bleu.py

#!/usr/bin/python
import re
import sys
import math
import xml.sax.saxutils

from typing import List, Pattern, Tuple, Union, Dict, Any, Optional

"""
This script was adapted from the original version by hieuhoang1972 which is part of MOSES.
"""

# $Id: bleu.py 1307 2007-03-14 22:22:36Z hieuhoang1972 $

"""Provides:

cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
score_cooked(alltest, n=4): Score a list of cooked test sentences.

score_set(s, testid, refids, n=4): Interface with dataset.py; calculate BLEU score of testid against refids.

The reason for breaking the BLEU computation into three phases cook_refs(), cook_test(), and score_cooked() is to allow the caller to calculate BLEU scores for multiple test sets as efficiently as possible.
"""

# Added to bypass NIST-style pre-processing of hyp and ref files -- wade
nonorm = 0

preserve_case = False
eff_ref_len = "shortest"

normalize1: List[Tuple[Union[Pattern[str], str], str]] = [
    ("<skipped>", ""),  # strip "skipped" tags
    (r"-\n", ""),  # strip end-of-line hyphenation and join lines
    (r"\n", " "),  # join lines
    #    (r'(\d)\s+(?=\d)', r'\1'), # join digits
]
normalize1 = [(re.compile(pattern), replace) for (pattern, replace) in normalize1]

normalize2: List[Tuple[Union[Pattern[str], str], str]] = [
    (
        r"([\{-\~\[-\` -\&\(-\+\:-\@\/])",
        r" \1 ",
    ),  # tokenize punctuation. apostrophe is missing
    (
        r"([^0-9])([\.,])",
        r"\1 \2 ",
    ),  # tokenize period and comma unless preceded by a digit
    (
        r"([\.,])([^0-9])",
        r" \1 \2",
    ),  # tokenize period and comma unless followed by a digit
    (r"([0-9])(-)", r"\1 \2 "),  # tokenize dash when preceded by a digit
]
normalize2 = [(re.compile(pattern), replace) for (pattern, replace) in normalize2]


def normalize(s):
    """Normalize and tokenize text. This is lifted from NIST mteval-v11a.pl."""
    # Added to bypass NIST-style pre-processing of hyp and ref files -- wade
    if nonorm:
        return s.split()
    if type(s) is not str:
        s = " ".join(s)
    # language-independent part:
    for pattern, replace in normalize1:
        s = re.sub(pattern, replace, s)
    s = xml.sax.saxutils.unescape(s, {"&quot;": '"'})
    # language-dependent part (assuming Western languages):
    s = " %s " % s
    if not preserve_case:
        s = s.lower()  # this might not be identical to the original
    for pattern, replace in normalize2:
        s = re.sub(pattern, replace, s)
    return s.split()


def count_ngrams(words, n=4):
    counts: Dict[Any, int] = {}
    for k in range(1, n + 1):
        for i in range(len(words) - k + 1):
            ngram = tuple(words[i : i + k])
            counts[ngram] = counts.get(ngram, 0) + 1
    return counts


def cook_refs(refs, n=4):
    """Takes a list of reference sentences for a single segment
    and returns an object that encapsulates everything that BLEU
    needs to know about them."""

    refs = [normalize(ref) for ref in refs]
    maxcounts: Dict[Tuple[str], int] = {}
    for ref in refs:
        counts = count_ngrams(ref, n)
        for ngram, count in counts.items():
            maxcounts[ngram] = max(maxcounts.get(ngram, 0), count)
    return ([len(ref) for ref in refs], maxcounts)


def cook_test(test, item, n=4):
    """Takes a test sentence and returns an object that
    encapsulates everything that BLEU needs to know about it."""
    (reflens, refmaxcounts) = item
    test = normalize(test)
    result: Dict[str, Any] = {}
    result["testlen"] = len(test)

    # Calculate effective reference sentence length.

    if eff_ref_len == "shortest":
        result["reflen"] = min(reflens)
    elif eff_ref_len == "average":
        result["reflen"] = float(sum(reflens)) / len(reflens)
    elif eff_ref_len == "closest":
        min_diff: Optional[int] = None
        for reflen in reflens:
            if min_diff is None or abs(reflen - len(test)) < min_diff:
                min_diff = abs(reflen - len(test))
                result["reflen"] = reflen

    result["guess"] = [max(len(test) - k + 1, 0) for k in range(1, n + 1)]

    result["correct"] = [0] * n
    counts = count_ngrams(test, n)
    for ngram, count in counts.items():
        result["correct"][len(ngram) - 1] += min(refmaxcounts.get(ngram, 0), count)

    return result


def score_cooked(allcomps, n=4, ground=0, smooth=1):
    totalcomps: Dict[str, Any] = {
        "testlen": 0,
        "reflen": 0,
        "guess": [0] * n,
        "correct": [0] * n,
    }
    for comps in allcomps:
        for key in ["testlen", "reflen"]:
            totalcomps[key] += comps[key]
        for key in ["guess", "correct"]:
            for k in range(n):
                totalcomps[key][k] += comps[key][k]
    logbleu = 0.0
    all_bleus: List[float] = []
    for k in range(n):
        correct = totalcomps["correct"][k]
        guess = totalcomps["guess"][k]
        addsmooth = 0
        if smooth == 1 and k > 0:
            addsmooth = 1
        logbleu += math.log(correct + addsmooth + sys.float_info.min) - math.log(
            guess + addsmooth + sys.float_info.min
        )
        if guess == 0:
            all_bleus.append(-10000000.0)
        else:
            all_bleus.append(math.log(correct + sys.float_info.min) - math.log(guess))

    logbleu /= float(n)
    all_bleus.insert(0, logbleu)

    brevPenalty = min(
        0, 1 - float(totalcomps["reflen"] + 1) / (totalcomps["testlen"] + 1)
    )
    for i in range(len(all_bleus)):
        if i == 0:
            all_bleus[i] += brevPenalty
        all_bleus[i] = math.exp(all_bleus[i])
    return all_bleus


def bleu(refs, candidate, ground=0, smooth=1):
    refs = cook_refs(refs)
    test = cook_test(candidate, refs)
    return score_cooked([test], ground=ground, smooth=smooth)


def splitPuncts(line):
    return " ".join(re.findall(r"[\w]+|[^\s\w]", line))


def computeMaps(predictions, goldfile):
    predictionMap: Dict[str, list] = {}
    goldMap: Dict[str, list] = {}
    gf = open(goldfile, "r")

    for row in predictions:
        cols = row.strip().split("\t")
        if len(cols) == 1:
            (rid, pred) = (cols[0], "")
        else:
            (rid, pred) = (cols[0], cols[1])
        predictionMap[rid] = [splitPuncts(pred.strip().lower())]

    for row in gf:
        (rid, pred) = row.split("\t")
        if rid in predictionMap:  # Only insert if the id exists for the method
            if rid not in goldMap:
                goldMap[rid] = []
            goldMap[rid].append(splitPuncts(pred.strip().lower()))

    sys.stderr.write("Total: " + str(len(goldMap)) + "\n")
    return (goldMap, predictionMap)


# m1 is the reference map
# m2 is the prediction map
def bleuFromMaps(m1, m2):
    score = [0] * 5
    num = 0.0

    for key in m1:
        if key in m2:
            bl = bleu(m1[key], m2[key][0])
            score = [score[i] + bl[i] for i in range(0, len(bl))]
            num += 1
    return [s * 100.0 / num for s in score]


def smoothed_bleu_4(references, predictions, **kwargs):
    predictionMap = {}
    goldMap = {}

    for rid, pred in enumerate(predictions):
        predictionMap[rid] = [splitPuncts(pred.strip().lower())]

    for rid, row in enumerate(references):
        goldMap[rid] = [splitPuncts(row.strip().lower())]

    return bleuFromMaps(goldMap, predictionMap)[0]


if __name__ == "__main__":
    reference_file = sys.argv[1]
    predictions = []
    for row in sys.stdin:
        predictions.append(row)
    (goldMap, predictionMap) = computeMaps(predictions, reference_file)
    print(bleuFromMaps(goldMap, predictionMap)[0])