deeppavlov/metrics/bleu.py
def bleu_advanced(y_true: List[Any], y_predicted: List[Any],
                  weights: Tuple=(1,), smoothing_function=SMOOTH.method1,
                  auto_reweigh=False, penalty=True) -> float:
    """Calculate BLEU score

    Parameters:
        y_true: list of reference tokens
        y_predicted: list of query tokens
        weights: n-gram weights
        smoothing_function: SmoothingFunction
        auto_reweigh: Option to re-normalize the weights uniformly
        penalty: either enable brevity penalty or not

    Return:
        BLEU score
    """

    bleu_measure = sentence_bleu([y_true], y_predicted, weights, smoothing_function, auto_reweigh)

    hyp_len = len(y_predicted)
    hyp_lengths = hyp_len
    ref_lengths = closest_ref_length([y_true], hyp_len)

    bpenalty = brevity_penalty(ref_lengths, hyp_lengths)

    if penalty is True or bpenalty == 0:
        return bleu_measure

    return bleu_measure/bpenalty
def bleu_advanced(y_true: List[Any], y_predicted: List[Any],
                  weights: Tuple=(1,), smoothing_function=SMOOTH.method1,
                  auto_reweigh=False, penalty=True) -> float:
    bleu_measure = sentence_bleu([y_true], y_predicted, weights, smoothing_function, auto_reweigh)

    hyp_len = len(y_predicted)
    hyp_lengths = hyp_len
    ref_lengths = closest_ref_length([y_true], hyp_len)

    bpenalty = brevity_penalty(ref_lengths, hyp_lengths)

    if penalty is True or bpenalty == 0:
        return bleu_measure

    return bleu_measure/bpenalty
def bleu_advanced ( y_true : List [ Any ] , y_predicted : List [ Any ] , weights : Tuple = ( 1 , ) , smoothing_function = SMOOTH . method1 , auto_reweigh = False , penalty = True ) -> float : bleu_measure = sentence_bleu ( [ y_true ] , y_predicted , weights , smoothing_function , auto_reweigh ) hyp_len = len ( y_predicted ) hyp_lengths = hyp_len ref_lengths = closest_ref_length ( [ y_true ] , hyp_len ) bpenalty = brevity_penalty ( ref_lengths , hyp_lengths ) if penalty is True or bpenalty == 0 : return bleu_measure return bleu_measure / bpenalty
Calculate BLEU score

    Parameters:
        y_true: list of reference tokens
        y_predicted: list of query tokens
        weights: n-gram weights
        smoothing_function: SmoothingFunction
        auto_reweigh: Option to re-normalize the weights uniformly
        penalty: either enable brevity penalty or not

    Return:
        BLEU score
Calculate BLEU score