def test_pdf(self): model = GaussianKDE() model.fit(self.data) sampled_data = model.sample(50) # Test PDF pdf = model.probability_density(sampled_data) assert (0 < pdf).all()
def test_probability_density(self, kde_mock): """Sample calls the gaussian_kde.resample method.""" instance = GaussianKDE() instance.fit(np.array([1, 2, 3, 4])) model = kde_mock.return_value model.evaluate.return_value = np.array([0.1, 0.2, 0.3]) pdf = instance.probability_density(np.array([1, 2, 3])) assert instance._model.evaluate.call_count == 1 input_array = instance._model.evaluate.call_args[0][0] np.testing.assert_equal(input_array, np.array([1, 2, 3])) np.testing.assert_equal(pdf, np.array([0.1, 0.2, 0.3]))
def test_to_dict_from_dict(self): model = GaussianKDE() model.fit(self.data) sampled_data = model.sample(50) params = model.to_dict() model2 = GaussianKDE.from_dict(params) pdf = model.probability_density(sampled_data) pdf2 = model2.probability_density(sampled_data) assert np.all(np.isclose(pdf, pdf2, atol=0.01)) cdf = model.cumulative_distribution(sampled_data) cdf2 = model2.cumulative_distribution(sampled_data) assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def test_save_load(self): model = GaussianKDE() model.fit(self.data) sampled_data = model.sample(50) path_to_model = os.path.join(self.test_dir.name, "model.pkl") model.save(path_to_model) model2 = GaussianKDE.load(path_to_model) pdf = model.probability_density(sampled_data) pdf2 = model2.probability_density(sampled_data) assert np.all(np.isclose(pdf, pdf2, atol=0.01)) cdf = model.cumulative_distribution(sampled_data) cdf2 = model2.cumulative_distribution(sampled_data) assert np.all(np.isclose(cdf, cdf2, atol=0.01))
def test_to_dict_from_dict_constant(self): model = GaussianKDE() model.fit(self.constant) sampled_data = model.sample(50) pdf = model.probability_density(sampled_data) cdf = model.cumulative_distribution(sampled_data) params = model.to_dict() model2 = GaussianKDE.from_dict(params) np.testing.assert_equal(np.full(50, 5), sampled_data) np.testing.assert_equal(np.full(50, 5), model2.sample(50)) np.testing.assert_equal(np.full(50, 1), pdf) np.testing.assert_equal(np.full(50, 1), model2.probability_density(sampled_data)) np.testing.assert_equal(np.full(50, 1), cdf) np.testing.assert_equal(np.full(50, 1), model2.cumulative_distribution(sampled_data))
def test_probability_density_constant(self, pdf_mock): """If constant_value, probability_density uses the degenerate version.""" # Setup instance = GaussianKDE() instance.fitted = True instance.constant_value = 3 instance._replace_constant_methods() X = np.array([0, 1, 2, 3, 4, 5]) expected_result = np.array([0, 0, 1, 0, 0]) pdf_mock.return_value = np.array([0, 0, 1, 0, 0]) # Run result = instance.probability_density(X) # Check compare_nested_iterables(result, expected_result) pdf_mock.assert_called_once_with(instance, X)
def test_probability_density(self, kde_mock): """probability_density evaluates with the model.""" # Setup model_mock = kde_mock.return_value model_mock.evaluate.return_value = np.array([0.0, 0.5, 1.0]) fit_data = np.array([1, 2, 3, 4, 5]) instance = GaussianKDE() instance.fit(fit_data) call_data = np.array([-10, 0, 10]) expected_result = np.array([0.0, 0.5, 1.0]) # Run result = instance.probability_density(call_data) # Check compare_nested_iterables(result, expected_result) kde_mock.assert_called_once_with(fit_data) model_mock.evaluate.assert_called_once_with(call_data)