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metrics/bleu.py
158
metrics/bleu.py
@ -28,98 +28,106 @@ import math
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def _get_ngrams(segment, max_order):
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"""Extracts all n-grams upto a given maximum order from an input segment.
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"""Extracts all n-grams upto a given maximum order from an input segment.
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Args:
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segment: text segment from which n-grams will be extracted.
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max_order: maximum length in tokens of the n-grams returned by this
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methods.
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Args:
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segment: text segment from which n-grams will be extracted.
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max_order: maximum length in tokens of the n-grams returned by this
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methods.
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Returns:
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The Counter containing all n-grams upto max_order in segment
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with a count of how many times each n-gram occurred.
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"""
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ngram_counts = collections.Counter()
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for order in range(1, max_order + 1):
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for i in range(0, len(segment) - order + 1):
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ngram = tuple(segment[i:i+order])
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ngram_counts[ngram] += 1
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return ngram_counts
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Returns:
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The Counter containing all n-grams upto max_order in segment
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with a count of how many times each n-gram occurred.
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"""
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ngram_counts = collections.Counter()
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for order in range(1, max_order + 1):
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for i in range(0, len(segment) - order + 1):
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ngram = tuple(segment[i : i + order])
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ngram_counts[ngram] += 1
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return ngram_counts
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def compute_bleu(reference_corpus, translation_corpus, max_order=4,
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smooth=True):
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"""Computes BLEU score of translated segments against one or more references.
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def compute_bleu(reference_corpus, translation_corpus, max_order=4, smooth=True):
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"""Computes BLEU score of translated segments against one or more references.
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Args:
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reference_corpus: list of lists of references for each translation. Each
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reference should be tokenized into a list of tokens.
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translation_corpus: list of translations to score. Each translation
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should be tokenized into a list of tokens.
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max_order: Maximum n-gram order to use when computing BLEU score.
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smooth: Whether or not to apply Lin et al. 2004 smoothing.
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Args:
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reference_corpus: list of lists of references for each translation. Each
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reference should be tokenized into a list of tokens.
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translation_corpus: list of translations to score. Each translation
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should be tokenized into a list of tokens.
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max_order: Maximum n-gram order to use when computing BLEU score.
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smooth: Whether or not to apply Lin et al. 2004 smoothing.
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Returns:
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3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
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precisions and brevity penalty.
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"""
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matches_by_order = [0] * max_order
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possible_matches_by_order = [0] * max_order
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reference_length = 0
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translation_length = 0
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for (references, translation) in zip(reference_corpus,
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translation_corpus):
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reference_length += min(len(r) for r in references)
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translation_length += len(translation)
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Returns:
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3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
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precisions and brevity penalty.
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"""
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matches_by_order = [0] * max_order
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possible_matches_by_order = [0] * max_order
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reference_length = 0
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translation_length = 0
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for (references, translation) in zip(reference_corpus, translation_corpus):
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reference_length += min(len(r) for r in references)
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translation_length += len(translation)
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merged_ref_ngram_counts = collections.Counter()
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for reference in references:
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merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
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translation_ngram_counts = _get_ngrams(translation, max_order)
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overlap = translation_ngram_counts & merged_ref_ngram_counts
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for ngram in overlap:
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matches_by_order[len(ngram)-1] += overlap[ngram]
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for order in range(1, max_order+1):
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possible_matches = len(translation) - order + 1
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if possible_matches > 0:
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possible_matches_by_order[order-1] += possible_matches
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merged_ref_ngram_counts = collections.Counter()
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for reference in references:
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merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
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translation_ngram_counts = _get_ngrams(translation, max_order)
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overlap = translation_ngram_counts & merged_ref_ngram_counts
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for ngram in overlap:
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matches_by_order[len(ngram) - 1] += overlap[ngram]
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for order in range(1, max_order + 1):
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possible_matches = len(translation) - order + 1
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if possible_matches > 0:
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possible_matches_by_order[order - 1] += possible_matches
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precisions = [0] * max_order
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for i in range(0, max_order):
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if smooth:
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precisions[i] = ((matches_by_order[i] + 1.) /
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(possible_matches_by_order[i] + 1.))
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precisions = [0] * max_order
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for i in range(0, max_order):
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if smooth:
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precisions[i] = (matches_by_order[i] + 1.0) / (
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possible_matches_by_order[i] + 1.0
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)
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else:
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if possible_matches_by_order[i] > 0:
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precisions[i] = (
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float(matches_by_order[i]) / possible_matches_by_order[i]
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)
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else:
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precisions[i] = 0.0
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if min(precisions) > 0:
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p_log_sum = sum((1.0 / max_order) * math.log(p) for p in precisions)
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geo_mean = math.exp(p_log_sum)
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else:
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if possible_matches_by_order[i] > 0:
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precisions[i] = (float(matches_by_order[i]) /
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possible_matches_by_order[i])
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else:
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precisions[i] = 0.0
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geo_mean = 0
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if min(precisions) > 0:
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p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions)
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geo_mean = math.exp(p_log_sum)
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else:
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geo_mean = 0
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ratio = float(translation_length) / reference_length
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ratio = float(translation_length) / reference_length
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if ratio > 1.0:
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bp = 1.0
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else:
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bp = math.exp(1 - 1.0 / ratio)
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bleu = geo_mean * bp
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bleu_score_dict = {
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"bleu": bleu,
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"precision": precisions,
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"bp": bp,
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"ratio": ratio,
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"trans_len": translation_length,
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"ref_len": reference_length,
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}
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return bleu_score_dict # (bleu, precisions, bp, ratio, translation_length, reference_length)
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if ratio > 1.0:
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bp = 1.
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else:
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bp = math.exp(1 - 1. / ratio)
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bleu = geo_mean * bp
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print(geo_mean)
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bleu_score_dict = {"bleu":bleu,"precision":precisions,"bp":bp,"ratio":ratio,"trans_len":translation_length,"ref_len":reference_length}
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return bleu_score_dict#(bleu, precisions, bp, ratio, translation_length, reference_length)
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def bleu_test_case():
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"""A simple functionality test case to evaluate BLEU"""
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generated = [[["a","=","b","\n","y","=","a","+","1"]]]
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reference = [["a","=","b","\n","print","a"]]
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score_dict = compute_bleu(generated,reference,smooth=False)
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generated = [[["a", "=", "b", "\n", "y", "=", "a", "+", "1"]]]
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reference = [["a", "=", "b", "\n", "print", "a"]]
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score_dict = compute_bleu(generated, reference, smooth=False)
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return score_dict
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if __name__ == "__main__":
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score_dict = bleu_test_case()
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print(score_dict)
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@ -1,7 +1,7 @@
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from metrics.bleu import compute_bleu
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def compute_exact_match(references,generated)->float:
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def compute_exact_match(references, generated) -> float:
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"""
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Computes Exact Match Accuracy.
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args:
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@ -12,15 +12,19 @@ def compute_exact_match(references,generated)->float:
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returns:
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exact_match_accuracy : Float
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"""
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assert(len(references[0])==len(generated),"Number of Samples should be equal in References and Synthesized Outputs..")
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assert (
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len(references[0]) == len(generated),
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"Number of Samples should be equal in References and Synthesized Outputs..",
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)
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exact_match_count = 0.0
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for gen,ref in zip(generated, references[0]):
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for gen, ref in zip(generated, references[0]):
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if gen == ref:
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exact_match_count += 1
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exact_match_acc = exact_match_count/len(generated)
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exact_match_acc = exact_match_count / len(generated)
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return exact_match_acc
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def compute_metrics(references,generated) -> dict:
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def compute_metrics(references, generated) -> dict:
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"""
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Calculates various metrics and returns the calculated dict of these matrics.
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args:
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@ -31,8 +35,12 @@ def compute_metrics(references,generated) -> dict:
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returns:
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A dicitonary with different metrics intact.
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"""
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metrics_dict = {} #Update as in new metrics are added over here.
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metrics_dict["smoothed_bleu_4"] = compute_bleu(references,generated,smooth=True)
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metrics_dict["bleu_4"] = compute_bleu(references,generated,smooth=False)
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metrics_dict["exact_match_acc"] = compute_exact_match(references,generated)
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metrics_dict = {
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"smoothed_bleu_4": None,
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"blue_4": None,
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"exact_match_acc": None,
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} # Update as in new metrics are computed.
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metrics_dict["smoothed_bleu_4"] = compute_bleu(references, generated, smooth=True)
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metrics_dict["bleu_4"] = compute_bleu(references, generated, smooth=False)
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metrics_dict["exact_match_acc"] = compute_exact_match(references, generated)
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return metrics_dict
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