def prepare_next_tree(self): """Prepare conditional U matrix for next tree.""" for edge in self.edges: copula_theta = edge.theta if self.level == 1: left_u = self.u_matrix[:, edge.L] right_u = self.u_matrix[:, edge.R] else: left_parent, right_parent = edge.parents left_u, right_u = Edge.get_conditional_uni( left_parent, right_parent) # compute conditional cdfs C(i|j) = dC(i,j)/duj and dC(i,j)/du left_u = [x for x in left_u if x is not None] right_u = [x for x in right_u if x is not None] X_left_right = np.array([[x, y] for x, y in zip(left_u, right_u)]) X_right_left = np.array([[x, y] for x, y in zip(right_u, left_u)]) copula = Bivariate(edge.name) copula.fit(X_left_right) left_given_right = copula.partial_derivative( X_left_right, copula_theta) right_given_left = copula.partial_derivative( X_right_left, copula_theta) # correction of 0 or 1 left_given_right[left_given_right == 0] = EPSILON right_given_left[right_given_left == 0] = EPSILON left_given_right[left_given_right == 1] = 1 - EPSILON right_given_left[right_given_left == 1] = 1 - EPSILON edge.U = np.array([left_given_right, right_given_left])
def test_fit(self): """fit checks that the given values are independent.""" # Setup instance = Bivariate(CopulaTypes.INDEPENDENCE) data = np.array([[1, 2], [4, 3]]) # Run instance.fit(data) # Check instance.tau is None instance.theta is None
def test_partial_derivative_scalar(self, derivative_mock): """partial_derivative_scalar calls partial_derivative with its arguments in an array.""" # Setup instance = Bivariate(copula_type=CopulaTypes.CLAYTON) instance.fit(self.X) # Run result = instance.partial_derivative_scalar(0.5, 0.1) # Check assert result == derivative_mock.return_value expected_args = ((np.array([[0.5, 0.1]]), 0), {}) assert len(expected_args) == len(derivative_mock.call_args) assert (derivative_mock.call_args[0][0] == expected_args[0][0]).all()
def test_to_dict(self): """To_dict returns the defining parameters of a copula in a dict.""" # Setup instance = Bivariate('frank') instance.fit(self.X) expected_result = { 'copula_type': 'FRANK', "tau": 0.014492753623188406, "theta": 0.13070829945417198 } # Run result = instance.to_dict() # Check assert result == expected_result
def test_to_dict(self): """To_dict returns the defining parameters of a copula in a dict.""" # Setup instance = Bivariate(copula_type='frank') instance.fit(self.X) expected_result = { 'copula_type': 'FRANK', "tau": 0.9128709291752769, "theta": 44.2003852484162 } # Run result = instance.to_dict() # Check assert result == expected_result
def test_save(self, json_mock): """Save stores the internal dictionary as a json in a file.""" # Setup instance = Bivariate('frank') instance.fit(self.X) expected_content = { "copula_type": "FRANK", "tau": 0.014492753623188406, "theta": 0.13070829945417198 } # Run instance.save('test.json') # Check assert json_mock.called compare_nested_dicts(json_mock.call_args[0][0], expected_content)
def test_save(self, json_mock, open_mock): """Save stores the internal dictionary as a json in a file.""" # Setup instance = Bivariate(copula_type='frank') instance.fit(self.X) expected_content = { "copula_type": "FRANK", "tau": 0.9128709291752769, "theta": 44.2003852484162 } # Run instance.save('test.json') # Check assert open_mock.called_once_with('test.json', 'w') assert json_mock.called compare_nested_dicts(json_mock.call_args[0][0], expected_content)
class TestFrank(TestCase): def setUp(self): self.X = np.array([ [2641.16233666, 180.2425623], [921.14476418, 192.35609972], [-651.32239137, 150.24830291], [1223.63536668, 156.62123653], [3233.37342355, 173.80311908], [1373.22400821, 191.0922843], [1959.28188858, 163.22252158], [1076.99295365, 190.73280428], [2029.25100261, 158.52982435], [1835.52188141, 163.0101334], [1170.03850556, 205.24904026], [739.42628394, 175.42916046], [1866.65810627, 208.31821984], [3703.49786503, 178.98351969], [1719.45232017, 160.50981075], [258.90206528, 163.19294974], [219.42363944, 173.30395132], [609.90212377, 215.18996298], [1618.44207239, 164.71141696], [2323.2775272, 178.84973821], [3251.78732274, 182.99902513], [1430.63989981, 217.5796917], [-180.57028875, 201.56983421], [-592.84497457, 174.92272693] ]) self.copula = Bivariate(CopulaTypes.FRANK) def test_fit(self): """On fit, theta and tau attributes are set.""" # Setup expected_theta = 0.1307082 expected_tau = 0.01449275 # Run self.copula.fit(self.X) actual_theta = self.copula.theta actual_tau = self.copula.tau # Check self.assertAlmostEqual(actual_theta, expected_theta, places=3) self.assertAlmostEqual(actual_tau, expected_tau) def test_probability_density(self): """Probability_density returns the probability density for the given values.""" # Setup self.copula.fit(self.X) expected_result = 0.999672586804842 # Run result = self.copula.probability_density(np.array([[0.1, 0.5]])) # Check assert np.isclose(result, expected_result).all() assert isinstance(result, np.ndarray) def test_cumulative_distribution(self): """Cumulative_density returns the probability distribution value for a point.""" # Setup self.copula.fit(self.X) expected_result = 0.05147003 # Run result = self.copula.cumulative_distribution(np.array([[0.1, 0.5]])) # Check assert np.isclose(result, expected_result).all() assert isinstance(result, np.ndarray) def test_sample(self): """After being fit, copula can produce samples.""" # Setup self.copula.fit(self.X) # Run result = self.copula.sample(10) # Check assert isinstance(result, np.ndarray) assert result.shape == (10, 2)
class TestFrank(TestCase): def setUp(self): self.X = np.array([[2641.16233666, 180.2425623], [921.14476418, 192.35609972], [-651.32239137, 150.24830291], [1223.63536668, 156.62123653], [3233.37342355, 173.80311908], [1373.22400821, 191.0922843], [1959.28188858, 163.22252158], [1076.99295365, 190.73280428], [2029.25100261, 158.52982435], [1835.52188141, 163.0101334], [1170.03850556, 205.24904026], [739.42628394, 175.42916046], [1866.65810627, 208.31821984], [3703.49786503, 178.98351969], [1719.45232017, 160.50981075], [258.90206528, 163.19294974], [219.42363944, 173.30395132], [609.90212377, 215.18996298], [1618.44207239, 164.71141696], [2323.2775272, 178.84973821], [3251.78732274, 182.99902513], [1430.63989981, 217.5796917], [-180.57028875, 201.56983421], [-592.84497457, 174.92272693]]) self.copula = Bivariate(CopulaTypes.FRANK) def test_fit(self): """On fit, theta and tau attributes are set.""" # Setup expected_theta = 0.1307082 expected_tau = 0.01449275 # Run self.copula.fit(self.X) actual_theta = self.copula.theta actual_tau = self.copula.tau # Check self.assertAlmostEqual(actual_theta, expected_theta, places=3) self.assertAlmostEqual(actual_tau, expected_tau) def test_probability_density(self): """Probability_density returns the probability density for the given values.""" # Setup self.copula.fit(self.X) expected_result = 0.999672586804842 # Run result = self.copula.probability_density(np.array([[0.1, 0.5]])) # Check assert np.isclose(result, expected_result).all() assert isinstance(result, np.ndarray) def test_cumulative_distribution(self): """Cumulative_density returns the probability distribution value for a point.""" # Setup self.copula.fit(self.X) expected_result = 0.05147003 # Run result = self.copula.cumulative_distribution(np.array([[0.1, 0.5]])) # Check assert np.isclose(result, expected_result).all() assert isinstance(result, np.ndarray) @patch('copulas.bivariate.base.np.random.uniform') def test_sample(self, uniform_mock): """Sample use the inverse-transform method to generate new samples.""" # Setup instance = Bivariate(CopulaTypes.FRANK) instance.tau = 0.5 instance.theta = instance.compute_theta() uniform_mock.return_value = np.array([0.1, 0.2, 0.4, 0.6, 0.8]) expected_result = np.array([[6.080069565509917e-06, 0.1], [6.080069565509917e-06, 0.2], [6.080069565509917e-06, 0.4], [6.080069565509917e-06, 0.6], [4.500185268624483e-06, 0.8]]) expected_uniform_call_args_list = [((0, 1, 5), {}), ((0, 1, 5), {})] # Run result = instance.sample(5) # Check assert isinstance(result, np.ndarray) assert result.shape == (5, 2) compare_nested_iterables(result, expected_result) assert uniform_mock.call_args_list == expected_uniform_call_args_list def test_cdf_zero_if_single_arg_is_zero(self): """Test of the analytical properties of copulas on a range of values of theta.""" # Setup instance = Bivariate(CopulaTypes.FRANK) tau_values = np.linspace(-1.0, 1.0, 20)[1:-1] # Run/Check for tau in tau_values: instance.tau = tau instance.theta = instance.compute_theta() copula_zero_if_arg_zero(instance) def test_cdf_value_if_all_other_arg_are_one(self): """Test of the analytical properties of copulas on a range of values of theta.""" # Setup instance = Bivariate(CopulaTypes.FRANK) tau_values = np.linspace(-1.0, 1.0, 20)[1:-1] # Run/Check for tau in tau_values: instance.tau = tau instance.theta = instance.compute_theta() copula_single_arg_not_one(instance, tolerance=1E-03) def test_sample_random_state(self): """If random_state is set, the samples are the same.""" # Setup instance = Bivariate(CopulaTypes.FRANK, random_seed=0) instance.tau = 0.5 instance.theta = instance.compute_theta() expected_result = np.array([[3.66330927e-06, 5.48813504e-01], [6.08006957e-06, 7.15189366e-01], [5.27582646e-06, 6.02763376e-01], [5.58315848e-06, 5.44883183e-01], [6.08006957e-06, 4.23654799e-01]]) # Run result = instance.sample(5) # Check compare_nested_iterables(result, expected_result)
class TestClayton(TestCase): def setUp(self): self.copula = Bivariate(CopulaTypes.CLAYTON) self.X = np.array([[2641.16233666, 180.2425623], [921.14476418, 192.35609972], [-651.32239137, 150.24830291], [1223.63536668, 156.62123653], [3233.37342355, 173.80311908], [1373.22400821, 191.0922843], [1959.28188858, 163.22252158], [1076.99295365, 190.73280428], [2029.25100261, 158.52982435], [1835.52188141, 163.0101334], [1170.03850556, 205.24904026], [739.42628394, 175.42916046], [1866.65810627, 208.31821984], [3703.49786503, 178.98351969], [1719.45232017, 160.50981075], [258.90206528, 163.19294974], [219.42363944, 173.30395132], [609.90212377, 215.18996298], [1618.44207239, 164.71141696], [2323.2775272, 178.84973821], [3251.78732274, 182.99902513], [1430.63989981, 217.5796917], [-180.57028875, 201.56983421], [-592.84497457, 174.92272693]]) def test_fit(self): """On fit, theta and tau attributes are set.""" # Setup expected_theta = 0.0294117 expected_tau = 0.01449275 # Run self.copula.fit(self.X) actual_theta = self.copula.theta actual_tau = self.copula.tau # Check self.assertAlmostEqual(actual_theta, expected_theta, places=3) self.assertAlmostEqual(actual_tau, expected_tau) def test_probability_density(self): """Probability_density returns the probability density for the given values.""" # Setup self.copula.fit(self.X) expected_result = np.array([0.98854645, 0.98607539]) # Run result = self.copula.probability_density( np.array([[0.1, 0.5], [0.2, 0.8]])) # Check assert isinstance(result, np.ndarray) assert np.isclose(result, expected_result).all() def test_cumulative_distribution(self): """Cumulative_density returns the probability distribution value for a point.""" # Setup self.copula.fit(self.X) expected_result = np.array([1.06658093e+06, 0.16165401]) # Run result = self.copula.cumulative_distribution( np.array([[1500, 180], [0.2, 0.8]])) # Check assert isinstance(result, np.ndarray) assert np.isclose(result, expected_result).all() def test_inverse_cumulative_percentile_point(self): """The percentile point and cumulative_distribution should be inverse one of the other.""" # Setup self.copula.fit(self.X) # Run # percentile = self.copula.percent_point(0.1, 0.5) # derivative = self.copula.partial_derivative([0.1], [0.5]) # result = self.copula.cumulative_distribution(derivative, percentile) # Check # assert point == result @patch('copulas.bivariate.base.np.random.uniform') def test_sample(self, uniform_mock): """Sample use the inverse-transform method to generate new samples.""" # Setup instance = Bivariate(CopulaTypes.CLAYTON) instance.tau = 0.5 instance.theta = instance.compute_theta() uniform_mock.return_value = np.array([0.1, 0.2, 0.4, 0.6, 0.8]) expected_result = np.array([[0.05233100, 0.1], [0.14271095, 0.2], [0.39959746, 0.4], [0.68567125, 0.6], [0.89420523, 0.8]]) expected_uniform_call_args_list = [((0, 1, 5), {}), ((0, 1, 5), {})] # Run result = instance.sample(5) # Check assert isinstance(result, np.ndarray) assert result.shape == (5, 2) compare_nested_iterables(result, expected_result) assert uniform_mock.call_args_list == expected_uniform_call_args_list def test_cdf_zero_if_single_arg_is_zero(self): """Test of the analytical properties of copulas on a range of values of theta.""" # Setup instance = Bivariate(CopulaTypes.CLAYTON) tau_values = np.linspace(-1.0, 1.0, 20)[1:-1] # Run/Check for tau in tau_values: instance.tau = tau instance.theta = instance.compute_theta() copula_zero_if_arg_zero(instance) def test_cdf_value_if_all_other_arg_are_one(self): """Test of the analytical properties of copulas on a range of values of theta.""" # Setup instance = Bivariate(CopulaTypes.CLAYTON) tau_values = np.linspace(-1.0, 1.0, 20)[1:-1] # Run/Check for tau in tau_values: instance.tau = tau instance.theta = instance.compute_theta() copula_single_arg_not_one(instance) def test_sample_random_state(self): """If random_state is set, the samples are the same.""" # Setup instance = Bivariate(CopulaTypes.CLAYTON, random_seed=0) instance.tau = 0.5 instance.theta = instance.compute_theta() expected_result = np.array([[0.68627770, 0.54881350], [0.64059280, 0.71518937], [0.90594782, 0.60276338], [0.96040856, 0.54488318], [0.40876969, 0.42365480]]) # Run result = instance.sample(5) # Check compare_nested_iterables(result, expected_result)