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# -*- coding: utf-8 -*-
# Natural Language Toolkit: BLEU Score
#
# Copyright (C) 2001-2020 NLTK Project
# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""BLEU score implementation."""
import math
import sys
from fractions import Fraction
import warnings
from collections import Counter
from evaluator.CodeBLEU.utils import ngrams
import pdb
def sentence_bleu(
references,
hypothesis,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None,
auto_reweigh=False,
):
"""
Calculate BLEU score (Bilingual Evaluation Understudy) from
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.
"BLEU: a method for automatic evaluation of machine translation."
In Proceedings of ACL. http://www.aclweb.org/anthology/P02-1040.pdf
>>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',
... 'forever', 'hearing', 'the', 'activity', 'guidebook',
... 'that', 'party', 'direct']
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will', 'forever',
... 'heed', 'Party', 'commands']
>>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the',
... 'Party']
>>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS
0.5045...
If there is no ngrams overlap for any order of n-grams, BLEU returns the
value 0. This is because the precision for the order of n-grams without
overlap is 0, and the geometric mean in the final BLEU score computation
multiplies the 0 with the precision of other n-grams. This results in 0
(independently of the precision of the othe n-gram orders). The following
example has zero 3-gram and 4-gram overlaps:
>>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS
0.0
To avoid this harsh behaviour when no ngram overlaps are found a smoothing
function can be used.
>>> chencherry = SmoothingFunction()
>>> sentence_bleu([reference1, reference2, reference3], hypothesis2,
... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS
0.0370...
The default BLEU calculates a score for up to 4-grams using uniform
weights (this is called BLEU-4). To evaluate your translations with
higher/lower order ngrams, use customized weights. E.g. when accounting
for up to 5-grams with uniform weights (this is called BLEU-5) use:
>>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.)
>>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS
0.3920...
:param references: reference sentences
:type references: list(list(str))
:param hypothesis: a hypothesis sentence
:type hypothesis: list(str)
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:param smoothing_function:
:type smoothing_function: SmoothingFunction
:param auto_reweigh: Option to re-normalize the weights uniformly.
:type auto_reweigh: bool
:return: The sentence-level BLEU score.
:rtype: float
"""
return corpus_bleu(
[references], [hypothesis], weights, smoothing_function, auto_reweigh
)
def corpus_bleu(
list_of_references,
hypotheses,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None,
auto_reweigh=False,
):
"""
Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all
the hypotheses and their respective references.
Instead of averaging the sentence level BLEU scores (i.e. marco-average
precision), the original BLEU metric (Papineni et al. 2002) accounts for
the micro-average precision (i.e. summing the numerators and denominators
for each hypothesis-reference(s) pairs before the division).
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will', 'forever',
... 'heed', 'Party', 'commands']
>>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS
0.5920...
The example below show that corpus_bleu() is different from averaging
sentence_bleu() for hypotheses
>>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1)
>>> score2 = sentence_bleu([ref2a], hyp2)
>>> (score1 + score2) / 2 # doctest: +ELLIPSIS
0.6223...
:param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses
:type list_of_references: list(list(list(str)))
:param hypotheses: a list of hypothesis sentences
:type hypotheses: list(list(str))
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:param smoothing_function:
:type smoothing_function: SmoothingFunction
:param auto_reweigh: Option to re-normalize the weights uniformly.
:type auto_reweigh: bool
:return: The corpus-level BLEU score.
:rtype: float
"""
# Before proceeding to compute BLEU, perform sanity checks.
p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches.
p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref.
hyp_lengths, ref_lengths = 0, 0
assert len(list_of_references) == len(hypotheses), (
"The number of hypotheses and their reference(s) should be the " "same "
)
# Iterate through each hypothesis and their corresponding references.
for references, hypothesis in zip(list_of_references, hypotheses):
# For each order of ngram, calculate the numerator and
# denominator for the corpus-level modified precision.
for i, _ in enumerate(weights, start=1):
p_i = modified_precision(references, hypothesis, i)
p_numerators[i] += p_i.numerator
p_denominators[i] += p_i.denominator
# Calculate the hypothesis length and the closest reference length.
# Adds them to the corpus-level hypothesis and reference counts.
hyp_len = len(hypothesis)
hyp_lengths += hyp_len
ref_lengths += closest_ref_length(references, hyp_len)
# Calculate corpus-level brevity penalty.
bp = brevity_penalty(ref_lengths, hyp_lengths)
# Uniformly re-weighting based on maximum hypothesis lengths if largest
# order of n-grams < 4 and weights is set at default.
if auto_reweigh:
if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25):
weights = (1 / hyp_lengths,) * hyp_lengths
# Collects the various precision values for the different ngram orders.
p_n = [
Fraction(p_numerators[i], p_denominators[i], _normalize=False)
for i, _ in enumerate(weights, start=1)
]
# Returns 0 if there's no matching n-grams
# We only need to check for p_numerators[1] == 0, since if there's
# no unigrams, there won't be any higher order ngrams.
if p_numerators[1] == 0:
return 0
# If there's no smoothing, set use method0 from SmoothinFunction class.
if not smoothing_function:
smoothing_function = SmoothingFunction().method1
# Smoothen the modified precision.
# Note: smoothing_function() may convert values into floats;
# it tries to retain the Fraction object as much as the
# smoothing method allows.
p_n = smoothing_function(
p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths
)
s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n))
s = bp * math.exp(math.fsum(s))
return s
def modified_precision(references, hypothesis, n):
"""
Calculate modified ngram precision.
The normal precision method may lead to some wrong translations with
high-precision, e.g., the translation, in which a word of reference
repeats several times, has very high precision.
This function only returns the Fraction object that contains the numerator
and denominator necessary to calculate the corpus-level precision.
To calculate the modified precision for a single pair of hypothesis and
references, cast the Fraction object into a float.
The famous "the the the ... " example shows that you can get BLEU precision
by duplicating high frequency words.
>>> reference1 = 'the cat is on the mat'.split()
>>> reference2 = 'there is a cat on the mat'.split()
>>> hypothesis1 = 'the the the the the the the'.split()
>>> references = [reference1, reference2]
>>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS
0.2857...
In the modified n-gram precision, a reference word will be considered
exhausted after a matching hypothesis word is identified, e.g.
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will',
... 'forever', 'heed', 'Party', 'commands']
>>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the',
... 'Party']
>>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> hypothesis = 'of the'.split()
>>> references = [reference1, reference2, reference3]
>>> float(modified_precision(references, hypothesis, n=1))
1.0
>>> float(modified_precision(references, hypothesis, n=2))
1.0
An example of a normal machine translation hypothesis:
>>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',
... 'forever', 'hearing', 'the', 'activity', 'guidebook',
... 'that', 'party', 'direct']
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will',
... 'forever', 'heed', 'Party', 'commands']
>>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the',
... 'Party']
>>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> references = [reference1, reference2, reference3]
>>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS
0.9444...
>>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS
0.5714...
>>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS
0.5882352941176471
>>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS
0.07692...
:param references: A list of reference translations.
:type references: list(list(str))
:param hypothesis: A hypothesis translation.
:type hypothesis: list(str)
:param n: The ngram order.
:type n: int
:return: BLEU's modified precision for the nth order ngram.
:rtype: Fraction
"""
# Extracts all ngrams in hypothesis
# Set an empty Counter if hypothesis is empty.
counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter()
# Extract a union of references' counts.
# max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references])
max_counts = {}
for reference in references:
reference_counts = (
Counter(ngrams(reference, n)) if len(reference) >= n else Counter()
)
for ngram in counts:
max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram])
# Assigns the intersection between hypothesis and references' counts.
clipped_counts = {
ngram: min(count, max_counts[ngram]) for ngram, count in counts.items()
}
numerator = sum(clipped_counts.values())
# Ensures that denominator is minimum 1 to avoid ZeroDivisionError.
# Usually this happens when the ngram order is > len(reference).
denominator = max(1, sum(counts.values()))
return Fraction(numerator, denominator, _normalize=False)
def closest_ref_length(references, hyp_len):
"""
This function finds the reference that is the closest length to the
hypothesis. The closest reference length is referred to as *r* variable
from the brevity penalty formula in Papineni et. al. (2002)
:param references: A list of reference translations.
:type references: list(list(str))
:param hyp_len: The length of the hypothesis.
:type hyp_len: int
:return: The length of the reference that's closest to the hypothesis.
:rtype: int
"""
ref_lens = (len(reference) for reference in references)
closest_ref_len = min(
ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)
)
return closest_ref_len
def brevity_penalty(closest_ref_len, hyp_len):
"""
Calculate brevity penalty.
As the modified n-gram precision still has the problem from the short
length sentence, brevity penalty is used to modify the overall BLEU
score according to length.
An example from the paper. There are three references with length 12, 15
and 17. And a concise hypothesis of the length 12. The brevity penalty is 1.
>>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
>>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15
>>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17
>>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
>>> references = [reference1, reference2, reference3]
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
1.0
In case a hypothesis translation is shorter than the references, penalty is
applied.
>>> references = [['a'] * 28, ['a'] * 28]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
0.2635971381157267
The length of the closest reference is used to compute the penalty. If the
length of a hypothesis is 12, and the reference lengths are 13 and 2, the
penalty is applied because the hypothesis length (12) is less then the
closest reference length (13).
>>> references = [['a'] * 13, ['a'] * 2]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
0.9200...
The brevity penalty doesn't depend on reference order. More importantly,
when two reference sentences are at the same distance, the shortest
reference sentence length is used.
>>> references = [['a'] * 13, ['a'] * 11]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> bp1 = brevity_penalty(closest_ref_len, hyp_len)
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(reversed(references), hyp_len)
>>> bp2 = brevity_penalty(closest_ref_len, hyp_len)
>>> bp1 == bp2 == 1
True
A test example from mteval-v13a.pl (starting from the line 705):
>>> references = [['a'] * 11, ['a'] * 8]
>>> hypothesis = ['a'] * 7
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
0.8668...
>>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]
>>> hypothesis = ['a'] * 7
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
1.0
:param hyp_len: The length of the hypothesis for a single sentence OR the
sum of all the hypotheses' lengths for a corpus
:type hyp_len: int
:param closest_ref_len: The length of the closest reference for a single
hypothesis OR the sum of all the closest references for every hypotheses.
:type closest_ref_len: int
:return: BLEU's brevity penalty.
:rtype: float
"""
if hyp_len > closest_ref_len:
return 1
# If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0
elif hyp_len == 0:
return 0
else:
return math.exp(1 - closest_ref_len / hyp_len)
class SmoothingFunction:
"""
This is an implementation of the smoothing techniques
for segment-level BLEU scores that was presented in
Boxing Chen and Collin Cherry (2014) A Systematic Comparison of
Smoothing Techniques for Sentence-Level BLEU. In WMT14.
http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf
"""
def __init__(self, epsilon=0.1, alpha=5, k=5):
"""
This will initialize the parameters required for the various smoothing
techniques, the default values are set to the numbers used in the
experiments from Chen and Cherry (2014).
>>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures',
... 'that', 'the', 'military', 'always', 'obeys', 'the',
... 'commands', 'of', 'the', 'party']
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures',
... 'that', 'the', 'military', 'will', 'forever', 'heed',
... 'Party', 'commands']
>>> chencherry = SmoothingFunction()
>>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS
0.4489...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS
0.4905...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS
0.4135...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS
0.4905...
:param epsilon: the epsilon value use in method 1
:type epsilon: float
:param alpha: the alpha value use in method 6
:type alpha: int
:param k: the k value use in method 4
:type k: int
"""
self.epsilon = epsilon
self.alpha = alpha
self.k = k
def method0(self, p_n, *args, **kwargs):
"""
No smoothing.
"""
p_n_new = []
for i, p_i in enumerate(p_n):
if p_i.numerator != 0:
p_n_new.append(p_i)
else:
_msg = str(
"\nThe hypothesis contains 0 counts of {}-gram overlaps.\n"
"Therefore the BLEU score evaluates to 0, independently of\n"
"how many N-gram overlaps of lower order it contains.\n"
"Consider using lower n-gram order or use "
"SmoothingFunction()"
).format(i + 1)
warnings.warn(_msg)
# When numerator==0 where denonminator==0 or !=0, the result
# for the precision score should be equal to 0 or undefined.
# Due to BLEU geometric mean computation in logarithm space,
# we we need to take the return sys.float_info.min such that
# math.log(sys.float_info.min) returns a 0 precision score.
p_n_new.append(sys.float_info.min)
return p_n_new
def method1(self, p_n, *args, **kwargs):
"""
Smoothing method 1: Add *epsilon* counts to precision with 0 counts.
"""
return [
(p_i.numerator + self.epsilon) / p_i.denominator
if p_i.numerator == 0
else p_i
for p_i in p_n
]
def method2(self, p_n, *args, **kwargs):
"""
Smoothing method 2: Add 1 to both numerator and denominator from
Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of
machine translation quality using longest common subsequence and
skip-bigram statistics. In ACL04.
"""
return [
Fraction(p_i.numerator + 1, p_i.denominator + 1, _normalize=False)
for p_i in p_n
]
def method3(self, p_n, *args, **kwargs):
"""
Smoothing method 3: NIST geometric sequence smoothing
The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each
precision score whose matching n-gram count is null.
k is 1 for the first 'n' value for which the n-gram match count is null/
For example, if the text contains:
- one 2-gram match
- and (consequently) two 1-gram matches
the n-gram count for each individual precision score would be:
- n=1 => prec_count = 2 (two unigrams)
- n=2 => prec_count = 1 (one bigram)
- n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1)
- n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2)
"""
incvnt = 1 # From the mteval-v13a.pl, it's referred to as k.
for i, p_i in enumerate(p_n):
if p_i.numerator == 0:
p_n[i] = 1 / (2 ** incvnt * p_i.denominator)
incvnt += 1
return p_n
def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 4:
Shorter translations may have inflated precision values due to having
smaller denominators; therefore, we give them proportionally
smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry
suggests dividing by 1/ln(len(T)), where T is the length of the translation.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
for i, p_i in enumerate(p_n):
if p_i.numerator == 0 and hyp_len != 0:
incvnt = i + 1 * self.k / math.log(
hyp_len
) # Note that this K is different from the K from NIST.
p_n[i] = incvnt / p_i.denominator
return p_n
def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 5:
The matched counts for similar values of n should be similar. To a
calculate the n-gram matched count, it averages the n−1, n and n+1 gram
matched counts.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
m = {}
# Requires an precision value for an addition ngram order.
p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)]
m[-1] = p_n[0] + 1
for i, p_i in enumerate(p_n):
p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3
m[i] = p_n[i]
return p_n
def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 6:
Interpolates the maximum likelihood estimate of the precision *p_n* with
a prior estimate *pi0*. The prior is estimated by assuming that the ratio
between pn and pn−1 will be the same as that between pn−1 and pn−2; from
Gao and He (2013) Training MRF-Based Phrase Translation Models using
Gradient Ascent. In NAACL.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
# This smoothing only works when p_1 and p_2 is non-zero.
# Raise an error with an appropriate message when the input is too short
# to use this smoothing technique.
assert p_n[2], "This smoothing method requires non-zero precision for bigrams."
for i, p_i in enumerate(p_n):
if i in [0, 1]: # Skips the first 2 orders of ngrams.
continue
else:
pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2]
# No. of ngrams in translation that matches the reference.
m = p_i.numerator
# No. of ngrams in translation.
l = sum(1 for _ in ngrams(hypothesis, i + 1))
# Calculates the interpolated precision.
p_n[i] = (m + self.alpha * pi0) / (l + self.alpha)
return p_n
def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 7:
Interpolates methods 4 and 5.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
p_n = self.method4(p_n, references, hypothesis, hyp_len)
p_n = self.method5(p_n, references, hypothesis, hyp_len)
return p_n

80
CodeBLEU/calc_code_bleu.py
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@ -1,80 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# -*- coding:utf-8 -*-
import argparse
from evaluator.CodeBLEU import bleu, weighted_ngram_match, syntax_match, dataflow_match
# import evaluator.CodeBLEU.weighted_ngram_match
# import evaluator.CodeBLEU.syntax_match
# import evaluator.CodeBLEU.dataflow_match
def get_codebleu(refs, hyp, lang, params='0.25,0.25,0.25,0.25'):
if not isinstance(refs, list):
refs = [refs]
alpha, beta, gamma, theta = [float(x) for x in params.split(',')]
# preprocess inputs
pre_references = [[x.strip() for x in open(file, 'r', encoding='utf-8').readlines()] for file in refs]
hypothesis = [x.strip() for x in open(hyp, 'r', encoding='utf-8').readlines()]
for i in range(len(pre_references)):
assert len(hypothesis) == len(pre_references[i])
references = []
for i in range(len(hypothesis)):
ref_for_instance = []
for j in range(len(pre_references)):
ref_for_instance.append(pre_references[j][i])
references.append(ref_for_instance)
assert len(references) == len(pre_references) * len(hypothesis)
# calculate ngram match (BLEU)
tokenized_hyps = [x.split() for x in hypothesis]
tokenized_refs = [[x.split() for x in reference] for reference in references]
ngram_match_score = bleu.corpus_bleu(tokenized_refs, tokenized_hyps)
# calculate weighted ngram match
keywords = [x.strip() for x in open('/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/keywords/' + lang + '.txt', 'r', encoding='utf-8').readlines()]
def make_weights(reference_tokens, key_word_list):
return {token: 1 if token in key_word_list else 0.2 for token in reference_tokens}
tokenized_refs_with_weights = [[[reference_tokens, make_weights(reference_tokens, keywords)] \
for reference_tokens in reference] for reference in tokenized_refs]
weighted_ngram_match_score = weighted_ngram_match.corpus_bleu(tokenized_refs_with_weights, tokenized_hyps)
# calculate syntax match
syntax_match_score = syntax_match.corpus_syntax_match(references, hypothesis, lang)
# calculate dataflow match
dataflow_match_score = dataflow_match.corpus_dataflow_match(references, hypothesis, lang)
print('ngram match: {0}, weighted ngram match: {1}, syntax_match: {2}, dataflow_match: {3}'. \
format(ngram_match_score, weighted_ngram_match_score, syntax_match_score, dataflow_match_score))
code_bleu_score = alpha * ngram_match_score \
+ beta * weighted_ngram_match_score \
+ gamma * syntax_match_score \
+ theta * dataflow_match_score
return code_bleu_score
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--refs', type=str, nargs='+', required=True,
help='reference files')
parser.add_argument('--hyp', type=str, required=True,
help='hypothesis file')
parser.add_argument('--lang', type=str, required=True,
choices=['java', 'js', 'c_sharp', 'php', 'go', 'python', 'ruby'],
help='programming language')
parser.add_argument('--params', type=str, default='0.25,0.25,0.25,0.25',
help='alpha, beta and gamma')
args = parser.parse_args()
code_bleu_score = get_codebleu(args.refs, args.hyp, args.lang, args.params)
print('CodeBLEU score: ', code_bleu_score)

148
CodeBLEU/dataflow_match.py
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@ -1,148 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp
from evaluator.CodeBLEU.parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from tree_sitter import Language, Parser
import pdb
parser_path = '/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/parser'
dfg_function = {
'python': DFG_python,
'java': DFG_java,
'ruby': DFG_ruby,
'go': DFG_go,
'php': DFG_php,
'javascript': DFG_javascript,
'c_sharp': DFG_csharp,
}
def calc_dataflow_match(references, candidate, lang):
return corpus_dataflow_match([references], [candidate], lang)
def corpus_dataflow_match(references, candidates, lang):
LANGUAGE = Language('{}/my-languages.so'.format(parser_path), lang)
parser = Parser()
parser.set_language(LANGUAGE)
parser = [parser, dfg_function[lang]]
match_count = 0
total_count = 0
for i in range(len(candidates)):
references_sample = references[i]
candidate = candidates[i]
for reference in references_sample:
try:
candidate = remove_comments_and_docstrings(candidate, 'java')
except:
pass
try:
reference = remove_comments_and_docstrings(reference, 'java')
except:
pass
cand_dfg = get_data_flow(candidate, parser)
ref_dfg = get_data_flow(reference, parser)
normalized_cand_dfg = normalize_dataflow(cand_dfg)
normalized_ref_dfg = normalize_dataflow(ref_dfg)
if len(normalized_ref_dfg) > 0:
total_count += len(normalized_ref_dfg)
for dataflow in normalized_ref_dfg:
if dataflow in normalized_cand_dfg:
match_count += 1
normalized_cand_dfg.remove(dataflow)
if total_count == 0:
print(
"WARNING: There is no reference data-flows extracted from the whole corpus, and the data-flow match score degenerates to 0. Please consider ignoring this score.")
return 0
score = match_count / total_count
return score
def get_data_flow(code, parser):
try:
tree = parser[0].parse(bytes(code, 'utf8'))
root_node = tree.root_node
tokens_index = tree_to_token_index(root_node)
code = code.split('\n')
code_tokens = [index_to_code_token(x, code) for x in tokens_index]
index_to_code = {}
for idx, (index, code) in enumerate(zip(tokens_index, code_tokens)):
index_to_code[index] = (idx, code)
try:
DFG, _ = parser[1](root_node, index_to_code, {})
except:
DFG = []
DFG = sorted(DFG, key=lambda x: x[1])
indexs = set()
for d in DFG:
if len(d[-1]) != 0:
indexs.add(d[1])
for x in d[-1]:
indexs.add(x)
new_DFG = []
for d in DFG:
if d[1] in indexs:
new_DFG.append(d)
codes = code_tokens
dfg = new_DFG
except:
codes = code.split()
dfg = []
# merge nodes
dic = {}
for d in dfg:
if d[1] not in dic:
dic[d[1]] = d
else:
dic[d[1]] = (d[0], d[1], d[2], list(set(dic[d[1]][3] + d[3])), list(set(dic[d[1]][4] + d[4])))
DFG = []
for d in dic:
DFG.append(dic[d])
dfg = DFG
return dfg
def normalize_dataflow_item(dataflow_item):
var_name = dataflow_item[0]
var_pos = dataflow_item[1]
relationship = dataflow_item[2]
par_vars_name_list = dataflow_item[3]
par_vars_pos_list = dataflow_item[4]
var_names = list(set(par_vars_name_list + [var_name]))
norm_names = {}
for i in range(len(var_names)):
norm_names[var_names[i]] = 'var_' + str(i)
norm_var_name = norm_names[var_name]
relationship = dataflow_item[2]
norm_par_vars_name_list = [norm_names[x] for x in par_vars_name_list]
return (norm_var_name, relationship, norm_par_vars_name_list)
def normalize_dataflow(dataflow):
var_dict = {}
i = 0
normalized_dataflow = []
for item in dataflow:
var_name = item[0]
relationship = item[2]
par_vars_name_list = item[3]
for name in par_vars_name_list:
if name not in var_dict:
var_dict[name] = 'var_' + str(i)
i += 1
if var_name not in var_dict:
var_dict[var_name] = 'var_' + str(i)
i += 1
normalized_dataflow.append((var_dict[var_name], relationship, [var_dict[x] for x in par_vars_name_list]))
return normalized_dataflow

107
CodeBLEU/keywords/c_sharp.txt
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@ -1,107 +0,0 @@
abstract
as
base
bool
break
byte
case
catch
char
checked
class
const
continue
decimal
default
delegate
do
double
else
enum
event
explicit
extern
false
finally
fixed
float
for
foreach
goto
if
implicit
in
int
interface
internal
is
lock
long
namespace
new
null
object
operator
out
override
params
private
protected
public
readonly
ref
return
sbyte
sealed
short
sizeof
stackalloc
static
string
struct
switch
this
throw
true
try
typeof
uint
ulong
unchecked
unsafe
ushort
using
virtual
void
volatile
while
add
alias
ascending
async
await
by
descending
dynamic
equals
from
get
global
group
into
join
let
nameof
notnull
on
orderby
partial
remove
select
set
unmanaged
value
var
when
where
yield

50
CodeBLEU/keywords/java.txt
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@ -1,50 +0,0 @@
abstract
assert
boolean
break
byte
case
catch
char
class
const
continue
default
do
double
else
enum
extends
final
finally
float
for
goto
if
implements
import
instanceof
int
interface
long
native
new
package
private
protected
public
return
short
static
strictfp
super
switch
synchronized
this
throw
throws
transient
try
void
volatile
while

1184
CodeBLEU/parser/DFG.py
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8
CodeBLEU/parser/__init__.py
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@ -1,8 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from .utils import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from .DFG import DFG_python,DFG_java,DFG_ruby,DFG_go,DFG_php,DFG_javascript,DFG_csharp

21
CodeBLEU/parser/build.py
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@ -1,21 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from tree_sitter import Language, Parser
Language.build_library(
# Store the library in the `build` directory
'my-languages.so',
# Include one or more languages
[
'tree-sitter-go',
'tree-sitter-javascript',
'tree-sitter-python',
'tree-sitter-php',
'tree-sitter-java',
'tree-sitter-ruby',
'tree-sitter-c-sharp',
]
)

8
CodeBLEU/parser/build.sh
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@ -1,8 +0,0 @@
git clone https://github.com/tree-sitter/tree-sitter-go
git clone https://github.com/tree-sitter/tree-sitter-javascript
git clone https://github.com/tree-sitter/tree-sitter-python
git clone https://github.com/tree-sitter/tree-sitter-ruby
git clone https://github.com/tree-sitter/tree-sitter-php
git clone https://github.com/tree-sitter/tree-sitter-java
git clone https://github.com/tree-sitter/tree-sitter-c-sharp
python build.py

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CodeBLEU/parser/my-languages.so
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108
CodeBLEU/parser/utils.py
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@ -1,108 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import re
from io import StringIO
import tokenize
def remove_comments_and_docstrings(source, lang):
if lang in ['python']:
"""
Returns 'source' minus comments and docstrings.
"""
io_obj = StringIO(source)
out = ""
prev_toktype = tokenize.INDENT
last_lineno = -1
last_col = 0
for tok in tokenize.generate_tokens(io_obj.readline):
token_type = tok[0]
token_string = tok[1]
start_line, start_col = tok[2]
end_line, end_col = tok[3]
ltext = tok[4]
if start_line > last_lineno:
last_col = 0
if start_col > last_col:
out += (" " * (start_col - last_col))
# Remove comments:
if token_type == tokenize.COMMENT:
pass
# This series of conditionals removes docstrings:
elif token_type == tokenize.STRING:
if prev_toktype != tokenize.INDENT:
# This is likely a docstring; double-check we're not inside an operator:
if prev_toktype != tokenize.NEWLINE:
if start_col > 0:
out += token_string
else:
out += token_string
prev_toktype = token_type
last_col = end_col
last_lineno = end_line
temp = []
for x in out.split('\n'):
if x.strip() != "":
temp.append(x)
return '\n'.join(temp)
elif lang in ['ruby']:
return source
else:
def replacer(match):
s = match.group(0)
if s.startswith('/'):
return " " # note: a space and not an empty string
else:
return s
pattern = re.compile(
r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"',
re.DOTALL | re.MULTILINE
)
temp = []
for x in re.sub(pattern, replacer, source).split('\n'):
if x.strip() != "":
temp.append(x)
return '\n'.join(temp)
def tree_to_token_index(root_node):
if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string',
'character_literal']) and root_node.type != 'comment':
return [(root_node.start_point, root_node.end_point)]
else:
code_tokens = []
for child in root_node.children:
code_tokens += tree_to_token_index(child)
return code_tokens
def tree_to_variable_index(root_node, index_to_code):
if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string',
'character_literal']) and root_node.type != 'comment':
index = (root_node.start_point, root_node.end_point)
_, code = index_to_code[index]
if root_node.type != code:
return [(root_node.start_point, root_node.end_point)]
else:
return []
else:
code_tokens = []
for child in root_node.children:
code_tokens += tree_to_variable_index(child, index_to_code)
return code_tokens
def index_to_code_token(index, code):
start_point = index[0]
end_point = index[1]
if start_point[0] == end_point[0]:
s = code[start_point[0]][start_point[1]:end_point[1]]
else:
s = ""
s += code[start_point[0]][start_point[1]:]
for i in range(start_point[0] + 1, end_point[0]):
s += code[i]
s += code[end_point[0]][:end_point[1]]
return s

1
CodeBLEU/readme.txt
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@ -1 +0,0 @@
python calc_code_bleu.py --refs reference_files --hyp candidate_file --language java ( or c_sharp) --params 0.25,0.25,0.25,0.25(default)

77
CodeBLEU/syntax_match.py
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@ -1,77 +0,0 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp
from evaluator.CodeBLEU.parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from tree_sitter import Language, Parser
parser_path = '/export/share/wang.y/workspace/CodeT5Full/finetune/evaluator/CodeBLEU/parser'
dfg_function = {
'python': DFG_python,
'java': DFG_java,
'ruby': DFG_ruby,
'go': DFG_go,
'php': DFG_php,
'javascript': DFG_javascript,
'c_sharp': DFG_csharp,
}
def calc_syntax_match(references, candidate, lang):
return corpus_syntax_match([references], [candidate], lang)
def corpus_syntax_match(references, candidates, lang):
JAVA_LANGUAGE = Language('{}/my-languages.so'.format(parser_path), lang)
parser = Parser()
parser.set_language(JAVA_LANGUAGE)
match_count = 0
total_count = 0
for i in range(len(candidates)):
references_sample = references[i]
candidate = candidates[i]
for reference in references_sample:
try:
candidate = remove_comments_and_docstrings(candidate, 'java')
except:
pass
try:
reference = remove_comments_and_docstrings(reference, 'java')
except:
pass
candidate_tree = parser.parse(bytes(candidate, 'utf8')).root_node
reference_tree = parser.parse(bytes(reference, 'utf8')).root_node
def get_all_sub_trees(root_node):
node_stack = []
sub_tree_sexp_list = []
depth = 1
node_stack.append([root_node, depth])
while len(node_stack) != 0:
cur_node, cur_depth = node_stack.pop()
sub_tree_sexp_list.append([cur_node.sexp(), cur_depth])
for child_node in cur_node.children:
if len(child_node.children) != 0:
depth = cur_depth + 1
node_stack.append([child_node, depth])
return sub_tree_sexp_list
cand_sexps = [x[0] for x in get_all_sub_trees(candidate_tree)]
ref_sexps = get_all_sub_trees(reference_tree)
# print(cand_sexps)
# print(ref_sexps)
for sub_tree, depth in ref_sexps:
if sub_tree in cand_sexps:
match_count += 1
total_count += len(ref_sexps)
score = match_count / total_count
return score

106
CodeBLEU/utils.py
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@ -1,106 +0,0 @@
# Natural Language Toolkit: Utility functions
#
# Copyright (C) 2001-2020 NLTK Project
# Author: Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from itertools import chain
def pad_sequence(
sequence,
n,
pad_left=False,
pad_right=False,
left_pad_symbol=None,
right_pad_symbol=None,
):
"""
Returns a padded sequence of items before ngram extraction.
>>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='<s>', right_pad_symbol='</s>'))
['<s>', 1, 2, 3, 4, 5, '</s>']
>>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='<s>'))
['<s>', 1, 2, 3, 4, 5]
>>> list(pad_sequence([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='</s>'))
[1, 2, 3, 4, 5, '</s>']
:param sequence: the source data to be padded
:type sequence: sequence or iter
:param n: the degree of the ngrams
:type n: int
:param pad_left: whether the ngrams should be left-padded
:type pad_left: bool
:param pad_right: whether the ngrams should be right-padded
:type pad_right: bool
:param left_pad_symbol: the symbol to use for left padding (default is None)
:type left_pad_symbol: any
:param right_pad_symbol: the symbol to use for right padding (default is None)
:type right_pad_symbol: any
:rtype: sequence or iter
"""
sequence = iter(sequence)
if pad_left:
sequence = chain((left_pad_symbol,) * (n - 1), sequence)
if pad_right:
sequence = chain(sequence, (right_pad_symbol,) * (n - 1))
return sequence
# add a flag to pad the sequence so we get peripheral ngrams?
def ngrams(
sequence,
n,
pad_left=False,
pad_right=False,
left_pad_symbol=None,
right_pad_symbol=None,
):
"""
Return the ngrams generated from a sequence of items, as an iterator.
For example:
>>> from nltk.util import ngrams
>>> list(ngrams([1,2,3,4,5], 3))
[(1, 2, 3), (2, 3, 4), (3, 4, 5)]
Wrap with list for a list version of this function. Set pad_left
or pad_right to true in order to get additional ngrams:
>>> list(ngrams([1,2,3,4,5], 2, pad_right=True))
[(1, 2), (2, 3), (3, 4), (4, 5), (5, None)]
>>> list(ngrams([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='</s>'))
[(1, 2), (2, 3), (3, 4), (4, 5), (5, '</s>')]
>>> list(ngrams([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='<s>'))
[('<s>', 1), (1, 2), (2, 3), (3, 4), (4, 5)]
>>> list(ngrams([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='<s>', right_pad_symbol='</s>'))
[('<s>', 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, '</s>')]
:param sequence: the source data to be converted into ngrams
:type sequence: sequence or iter
:param n: the degree of the ngrams
:type n: int
:param pad_left: whether the ngrams should be left-padded
:type pad_left: bool
:param pad_right: whether the ngrams should be right-padded
:type pad_right: bool
:param left_pad_symbol: the symbol to use for left padding (default is None)
:type left_pad_symbol: any
:param right_pad_symbol: the symbol to use for right padding (default is None)
:type right_pad_symbol: any
:rtype: sequence or iter
"""
sequence = pad_sequence(
sequence, n, pad_left, pad_right, left_pad_symbol, right_pad_symbol
)
history = []
while n > 1:
# PEP 479, prevent RuntimeError from being raised when StopIteration bubbles out of generator
try:
next_item = next(sequence)
except StopIteration:
# no more data, terminate the generator
return
history.append(next_item)
n -= 1
for item in sequence:
history.append(item)
yield tuple(history)
del history[0]

558
CodeBLEU/weighted_ngram_match.py
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@ -1,558 +0,0 @@
# -*- coding: utf-8 -*-
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Natural Language Toolkit: BLEU Score
#
# Copyright (C) 2001-2020 NLTK Project
# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""BLEU score implementation."""
import math
import sys
from fractions import Fraction
import warnings
from collections import Counter
from evaluator.CodeBLEU.utils import ngrams
import pdb
def sentence_bleu(
references,
hypothesis,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None,
auto_reweigh=False,
):
"""
Calculate BLEU score (Bilingual Evaluation Understudy) from
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.
"BLEU: a method for automatic evaluation of machine translation."
In Proceedings of ACL. http://www.aclweb.org/anthology/P02-1040.pdf
>>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',
... 'forever', 'hearing', 'the', 'activity', 'guidebook',
... 'that', 'party', 'direct']
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will', 'forever',
... 'heed', 'Party', 'commands']
>>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the',
... 'Party']
>>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS
0.5045...
If there is no ngrams overlap for any order of n-grams, BLEU returns the
value 0. This is because the precision for the order of n-grams without
overlap is 0, and the geometric mean in the final BLEU score computation
multiplies the 0 with the precision of other n-grams. This results in 0
(independently of the precision of the othe n-gram orders). The following
example has zero 3-gram and 4-gram overlaps:
>>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS
0.0
To avoid this harsh behaviour when no ngram overlaps are found a smoothing
function can be used.
>>> chencherry = SmoothingFunction()
>>> sentence_bleu([reference1, reference2, reference3], hypothesis2,
... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS
0.0370...
The default BLEU calculates a score for up to 4-grams using uniform
weights (this is called BLEU-4). To evaluate your translations with
higher/lower order ngrams, use customized weights. E.g. when accounting
for up to 5-grams with uniform weights (this is called BLEU-5) use:
>>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.)
>>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS
0.3920...
:param references: reference sentences
:type references: list(list(str))
:param hypothesis: a hypothesis sentence
:type hypothesis: list(str)
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:param smoothing_function:
:type smoothing_function: SmoothingFunction
:param auto_reweigh: Option to re-normalize the weights uniformly.
:type auto_reweigh: bool
:return: The sentence-level BLEU score.
:rtype: float
"""
return corpus_bleu(
[references], [hypothesis], weights, smoothing_function, auto_reweigh
)
def corpus_bleu(
list_of_references,
hypotheses,
weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None,
auto_reweigh=False,
):
"""
Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all
the hypotheses and their respective references.
Instead of averaging the sentence level BLEU scores (i.e. marco-average
precision), the original BLEU metric (Papineni et al. 2002) accounts for
the micro-average precision (i.e. summing the numerators and denominators
for each hypothesis-reference(s) pairs before the division).
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will', 'forever',
... 'heed', 'Party', 'commands']
>>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS
0.5920...
The example below show that corpus_bleu() is different from averaging
sentence_bleu() for hypotheses
>>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1)
>>> score2 = sentence_bleu([ref2a], hyp2)
>>> (score1 + score2) / 2 # doctest: +ELLIPSIS
0.6223...
:param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses
:type list_of_references: list(list(list(str)))
:param hypotheses: a list of hypothesis sentences
:type hypotheses: list(list(str))
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:param smoothing_function:
:type smoothing_function: SmoothingFunction
:param auto_reweigh: Option to re-normalize the weights uniformly.
:type auto_reweigh: bool
:return: The corpus-level BLEU score.
:rtype: float
"""
# Before proceeding to compute BLEU, perform sanity checks.
p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches.
p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref.
hyp_lengths, ref_lengths = 0, 0
assert len(list_of_references) == len(hypotheses), (
"The number of hypotheses and their reference(s) should be the " "same "
)
# Iterate through each hypothesis and their corresponding references.
for references, hypothesis in zip(list_of_references, hypotheses):
# For each order of ngram, calculate the numerator and
# denominator for the corpus-level modified precision.
for i, _ in enumerate(weights, start=1):
p_i_numeraotr, p_i_denominator = modified_recall(references, hypothesis, i)
p_numerators[i] += p_i_numeraotr
p_denominators[i] += p_i_denominator
# Calculate the hypothesis length and the closest reference length.
# Adds them to the corpus-level hypothesis and reference counts.
hyp_len = len(hypothesis)
hyp_lengths += hyp_len
ref_lengths += closest_ref_length(references, hyp_len)
# Calculate corpus-level brevity penalty.
bp = brevity_penalty(ref_lengths, hyp_lengths)
# Uniformly re-weighting based on maximum hypothesis lengths if largest
# order of n-grams < 4 and weights is set at default.
if auto_reweigh:
if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25):
weights = (1 / hyp_lengths,) * hyp_lengths
# Collects the various recall values for the different ngram orders.
p_n = [
(p_numerators[i], p_denominators[i])
for i, _ in enumerate(weights, start=1)
]
# Returns 0 if there's no matching n-grams
# We only need to check for p_numerators[1] == 0, since if there's
# no unigrams, there won't be any higher order ngrams.
if p_numerators[1] == 0:
return 0
# If there's no smoothing, set use method0 from SmoothinFunction class.
if not smoothing_function:
smoothing_function = SmoothingFunction().method1
# Smoothen the modified precision.
# Note: smoothing_function() may convert values into floats;
# it tries to retain the Fraction object as much as the
# smoothing method allows.
p_n = smoothing_function(
p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths
)
# pdb.set_trace()
s = (w_i * math.log(p_i[0]/p_i[1]) for w_i, p_i in zip(weights, p_n))
s = bp * math.exp(math.fsum(s))
return s
def modified_recall(references, hypothesis, n):
"""
Calculate modified ngram recall.
:param references: A list of reference translations.
:type references: list(list(str))
:param hypothesis: A hypothesis translation.
:type hypothesis: list(str)
:param n: The ngram order.
:type n: int
:return: BLEU's modified precision for the nth order ngram.
:rtype: Fraction
"""
# Extracts all ngrams in hypothesis
# Set an empty Counter if hypothesis is empty.
# pdb.set_trace()
numerator = 0
denominator = 0
counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter()
# Extract a union of references' counts.
# max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references])
max_counts = {}
for reference_and_weights in references:
reference = reference_and_weights[0]
weights = reference_and_weights[1]
reference_counts = (
Counter(ngrams(reference, n)) if len(reference) >= n else Counter()
)
# for ngram in reference_counts:
# max_counts[ngram] = max(max_counts.get(ngram, 0), counts[ngram])
clipped_counts = {
ngram: min(count, counts[ngram]) for ngram, count in reference_counts.items()
}
# reweight
if n == 1 and len(weights) == len(reference_counts):
def weighted_sum(weights, counts):
sum_counts = 0
for ngram, count in counts.items():
sum_counts += count * (weights[ngram[0]] if ngram[0] in weights else 1)
return sum_counts
numerator += weighted_sum(weights, clipped_counts)
denominator += max(1, weighted_sum(weights, reference_counts))
else:
numerator += sum(clipped_counts.values())
denominator += max(1, sum(reference_counts.values()))
# # Assigns the intersection between hypothesis and references' counts.
# clipped_counts = {
# ngram: min(count, max_counts[ngram]) for ngram, count in counts.items()
# }
# numerator += sum(clipped_counts.values())
# # Ensures that denominator is minimum 1 to avoid ZeroDivisionError.
# # Usually this happens when the ngram order is > len(reference).
# denominator += max(1, sum(counts.values()))
#return Fraction(numerator, denominator, _normalize=False)
return numerator, denominator
def closest_ref_length(references, hyp_len):
"""
This function finds the reference that is the closest length to the
hypothesis. The closest reference length is referred to as *r* variable
from the brevity penalty formula in Papineni et. al. (2002)
:param references: A list of reference translations.
:type references: list(list(str))
:param hyp_len: The length of the hypothesis.
:type hyp_len: int
:return: The length of the reference that's closest to the hypothesis.
:rtype: int
"""
ref_lens = (len(reference) for reference in references)
closest_ref_len = min(
ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)
)
return closest_ref_len
def brevity_penalty(closest_ref_len, hyp_len):
"""
Calculate brevity penalty.
As the modified n-gram precision still has the problem from the short
length sentence, brevity penalty is used to modify the overall BLEU
score according to length.
An example from the paper. There are three references with length 12, 15
and 17. And a concise hypothesis of the length 12. The brevity penalty is 1.
>>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
>>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15
>>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17
>>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
>>> references = [reference1, reference2, reference3]
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
1.0
In case a hypothesis translation is shorter than the references, penalty is
applied.
>>> references = [['a'] * 28, ['a'] * 28]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
0.2635971381157267
The length of the closest reference is used to compute the penalty. If the
length of a hypothesis is 12, and the reference lengths are 13 and 2, the
penalty is applied because the hypothesis length (12) is less then the
closest reference length (13).
>>> references = [['a'] * 13, ['a'] * 2]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
0.9200...
The brevity penalty doesn't depend on reference order. More importantly,
when two reference sentences are at the same distance, the shortest
reference sentence length is used.
>>> references = [['a'] * 13, ['a'] * 11]
>>> hypothesis = ['a'] * 12
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> bp1 = brevity_penalty(closest_ref_len, hyp_len)
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(reversed(references), hyp_len)
>>> bp2 = brevity_penalty(closest_ref_len, hyp_len)
>>> bp1 == bp2 == 1
True
A test example from mteval-v13a.pl (starting from the line 705):
>>> references = [['a'] * 11, ['a'] * 8]
>>> hypothesis = ['a'] * 7
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
0.8668...
>>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]
>>> hypothesis = ['a'] * 7
>>> hyp_len = len(hypothesis)
>>> closest_ref_len = closest_ref_length(references, hyp_len)
>>> brevity_penalty(closest_ref_len, hyp_len)
1.0
:param hyp_len: The length of the hypothesis for a single sentence OR the
sum of all the hypotheses' lengths for a corpus
:type hyp_len: int
:param closest_ref_len: The length of the closest reference for a single
hypothesis OR the sum of all the closest references for every hypotheses.
:type closest_ref_len: int
:return: BLEU's brevity penalty.
:rtype: float
"""
if hyp_len > closest_ref_len:
return 1
# If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0
elif hyp_len == 0:
return 0
else:
return math.exp(1 - closest_ref_len / hyp_len)
class SmoothingFunction:
"""
This is an implementation of the smoothing techniques
for segment-level BLEU scores that was presented in
Boxing Chen and Collin Cherry (2014) A Systematic Comparison of
Smoothing Techniques for Sentence-Level BLEU. In WMT14.
http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf
"""
def __init__(self, epsilon=0.1, alpha=5, k=5):
"""
This will initialize the parameters required for the various smoothing
techniques, the default values are set to the numbers used in the
experiments from Chen and Cherry (2014).
>>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures',
... 'that', 'the', 'military', 'always', 'obeys', 'the',
... 'commands', 'of', 'the', 'party']
>>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures',
... 'that', 'the', 'military', 'will', 'forever', 'heed',
... 'Party', 'commands']
>>> chencherry = SmoothingFunction()
>>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS
0.4489...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS
0.4118...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS
0.4905...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS
0.4135...
>>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS
0.4905...
:param epsilon: the epsilon value use in method 1
:type epsilon: float
:param alpha: the alpha value use in method 6
:type alpha: int
:param k: the k value use in method 4
:type k: int
"""
self.epsilon = epsilon
self.alpha = alpha
self.k = k
def method0(self, p_n, *args, **kwargs):
"""
No smoothing.
"""
p_n_new = []
for i, p_i in enumerate(p_n):
if p_i[0] != 0:
p_n_new.append(p_i)
else:
_msg = str(
"\nThe hypothesis contains 0 counts of {}-gram overlaps.\n"
"Therefore the BLEU score evaluates to 0, independently of\n"
"how many N-gram overlaps of lower order it contains.\n"
"Consider using lower n-gram order or use "
"SmoothingFunction()"
).format(i + 1)
warnings.warn(_msg)
# When numerator==0 where denonminator==0 or !=0, the result
# for the precision score should be equal to 0 or undefined.
# Due to BLEU geometric mean computation in logarithm space,
# we we need to take the return sys.float_info.min such that
# math.log(sys.float_info.min) returns a 0 precision score.
p_n_new.append(sys.float_info.min)
return p_n_new
def method1(self, p_n, *args, **kwargs):
"""
Smoothing method 1: Add *epsilon* counts to precision with 0 counts.
"""
return [
((p_i[0] + self.epsilon), p_i[1])
if p_i[0] == 0
else p_i
for p_i in p_n
]
def method2(self, p_n, *args, **kwargs):
"""
Smoothing method 2: Add 1 to both numerator and denominator from
Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of
machine translation quality using longest common subsequence and
skip-bigram statistics. In ACL04.
"""
return [
(p_i[0] + 1, p_i[1] + 1)
for p_i in p_n
]
def method3(self, p_n, *args, **kwargs):
"""
Smoothing method 3: NIST geometric sequence smoothing
The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each
precision score whose matching n-gram count is null.
k is 1 for the first 'n' value for which the n-gram match count is null/
For example, if the text contains:
- one 2-gram match
- and (consequently) two 1-gram matches
the n-gram count for each individual precision score would be:
- n=1 => prec_count = 2 (two unigrams)
- n=2 => prec_count = 1 (one bigram)
- n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1)
- n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2)
"""
incvnt = 1 # From the mteval-v13a.pl, it's referred to as k.
for i, p_i in enumerate(p_n):
if p_i.numerator == 0:
p_n[i] = 1 / (2 ** incvnt * p_i.denominator)
incvnt += 1
return p_n
def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 4:
Shorter translations may have inflated precision values due to having
smaller denominators; therefore, we give them proportionally
smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry
suggests dividing by 1/ln(len(T)), where T is the length of the translation.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
for i, p_i in enumerate(p_n):
if p_i.numerator == 0 and hyp_len != 0:
incvnt = i + 1 * self.k / math.log(
hyp_len
) # Note that this K is different from the K from NIST.
p_n[i] = incvnt / p_i.denominator
return p_n
def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 5:
The matched counts for similar values of n should be similar. To a
calculate the n-gram matched count, it averages the n−1, n and n+1 gram
matched counts.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
m = {}
# Requires an precision value for an addition ngram order.
p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)]
m[-1] = p_n[0] + 1
for i, p_i in enumerate(p_n):
p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3
m[i] = p_n[i]
return p_n
def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 6:
Interpolates the maximum likelihood estimate of the precision *p_n* with
a prior estimate *pi0*. The prior is estimated by assuming that the ratio
between pn and pn−1 will be the same as that between pn−1 and pn−2; from
Gao and He (2013) Training MRF-Based Phrase Translation Models using
Gradient Ascent. In NAACL.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
# This smoothing only works when p_1 and p_2 is non-zero.
# Raise an error with an appropriate message when the input is too short
# to use this smoothing technique.
assert p_n[2], "This smoothing method requires non-zero precision for bigrams."
for i, p_i in enumerate(p_n):
if i in [0, 1]: # Skips the first 2 orders of ngrams.
continue
else:
pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2]
# No. of ngrams in translation that matches the reference.
m = p_i.numerator
# No. of ngrams in translation.
l = sum(1 for _ in ngrams(hypothesis, i + 1))
# Calculates the interpolated precision.
p_n[i] = (m + self.alpha * pi0) / (l + self.alpha)
return p_n
def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
"""
Smoothing method 7:
Interpolates methods 4 and 5.
"""
hyp_len = hyp_len if hyp_len else len(hypothesis)
p_n = self.method4(p_n, references, hypothesis, hyp_len)
p_n = self.method5(p_n, references, hypothesis, hyp_len)
return p_n

17
apply_tokenizer.py
View File

@ -1,17 +0,0 @@
from tokenizers import ByteLevelBPETokenizer
tokenizer = ByteLevelBPETokenizer.from_file(
"./salesforce/codet5-vocab.json",
"./salesforce/codet5-merges.txt"
)
tokenizer.add_special_tokens([
"<pad>",
"<s>",
"</s>",
"<unk>",
"<mask>"
])
print(
tokenizer.encode("<s> hello <unk> Don't you love 🤗 Transformers <mask> yes . </s>").tokens
)

134
bleu.py
View File

@ -1,134 +0,0 @@
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Python implementation of BLEU and smooth-BLEU.
This module provides a Python implementation of BLEU and smooth-BLEU.
Smooth BLEU is computed following the method outlined in the paper:
Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic
evaluation metrics for machine translation. COLING 2004.
"""
import collections
import math
def _get_ngrams(segment, max_order):
"""Extracts all n-grams upto a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.
Returns:
The Counter containing all n-grams upto max_order in segment
with a count of how many times each n-gram occurred.
"""
ngram_counts = collections.Counter()
for order in range(1, max_order + 1):
for i in range(0, len(segment) - order + 1):
ngram = tuple(segment[i:i+order])
ngram_counts[ngram] += 1
return ngram_counts
def compute_bleu(reference_corpus, translation_corpus, max_order=4,
smooth=False):
"""Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of lists of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
precisions and brevity penalty.
"""
matches_by_order = [0] * max_order
possible_matches_by_order = [0] * max_order
reference_length = 0
translation_length = 0
for (references, translation) in zip(reference_corpus,
translation_corpus):
reference_length += min(len(r) for r in references)
translation_length += len(translation)
merged_ref_ngram_counts = collections.Counter()
for reference in references:
merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
translation_ngram_counts = _get_ngrams(translation, max_order)
overlap = translation_ngram_counts & merged_ref_ngram_counts
for ngram in overlap:
matches_by_order[len(ngram)-1] += overlap[ngram]
for order in range(1, max_order+1):
possible_matches = len(translation) - order + 1
if possible_matches > 0:
possible_matches_by_order[order-1] += possible_matches
precisions = [0] * max_order
for i in range(0, max_order):
if smooth:
precisions[i] = ((matches_by_order[i] + 1.) /
(possible_matches_by_order[i] + 1.))
else:
if possible_matches_by_order[i] > 0:
precisions[i] = (float(matches_by_order[i]) /
possible_matches_by_order[i])
else:
precisions[i] = 0.0
if min(precisions) > 0:
p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions)
geo_mean = math.exp(p_log_sum)
else:
geo_mean = 0
ratio = float(translation_length) / reference_length
if ratio > 1.0:
bp = 1.
else:
bp = math.exp(1 - 1. / ratio)
bleu = geo_mean * bp
return (bleu, precisions, bp, ratio, translation_length, reference_length)
def _bleu(ref_file, trans_file, subword_option=None):
max_order = 4
smooth = True
ref_files = [ref_file]
reference_text = []
for reference_filename in ref_files:
with open(reference_filename) as fh:
reference_text.append(fh.readlines())
per_segment_references = []
for references in zip(*reference_text):
reference_list = []
for reference in references:
reference_list.append(reference.strip().split())
per_segment_references.append(reference_list)
translations = []
with open(trans_file) as fh:
for line in fh:
translations.append(line.strip().split())
bleu_score, _, _, _, _, _ = compute_bleu(per_segment_references, translations, max_order, smooth)
return round(100 * bleu_score,2)

31740
salesforce/codet5-merges.txt
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salesforce/codet5-vocab.json
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208
smooth_bleu.py
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#!/usr/bin/python
'''
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.
'''
import sys, math, re, xml.sax.saxutils
import subprocess
import os
# Added to bypass NIST-style pre-processing of hyp and ref files -- wade
nonorm = 0
preserve_case = False
eff_ref_len = "shortest"
normalize1 = [
('<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 = [
(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 = {}
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 = {}
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 = {}
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 = 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 = {'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 = []
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)
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 = {}
goldMap = {}
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]
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])

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train_tokenizer.py
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from tokenizers import ByteLevelBPETokenizer
paths = ['train_code.txt', 'train_doc.txt']
# Initialize a tokenizer
tokenizer = ByteLevelBPETokenizer()
# Customize training
tokenizer.train(files=paths, vocab_size=32000, min_frequency=3, special_tokens=[
"<pad>",
"<s>",
"</s>",
"<unk>",
"<mask>"
])
# Save files to disk
tokenizer.save_model("./salesforce", "codet5")
print(
tokenizer.encode("<s> hello <unk> Don't you love 🤗 Transformers <mask> yes . </s>").tokens
)