def test_unigram_test_str_untrained(self):
unigram_model = UnigramModel(smoothing=0.0)
unigram_model_smooth = UnigramModel()
test_sentence_op = unigram_model.get_string_prob(
UnigramTests.input_str)
test_sentence_op_smooth = unigram_model_smooth.get_string_prob(
UnigramTests.input_str)
self.assertAlmostEqual(0.0, test_sentence_op[0], places=2)
self.assertAlmostEqual(0.0, test_sentence_op_smooth[0], places=2)
self.assertEqual({}, test_sentence_op[1])
self.assertEqual({}, test_sentence_op_smooth[1])
self.assertEqual([], test_sentence_op[2])
self.assertEqual([], test_sentence_op_smooth[2])
def test_unigram_test_str_smooth(self):
unigram_model = UnigramModel(UnigramTests.train_str)
test_sentence_op = unigram_model.get_string_prob(
UnigramTests.input_str)
total_prob_exp = 0.0
cumul_prob_exp = []
single_prob_exp = {}
char_dict = dict()
char_dict.update(unigram_model.char_dict)
for char in UnigramTests.input_str:
char_dict[char] = 0
for char in UnigramTests.input_str:
if char in unigram_model.char_dict:
current_prob = UnigramModel.calc_prob(
char_count=unigram_model.char_dict[char],
total_count=unigram_model.training_size,
smoothing=UnigramModel.SMOOTHING_DEFAULT,
vocab_size=len(char_dict))
else:
current_prob = UnigramModel.calc_prob(
char_count=0,
total_count=unigram_model.training_size,
smoothing=UnigramModel.SMOOTHING_DEFAULT,
vocab_size=len(char_dict))
single_prob_exp[char] = current_prob
total_prob_exp += current_prob
cumul_prob_exp.append((char, total_prob_exp))
self.assertEqual(len(char_dict), len(unigram_model.char_dict))
self.assertEqual(len(unigram_model.probs_dict),
len(unigram_model.char_dict))
self.assertAlmostEqual(test_sentence_op[0], total_prob_exp, places=2)
self.assertEqual(single_prob_exp, test_sentence_op[1])
self.assertEqual(cumul_prob_exp, test_sentence_op[2])
def test_unigram_test_str(self):
unigram_model = UnigramModel(UnigramTests.train_str, smoothing=0.0)
test_sentence_op = unigram_model.get_string_prob(
UnigramTests.input_str)
self.assertEqual(len(unigram_model.probs_dict),
len(unigram_model.char_dict))
total_prob_exp = 0.0
cumul_prob_exp = []
single_prob_exp = {}
for char in UnigramTests.input_str:
if char in unigram_model.probs_dict:
current_prob = UnigramModel.calc_prob(
char_count=unigram_model.char_dict[char],
total_count=unigram_model.training_size)
total_prob_exp += current_prob
cumul_prob_exp.append((char, total_prob_exp))
single_prob_exp[char] = current_prob
self.assertAlmostEqual(test_sentence_op[0], total_prob_exp, places=2)
self.assertEqual(single_prob_exp, test_sentence_op[1])
self.assertEqual(cumul_prob_exp, test_sentence_op[2])