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"""
Augmenter Class
===================
"""
import random
import tqdm
from textattack.constraints import PreTransformationConstraint
from textattack.metrics.quality_metrics import Perplexity, USEMetric
from textattack.shared import AttackedText, utils
class Augmenter:
"""A class for performing data augmentation using TextAttack.
Returns all possible transformations for a given string. Currently only
supports transformations which are word swaps.
Args:
transformation (textattack.Transformation): the transformation
that suggests new texts from an input.
constraints: (list(textattack.Constraint)): constraints
that each transformation must meet
pct_words_to_swap: (float): [0., 1.], percentage of words to swap per augmented example
transformations_per_example: (int): Maximum number of augmentations
per input
high_yield: Whether to return a set of augmented texts that will be relatively similar, or to return only a
single one.
fast_augment: Stops additional transformation runs when number of successful augmentations reaches
transformations_per_example
advanced_metrics: return perplexity and USE Score of augmentation
Example::
>>> from textattack.transformations import WordSwapRandomCharacterDeletion, WordSwapQWERTY, CompositeTransformation
>>> from textattack.constraints.pre_transformation import RepeatModification, StopwordModification
>>> from textattack.augmentation import Augmenter
>>> transformation = CompositeTransformation([WordSwapRandomCharacterDeletion(), WordSwapQWERTY()])
>>> constraints = [RepeatModification(), StopwordModification()]
>>> # initiate augmenter
>>> augmenter = Augmenter(
... transformation=transformation,
... constraints=constraints,
... pct_words_to_swap=0.5,
... transformations_per_example=3
... )
>>> # additional parameters can be modified if not during initiation
>>> augmenter.enable_advanced_metrics = True
>>> augmenter.fast_augment = True
>>> augmenter.high_yield = True
>>> s = 'What I cannot create, I do not understand.'
>>> results = augmenter.augment(s)
>>> augmentations = results[0]
>>> perplexity_score = results[1]
>>> use_score = results[2]
"""
def __init__(
self,
transformation,
constraints=[],
pct_words_to_swap=0.1,
transformations_per_example=1,
high_yield=False,
fast_augment=False,
enable_advanced_metrics=False,
):
assert (
transformations_per_example > 0
), "transformations_per_example must be a positive integer"
assert 0.0 <= pct_words_to_swap <= 1.0, "pct_words_to_swap must be in [0., 1.]"
self.transformation = transformation
self.pct_words_to_swap = pct_words_to_swap
self.transformations_per_example = transformations_per_example
self.constraints = []
self.pre_transformation_constraints = []
self.high_yield = high_yield
self.fast_augment = fast_augment
self.advanced_metrics = enable_advanced_metrics
for constraint in constraints:
if isinstance(constraint, PreTransformationConstraint):
self.pre_transformation_constraints.append(constraint)
else:
self.constraints.append(constraint)
def _filter_transformations(self, transformed_texts, current_text, original_text):
"""Filters a list of ``AttackedText`` objects to include only the ones
that pass ``self.constraints``."""
for C in self.constraints:
if len(transformed_texts) == 0:
break
if C.compare_against_original:
if not original_text:
raise ValueError(
f"Missing `original_text` argument when constraint {type(C)} is set to compare against "
f"`original_text` "
)
transformed_texts = C.call_many(transformed_texts, original_text)
else:
transformed_texts = C.call_many(transformed_texts, current_text)
return transformed_texts
def augment(self, text):
"""Returns all possible augmentations of ``text`` according to
``self.transformation``."""
attacked_text = AttackedText(text)
original_text = attacked_text
all_transformed_texts = set()
num_words_to_swap = max(
int(self.pct_words_to_swap * len(attacked_text.words)), 1
)
augmentation_results = []
for _ in range(self.transformations_per_example):
current_text = attacked_text
words_swapped = len(current_text.attack_attrs["modified_indices"])
while words_swapped < num_words_to_swap:
transformed_texts = self.transformation(
current_text, self.pre_transformation_constraints
)
# Get rid of transformations we already have
transformed_texts = [
t for t in transformed_texts if t not in all_transformed_texts
]
# Filter out transformations that don't match the constraints.
transformed_texts = self._filter_transformations(
transformed_texts, current_text, original_text
)
# if there's no more transformed texts after filter, terminate
if not len(transformed_texts):
break
# look for all transformed_texts that has enough words swapped
if self.high_yield or self.fast_augment:
ready_texts = [
text
for text in transformed_texts
if len(text.attack_attrs["modified_indices"])
>= num_words_to_swap
]
for text in ready_texts:
all_transformed_texts.add(text)
unfinished_texts = [
text for text in transformed_texts if text not in ready_texts
]
if len(unfinished_texts):
current_text = random.choice(unfinished_texts)
else:
# no need for further augmentations if all of transformed_texts meet `num_words_to_swap`
break
else:
current_text = random.choice(transformed_texts)
# update words_swapped based on modified indices
words_swapped = max(
len(current_text.attack_attrs["modified_indices"]),
words_swapped + 1,
)
all_transformed_texts.add(current_text)
# when with fast_augment, terminate early if there're enough successful augmentations
if (
self.fast_augment
and len(all_transformed_texts) >= self.transformations_per_example
):
if not self.high_yield:
all_transformed_texts = random.sample(
all_transformed_texts, self.transformations_per_example
)
break
perturbed_texts = sorted([at.printable_text() for at in all_transformed_texts])
if self.advanced_metrics:
for transformed_texts in all_transformed_texts:
augmentation_results.append(
AugmentationResult(original_text, transformed_texts)
)
perplexity_stats = Perplexity().calculate(augmentation_results)
use_stats = USEMetric().calculate(augmentation_results)
return perturbed_texts, perplexity_stats, use_stats
return perturbed_texts
def augment_many(self, text_list, show_progress=False):
"""Returns all possible augmentations of a list of strings according to
``self.transformation``.
Args:
text_list (list(string)): a list of strings for data augmentation
Returns a list(string) of augmented texts.
:param show_progress: show process during augmentation
"""
if show_progress:
text_list = tqdm.tqdm(text_list, desc="Augmenting data...")
return [self.augment(text) for text in text_list]
def augment_text_with_ids(self, text_list, id_list, show_progress=True):
"""Supplements a list of text with more text data.
Returns the augmented text along with the corresponding IDs for
each augmented example.
"""
if len(text_list) != len(id_list):
raise ValueError("List of text must be same length as list of IDs")
if self.transformations_per_example == 0:
return text_list, id_list
all_text_list = []
all_id_list = []
if show_progress:
text_list = tqdm.tqdm(text_list, desc="Augmenting data...")
for text, _id in zip(text_list, id_list):
all_text_list.append(text)
all_id_list.append(_id)
augmented_texts = self.augment(text)
all_text_list.extend
all_text_list.extend([text] + augmented_texts)
all_id_list.extend([_id] * (1 + len(augmented_texts)))
return all_text_list, all_id_list
def __repr__(self):
main_str = "Augmenter" + "("
lines = []
# self.transformation
lines.append(utils.add_indent(f"(transformation): {self.transformation}", 2))
# self.constraints
constraints_lines = []
constraints = self.constraints + self.pre_transformation_constraints
if len(constraints):
for i, constraint in enumerate(constraints):
constraints_lines.append(utils.add_indent(f"({i}): {constraint}", 2))
constraints_str = utils.add_indent("\n" + "\n".join(constraints_lines), 2)
else:
constraints_str = "None"
lines.append(utils.add_indent(f"(constraints): {constraints_str}", 2))
main_str += "\n " + "\n ".join(lines) + "\n"
main_str += ")"
return main_str
class AugmentationResult:
def __init__(self, text1, text2):
self.original_result = self.tempResult(text1)
self.perturbed_result = self.tempResult(text2)
class tempResult:
def __init__(self, text):
self.attacked_text = text
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