Source code for

import math
import collections
import torch
from import ngrams_iterator

def _compute_ngram_counter(tokens, max_n):
    """ Create a Counter with a count of unique n-grams in the tokens list

        tokens: a list of tokens (typically a string split on whitespaces)
        max_n: the maximum order of n-gram wanted

        output: a collections.Counter object with the unique n-grams and their
            associated count

        >>> from import _compute_ngram_counter
        >>> tokens = ['me', 'me', 'you']
        >>> _compute_ngram_counter(tokens, 2)
            Counter({('me',): 2,
             ('you',): 1,
             ('me', 'me'): 1,
             ('me', 'you'): 1,
             ('me', 'me', 'you'): 1})
    assert max_n > 0
    ngrams_counter = collections.Counter(tuple(x.split(' '))
                                         for x in ngrams_iterator(tokens, max_n))

    return ngrams_counter

[docs]def bleu_score(candidate_corpus, references_corpus, max_n=4, weights=[0.25] * 4): """Computes the BLEU score between a candidate translation corpus and a references translation corpus. Based on Args: candidate_corpus: an iterable of candidate translations. Each translation is an iterable of tokens references_corpus: an iterable of iterables of reference translations. Each translation is an iterable of tokens max_n: the maximum n-gram we want to use. E.g. if max_n=3, we will use unigrams, bigrams and trigrams weights: a list of weights used for each n-gram category (uniform by default) Examples: >>> from import bleu_score >>> candidate_corpus = [['My', 'full', 'pytorch', 'test'], ['Another', 'Sentence']] >>> references_corpus = [[['My', 'full', 'pytorch', 'test'], ['Completely', 'Different']], [['No', 'Match']]] >>> bleu_score(candidate_corpus, references_corpus) 0.8408964276313782 """ assert max_n == len(weights), 'Length of the "weights" list has be equal to max_n' assert len(candidate_corpus) == len(references_corpus),\ 'The length of candidate and reference corpus should be the same' clipped_counts = torch.zeros(max_n) total_counts = torch.zeros(max_n) weights = torch.tensor(weights) candidate_len = 0.0 refs_len = 0.0 for (candidate, refs) in zip(candidate_corpus, references_corpus): candidate_len += len(candidate) # Get the length of the reference that's closest in length to the candidate refs_len_list = [float(len(ref)) for ref in refs] refs_len += min(refs_len_list, key=lambda x: abs(len(candidate) - x)) reference_counters = _compute_ngram_counter(refs[0], max_n) for ref in refs[1:]: reference_counters = reference_counters | _compute_ngram_counter(ref, max_n) candidate_counter = _compute_ngram_counter(candidate, max_n) clipped_counter = candidate_counter & reference_counters for ngram in clipped_counter: clipped_counts[len(ngram) - 1] += clipped_counter[ngram] for ngram in candidate_counter: # TODO: no need to loop through the whole counter total_counts[len(ngram) - 1] += candidate_counter[ngram] if min(clipped_counts) == 0: return 0.0 else: pn = clipped_counts / total_counts log_pn = weights * torch.log(pn) score = torch.exp(sum(log_pn)) bp = math.exp(min(1 - refs_len / candidate_len, 0)) return bp * score.item()


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