Source code for textattack.datasets.dataset

from collections import OrderedDict
import random

import torch

[docs]class Dataset( """Basic class for dataset. It operates as a map-style dataset, fetching data via :meth:`__getitem__` and :meth:`__len__` methods. .. note:: This class subclasses :obj:`` and therefore can be treated as a regular PyTorch Dataset. Args: dataset (:obj:`list[tuple]`): A list of :obj:`(input, output)` pairs. If :obj:`input` consists of multiple fields (e.g. "premise" and "hypothesis" for SNLI), :obj:`input` must be of the form :obj:`(input_1, input_2, ...)` and :obj:`input_columns` parameter must be set. :obj:`output` can either be an integer representing labels for classification or a string for seq2seq tasks. input_columns (:obj:`list[str]`, `optional`, defaults to :obj:`["text"]`): List of column names of inputs in order. label_map (:obj:`dict[int, int]`, `optional`, defaults to :obj:`None`): Mapping if output labels of the dataset should be re-mapped. Useful if model was trained with a different label arrangement. For example, if dataset's arrangement is 0 for `Negative` and 1 for `Positive`, but model's label arrangement is 1 for `Negative` and 0 for `Positive`, passing :obj:`{0: 1, 1: 0}` will remap the dataset's label to match with model's arrangements. Could also be used to remap literal labels to numerical labels (e.g. :obj:`{"positive": 1, "negative": 0}`). label_names (:obj:`list[str]`, `optional`, defaults to :obj:`None`): List of label names in corresponding order (e.g. :obj:`["World", "Sports", "Business", "Sci/Tech"]` for AG-News dataset). If not set, labels will printed as is (e.g. "0", "1", ...). This should be set to :obj:`None` for non-classification datasets. output_scale_factor (:obj:`float`, `optional`, defaults to :obj:`None`): Factor to divide ground-truth outputs by. Generally, TextAttack goal functions require model outputs between 0 and 1. Some datasets are regression tasks, in which case this is necessary. shuffle (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to shuffle the underlying dataset. .. note:: Generally not recommended to shuffle the underlying dataset. Shuffling can be performed using DataLoader or by shuffling the order of indices we attack. Examples:: >>> import textattack >>> # Example of sentiment-classification dataset >>> data = [("I enjoyed the movie a lot!", 1), ("Absolutely horrible film.", 0), ("Our family had a fun time!", 1)] >>> dataset = textattack.datasets.Dataset(data) >>> dataset[1:2] >>> # Example for pair of sequence inputs (e.g. SNLI) >>> data = [("A man inspects the uniform of a figure in some East Asian country.", "The man is sleeping"), 1)] >>> dataset = textattack.datasets.Dataset(data, input_columns=("premise", "hypothesis")) >>> # Example for seq2seq >>> data = [("J'aime le film.", "I love the movie.")] >>> dataset = textattack.datasets.Dataset(data) """ def __init__( self, dataset, input_columns=["text"], label_map=None, label_names=None, output_scale_factor=None, shuffle=False, ): self._dataset = dataset self.input_columns = input_columns self.label_map = label_map self.label_names = label_names if label_map: # If labels are remapped, the label names have to be remapped as well. self.label_names = [ self.label_names[self.label_map[i]] for i in self.label_map ] self.shuffled = shuffle self.output_scale_factor = output_scale_factor if shuffle: random.shuffle(self._dataset) def _format_as_dict(self, example): output = example[1] if self.label_map: output = self.label_map[output] if self.output_scale_factor: output = output / self.output_scale_factor if isinstance(example[0], str): if len(self.input_columns) != 1: raise ValueError( "Mismatch between the number of columns in `input_columns` and number of columns of actual input." ) input_dict = OrderedDict([(self.input_columns[0], example[0])]) else: if len(self.input_columns) != len(example[0]): raise ValueError( "Mismatch between the number of columns in `input_columns` and number of columns of actual input." ) input_dict = OrderedDict( [(c, example[0][i]) for i, c in enumerate(self.input_columns)] ) return input_dict, output
[docs] def shuffle(self): random.shuffle(self._dataset) self.shuffled = True
[docs] def filter_by_labels_(self, labels_to_keep): """Filter items by their labels for classification datasets. Performs in-place filtering. Args: labels_to_keep (:obj:`Union[Set, Tuple, List, Iterable]`): Set, tuple, list, or iterable of integers representing labels. """ if not isinstance(labels_to_keep, set): labels_to_keep = set(labels_to_keep) self._dataset = filter(lambda x: x[1] in labels_to_keep, self._dataset)
[docs] def __getitem__(self, i): """Return i-th sample.""" if isinstance(i, int): return self._format_as_dict(self._dataset[i]) else: # `idx` could be a slice or an integer. if it's a slice, # return the formatted version of the proper slice of the list return [self._format_as_dict(ex) for ex in self._dataset[i]]
[docs] def __len__(self): """Returns the size of dataset.""" return len(self._dataset)