def test_imputed_values(self):
""" Assert values are as expected"""
imputed = complete_case(self.data_m)
expected = np.array([[5., 6., 7., 8., 9.], [10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]])
self.assertTrue(np.equal(imputed, expected).all())
def test_impute_no_missing_values(self):
""" After imputation, no change should occur"""
imputed = complete_case(self.data_c)
self.assertTrue(np.array_equal(imputed, self.data_c))
def test_impute_missing_values(self):
""" After imputation, no NaN's should exist"""
imputed = complete_case(self.data_m)
self.assertTrue(np.shape(imputed) == (4, 5))
def test_complete_case_(test_data):
data = test_data(SHAPE)
imputed = complete_case(data)
return_na_check(imputed)
def test_return_type(self):
""" Check return type, should return an np.ndarray"""
imputed = complete_case(self.data_m)
self.assertTrue(isinstance(imputed, np.ndarray))
def test_imputed_values(test_data):
data = test_data(SHAPE)
imputed = complete_case(data)
expected = np.arange(5, 25, dtype=float).reshape(4, 5)
assert np.equal(imputed, expected).all()
def test_impute_missing_values(test_data):
data = test_data(SHAPE)
imputed = complete_case(data)
assert np.shape(imputed) == (4, 5)
def test_impute_missing_values():
""" After imputation, no NaN's should exist"""
imputed = complete_case(data_m)
assert np.shape(imputed) == (4, 5)
def test_impute_no_missing_values():
""" After imputation, no change should occur"""
imputed = complete_case(data_m)
assert not np.isnan(imputed).any()
def test_return_type():
""" Check return type, should return an np.ndarray"""
imputed = complete_case(data_m)
assert isinstance(imputed, np.ndarray)
