def test_ScorePositiveExampleWithUnkowns(self):
#Tests the Probability Distribution of each class for a positive example
sa = hw3.SentimentAnalysis()
examples = hw3.generate_tuples_from_file(self.trainingFilePath)
#Trains the Naive Bayes Classifier based on the tuples from the training data
sa.train(examples)
#Returns a probability distribution of each class for the given test sentence
score = sa.score("I loved the hotel a lot")
#P(C|text)=P(I|C)*P(loved|C)*P(the|C)*P(hotel|C)*P(a|C)*P(lot|C)*P(C),where C is either 0 or 1(Classifier)
pos = ((1 + 1) /
(8 + 12)) * ((1 + 1) /
(8 + 12)) * ((1 + 1) /
(8 + 12)) * ((2 + 1) /
(8 + 12)) * (2 / 4)
neg = ((1 + 1) /
(11 + 12)) * ((0 + 1) /
(11 + 12)) * ((1 + 1) /
(11 + 12)) * ((2 + 1) /
(11 + 12)) * (2 / 4)
actualScoreDistribution = {'1': pos, '0': neg}
self.assertAlmostEqual(actualScoreDistribution['0'],
score['0'],
places=5)
self.assertAlmostEqual(actualScoreDistribution['1'],
score['1'],
places=5)
def test_ClassifyForNegativeExample(self):
#Tests the label classified for the negative test sentence
sa = hw3.SentimentAnalysis()
examples = hw3.generate_tuples_from_file(self.trainingFilePath)
sa.train(examples)
label = sa.classify("I hated the hotel")
actualLabel = '0'
self.assertEqual(actualLabel, label)
def test_GenerateTuplesFromTrainingFile(self):
#Tests the tuple generation from the sentences
sa = hw3.SentimentAnalysis()
examples = hw3.generate_tuples_from_file(self.trainingFilePath)
actualExamples = [('ID-2', 'The hotel was not liked by me', '0'), ('ID-3', 'I loved the hotel', '1'), ('ID-1', 'The hotel was great', '1'), ('ID-4', 'I hated the hotel', '0')]
print("Tuple generation test")
self.assertListEqual(sorted(examples), sorted(actualExamples))
print("Done")
def test_ClassifyForPositiveExample(self):
#Tests the label classified for the positive test sentence
sa = hw3.SentimentAnalysis()
examples = hw3.generate_tuples_from_file(self.trainingFilePath)
sa.train(examples)
#Classifies the test sentence based on the probability distribution of each class
label = sa.classify("I loved the hotel a lot")
actualLabel = '1'
self.assertEqual(actualLabel, label)
def test_ScoreForNegativeExample(self):
#Tests the Probability Distribution of each class for a negative example
print("tests probability distribution of each class for negative example")
sa = hw3.SentimentAnalysis()
examples = hw3.generate_tuples_from_file(self.trainingFilePath)
sa.train(examples)
score=sa.score("I hated the hotel")
#P(C|text)=P(I|C)*P(hated|C)*P(the|C)*P(hotel|C)*P(C),where C is either 0 or 1(Classifier)
pos = ((1+1)/(8+12))*((0+1)/(8+12))*((1+1)/(8+12))*((2+1)/(8+12))*(2/4)
neg = ((1+1)/(11+12))*((1+1)/(11+12))*((1+1)/(11+12))*((2+1)/(11+12))*(2/4)
actualScoreDistribution={'1': pos, '0': neg}
self.assertAlmostEqual(score['0'], actualScoreDistribution['0'], places=5)
self.assertAlmostEqual(score['1'], actualScoreDistribution['1'], places=5)
print("Done")
