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mirror of https://github.com/QData/TextAttack.git synced 2021-10-13 00:05:06 +03:00

makefile and setup; need to fix imports

This commit is contained in:
Jack Morris
2020-06-15 17:07:12 -04:00
parent 10539ece4e
commit fcb82dac5e
185 changed files with 3894 additions and 2832 deletions

View File

@@ -1,8 +1,10 @@
import math
import torch
from textattack.constraints import Constraint
class LanguageModelConstraint(Constraint):
"""
Determines if two sentences have a swapped word that has a similar
@@ -12,35 +14,39 @@ class LanguageModelConstraint(Constraint):
max_log_prob_diff (float): the maximum difference in log-probability
between x and x_adv
"""
def __init__(self, max_log_prob_diff=None):
if max_log_prob_diff is None:
raise ValueError('Must set max_log_prob_diff')
raise ValueError("Must set max_log_prob_diff")
self.max_log_prob_diff = max_log_prob_diff
def get_log_probs_at_index(self, text_list, word_index):
""" Gets the log-probability of items in `text_list` at index
`word_index` according to a language model.
"""
raise NotImplementedError()
def _check_constraint(self, transformed_text, current_text, original_text=None):
try:
indices = transformed_text.attack_attrs['newly_modified_indices']
indices = transformed_text.attack_attrs["newly_modified_indices"]
except KeyError:
raise KeyError('Cannot apply language model constraint without `newly_modified_indices`')
raise KeyError(
"Cannot apply language model constraint without `newly_modified_indices`"
)
for i in indices:
probs = self.get_log_probs_at_index((current_text, transformed_text), i)
if len(probs) != 2:
raise ValueError(f'Error: get_log_probs_at_index returned {len(probs)} values for 2 inputs')
raise ValueError(
f"Error: get_log_probs_at_index returned {len(probs)} values for 2 inputs"
)
cur_prob, transformed_prob = probs
if self.max_log_prob_diff is None:
cur_prob, transformed_prob = math.log(p1), math.log(p2)
if abs(cur_prob - transformed_prob) > self.max_log_prob_diff:
return False
return True
def extra_repr_keys(self):
return ['max_log_prob_diff']
return ["max_log_prob_diff"]