def test_fit_sample_constant(self): model = StudentTUnivariate() model.fit(self.constant) sampled_data = model.sample(50) assert isinstance(sampled_data, np.ndarray) assert sampled_data.shape == (50, ) assert model._constant_value == 5 np.testing.assert_allclose(np.full(50, 5), model.sample(50), atol=0.2)
def test_pdf(self): model = StudentTUnivariate() model.fit(self.data) sampled_data = model.sample(50) # Test PDF pdf = model.probability_density(sampled_data) assert (0 < pdf).all()
def test_fit_sample(self): model = StudentTUnivariate() model.fit(self.data) np.testing.assert_allclose(model._params['df'], 3.0, rtol=0.2) np.testing.assert_allclose(model._params['loc'], 1.0, atol=0.2) np.testing.assert_allclose(model._params['scale'], 0.5, atol=0.2) sampled_data = model.sample(50) assert isinstance(sampled_data, np.ndarray) assert sampled_data.shape == (50, )
def test_cdf(self): model = StudentTUnivariate() model.fit(self.data) sampled_data = model.sample(50) # Test CDF cdf = model.cumulative_distribution(sampled_data) assert (0 <= cdf).all() and (cdf <= 1).all() # Test CDF increasing function sorted_data = sorted(sampled_data) cdf = model.cumulative_distribution(sorted_data) assert (np.diff(cdf) >= 0).all()
def test_to_dict_from_dict(self): model = StudentTUnivariate() model.fit(self.data) sampled_data = model.sample(50) params = model.to_dict() model2 = StudentTUnivariate.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_to_dict_from_dict_constant(self): model = StudentTUnivariate() 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 = StudentTUnivariate.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_save_load(self): model = StudentTUnivariate() 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 = StudentTUnivariate.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))