def test_dont_change_labels_when_run_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0")
classifications = np.matrix([[1], [2], [1], [2]])
labels = [0, 1, 2]
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications, labels)
expected_labels = [0, 1, 2]
self.assertEquals(expected_labels, labels)
def run_train(cls, pkgs_classifications):
all_matrix = (np.matrix(pkgs_classifications.values()))
data_matrix = all_matrix[0:, 0:-1]
classifications = all_matrix[0:, -1]
order_of_classifications = ['NU', 'U', 'RU']
bayes_matrix = BayesMatrix()
bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
BayesMatrix.save(bayes_matrix,
MachineLearningData.MACHINE_LEARNING_TRAINING)
def get_model(self, cross_item_score):
super(CrossValidationBVA, self).get_model(cross_item_score)
bayes_matrix = BayesMatrix()
all_matrix = (np.matrix(cross_item_score.values()))
data_matrix = all_matrix[0:, 0:-1]
classifications = all_matrix[0:, -1]
bayes_matrix.training(data_matrix, classifications, self.labels)
return bayes_matrix
def get_model(self, cross_item_score):
super(CrossValidationBVA, self).get_model(cross_item_score)
bayes_matrix = BayesMatrix()
all_matrix = (np.matrix(cross_item_score.values()))
data_matrix = all_matrix[0:, 0:-1]
classifications = all_matrix[0:, -1]
bayes_matrix.training(data_matrix, classifications,
self.labels)
return bayes_matrix
def test_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0")
classifications = np.matrix([[1], [2], [0], [1], [2]])
order_of_classifications = [0, 1, 2]
prob = np.matrix("1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(
float)
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
self.assertTrue(np.allclose(prob, self.bayes_matrix.prob))
def test_get_classification(self):
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.data = np.matrix(
"1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0").astype(float)
self.bayes_matrix.labels = np.matrix("0; 1; 2").astype(float)
self.bayes_matrix.label_probability = (
np.matrix("0.2; 0.4; 0.4").astype(float))
self.bayes_matrix.order_of_classifications = [0, 1, 2]
self.bayes_matrix.used_order_of_classifications = [0, 1, 2]
self.bayes_matrix.prob = np.matrix(
"1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(float)
attribute_vector = np.matrix("1 0 1 1 0")
self.assertEqual(
1, self.bayes_matrix.get_classification(attribute_vector))
def test_dont_change_labels_when_run_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0")
classifications = np.matrix([[1], [2], [1], [2]])
labels = [0, 1, 2]
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
labels)
expected_labels = [0, 1, 2]
self.assertEquals(expected_labels, labels)
def test_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0")
classifications = np.matrix([[1], [2], [0], [1], [2]])
order_of_classifications = [0, 1, 2]
prob = np.matrix("1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(
float)
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
self.assertTrue(np.allclose(prob, self.bayes_matrix.prob))
def test_get_classification(self):
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.data = np.matrix(
"1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0").astype(float)
self.bayes_matrix.labels = np.matrix("0; 1; 2").astype(float)
self.bayes_matrix.label_probability = (np.matrix(
"0.2; 0.4; 0.4").astype(float))
self.bayes_matrix.order_of_classifications = [0, 1, 2]
self.bayes_matrix.used_order_of_classifications = [0, 1, 2]
self.bayes_matrix.prob = np.matrix(
"1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(float)
attribute_vector = np.matrix("1 0 1 1 0")
self.assertEqual(
1, self.bayes_matrix.get_classification(attribute_vector))
def run_train(cls, pkgs_classifications):
all_matrix = (np.matrix(pkgs_classifications.values()))
data_matrix = all_matrix[0:, 0:-1]
classifications = all_matrix[0:, -1]
order_of_classifications = ['NU', 'U', 'RU']
bayes_matrix = BayesMatrix()
bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
BayesMatrix.save(bayes_matrix,
MachineLearningData.MACHINE_LEARNING_TRAINING)
def get_ml_strategy(self):
return BayesMatrix.load(
MachineLearningData.MACHINE_LEARNING_TRAINING)
class PkgClassificationTests(unittest.TestCase):
def setUp(self):
self.bayes_matrix = BayesMatrix()
def test_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0")
classifications = np.matrix([[1], [2], [0], [1], [2]])
order_of_classifications = [0, 1, 2]
prob = np.matrix("1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(
float)
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
self.assertTrue(np.allclose(prob, self.bayes_matrix.prob))
def test_get_classification(self):
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.data = np.matrix(
"1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0").astype(float)
self.bayes_matrix.labels = np.matrix("0; 1; 2").astype(float)
self.bayes_matrix.label_probability = (np.matrix(
"0.2; 0.4; 0.4").astype(float))
self.bayes_matrix.order_of_classifications = [0, 1, 2]
self.bayes_matrix.used_order_of_classifications = [0, 1, 2]
self.bayes_matrix.prob = np.matrix(
"1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(float)
attribute_vector = np.matrix("1 0 1 1 0")
self.assertEqual(
1, self.bayes_matrix.get_classification(attribute_vector))
def test_convert_classifications_to_numbers(self):
classifications = np.matrix([['M'], ['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = np.array([[1], [0], [2]])
actual = self.bayes_matrix.convert_classifications_to_number(
classifications, order_of_classifications)
for index, label in enumerate(expected):
self.assertEqual(label[0], actual[index, 0])
def test_get_used_order_of_classifications(self):
classifications = np.matrix([['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['M'], ['M'], ['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B', 'M', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['M'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['M', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
classifications = np.matrix([['B']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
classifications = np.matrix([['B'], ['D'], ['F']])
order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
expected = ['B', 'D', 'F']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['B'], ['D'], ['F']])
order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F']
expected = ['B', 'D', 'F']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
def test_dont_change_labels_when_run_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0")
classifications = np.matrix([[1], [2], [1], [2]])
labels = [0, 1, 2]
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
labels)
expected_labels = [0, 1, 2]
self.assertEquals(expected_labels, labels)
def setUp(self):
self.bayes_matrix = BayesMatrix()
def setUp(self):
self.bayes_matrix = BayesMatrix()
class PkgClassificationTests(unittest.TestCase):
def setUp(self):
self.bayes_matrix = BayesMatrix()
def test_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0")
classifications = np.matrix([[1], [2], [0], [1], [2]])
order_of_classifications = [0, 1, 2]
prob = np.matrix("1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(
float)
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications,
order_of_classifications)
self.assertTrue(np.allclose(prob, self.bayes_matrix.prob))
def test_get_classification(self):
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.data = np.matrix(
"1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\
0 1 1 1 0").astype(float)
self.bayes_matrix.labels = np.matrix("0; 1; 2").astype(float)
self.bayes_matrix.label_probability = (
np.matrix("0.2; 0.4; 0.4").astype(float))
self.bayes_matrix.order_of_classifications = [0, 1, 2]
self.bayes_matrix.used_order_of_classifications = [0, 1, 2]
self.bayes_matrix.prob = np.matrix(
"1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(float)
attribute_vector = np.matrix("1 0 1 1 0")
self.assertEqual(
1, self.bayes_matrix.get_classification(attribute_vector))
def test_convert_classifications_to_numbers(self):
classifications = np.matrix([['M'], ['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = np.array([[1], [0], [2]])
actual = self.bayes_matrix.convert_classifications_to_number(
classifications, order_of_classifications)
for index, label in enumerate(expected):
self.assertEqual(label[0], actual[index, 0])
def test_get_used_order_of_classifications(self):
classifications = np.matrix([['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['M'], ['M'], ['B'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B', 'M', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['M'], ['G']])
order_of_classifications = ['B', 'M', 'G']
expected = ['M', 'G']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
classifications = np.matrix([['B']])
order_of_classifications = ['B', 'M', 'G']
expected = ['B']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
classifications = np.matrix([['B'], ['D'], ['F']])
order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
expected = ['B', 'D', 'F']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
classifications = np.matrix([['B'], ['D'], ['F']])
order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F']
expected = ['B', 'D', 'F']
actual = self.bayes_matrix.get_used_order_of_classifications(
classifications, order_of_classifications)
self.assertEquals(expected, actual)
def test_dont_change_labels_when_run_training(self):
data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0")
classifications = np.matrix([[1], [2], [1], [2]])
labels = [0, 1, 2]
self.bayes_matrix = BayesMatrix()
self.bayes_matrix.training(data_matrix, classifications, labels)
expected_labels = [0, 1, 2]
self.assertEquals(expected_labels, labels)
def get_ml_strategy(self):
return BayesMatrix.load(
MachineLearningData.MACHINE_LEARNING_TRAINING)
