def test_transform_multiple_observations_left_tailed(self):
self.estimator = Exponential(direction='left-tailed')
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
observations = np.array([[2 * math.log(4 / 3), 3 * math.log(4 / 3)],
[2 * math.log(2), 3 * math.log(2)],
[2 * math.log(4), 3 * math.log(4)]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations),
np.array([[0.25, 0.25], [0.50, 0.50], [0.75, 0.75]]))
def setUp(self):
self.estimator = Exponential()
def test_init_two_tailed(self):
self.estimator = Exponential(direction='two-tailed')
self.assertEqual(self.estimator.direction, 'two-tailed')
self.assertFalse(hasattr(self.estimator, "means"))
class TestExponential(TestCase):
def setUp(self):
self.estimator = Exponential()
def test_init_default(self):
self.estimator = Exponential()
self.assertEqual(self.estimator.direction, 'right-tailed')
self.assertFalse(hasattr(self.estimator, "means"))
def test_init_left_tailed(self):
self.estimator = Exponential(direction='left-tailed')
self.assertEqual(self.estimator.direction, 'left-tailed')
self.assertFalse(hasattr(self.estimator, "means"))
def test_init_right_tailed(self):
self.estimator = Exponential(direction='right-tailed')
self.assertEqual(self.estimator.direction, 'right-tailed')
self.assertFalse(hasattr(self.estimator, "means"))
def test_init_two_tailed(self):
self.estimator = Exponential(direction='two-tailed')
self.assertEqual(self.estimator.direction, 'two-tailed')
self.assertFalse(hasattr(self.estimator, "means"))
def test_init_unknown_direction(self):
self.assertRaises(ValueError, Exponential, 'unknown-tailed')
def test_fit_empty_ref_observations(self):
ref_observations = np.array([[]])
weights = np.array([])
self.assertRaises(ValueError, self.estimator.fit, ref_observations,
weights)
def test_fit_inconsistent_length_ref_observations_and_weights(self):
ref_observations = np.array([[1, 2], [2, 3]])
weights = np.array([1, 2, 3])
self.assertRaises(ValueError, self.estimator.fit, ref_observations,
weights)
def test_fit_single_ref_observation(self):
ref_observations = np.array([[1, 2]])
weights = np.array([1])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_equal(self.estimator.means, np.array([1, 2]))
def test_fit_multiple_ref_observations_equal_weights(self):
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_equal(self.estimator.means, np.array([2, 3]))
def test_fit_multiple_ref_observations_various_weights(self):
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 2, 3])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(self.estimator.means,
np.array([14 / 6, 20 / 6]))
def test_without_fit(self):
observations = np.array([[1, 2, 3], [2, 3, 4]])
self.assertRaises(ValueError, self.estimator.transform, observations)
def test_transform_inconsistent_number_of_features(self):
ref_observations = np.array([[1, 2], [2, 3]])
weights = np.array([1, 1])
observations = np.array([[1, 2, 3], [2, 3, 4]])
self.estimator.fit(ref_observations, weights)
self.assertRaises(ValueError, self.estimator.transform, observations)
def test_transform_zero_means(self):
ref_observations = np.array([[0, 0]])
weights = np.array([1])
observations = np.array([[1, 2], [2, 3]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations),
np.array([[1.0, 1.0], [1.0, 1.0]]))
def test_transform_single_observation(self):
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
observations = np.array([[2 * math.log(2), 3 * math.log(2)]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations), np.array([[0.5, 0.5]]))
def test_transform_multiple_observations_left_tailed(self):
self.estimator = Exponential(direction='left-tailed')
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
observations = np.array([[2 * math.log(4 / 3), 3 * math.log(4 / 3)],
[2 * math.log(2), 3 * math.log(2)],
[2 * math.log(4), 3 * math.log(4)]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations),
np.array([[0.25, 0.25], [0.50, 0.50], [0.75, 0.75]]))
def test_transform_multiple_observations_right_tailed(self):
self.estimator = Exponential(direction='right-tailed')
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
observations = np.array([[2 * math.log(4 / 3), 3 * math.log(4 / 3)],
[2 * math.log(2), 3 * math.log(2)],
[2 * math.log(4), 3 * math.log(4)]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations),
np.array([[0.75, 0.75], [0.50, 0.50], [0.25, 0.25]]))
def test_transform_multiple_observations_two_tailed(self):
self.estimator = Exponential(direction='two-tailed')
ref_observations = np.array([[1, 2], [2, 3], [3, 4]])
weights = np.array([1, 1, 1])
observations = np.array([[2 * math.log(4 / 3), 3 * math.log(4 / 3)],
[2 * math.log(2), 3 * math.log(2)],
[2 * math.log(4), 3 * math.log(4)]])
self.estimator.fit(ref_observations, weights)
np.testing.assert_array_almost_equal(
self.estimator.transform(observations),
np.array([[0.5, 0.5], [1.0, 1.0], [0.5, 0.5]]))
def test_init_default(self):
self.estimator = Exponential()
self.assertEqual(self.estimator.direction, 'right-tailed')
self.assertFalse(hasattr(self.estimator, "means"))