def test_test_fit_equal_values(self): """If it's fit with constant data, contant_value is set.""" instance = BetaUnivariate() instance.fit(np.array([5, 5, 5, 5, 5, 5])) assert instance.a is None assert instance.b is None assert instance.constant_value == 5
def test_pdf(self): model = BetaUnivariate() 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_constant(self): model = BetaUnivariate() 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_equal(np.full(50, 5), model.sample(50))
def test_fit_sample(self): model = BetaUnivariate() model.fit(self.data) np.testing.assert_allclose(model._params['loc'], 1.0, atol=0.2) np.testing.assert_allclose(model._params['scale'], 1.0, atol=0.2) np.testing.assert_allclose(model._params['a'], 1.0, atol=0.2) np.testing.assert_allclose(model._params['b'], 1.0, atol=0.2) sampled_data = model.sample(50) assert isinstance(sampled_data, np.ndarray) assert sampled_data.shape == (50, )
def test_to_dict_constant(self): model = BetaUnivariate() model.fit(self.constant) params = model.to_dict() assert params == { 'type': 'copulas.univariate.beta.BetaUnivariate', 'loc': 5, 'scale': 0, 'a': 1, 'b': 1 }
def test_cdf(self): model = BetaUnivariate() model.fit(self.data) sampled_data = model.sample(50) # Test the 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_fit(self): """On fit, stats from fit data are set in the model.""" # Generate data with known parameters np.random.seed(42) a, b, loc, scale = 1.0, 1.0, 10.0, 11.0 data = beta.rvs(a, b, loc, scale, size=100000) # Fit the model and check parameters copula = BetaUnivariate() copula.fit(data) self.assertAlmostEqual(copula.a, a, places=1) self.assertAlmostEqual(copula.b, b, places=1) self.assertAlmostEqual(copula.loc, loc, places=1) self.assertAlmostEqual(copula.scale, scale, places=1)
def test_to_dict_from_dict(self): model = BetaUnivariate() model.fit(self.data) sampled_data = model.sample(50) params = model.to_dict() model2 = BetaUnivariate.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 = BetaUnivariate() 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 = BetaUnivariate.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 = BetaUnivariate() 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 = BetaUnivariate.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__is_constant_false(self): distribution = BetaUnivariate() distribution.fit(np.array([1, 2, 3, 4])) assert not distribution._is_constant()
def test__is_constant_true(self): distribution = BetaUnivariate() distribution.fit(np.array([1, 1, 1, 1])) assert distribution._is_constant()
def test_valid_serialization_fit_model(self): """For a fitted model to_dict and from_dict are opposites.""" instance = BetaUnivariate() instance.fit(np.array([1, 2, 3, 2, 1])) result = BetaUnivariate.from_dict(instance.to_dict()) assert instance.to_dict() == result.to_dict()