def test_most_freq_word_end(self):
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
five = NGramTrie(5, encoded)
trie = NGramTrie(3, encoded)
four = NGramTrie(4, encoded)
expected_word = storage.get_id('<END>')
context = (
storage.get_id('his'),
storage.get_id('name'),
storage.get_id('is'),
storage.get_id('bruno'),
)
generator = BackOffGenerator(storage, five, trie, four)
actual = generator.most_freq_word(context)
self.assertEqual(expected_word, actual)
def test_load_model_takes_less_time(self):
with open('lab_1/data.txt', 'r', encoding='utf-8') as big_file:
big_data = big_file.read()
tokenized_data = tokenize_by_sentence(big_data)
storage = WordStorage()
storage.update(tokenized_data)
context = (
storage.get_id('despite'),
storage.get_id('the'),
)
start_time_generate = time()
encoded = encode_text(storage, tokenized_data)
trie = NGramTrie(3, encoded)
generator = NGramTextGenerator(storage, trie)
generated_text = generator.generate_text(context, 3)
end_time_generate = time() - start_time_generate
save_model(generator, 'model_training')
start_time_saved = time()
loaded_model = load_model('model_training')
new_result = loaded_model.generate_text(context, 3)
end_time_saved = time() - start_time_saved
self.assertGreater(end_time_generate, end_time_saved)
self.assertEqual(generated_text, new_result)
def test_end(self):
"""
Checks that after decoding no end in result
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'cat', '<END>', 'his',
'name', 'is', 'rex', '<END>', 'her', 'name', 'is', 'cat',
'<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (
storage.get_id('a'),
storage.get_id('cat'),
)
generator = LikelihoodBasedTextGenerator(storage, trie)
to_decode = generator.generate_text(context, 1)
actual = decode_text(storage, to_decode)
expected = ('A cat', )
self.assertEqual(expected, actual)
def test_decode_text_ideal(self):
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (
storage.get_id('name'),
storage.get_id('is'),
)
print('Я ТЕСТ', context)
end = storage.get_id('<END>')
generator = LikelihoodBasedTextGenerator(storage, trie)
to_decode = generator.generate_text(context, 2)
self.assertEqual(to_decode[-1], end)
expected = ('Name is rex', 'Her name is rex')
actual = decode_text(storage, to_decode)
self.assertEqual(expected, actual)
def test_decode_text_incorrect_storage(self):
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (
storage.get_id('name'),
storage.get_id('is'),
)
generator = LikelihoodBasedTextGenerator(storage, trie)
to_decode = generator.generate_text(context, 2)
bad_inputs = [(), [], 123, None, NGramTrie]
for bad_storage in bad_inputs:
self.assertRaises(ValueError, decode_text, bad_storage, to_decode)
def test_generate_text_large_context(self):
"""
should generate simple case with three sentences out of small corpus
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(5, encoded)
generator = NGramTextGenerator(storage, trie)
context = (
storage.get_id('i'),
storage.get_id('have'),
storage.get_id('a'),
storage.get_id('bruno'),
)
end = storage.get_id('<END>')
actual = generator.generate_text(context, 3)
self.assertEqual(actual.count(end), 3)
def test_decode_text_ideal_conditions(self):
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (
storage.get_id('name'),
storage.get_id('is'),
)
generator = LikelihoodBasedTextGenerator(storage, trie)
to_decode = generator.generate_text(context, 2)
actual = decode_text(storage, to_decode)
for sentence in actual:
self.assertTrue('<END>' not in sentence)
self.assertTrue(sentence[0].isupper())
self.assertTrue(sentence[-1].isalpha())
def test_generate_next_word_context_incorrect(self):
corpus = ('i', 'have', 'a', 'cat', '<END>',
'his', 'name', 'is', 'bruno', '<END>',
'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>',
'her', 'name', 'is', 'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
four = NGramTrie(4, encoded)
trie = NGramTrie(3, encoded)
two = NGramTrie(2, encoded)
expected_word = storage.get_id('rex')
context = (storage.get_id('name'),
storage.get_id('is'),
storage.get_id('cat'))
generator = BackOffGenerator(storage, four, two, trie)
actual = generator._generate_next_word(context)
self.assertEqual(expected_word, actual)
def test_text_generator_generate_sentence_proper_beginning(self):
"""
Checks that class creates correct sentence from a context '<END>' without '<END>' in the beginning
"""
corpus = ('my', 'favourite', 'subject', 'is', 'maths', '<END>', 'his',
'favourite', 'thing', 'is', 'music'
'<END>', 'i', 'have', 'a', 'favourite', 'film', '<END>',
'my', 'family', 'likes', 'avatar', '<END>', 'my',
'favourite', 'subject', 'is', 'music', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(2, encoded)
context = (storage.get_id('<END>'), )
first_generated = storage.get_id('my')
last_generated = storage.get_id('<END>')
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertNotEqual(storage.get_id('<END>'), actual[0])
self.assertEqual(first_generated, actual[0])
self.assertEqual(last_generated, actual[-1])
def test_text_generator_no_context(self):
"""
checks if the program can generate sentences without given context
"""
corpus = ('cat', 'has', 'paws', '<END>', 'dogs', 'have', 'noses',
'<END>', 'cat', 'has', 'whiskers', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
two = NGramTrie(2, encoded)
four = NGramTrie(4, encoded)
context = (
storage.get_id('cat'),
storage.get_id('dogs'),
)
generator = BackOffGenerator(storage, trie, two, four)
actual = generator.generate_text(context, 3)
self.assertTrue(all(actual))
def main():
text = ('I have a cat. His name is Bruno. '
'I have a dog too. His name is Rex. '
'Her name is Rex too.')
corpus = tokenize_by_sentence(text)
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
two = NGramTrie(2, encoded)
trie = NGramTrie(3, encoded)
context = (
storage.get_id('name'),
storage.get_id('is'),
)
generator = BackOffGenerator(storage, trie, two)
expected = 'rex'
actual = storage.get_word(generator._generate_next_word(context))
print(f'TEXT:\n{text}')
print(f'\nEXPECTED WORD AFTER name is IS {expected}')
print(f'ACTUAL WORD AFTER name is IS {actual}')
save_model(generator, 'model.txt')
load_model('model.txt')
return actual == expected
def realize_n_gram_text_generator(text):
n_gram_storage = WordStorage()
n_gram_storage.update(text)
n_gram_context = (n_gram_storage.get_id('my'), n_gram_storage.get_id('dear'))
n_gram_encoded = encode_text(n_gram_storage, text)
n_gram_trie = NGramTrie(3, n_gram_encoded)
n_gram_generator = NGramTextGenerator(n_gram_storage, n_gram_trie)
n_gram_text_generated = n_gram_generator.generate_text(n_gram_context, 3)
return decode_text(n_gram_storage, n_gram_text_generated)
def realize_likelihood_generator(text):
likelihood_storage = WordStorage()
likelihood_storage.update(text)
context = (likelihood_storage.get_id('i'),
likelihood_storage.get_id('shall'),)
model = load_model('lab_4/likelihood_model.json')
generator = LikelihoodBasedTextGenerator(model.word_storage, model.n_gram_trie)
likelihood_text_generated = generator.generate_text(context, 3)
return decode_text(likelihood_storage, likelihood_text_generated)
def realize_backoff_generator(text):
backoff_storage = WordStorage()
backoff_storage.update(text)
backoff_encoded = encode_text(backoff_storage, text)
two = NGramTrie(2, backoff_encoded)
trie = NGramTrie(3, backoff_encoded)
backoff_context = (backoff_storage.get_id('if'),
backoff_storage.get_id('you'),)
backoff_generator = BackOffGenerator(backoff_storage, trie, two)
backoff_text_generated = backoff_generator.generate_text(backoff_context, 3)
return decode_text(backoff_storage, backoff_text_generated)
def test_ngram_text_generator_identical_words(self):
corpus = ('deadline', 'deadline', 'deadline', 'deadline', 'deadline',
'<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (storage.get_id('deadline'), storage.get_id('deadline'))
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(20 + len(context) + 1,
len(actual)) # +1 it is for <END>
def test_ngram_text_generator_generate_next_word(self):
"""
Checks that next word generates properly
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'i', 'have', 'a', 'bruno',
'cat', '<END>')
word_storage = WordStorage()
word_storage.update(corpus)
encoded = encode_text(word_storage, corpus)
ngram = NGramTrie(3, encoded)
generator = NGramTextGenerator(word_storage, ngram)
context = (word_storage.get_id('i'), word_storage.get_id('have'))
expected = word_storage.get_id('a')
actual = generator._generate_next_word(context)
self.assertEqual(expected, actual)
def test_ngram_text_generator_generate_sentence_properly(self):
"""
generates correct output according to simple case
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>')
word_storage = WordStorage()
word_storage.update(corpus)
encoded = encode_text(word_storage, corpus)
trie = NGramTrie(2, encoded)
context = (word_storage.get_id('i'), )
end = word_storage.get_id('<END>')
generator = NGramTextGenerator(word_storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(actual[-1], end)
def test_get_most_frequent_gram_ideal(self):
"""
Checks that most frequent ngram gets properly
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'i', 'have', 'a', 'bruno',
'cat', '<END>')
word_storage = WordStorage()
word_storage.update(corpus)
encoded = encode_text(word_storage, corpus)
ngram = NGramTrie(3, encoded)
generator = NGramTextGenerator(word_storage, ngram)
context = (word_storage.get_id('i'), word_storage.get_id('have'))
expected = (word_storage.get_id('i'), word_storage.get_id('have'),
word_storage.get_id('a'))
actual = generator.get_most_frequent_gram(context)
self.assertEqual(expected, actual)
def test_word_storage_get_id_ideal(self):
"""
ideal case for get_id
"""
word_storage = WordStorage()
word_storage.storage = {'word': 1}
expected = 1
actual = word_storage.get_id('word')
self.assertEqual(expected, actual)
def test_decode_text_upper_first_letter(self):
'''
Tests that number all the letters except
first one in a sentence are in a lower case
'''
corpus = ('first', 'sentence', 'here', '<END>', 'second', 'sentence',
'here', '<END>', 'third', 'sentence', 'here', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded_text = encode_text(storage, corpus)
trie = NGramTrie(3, encoded_text)
context = (storage.get_id('first'), storage.get_id('sentence'))
likelihood_generator = LikelihoodBasedTextGenerator(storage, trie)
generated_encoded_text = likelihood_generator.generate_text(context, 1)
decoded_text = decode_text(storage, generated_encoded_text)
self.assertFalse(decoded_text[0][1:].isupper())
def test_generate_next_word_short_context(self):
corpus = ('bye', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
four = NGramTrie(4, encoded)
trie = NGramTrie(3, encoded)
two = NGramTrie(2, encoded)
expected_word = storage.get_id('bye')
context = (storage.get_id('<END>'),)
generator = BackOffGenerator(storage, two, four, trie)
actual = generator._generate_next_word(context)
self.assertEqual(expected_word, actual)
def test_get_most_frequent_gram_no_such_context(self):
"""
Checks that returns empty tuple with no context in the corpus
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>')
word_storage = WordStorage()
word_storage.update(corpus)
encoded = encode_text(word_storage, corpus)
ngram = NGramTrie(3, encoded)
generator = NGramTextGenerator(word_storage, ngram)
context = (
word_storage.get_id('i'),
word_storage.get_id('name'),
) # there is no such context in ngrams
expected = ()
actual = generator.get_most_frequent_gram(context)
self.assertEqual(expected, actual)
def test_word_storage_put_word_ideal(self):
"""
word is added to storage
"""
word_storage = WordStorage()
word = 'word'
actual = word_storage._put_word(word)
self.assertTrue(word in word_storage.storage)
expected = word_storage.get_id(word)
self.assertEqual(expected, actual)
def test_ngram_text_generator_generate_next_word_no_such_context(self):
"""
Checks that next word generates properly if no context found
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>')
word_storage = WordStorage()
word_storage.update(corpus)
encoded = encode_text(word_storage, corpus)
ngram = NGramTrie(3, encoded)
generator = NGramTextGenerator(word_storage, ngram)
context = (
word_storage.get_id('i'),
word_storage.get_id('name'),
) # there is no such context in ngrams, so return most frequent option
expected_top_freq = word_storage.get_id('<END>') # as it appears twice
actual = generator._generate_next_word(context)
self.assertEqual(expected_top_freq, actual)
def test_text_generator_generate_sentence_proper_number_of_end(self):
"""
Checks that class creates correct sentence with only one <END>
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'there', 'are', 'a',
'cat', 'outside', '<END>', 'here', 'is', 'a', 'cat',
'outside', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(3, encoded)
context = (storage.get_id('a'), storage.get_id('is'),
storage.get_id('<END>'))
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(1, actual.count(storage.get_id('<END>')))
def test_ngram_text_generator_generate_sentence_ideal(self):
"""
first and last generated words as expected
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(2, encoded)
context = (storage.get_id('i'), )
first_generated = storage.get_id('have')
last_generated = storage.get_id('<END>')
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(actual[1], first_generated)
self.assertEqual(actual[-1], last_generated)
def test_float_result(self):
"""
Checks that returned result is float
"""
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(2, encoded)
context = (storage.get_id('i'), )
word = storage.get_id('have')
generator = LikelihoodBasedTextGenerator(storage, trie)
actual = generator._calculate_maximum_likelihood(word, context)
self.assertEqual(float, type(actual))
def test_generate_next_word_larger_context(self):
corpus = ('i', 'have', 'a', 'cat', '<END>', 'his', 'name', 'is',
'bruno', '<END>', 'i', 'have', 'a', 'dog', 'too', '<END>',
'his', 'name', 'is', 'rex', '<END>', 'her', 'name', 'is',
'rex', 'too', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(4, encoded)
expected_word = storage.get_id('bruno')
context = (storage.get_id('his'), storage.get_id('name'),
storage.get_id('is'))
generator = LikelihoodBasedTextGenerator(storage, trie)
actual = generator._generate_next_word(context)
self.assertEqual(expected_word, actual)
def test_ngram_text_generator_duplicates_words(self):
corpus = ('stop', 'it', 'stop', 'it', 'stop', 'it', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(2, encoded)
context = (storage.get_id('stop'), )
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(20 + len(context) + 1, len(actual))
def test_ngram_text_generator_end_at_the_beginning(self):
""""
should generate a sentence without <END> in any other position except the end of the sentence
"""
corpus = ('i', 'like', 'to', 'read', '<END>', 'he', 'likes', 'to',
'read', 'too', 'i', 'like', 'a', 'book', 'called',
'"Harry Potter"', '<END>', 'he', 'likes', 'another', 'book',
'<END>', 'he', 'does', 'not', 'tell', 'me', 'name', '<END>')
storage = WordStorage()
storage.update(corpus)
encoded = encode_text(storage, corpus)
trie = NGramTrie(2, encoded)
context = (storage.get_id('<END>'), )
last_generated = storage.get_id('<END>')
generator = NGramTextGenerator(storage, trie)
actual = generator._generate_sentence(context)
self.assertEqual(last_generated, actual[-1])
self.assertEqual(1, actual.count(storage.get_id('<END>')))
