def q5_calculate_perplexity_of_reuters_corpus():
training = ReutersTrainingCorpus()
test = ReutersTestCorpus()
language_model_bigram = BigramLanguageModel(training)
perplexity_calculator = PerplexityCalculator()
training_perplexity_bigram = perplexity_calculator.calculate_corpus_perplexity(language_model_bigram, training)
test_perplexity_bigram = perplexity_calculator.calculate_corpus_perplexity(language_model_bigram, test)
print("Training perplexity basic model: ", training_perplexity_bigram)
print("Test perplexity basic model: ", test_perplexity_bigram)
def q5_calculate_perplexity_of_reuters_corpus():
training = ReutersTrainingCorpus()
test = ReutersTestCorpus()
language_model_bigram = BigramLanguageModel(training)
perplexity_calculator = PerplexityCalculator()
training_perplexity_bigram = perplexity_calculator.calculate_corpus_perplexity(
language_model_bigram, training)
test_perplexity_bigram = perplexity_calculator.calculate_corpus_perplexity(
language_model_bigram, test)
print("Training perplexity basic model: ", training_perplexity_bigram)
print("Test perplexity basic model: ", test_perplexity_bigram)
def find_lambdas_max_estimation_multiple_slicings(self, corpus, n, slicings):
sentences = corpus.get_sentences()
slice_size = round(len(sentences) * (1/slicings))
guesses = []
for i in range(slicings):
slice_index = round(slice_size * i)
slice_end = slice_index + slice_size
training = Corpus(sentences[:slice_index] + sentences[slice_end:])
holdout = Corpus(sentences[slice_index:slice_end])
best_lambdas = InterpolationTrainer().find_lambdas_max_estimation(training, holdout, 3)
guesses.append(best_lambdas)
perplexities = [0] * slicings
for slicing in range(slicings):
slice_index = round(slice_size * i)
slice_end = slice_index + slice_size
training = Corpus(sentences[:slice_index] + sentences[slice_end:])
holdout = Corpus(sentences[slice_index:slice_end])
for guess in range(slicings):
calculator = InterpolatingNgramProbabilityCalculator(NgramCalculatorContainer(training, 3), guesses[i])
max_estimation_language_model = NgramLanguageModel(training, 3, ngram_probability_calculator=calculator)
perplexity = PerplexityCalculator().calculate_corpus_perplexity(max_estimation_language_model, holdout)
perplexities[guess] += perplexity
return guesses[perplexities.index(min(perplexities))]
def q6_minimize_perplexity_of_reuters_corpus():
training = ReutersTrainingCorpus()
test = ReutersTestCorpus()
lambdas = [0.2518, 0.4691, 0.2791]
calculator = InterpolatingNgramProbabilityCalculator(
NgramCalculatorContainer(training, 3), lambdas)
language_model = NgramLanguageModel(
training, 3, ngram_probability_calculator=calculator)
perplexity = PerplexityCalculator().calculate_corpus_perplexity(
language_model, test)
print("Test perplexity best model: ", perplexity)
def find_lambdas_brute_force(self, training_corpus, holdout_corpus, n):
ngram_counter = NgramCalculatorContainer(training_corpus, n)
minimum_perplexity = sys.maxsize
best_lambdas = []
perplexity_calculator = PerplexityCalculator()
increment = .1
tries = round(1 / increment)
for i in range(tries + 1):
l1 = round(increment * i, 2)
for j in range(tries-i):
l2 = round(increment * j, 2)
l3 = round(1 - l1 - l2, 2)
lambdas = [l1, l2, l3]
ngram_probability_calculator = InterpolatingNgramProbabilityCalculator(ngram_counter, lambdas)
language_model = NgramLanguageModel(
training_corpus, n, ngram_probability_calculator=ngram_probability_calculator
)
perplexity = perplexity_calculator.calculate_corpus_perplexity(language_model, holdout_corpus)
if perplexity < minimum_perplexity:
minimum_perplexity = perplexity
best_lambdas = lambdas
return best_lambdas, minimum_perplexity
def q7_perplexity_oov_of_brown_corpus():
training = ReutersTrainingCorpus()
brown = BrownCorpus()
lambdas = [0.2518, 0.4691, 0.2791]
calculator = InterpolatingNgramProbabilityCalculator(
NgramCalculatorContainer(training, 3), lambdas)
language_model = NgramLanguageModel(
training, 3, ngram_probability_calculator=calculator)
perplexity = PerplexityCalculator().calculate_corpus_perplexity(
language_model, brown)
oov_calculator = OutOfVocabularyRateCalculator()
oov_rate = oov_calculator.calculate_out_of_vocabulary_rate(training, brown)
print("Brown perplexity best model: ", perplexity)
print("Brown out of vocabulary rate: ", oov_rate)
def test_trainer_on_reuters_corpus(self):
reuters = ReutersTrainingCorpus().get_sentences()
slice_size = round(len(reuters) * .1)
guesses = []
for i in range(10):
slice_index = round(slice_size * i)
slice_end = slice_index + slice_size
training = Corpus(reuters[:slice_index] + reuters[slice_end:])
holdout = Corpus(reuters[slice_index:slice_end])
best_lambdas = InterpolationTrainer().find_lambdas_max_estimation(
training, holdout, 3)
guesses.append(best_lambdas)
perplexities = [0] * 10
for slicing in range(10):
slice_index = round(slice_size * i)
slice_end = slice_index + slice_size
training = Corpus(reuters[:slice_index] + reuters[slice_end:])
holdout = Corpus(reuters[slice_index:slice_end])
for guess in range(10):
calculator = InterpolatingNgramProbabilityCalculator(
NgramCalculatorContainer(training, 3), guesses[guess])
max_estimation_language_model = NgramLanguageModel(
training, 3, ngram_probability_calculator=calculator)
perplexity = PerplexityCalculator(
).calculate_corpus_perplexity(max_estimation_language_model,
holdout)
perplexities[guess] += perplexity
min_perplexity = min(perplexities)
best_lambdas = guesses[perplexities.index(min_perplexity)]
for i in range(10):
print(perplexities[i] / 10, guesses[i])
print(min_perplexity, best_lambdas)
class TestLanguageModels:
calculator = PerplexityCalculator()
def test_language_models_on_very_simple_corpus(self):
corpus = Corpus([['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']])
unigram_language_model = UnigramLanguageModel(corpus)
bigram_language_model = BigramLanguageModel(corpus)
trigram_language_model = NgramLanguageModel(
corpus, 3, NgramProbabilityCalculator)
assert 10 == round(
self.calculator.calculate_corpus_perplexity(
unigram_language_model, corpus), 10)
assert 1 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model, corpus), 10)
assert 1 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model, corpus), 10)
def test_language_models_on_somewhat_simple_corpus(self):
training = Corpus([['a', 'b', 'c'], ['b', 'c', 'd'], ['c', 'd', 'e']])
test = Corpus([['a', 'b', 'c'], ['a', 'c', 'd']])
unigram_language_model = UnigramLanguageModel(training)
bigram_language_model = BigramLanguageModel(training)
bigram_language_model_smoothing = NgramLanguageModel(
training, 2, LaplaceSmoothingNgramProbabilityCalculator)
trigram_language_model = NgramLanguageModel(
training, 3, NgramProbabilityCalculator)
trigram_language_model_backoff = NgramLanguageModel(
training, 3, BackoffNgramProbabilityCalculator)
trigram_language_model_interpolating = NgramLanguageModel(
training, 3, InterpolatingNgramProbabilityCalculator)
assert 4.95 == round(
self.calculator.calculate_corpus_perplexity(
unigram_language_model, test), 2)
assert 1.68 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model, test), 2)
assert 3.49 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model_smoothing, test), 2)
assert 1.73 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model, test), 2)
assert 2.93 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model_backoff, test), 2)
assert 3.04 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model_interpolating, test), 2)
def test_language_models_on_brown_corpus(self):
corpus = BrownCorpus()
unigram_language_model = UnigramLanguageModel(corpus)
bigram_language_model = BigramLanguageModel(corpus)
bigram_language_model_smoothing = NgramLanguageModel(
corpus, 2, LaplaceSmoothingNgramProbabilityCalculator)
trigram_language_model = NgramLanguageModel(
corpus, 3, BackoffNgramProbabilityCalculator)
trigram_language_model_interpolating = NgramLanguageModel(
corpus, 3, InterpolatingNgramProbabilityCalculator)
quadrigram_language_model = NgramLanguageModel(
corpus, 4, BackoffNgramProbabilityCalculator)
assert 1548.66 == round(
self.calculator.calculate_corpus_perplexity(
unigram_language_model, corpus), 2)
assert 100.10 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model, corpus), 2)
assert 4659.28 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model_smoothing, corpus), 2)
assert 7.77 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model, corpus), 2)
assert 17.56 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model_interpolating, corpus), 2)
assert 2.40 == round(
self.calculator.calculate_corpus_perplexity(
quadrigram_language_model, corpus), 2)
def test_language_models_on_reuters_corpus(self):
reuters = ReutersTrainingCorpus().get_sentences()
slice_index = round(len(reuters) * .9)
training = Corpus(reuters[:slice_index])
test = Corpus(reuters[slice_index:])
unigram_language_model = UnigramLanguageModel(training)
bigram_language_model = NgramLanguageModel(
training, 2, BackoffNgramProbabilityCalculator)
trigram_language_model = NgramLanguageModel(
training, 3, BackoffNgramProbabilityCalculator)
trigram_language_model_interpolating = NgramLanguageModel(
training, 3, InterpolatingNgramProbabilityCalculator)
quadrigram_language_model = NgramLanguageModel(
training, 4, BackoffNgramProbabilityCalculator)
assert 1021.63 == round(
self.calculator.calculate_corpus_perplexity(
unigram_language_model, test), 2)
assert 176.52 == round(
self.calculator.calculate_corpus_perplexity(
bigram_language_model, test), 2)
assert 171.96 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model, test), 2)
assert 189.32 == round(
self.calculator.calculate_corpus_perplexity(
trigram_language_model_interpolating, test), 2)
assert 298.98 == round(
self.calculator.calculate_corpus_perplexity(
quadrigram_language_model, test), 2)
