // batch_size + 1): yield tuple(zip(*[data[o] for o in order[i * batch_size:(i + 1) * batch_size]])) def get_instances(self, data_type: str = 'train') -> Tuple[tuple, tuple]: """Get all data for a selected data type Args: data_type (str): can be either ``'train'``, ``'test'``, ``'valid'`` or ``'all'`` Returns: a tuple of all inputs for a data type and all expected outputs for a data type """ data =[data_type] return tuple(zip(*data))