def test_sample_constant(self):
"""If constant_value is set, all the sample have the same value."""
# Setup
instance = KDEUnivariate()
instance.fitted = True
instance.constant_value = 3
instance._replace_constant_methods()
expected_result = np.array([3, 3, 3, 3, 3])
# Run
result = instance.sample(5)
# Check
compare_nested_iterables(result, expected_result)
def test_sample_random_state(self):
"""If random_state is set, samples will generate the exact same values."""
# Setup
instance = KDEUnivariate(random_seed=0)
X = np.array([1, 2, 3, 4, 5])
instance.fit(X)
expected_result_random_state = np.array(
[[5.02156389, 5.45857107, 6.12161148, 4.56801267, 6.14017901]])
# Run
result = instance.sample(5)
# Check
compare_nested_iterables(result, expected_result_random_state)
def test_sample(self, kde_mock):
"""When fitted, we are able to use the model to get samples."""
# Setup
model_mock = kde_mock.return_value
model_mock.resample.return_value = np.array([0, 1, 0, 1, 0])
instance = KDEUnivariate()
X = np.array([1, 2, 3, 4, 5])
instance.fit(X)
expected_result = np.array([0, 1, 0, 1, 0])
# Run
result = instance.sample(5)
# Check
compare_nested_iterables(result, expected_result)
assert instance.model == model_mock
kde_mock.assert_called_once_with(X)
model_mock.resample.assert_called_once_with(5)
