nlpaug.augmenter.word.random

Augmenter that apply random word operation to textual input.

class nlpaug.augmenter.word.random.RandomWordAug(action='delete', name='RandomWord_Aug', aug_min=1, aug_max=10, aug_p=0.3, stopwords=None, target_words=None, tokenizer=None, reverse_tokenizer=None, stopwords_regex=None, verbose=0)[source]

Bases: nlpaug.augmenter.word.word_augmenter.WordAugmenter

Augmenter that apply randomly behavior for augmentation.

Parameters:
  • action (str) – ‘substitute’, ‘swap’, ‘delete’ or ‘crop’. If value is ‘swap’, adjacent words will be swapped randomly. If value is ‘delete’, word will be removed randomly. If value is ‘crop’, a set of contunous word will be removed randomly.
  • aug_p (float) – Percentage of word will be augmented.
  • aug_min (int) – Minimum number of word will be augmented.
  • aug_max (int) – Maximum number of word will be augmented. If None is passed, number of augmentation is calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from aug_p. Otherwise, using aug_max.
  • stopwords (list) – List of words which will be skipped from augment operation. Not effective if action is ‘crop’
  • stopwords_regex (str) – Regular expression for matching words which will be skipped from augment operation. Not effective if action is ‘crop’
  • target_words (list) – List of word for replacement (used for substitute operation only). Default value is _.
  • tokenizer (func) – Customize tokenization process
  • reverse_tokenizer (func) – Customize reverse of tokenization process
  • name (str) – Name of this augmenter
>>> import nlpaug.augmenter.word as naw
>>> aug = naw.RandomWordAug()
augment(data, n=1, num_thread=1)
Parameters:
  • data (object/list) – Data for augmentation. It can be list of data (e.g. list of string or numpy) or single element (e.g. string or numpy)
  • n (int) – Default is 1. Number of unique augmented output. Will be force to 1 if input is list of data
  • num_thread (int) – Number of thread for data augmentation. Use this option when you are using CPU and n is larger than 1
Returns:

Augmented data

>>> augmented_data = aug.augment(data)