Python sacrebleu.corpus_bleu方法代碼示例

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def calc_bleu_from_stats(sentence_stats: pd.DataFrame) -> sacrebleu.BLEU:
    corpus_stats = sentence_stats.sum(axis=0)
    corpus_bleu = sacrebleu.compute_bleu(
        correct=[
            corpus_stats.correct_1_grams,
            corpus_stats.correct_2_grams,
            corpus_stats.correct_3_grams,
            corpus_stats.correct_4_grams,
        ],
        total=[
            corpus_stats.total_1_grams,
            corpus_stats.total_2_grams,
            corpus_stats.total_3_grams,
            corpus_stats.total_4_grams,
        ],
        sys_len=corpus_stats.translation_length,
        ref_len=corpus_stats.reference_length,
    )
    return corpus_bleu 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def get_all_scores(
        orig_sents: List[str], sys_sents: List[str], refs_sents: List[List[str]],
        lowercase: bool = False, tokenizer: str = '13a', metrics: List[str] = DEFAULT_METRICS,
        ):
    scores = OrderedDict()
    if 'bleu' in metrics:
        scores['BLEU'] = corpus_bleu(sys_sents, refs_sents, force=True, tokenize=tokenizer, lowercase=lowercase).score
    if 'sari' in metrics:
        scores['SARI'] = corpus_sari(orig_sents, sys_sents, refs_sents, tokenizer=tokenizer, lowercase=lowercase)
    if 'samsa' in metrics:
        scores['SAMSA'] = corpus_samsa(orig_sents, sys_sents, tokenizer=tokenizer, verbose=True, lowercase=lowercase)
    if 'fkgl' in metrics:
        scores['FKGL'] = corpus_fkgl(sys_sents, tokenizer=tokenizer)
    quality_estimation_scores = corpus_quality_estimation(
            orig_sents,
            sys_sents,
            tokenizer=tokenizer,
            lowercase=lowercase
            )
    scores = add_dicts(
            scores,
            quality_estimation_scores,
            )
    return {key: round(value, 2) for key, value in scores.items()} 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def score_batch(self,
                    hypotheses: List[List[str]],
                    references: List[List[str]]) -> float:

        hyp_joined = [" ".join(hyp) for hyp in hypotheses]
        ref_joined = [" ".join(ref) for ref in references]

        bleu = corpus_bleu(hyp_joined, [ref_joined],
                           smooth_method=self.smooth_method,
                           smooth_value=self.smooth_value,
                           force=self.force,
                           lowercase=self.lowercase,
                           tokenize=self.tokenize,
                           use_effective_order=self.use_effective_order)

        return bleu.score


# pylint: disable=invalid-name 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def validate(model, test_data, beam_size=8, alpha=0.6, max_time_step=100):
    """For development Only"""
    pp = PostProcess()

    ref_stream = []
    sys_stream = []
    for batch in test_data:
        res = generate_batch(model, batch, beam_size, alpha, max_time_step)
        sys_stream.extend(res['token'])
        ref_stream.extend(batch['target'])

    assert len(sys_stream) == len(ref_stream)
    sys_stream = [ pp.post_process(o) for o in sys_stream]
    ref_stream = [ ' '.join(o) for i in ref_stream]
    ref_streams = [ref_stream]

    bleu = sacrebleu.corpus_bleu(sys_stream, ref_streams, 
                          force=True, lowercase=False, 
                          tokenize='none').score
    chrf = sacrebleu.corpus_chrf(sys_stream, ref_stream)

    return bleu, chrf 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def evaluate(self, ignore_missing=False):
        if ignore_missing:
            keep = set(self.answers.keys())
            target_segments = {sid: text for sid, text in self._target_segments.items() if sid in keep}
        else:
            target_segments = self._target_segments
        answers = [self.answers.get(sid, "") for sid in target_segments]
        references = [target for target in target_segments.values()]
        bleu = corpus_bleu(answers, [references])
        self._results = {'SacreBLEU': bleu.score}

        if self._tokenization is not None:
            tok_answers = [self._tokenization(answer) for answer in answers]
            tok_references = [self._tokenization(target) for target in references]
            tok_bleu = corpus_bleu(tok_answers, [tok_references], tokenize='none', force=True)
            self._results['BLEU score'] = tok_bleu.score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def bleu(targets, predictions):
  """Computes BLEU score.

  Args:
    targets: list of strings or list of list of strings if multiple references
      are present.
    predictions: list of strings

  Returns:
    bleu_score across all targets and predictions
  """
  if isinstance(targets[0], list):
    targets = [[x for x in target] for target in targets]
  else:
    # Need to wrap targets in another list for corpus_bleu.
    targets = [targets]

  bleu_score = sacrebleu.corpus_bleu(predictions, targets,
                                     smooth_method="exp",
                                     smooth_value=0.0,
                                     force=False,
                                     lowercase=False,
                                     tokenize="intl",
                                     use_effective_order=False)
  return {"bleu": bleu_score.score} 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def cache_stats(self, ref, out):
    """
    Cache sufficient statistics for caculating SacreBLEU score

    Args:
      ref: A reference corpus
      out: An output corpus

    Returns:
      A list of cached statistics
    """
    if self.case_insensitive:
      ref = corpus_utils.lower(ref)
      out = corpus_utils.lower(out)

    cached_stats = []
    for r, o in zip(ref, out):
      re = sacrebleu.corpus_bleu(" ".join(o), " ".join(r))
      cached_stats.append( (re.counts, re.totals, re.sys_len, re.ref_len) )

    return cached_stats 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def computeBLEU(outputs, targets):
    targets = [[t[i] for t in targets] for i in range(len(targets[0]))]
    return corpus_bleu(outputs, targets, lowercase=True).score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def corpus_bleu(sys_stream, ref_streams):
    bleu = _corpus_bleu(sys_stream, ref_streams, tokenize='none')
    return bleu.score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def sentence_bleu(hypothesis, reference):
    bleu = _corpus_bleu(hypothesis, reference)
    for i in range(1, 4):
        bleu.counts[i] += 1
        bleu.totals[i] += 1
    bleu = compute_bleu(
        bleu.counts, bleu.totals,
        bleu.sys_len, bleu.ref_len,
        smooth='exp', smooth_floor=0.0,
    )
    return bleu.score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def _inference_with_bleu(self, generator, sample, model):
        import sacrebleu

        def decode(toks, escape_unk=False):
            s = self.tgt_dict.string(
                toks.int().cpu(),
                self.args.eval_bleu_remove_bpe,
                # The default unknown string in fairseq is `<unk>`, but
                # this is tokenized by sacrebleu as `< unk >`, inflating
                # BLEU scores. Instead, we use a somewhat more verbose
                # alternative that is unlikely to appear in the real
                # reference, but doesn't get split into multiple tokens.
                unk_string=(
                    "UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP"
                ),
            )
            if self.tokenizer:
                s = self.tokenizer.decode(s)
            return s

        gen_out = self.inference_step(generator, [model], sample, None)
        hyps, refs = [], []
        for i in range(len(gen_out)):
            hyps.append(decode(gen_out[i][0]['tokens']))
            refs.append(decode(
                utils.strip_pad(sample['target'][i], self.tgt_dict.pad()),
                escape_unk=True,  # don't count <unk> as matches to the hypo
            ))
        if self.args.eval_bleu_print_samples:
            logger.info('example hypothesis: ' + hyps[0])
            logger.info('example reference: ' + refs[0])
        if self.args.eval_tokenized_bleu:
            return sacrebleu.corpus_bleu(hyps, [refs], tokenize='none')
        else:
            return sacrebleu.corpus_bleu(hyps, [refs]) 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def __call__(self, ref_path, hyp_path):
    with tf.io.gfile.GFile(ref_path) as ref_stream, tf.io.gfile.GFile(hyp_path) as sys_stream:
      bleu = corpus_bleu(sys_stream, [ref_stream], force=True)
      return bleu.score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def corpus_bleu(sys_sents: List[str],
                refs_sents: List[List[str]],
                smooth_method: str = 'exp',
                smooth_value: float = None,
                force: bool = True,
                lowercase: bool = False,
                tokenizer: str = '13a',
                use_effective_order: bool = False):

    sys_sents = [utils_prep.normalize(sent, lowercase, tokenizer) for sent in sys_sents]
    refs_sents = [[utils_prep.normalize(sent, lowercase, tokenizer) for sent in ref_sents]
                  for ref_sents in refs_sents]

    return sacrebleu.corpus_bleu(sys_sents, refs_sents, smooth_method, smooth_value, force,
                                 lowercase=False, tokenize='none', use_effective_order=use_effective_order).score 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def sentence_bleu(sys_sent: str,
                  ref_sents: List[str],
                  smooth_method: str = 'floor',
                  smooth_value: float = None,
                  lowercase: bool = False,
                  tokenizer: str = '13a',
                  use_effective_order: bool = True):

    return corpus_bleu([sys_sent], [[ref] for ref in ref_sents], smooth_method, smooth_value, force=True,
                       lowercase=lowercase, tokenizer=tokenizer, use_effective_order=use_effective_order) 

# 需要導入模塊: import sacrebleu [as 別名]
# 或者: from sacrebleu import corpus_bleu [as 別名]
def validate(model, test_data, golden_file, beam_size=8, alpha=0.6, max_time_step=100):
    """For development Only"""
    pp = PostProcess()

    ref_stream = []
    for line in open(golden_file+'.input_clean'):
        if line.startswith('# ::tokens '):
            o = json.loads(line[len('# ::tokens '):].strip())
            ref_stream.append(' '.join(o).lower())
    # gold model output
    graph, gold_sys_stream, _, abstract = read_file(golden_file+'.preproc')
    ref_streams = [ref_stream]

    sys_stream = []
    for batch in test_data:
        res = generate_batch(model, batch, beam_size, alpha, max_time_step)
        sys_stream.extend(res['token'])

    assert len(sys_stream) == len(ref_stream)
    sys_stream = [ pp.post_process(o, abstract[i], graph[i]) for i, o in enumerate(sys_stream)]

    bleu = sacrebleu.corpus_bleu(sys_stream, ref_streams, 
                          force=True, lowercase=True, 
                          tokenize='none').score
    chrf = sacrebleu.corpus_chrf(sys_stream, ref_stream)

    return bleu, chrf