class TestData(unittest.TestCase):
def setUp(self):
self.y = np.array([1, 2, 3, 4, 5])
self.X = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 1, 1, 1, 1], "c": [1, 2, 3, 3, 4]})
self.data = Data(self.X, self.y, normalize=True)
def test_unnormalization(self):
self.assertListEqual(list(self.data.unnormalized_y), list(self.y))
self.assertListEqual(list(self.data.unnormalize_y(np.array([0, 0.25, 0.5, 0.75]))), [3, 4, 5, 6])
def test_unique_proportion_of_value_in_variable(self):
self.assertEqual(self.data.proportion_of_value_in_variable(0, 1), 0.2)
def test_non_unique_proportion_of_value_in_variable(self):
self.assertEqual(self.data.proportion_of_value_in_variable(2, 1), 0.2)
self.assertEqual(self.data.proportion_of_value_in_variable(2, 3), 0.4)
def test_unique_columns(self):
self.assertEqual(self.data.unique_columns, [0])
def test_covariates_stored_as_matrix(self):
self.assertEqual(type(self.data.X), np.ndarray)
def test_is_not_constant(self):
self.assertTrue(is_not_constant(np.array([1, 1, 2, 3])))
self.assertFalse(is_not_constant(np.array([1, 1, 1, 1])))
def test_n_obsv(self):
self.assertEqual(self.data.n_obsv, 5)
def test_normalization(self):
self.assertEqual(-0.5, self.data.y.min())
self.assertEqual(0.5, self.data.y.max())
def test_splittable_variables(self):
self.assertListEqual(list(self.data.splittable_variables()), [0, 2])
def test_random_splittable_value(self):
for a in range(10000):
self.assertIn(self.data.random_splittable_value(0), [1, 2, 3, 4])
self.assertIsNone(self.data.random_splittable_value(1))
def test_random_splittable_variable(self):
for a in range(100):
self.assertIn(self.data.random_splittable_variable(), [0, 2])
self.filtered_data = Data(self.data.X[:,[1]], self.data.y)
with self.assertRaises(NoSplittableVariableException):
self.filtered_data.random_splittable_variable()
def test_n_splittable_variables(self):
self.assertEqual(self.data.n_splittable_variables, 2)
def test_variables(self):
self.assertEqual(self.data.variables, [0, 1, 2])
class TestDataNormalization(unittest.TestCase):
def setUp(self):
self.y = np.array([1, 2, 3, 4, 5])
self.X = pd.DataFrame({
"a": [1, 2, 3, 4, 5],
"b": [1, 1, 1, 1, 1],
"c": [1, 2, 3, 3, 4]
})
self.X = format_covariate_matrix(self.X)
self.data = Data(self.X, self.y, normalize=True)
def test_unnormalization(self):
self.assertListEqual(list(self.data.unnormalized_y), list(self.y))
self.assertListEqual(
list(self.data.unnormalize_y(np.array([0, 0.25, 0.5, 0.75]))),
[3, 4, 5, 6])
def test_normalization(self):
self.assertEqual(-0.5, self.data.y.min())
self.assertEqual(0.5, self.data.y.max())