class NaiveBayesClassifierTests(TestCase):
"""Tests for the NaiveBayesClassifier class."""
def setUp(self):
"""Define some sample data that will be used by the tests."""
self.ref_seqs1 = StringIO(ref_seqs1)
self.tax_map1 = StringIO(tax_map1)
self.training_data1 = StringIO(training_data1)
self.classifier = NaiveBayesClassifier(ref_seqs_f=self.ref_seqs1,
tax_map_f=self.tax_map1)
self.ref_seqs_tiny = StringIO(ref_seqs_tiny)
self.tax_map_tiny = StringIO(tax_map_tiny)
self.classifier_ws1 = NaiveBayesClassifier(
ref_seqs_f=self.ref_seqs_tiny, tax_map_f=self.tax_map_tiny,
WordSize=1)
def test_init(self):
"""Test raises error on invalid constructor arguments."""
self.assertRaises(MissingTrainingSetError, NaiveBayesClassifier)
self.assertRaises(AmbiguousTrainingSetError, NaiveBayesClassifier,
ref_seqs_f=self.ref_seqs1, tax_map_f=self.tax_map1,
training_data_f=self.training_data1)
def test_call(self):
"""Test classifying sequences."""
obs = self.classifier(self.ref_seqs1)
#self.assertEqual(obs, exp_classifications1)
def test_calculate_prior_word_probabilities(self):
"""Test calculating probability of seeing words in training set."""
self.classifier_ws1._calculate_prior_word_probabilities()
self.assertFloatEqual(self.classifier_ws1._prior_word_probabilities,
{'A': 0.83333333, 'T': 0.83333333, 'G': 0.5, 'C': 0.16666667})
def setUp(self):
"""Define some sample data that will be used by the tests."""
self.ref_seqs1 = StringIO(ref_seqs1)
self.tax_map1 = StringIO(tax_map1)
self.training_data1 = StringIO(training_data1)
self.classifier = NaiveBayesClassifier(ref_seqs_f=self.ref_seqs1,
tax_map_f=self.tax_map1)
self.ref_seqs_tiny = StringIO(ref_seqs_tiny)
self.tax_map_tiny = StringIO(tax_map_tiny)
self.classifier_ws1 = NaiveBayesClassifier(
ref_seqs_f=self.ref_seqs_tiny, tax_map_f=self.tax_map_tiny,
WordSize=1)
