def test_missing_arguments(self):
with self.assertRaises(TypeError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]))
self.assertTrue(
"__init__() missing 1 required positional argument: 'clusters'" in
str(context.exception))
with self.assertRaises(TypeError) as context:
KMedoidsModel(clusters=5)
self.assertTrue(
"__init__() missing 1 required positional argument: 'data'" in str(
context.exception))
def test_float_initializations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
initializations=10.6)
self.assertTrue('Initializations must be an integer greater than zero'
in str(context.exception))
def test_negative_iterations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
iterations=-10)
self.assertTrue('Iterations must be an integer greater than zero' in
str(context.exception))
def test_sum_second_column(self):
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 1], [1., 2], [1., 3]])), 6.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 1], [1., -2], [1., 1]])), 0.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 5.6], [1., 23], [1., 0]])), 28.6)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 0], [1., 0], [1., 0]])), 0.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., -1], [1., -2], [1., -3]])), -6.0)
def test_plotting_non_2_d(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2, 2], [3, 4, 7], [5, 6, 1]]),
2,
plot=True)
self.assertTrue('Data must be two-dimensional in order to plot' in str(
context.exception))
def test_shuffle_array(self):
original_array = np.array([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20
])
self.assertFalse(
np.array_equal(original_array,
KMedoidsModel.shuffle_array(original_array)))
def test_calculate_distance_matrix(self):
model = KMedoidsModel(TestKMedoidsModel.dataset_1, 4)
true_distances = np.array(
[[0., 1.41421356, 1.41421356, 1.41421356, 1.41421356],
[1.41421356, 0., 2.82842712, 2., 2.],
[1.41421356, 2.82842712, 0., 2., 2.],
[1.41421356, 2., 2., 0., 2.82842712],
[1.41421356, 2., 2., 2.82842712, 0.]])
self.assertTrue(
np.allclose(model.distances,
true_distances,
rtol=1e-05,
atol=1e-08))
self.assertTrue(
np.allclose(model.calculate_distance_matrix(),
true_distances,
rtol=1e-05,
atol=1e-08))
def test_float_clusters(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]), 1.2)
self.assertTrue('Clusters must be an integer greater than zero' in str(
context.exception))
class TestKMedoidsModel(unittest.TestCase):
# class variables pre-load models and datasets used in tests
model_1 = KMedoidsModel(
np.array([[0, 0], [0.03, 0.01], [0.1, -0.4], [-0.47, -0.09],
[-0.33, 0.4], [1, 1], [1.03, 1.01], [1.1, 0.7], [0.77, 0.91],
[0.88, 1.2], [-1, -1], [-1.05, -1.03], [-1.2, -0.68],
[-0.67, -0.93], [-0.89, -1.3], [-1, 1], [-1.1, 1.12],
[-1.23, 0.59], [-0.68, 0.75], [-0.93, 1.21], [1, -1],
[1.15, -1.23], [0.85, -1.14], [0.77, -0.87], [1.45, -0.9]]),
5)
model_2 = KMedoidsModel(
np.array([[7, 6], [2, 6], [3, 8], [8, 5], [7, 4], [4, 7], [6, 2],
[7, 3], [6, 4], [3, 4]]), 2)
dataset_1 = np.array([[0, 0], [1, 1], [-1, -1], [-1, 1], [1, -1]])
model_3 = KMedoidsModel(dataset_1, 3)
# validation tests
def test_no_arguments(self):
with self.assertRaises(TypeError) as context:
KMedoidsModel()
self.assertTrue(
"__init__() missing 2 required positional arguments: 'data' and 'clusters'"
in str(context.exception))
def test_missing_arguments(self):
with self.assertRaises(TypeError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]))
self.assertTrue(
"__init__() missing 1 required positional argument: 'clusters'" in
str(context.exception))
with self.assertRaises(TypeError) as context:
KMedoidsModel(clusters=5)
self.assertTrue(
"__init__() missing 1 required positional argument: 'data'" in str(
context.exception))
def test_non_numpy_array(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel([[1, 2], [3, 4], [5, 6]], 2)
self.assertTrue('Data must be in the form of a numpy array' in str(
context.exception))
def test_zero_element_array(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([]), 2)
self.assertTrue('Numpy array must contain at least one element' in str(
context.exception))
def test_negative_clusters(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]), -2)
self.assertTrue('Clusters must be an integer greater than zero' in str(
context.exception))
def test_float_clusters(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]), 1.2)
self.assertTrue('Clusters must be an integer greater than zero' in str(
context.exception))
def test_negative_iterations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
iterations=-10)
self.assertTrue('Iterations must be an integer greater than zero' in
str(context.exception))
def test_float_iterations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
iterations=10.4)
self.assertTrue('Iterations must be an integer greater than zero' in
str(context.exception))
def test_negative_initializations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
initializations=-10)
self.assertTrue('Initializations must be an integer greater than zero'
in str(context.exception))
def test_float_initializations(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]),
2,
initializations=10.6)
self.assertTrue('Initializations must be an integer greater than zero'
in str(context.exception))
def test_too_many_clusters(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]), 4)
self.assertTrue(
'Clusters outnumber data points: 4 > 3' in str(context.exception))
def test_plotting_non_2_d(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2, 2], [3, 4, 7], [5, 6, 1]]),
2,
plot=True)
self.assertTrue('Data must be two-dimensional in order to plot' in str(
context.exception))
def test_raise_exception_for(self):
with self.assertRaises(AttributeError) as context:
KMedoidsModel.raise_exception_for(AttributeError, True, 'Testing')
self.assertTrue('Testing' in str(context.exception))
def test_predict_cluster_error(self):
model = TestKMedoidsModel.model_3
with self.assertRaises(ValueError) as context:
model.predict_cluster(np.array([1, 2, 3]))
self.assertTrue('Shape of point does not match training data' in str(
context.exception))
# distance tests
def test_calculate_distance_matrix(self):
model = KMedoidsModel(TestKMedoidsModel.dataset_1, 4)
true_distances = np.array(
[[0., 1.41421356, 1.41421356, 1.41421356, 1.41421356],
[1.41421356, 0., 2.82842712, 2., 2.],
[1.41421356, 2.82842712, 0., 2., 2.],
[1.41421356, 2., 2., 0., 2.82842712],
[1.41421356, 2., 2., 2.82842712, 0.]])
self.assertTrue(
np.allclose(model.distances,
true_distances,
rtol=1e-05,
atol=1e-08))
self.assertTrue(
np.allclose(model.calculate_distance_matrix(),
true_distances,
rtol=1e-05,
atol=1e-08))
# KMedoidsModel class methods tests
def test_sum_second_column(self):
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 1], [1., 2], [1., 3]])), 6.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 1], [1., -2], [1., 1]])), 0.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 5.6], [1., 23], [1., 0]])), 28.6)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., 0], [1., 0], [1., 0]])), 0.0)
self.assertEqual(
KMedoidsModel.sum_second_column(
np.array([[1., -1], [1., -2], [1., -3]])), -6.0)
def test_shuffle_array(self):
original_array = np.array([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20
])
self.assertFalse(
np.array_equal(original_array,
KMedoidsModel.shuffle_array(original_array)))
# k-medoids algorithm tests
def test_initialize_medoids(self):
model = TestKMedoidsModel.model_3
self.assertEqual(len(model.medoids), 3)
self.assertEqual(model.medoids[0] >= 0, True)
self.assertEqual(model.medoids[1] >= 0, True)
self.assertEqual(model.medoids[2] >= 0, True)
self.assertEqual(model.medoids[0] < 5, True)
self.assertEqual(model.medoids[1] < 5, True)
self.assertEqual(model.medoids[2] < 5, True)
self.assertEqual(
model.medoids[0] != model.medoids[1] != model.medoids[2], True)
def test_run_expectation_step(self):
model = TestKMedoidsModel.model_3
model.medoids = np.array([1, 2, 4])
true_labels_with_medoid_distances = np.array([[1., 1.41421356],
[1., 0.], [2., 0.],
[1., 2.], [4., 0.]])
self.assertTrue(
np.allclose(model.run_expectation_step()[0],
true_labels_with_medoid_distances,
rtol=1e-05,
atol=1e-08))
self.assertTrue(
np.allclose(model.run_expectation_step()[1],
3.41421356,
rtol=1e-05,
atol=1e-08))
def test_run(self):
model = TestKMedoidsModel.model_1
medoids = model.medoids.sort()
true_medoids = np.array([0, 15, 20, 10, 5]).sort()
self.assertEqual(round(model.cost, 2), 5.82)
self.assertTrue(np.array_equal(medoids, true_medoids))
self.assertTrue(
np.allclose(model.labels_with_medoid_distances,
np.array([[0., 0.], [0., 0.03162278], [0., 0.41231056],
[0., 0.47853944], [0., 0.51855569], [5., 0.],
[5., 0.03162278], [5., 0.31622777],
[5., 0.24698178], [5., 0.23323808],
[10., 0.], [10., 0.05830952],
[10., 0.37735925], [10., 0.33734256],
[10., 0.31953091], [15., 0.],
[15., 0.15620499], [15., 0.47010637],
[15., 0.40607881], [15., 0.22135944],
[20., 0.], [20.,
0.2745906], [20., 0.20518285],
[20., 0.2641969], [20., 0.46097722]]),
rtol=1e-05,
atol=1e-08))
model = TestKMedoidsModel.model_2
medoids = model.medoids.sort()
true_medoids = np.array([4, 1]).sort()
self.assertEqual(round(model.cost, 2), 14.36)
self.assertTrue(np.array_equal(medoids, true_medoids))
self.assertTrue(
np.allclose(model.labels_with_medoid_distances,
np.array([[4., 2.], [1., 0.], [1., 2.23606798],
[4., 1.41421356], [4., 0.], [1., 2.23606798],
[4., 2.23606798], [4., 1.], [4., 1.],
[1., 2.23606798]]),
rtol=1e-05,
atol=1e-08))
# prediction tests
def test_predict_cluster(self):
self.assertEqual(
TestKMedoidsModel.model_1.predict_cluster(np.array([1, 2])),
(5, 1.0))
def test_zero_element_array(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([]), 2)
self.assertTrue('Numpy array must contain at least one element' in str(
context.exception))
def test_non_numpy_array(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel([[1, 2], [3, 4], [5, 6]], 2)
self.assertTrue('Data must be in the form of a numpy array' in str(
context.exception))
def test_no_arguments(self):
with self.assertRaises(TypeError) as context:
KMedoidsModel()
self.assertTrue(
"__init__() missing 2 required positional arguments: 'data' and 'clusters'"
in str(context.exception))
def test_raise_exception_for(self):
with self.assertRaises(AttributeError) as context:
KMedoidsModel.raise_exception_for(AttributeError, True, 'Testing')
self.assertTrue('Testing' in str(context.exception))
def test_too_many_clusters(self):
with self.assertRaises(ValueError) as context:
KMedoidsModel(np.array([[1, 2], [3, 4], [5, 6]]), 4)
self.assertTrue(
'Clusters outnumber data points: 4 > 3' in str(context.exception))