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#!/usr/bin/env python
import os
import kenlm
LM = os.path.join(os.path.dirname(__file__), '..', 'lm', '')
model = kenlm.LanguageModel(LM)
print('{0}-gram model'.format(model.order))
sentence = 'language modeling is fun .'
# Check that total full score = direct score
def score(s):
return sum(prob for prob, _, _ in model.full_scores(s))
assert (abs(score(sentence) - model.score(sentence)) < 1e-3)
# Show scores and n-gram matches
words = ['<s>'] + sentence.split() + ['</s>']
for i, (prob, length, oov) in enumerate(model.full_scores(sentence)):
print('{0} {1}: {2}'.format(prob, length, ' '.join(words[i+2-length:i+2])))
if oov:
print('\t"{0}" is an OOV'.format(words[i+1]))
# Find out-of-vocabulary words
for w in words:
if not w in model:
print('"{0}" is an OOV'.format(w))
#Stateful query
state = kenlm.State()
state2 = kenlm.State()
#Use <s> as context. If you don't want <s>, use model.NullContextWrite(state).
accum = 0.0
accum += model.BaseScore(state, "a", state2)
accum += model.BaseScore(state2, "sentence", state)
#score defaults to bos = True and eos = True. Here we'll check without the end
#of sentence marker.
assert (abs(accum - model.score("a sentence", eos = False)) < 1e-3)
accum += model.BaseScore(state, "</s>", state2)
assert (abs(accum - model.score("a sentence")) < 1e-3)
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