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from nmtscore import NMTScorer
from dataclasses import dataclass
from typing import List, Union, Optional
import numpy as np
from scipy.special import softmax
from scipy.stats import permutation_test
@dataclass
class TranslationDirectionResult:
sentence1: Union[str, List[str]]
sentence2: Union[str, List[str]]
lang1: str
lang2: str
raw_prob_1_to_2: float
raw_prob_2_to_1: float
pvalue: Optional[float] = None
@property
def num_sentences(self):
return len(self.sentence1) if isinstance(self.sentence1, list) else 1
@property
def prob_1_to_2(self):
return softmax([self.raw_prob_1_to_2, self.raw_prob_2_to_1])[0]
@property
def prob_2_to_1(self):
return softmax([self.raw_prob_1_to_2, self.raw_prob_2_to_1])[1]
@property
def predicted_direction(self) -> str:
if self.raw_prob_1_to_2 >= self.raw_prob_2_to_1:
return self.lang1 + '→' + self.lang2
else:
return self.lang2 + '→' + self.lang1
def __str__(self):
s = f"""\
Predicted direction: {self.predicted_direction}
{self.num_sentences} sentence pair{"s" if self.num_sentences > 1 else ""}
{self.lang1}→{self.lang2}: {self.prob_1_to_2:.3f}
{self.lang2}→{self.lang1}: {self.prob_2_to_1:.3f}"""
if self.pvalue is not None:
s += f"\np-value: {self.pvalue}\n"
return s
class TranslationDirectionDetector:
def __init__(self, scorer: NMTScorer = None, use_normalization: bool = False):
self.scorer = scorer or NMTScorer()
self.use_normalization = use_normalization
def detect(self,
sentence1: Union[str, List[str]],
sentence2: Union[str, List[str]],
lang1: str,
lang2: str,
return_pvalue: bool = False,
pvalue_n_resamples: int = 9999,
score_kwargs: dict = None
) -> TranslationDirectionResult:
if isinstance(sentence1, list) and isinstance(sentence2, list):
if len(sentence1) != len(sentence2):
raise ValueError("Lists sentence1 and sentence2 must have same length")
if len(sentence1) == 0:
raise ValueError("Lists sentence1 and sentence2 must not be empty")
if len(sentence1) == 1 and return_pvalue:
raise ValueError("return_pvalue=True requires the documents to have multiple sentences")
if lang1 == lang2:
raise ValueError("lang1 and lang2 must be different")
prob_1_to_2 = self.scorer.score_direct(
sentence2, sentence1,
lang2, lang1,
normalize=self.use_normalization,
both_directions=False,
score_kwargs=score_kwargs
)
prob_2_to_1 = self.scorer.score_direct(
sentence1, sentence2,
lang1, lang2,
normalize=self.use_normalization,
both_directions=False,
score_kwargs=score_kwargs
)
pvalue = None
if isinstance(sentence1, list): # document-level
# Compute the average probability per target token, across the complete document
# 1. Convert probabilities back to log probabilities
log_prob_1_to_2 = np.log2(np.array(prob_1_to_2))
log_prob_2_to_1 = np.log2(np.array(prob_2_to_1))
# 2. Reverse the sentence-level length normalization
sentence1_lengths = np.array([self._get_sentence_length(s) for s in sentence1])
sentence2_lengths = np.array([self._get_sentence_length(s) for s in sentence2])
log_prob_1_to_2 = sentence2_lengths * log_prob_1_to_2
log_prob_2_to_1 = sentence1_lengths * log_prob_2_to_1
# 4. Sum up the log probabilities across the document
total_log_prob_1_to_2 = log_prob_1_to_2.sum()
total_log_prob_2_to_1 = log_prob_2_to_1.sum()
# 3. Document-level length normalization
avg_log_prob_1_to_2 = total_log_prob_1_to_2 / sum(sentence2_lengths)
avg_log_prob_2_to_1 = total_log_prob_2_to_1 / sum(sentence1_lengths)
# 4. Convert back to probabilities
prob_1_to_2 = 2 ** avg_log_prob_1_to_2
prob_2_to_1 = 2 ** avg_log_prob_2_to_1
if return_pvalue:
x = np.vstack([log_prob_1_to_2, sentence2_lengths]).T
y = np.vstack([log_prob_2_to_1, sentence1_lengths]).T
result = permutation_test(
data=(x, y),
statistic=self._statistic_token_mean,
permutation_type="samples",
n_resamples=pvalue_n_resamples,
)
pvalue = result.pvalue
else:
if return_pvalue:
raise ValueError("return_pvalue=True requires sentence1 and sentence2 to be lists of sentences")
return TranslationDirectionResult(
sentence1=sentence1,
sentence2=sentence2,
lang1=lang1,
lang2=lang2,
raw_prob_1_to_2=prob_1_to_2,
raw_prob_2_to_1=prob_2_to_1,
pvalue=pvalue,
)
def _get_sentence_length(self, sentence: str) -> int:
tokens = self.scorer.model.tokenizer.tokenize(sentence)
return len(tokens)
@staticmethod
def _statistic_token_mean(x: np.ndarray, y: np.ndarray, axis: int = -1) -> float:
"""
Statistic for scipy.stats.permutation_test
:param x: Matrix of shape (2 x num_sentences). The first row contains the unnormalized log probability
for lang1→lang2, the second row contains the sentence lengths in lang2.
:param y: Same as x, but for lang2→lang1
:return: Difference between lang1→lang2 and lang2→lang1
"""
if axis != -1:
raise NotImplementedError("Only axis=-1 is supported")
# Add batch dim
if x.ndim == 2:
x = x[np.newaxis, ...]
y = y[np.newaxis, ...]
# Sum up the log probabilities across the document
total_log_prob_1_to_2 = x[:, 0].sum(axis=axis)
total_log_prob_2_to_1 = y[:, 0].sum(axis=axis)
# Document-level length normalization
avg_log_prob_1_to_2 = total_log_prob_1_to_2 / x[:, 1].sum(axis=axis)
avg_log_prob_2_to_1 = total_log_prob_2_to_1 / y[:, 1].sum(axis=axis)
# Convert to probabilities
prob_1_to_2 = 2 ** avg_log_prob_1_to_2
prob_2_to_1 = 2 ** avg_log_prob_2_to_1
# Compute difference
return prob_1_to_2 - prob_2_to_1
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