def __init__(self, corpus):
"""
:param corpus: corpus over which to create the language model
"""
super(BigramLanguageModel, self).__init__(corpus)
self.ngram_calculator = BasicNgramCalculator(corpus)
self.bigrams = self.ngram_calculator.calculate_ngrams(2, True, False)
def q2_calculate_unique_unigrams_in_reuters_training():
corpus = ReutersTrainingCorpus()
unigram_calculator = UnigramCalculator(corpus)
ngram_calculator = BasicNgramCalculator(corpus)
unique_unigrams = unigram_calculator.get_percentage_unique_unigrams()
unique_unigrams_ngram = ngram_calculator.get_percentage_unique_ngrams(1)
print("Percentage of unique unigrams (unigram calculator): ", unique_unigrams)
print("Percentage of unique unigrams (ngram calculator): ", unique_unigrams_ngram)
def q2_calculate_unique_unigrams_in_reuters_training():
corpus = ReutersTrainingCorpus()
unigram_calculator = UnigramCalculator(corpus)
ngram_calculator = BasicNgramCalculator(corpus)
unique_unigrams = unigram_calculator.get_percentage_unique_unigrams()
unique_unigrams_ngram = ngram_calculator.get_percentage_unique_ngrams(1)
print("Percentage of unique unigrams (unigram calculator): ",
unique_unigrams)
print("Percentage of unique unigrams (ngram calculator): ",
unique_unigrams_ngram)
def q3_calculate_unique_ngrams_in_reuters_training_for_n_2_3_4_5_6():
corpus = ReutersTrainingCorpus()
ngram_calculator = BasicNgramCalculator(corpus)
unique_ngrams_2 = ngram_calculator.get_percentage_unique_ngrams(2)
unique_ngrams_3 = ngram_calculator.get_percentage_unique_ngrams(3)
unique_ngrams_4 = ngram_calculator.get_percentage_unique_ngrams(4)
unique_ngrams_5 = ngram_calculator.get_percentage_unique_ngrams(5)
unique_ngrams_6 = ngram_calculator.get_percentage_unique_ngrams(6)
print("Percentage of unique bigrams: ", unique_ngrams_2)
print("Percentage of unique trigrams: ", unique_ngrams_3)
print("Percentage of unique 4-grams: ", unique_ngrams_4)
print("Percentage of unique 5-grams: ", unique_ngrams_5)
print("Percentage of unique 6-grams: ", unique_ngrams_6)
def q3_calculate_unique_ngrams_in_reuters_training_for_n_2_3_4_5_6():
corpus = ReutersTrainingCorpus()
ngram_calculator = BasicNgramCalculator(corpus)
unique_ngrams_2 = ngram_calculator.get_percentage_unique_ngrams(2)
unique_ngrams_3 = ngram_calculator.get_percentage_unique_ngrams(3)
unique_ngrams_4 = ngram_calculator.get_percentage_unique_ngrams(4)
unique_ngrams_5 = ngram_calculator.get_percentage_unique_ngrams(5)
unique_ngrams_6 = ngram_calculator.get_percentage_unique_ngrams(6)
print("Percentage of unique bigrams: ", unique_ngrams_2)
print("Percentage of unique trigrams: ", unique_ngrams_3)
print("Percentage of unique 4-grams: ", unique_ngrams_4)
print("Percentage of unique 5-grams: ", unique_ngrams_5)
print("Percentage of unique 6-grams: ", unique_ngrams_6)
class BigramLanguageModel(UnigramLanguageModel):
"""
Language model using bigram probabilities
"""
def __init__(self, corpus):
"""
:param corpus: corpus over which to create the language model
"""
super(BigramLanguageModel, self).__init__(corpus)
self.ngram_calculator = BasicNgramCalculator(corpus)
self.bigrams = self.ngram_calculator.calculate_ngrams(2, True, False)
def get_bigram_count(self, bigram):
"""
Gets the count of the bigram in the corpus
:param bigram: bigram to find in the corpus
:return: number of times the bigram is found in the corpus; 1 if it is not found TODO should be zero
"""
return self.bigrams[bigram] if bigram in self.bigrams else 0
def get_bigram_probability(self, bigram):
"""
Gets the probability of a bigram as calculated by count(bigram) / count(first word of bigram)
:param bigram: bigram to get the probability for
:return: probability of the bigram for the language model
"""
x, y = bigram
x_count = self.get_unigram_count(x)
return self.get_bigram_count(bigram) / x_count if x_count != 0 else 0
def get_bigram_log_probability(self, bigram):
"""
Gets the log of the probability of a bigram; see get_bigram_probability for details
:param bigram: bigram to get the probability for
:return: log base 2 of the probability of the bigram for the language model; None if bigram probability is 0
"""
bigram_probability = self.get_bigram_probability(bigram)
return log(bigram_probability, self.BASE) if bigram_probability != 0 else None
def get_sentence_log_probability(self, sentence):
"""
Calculates the log probability of a sentence
:param sentence: list of words
:return: log of the probability of the sentence (to be used to calculate entropy, perplexity)
"""
probability = 0
found_words = 0
bigram_probability = self.get_bigram_log_probability(('<s>', sentence[0]))
probability += bigram_probability if bigram_probability is not None else 0
found_words += 1 if bigram_probability is not None else 0
for i in range(1, len(sentence)):
bigram = (sentence[i-1], sentence[i])
bigram_probability = self.get_bigram_log_probability(bigram)
probability += bigram_probability if bigram_probability is not None else 0
found_words += 1 if bigram_probability is not None else 0
return probability, found_words
class TestNgramCalculator:
corpus = Corpus()
basic_ngram_calculator = BasicNgramCalculator(corpus)
ngram_calculator = NgramCalculator(corpus)
flattened_sentences = [
item for sublist in corpus.get_sentences() for item in sublist
]
frequencies = FreqDist(flattened_sentences)
def test_calculate_unigrams(self):
assert dict(
self.frequencies) == self.ngram_calculator.calculate_ngrams(1)
def test_get_percentage_unique_unigrams(self):
assert round(27 / len(list(self.frequencies)), 2) == round(
self.ngram_calculator.get_percentage_unique_ngrams(1), 2)