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multiset_distances.py
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/
multiset_distances.py
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from collections import Counter
from functools import reduce
import numpy as np
from nltk.translate.bleu_score import ngrams
class metric_names:
jaccard = "Jaccard"
sorensen = "Sorensen"
canberra = "Canberra"
minkowski = "Minkowski"
BLEU = "BLEU"
SelfBLEU = "SelfBLEU"
def get_ngrams(sentences, n):
f = lambda x: (list(ngrams(x, n)) if len(x) >= n else [])
return list(map(f, sentences))
class MultisetDistances:
def __init__(self, references, min_n=1, max_n=5):
super().__init__()
# print('multiset distances init upto {}!'.format(max_n))
self.references = references
self.max_n = max_n
self.min_n = min_n
assert self.max_n >= self.min_n
assert self.min_n >= 1
self.ref_ngrams = self._get_ngrams(references)
def get_cached_fields(self):
return self.ref_ngrams,
def _get_ngrams(self, sentences):
samples_size = len(sentences)
all_counters = [Counter([x for y in get_ngrams(sentences, n + 1) for x in y])
for n in range(self.max_n)]
for n_counter in all_counters:
for k in n_counter.keys():
n_counter[k] /= samples_size
return all_counters
def get_ngram_stuff(self, sentences):
sample_ngrams = self._get_ngrams(sentences)
ngrams_intersection = [sample_ngrams[i] & self.ref_ngrams[i]
for i in range(self.max_n)] # intersection: min(c[x], d[x])
ngrams_union = [sample_ngrams[i] | self.ref_ngrams[i]
for i in range(self.max_n)] # union: max(c[x], d[x])
ngrams_abs_diff = [ngrams_union[i] - ngrams_intersection[i] \
for i in range(self.max_n)]
ngrams_added = [sample_ngrams[i] + self.ref_ngrams[i]
for i in range(self.max_n)]
return ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added
def _final_average(self, score_value):
return np.power(reduce(lambda x, y: x * y, score_value), 1. / float(len(score_value)))
def _jaccard(self, ngrams_intersection, ngrams_union):
jaccard_value = [float(sum(ngrams_intersection[n].values())) / sum(ngrams_union[n].values()) for n in
range(self.max_n)]
return jaccard_value
def get_jaccard_score(self, sentences):
ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added = self.get_ngram_stuff(sentences)
jaccard_value = self._jaccard(ngrams_intersection=ngrams_intersection, ngrams_union=ngrams_union)
return {n: self._final_average(jaccard_value[:n]) for n in range(self.min_n, self.max_n + 1)}
def _sorensen(self, ngrams_abs_diff, ngrams_added):
sorensen_value = [float(sum(ngrams_abs_diff[n].values())) / sum(ngrams_added[n].values()) for n in
range(self.max_n)]
return sorensen_value
def get_sorensen_score(self, sentences):
print('Sorensen distances preprocess upto {}!'.format(self.max_n))
ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added = self.get_ngram_stuff(sentences)
sorensen_value = self._sorensen(ngrams_abs_diff=ngrams_abs_diff, ngrams_added=ngrams_added)
return {n: self._final_average(sorensen_value[:n]) for n in range(self.min_n, self.max_n + 1)}
def _canberra(self, ngrams_abs_diff, ngrams_added):
canberra_value = [np.sum([ngrams_abs_diff[n][key] / float(ngrams_added[n][key]) for key in ngrams_abs_diff[n]])
for n in range(self.max_n)]
return canberra_value
def get_canberra_score(self, sentences):
print('Canberra distances preprocess upto {}!'.format(self.max_n))
ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added = self.get_ngram_stuff(sentences)
canberra_value = self._canberra(ngrams_abs_diff=ngrams_abs_diff, ngrams_added=ngrams_added)
return {n: self._final_average(canberra_value[:n]) for n in range(self.min_n, self.max_n + 1)}
def _minkowski(self, ngrams_abs_diff, p):
minkowski_value = [np.power(np.sum(np.power(list(ngrams_abs_diff[n].values()), p)), 1. / p) for n in
range(self.max_n)]
return minkowski_value
def get_minkowski_score(self, sentences, p):
print('Minkowski (p={}) distances preprocess upto {}!'.format(p, self.max_n))
ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added = self.get_ngram_stuff(sentences)
minkowski_value = self._minkowski(ngrams_abs_diff=ngrams_abs_diff, p=p)
return {n: self._final_average(minkowski_value[:n]) for n in range(self.min_n, self.max_n + 1)}
def get_all_score(self, sentences, max_mikowski_order=3):
print('multiset distances preprocess upto {}!'.format(self.max_n))
ngrams_intersection, ngrams_union, ngrams_abs_diff, ngrams_added = self.get_ngram_stuff(sentences)
temp_results = {}
print('multiset distances evaluating upto {}!'.format(self.max_n))
temp_results[metric_names.jaccard] = self._jaccard(ngrams_intersection=ngrams_intersection,
ngrams_union=ngrams_union)
temp_results[metric_names.sorensen] = self._sorensen(ngrams_abs_diff=ngrams_abs_diff, ngrams_added=ngrams_added)
temp_results[metric_names.canberra] = self._canberra(ngrams_abs_diff=ngrams_abs_diff, ngrams_added=ngrams_added)
for p in range(1, max_mikowski_order + 1):
temp_results['p%d-%s' % (p, metric_names.minkowski)] = self._minkowski(ngrams_abs_diff=ngrams_abs_diff, p=p)
result = {}
for key in temp_results:
for n in range(self.min_n, self.max_n + 1):
result[key + '%d' % n] = self._final_average(temp_results[key][:n])
return result
if __name__ == "__main__":
# print(MultisetDistances([['saadi', 'saadi']], max_n=2).get_all_score([['saadi', 'ferdowsi']]))
# print(MultisetDistances([['saadi', 'saadi']], max_n=2).get_all_score([['saadi', 'saadi']]))
ref1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures', 'that', 'the', 'military', 'will', 'forever',
'heed', 'Party', 'commands']
ref2 = ['It', 'is', 'the', 'guiding', 'principle', 'which', 'guarantees', 'the', 'military', 'forces', 'always',
'being', 'under', 'the', 'command', 'of', 'the', 'Party']
ref3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', 'army', 'always', 'to', 'heed', 'the', 'directions',
'of', 'the', 'party']
sen1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures', 'that', 'the', 'military', 'always', 'obeys',
'the', 'commands', 'of', 'the', 'party']
sen2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', 'interested', 'in', 'world', 'history']
references = [ref1, ref2, ref3]
sentences = [sen1, sen2]
msd = MultisetDistances(references=references)
print(msd.get_jaccard_score(sentences=sentences))