def test_sample(self): model = MC(['a', 'b'], [2, 2]) model.transition_models['a'] = { 0: { 0: 0.1, 1: 0.9 }, 1: { 0: 0.2, 1: 0.8 } } model.transition_models['b'] = { 0: { 0: 0.3, 1: 0.7 }, 1: { 0: 0.4, 1: 0.6 } } sample = model.sample(start_state=[State('a', 0), State('b', 1)], size=2) self.assertEqual(len(sample), 2) self.assertEqual(list(sample.columns), ['a', 'b']) self.assertTrue( list(sample.loc[0]) in [[0, 0], [0, 1], [1, 0], [1, 1]]) self.assertTrue( list(sample.loc[1]) in [[0, 0], [0, 1], [1, 0], [1, 1]])
def test_generate_sample_less_arg(self, random_state, sample_discrete): model = MC(['a', 'b'], [2, 2]) model.transition_models['a'] = { 0: { 0: 0.1, 1: 0.9 }, 1: { 0: 0.2, 1: 0.8 } } model.transition_models['b'] = { 0: { 0: 0.3, 1: 0.7 }, 1: { 0: 0.4, 1: 0.6 } } random_state.return_value = [State('a', 0), State('b', 1)] sample_discrete.side_effect = [[1], [0]] * 2 gen = model.generate_sample(size=2) samples = [sample for sample in gen] expected_samples = [[State('a', 1), State('b', 0)]] * 2 self.assertEqual(samples, expected_samples)
def test_set_start_state_list(self, check_state): model = MC(['b', 'a'], [1, 2]) check_state.return_value = True model.set_start_state([State('a', 0), State('b', 1)]) model_state = [State('b', 1), State('a', 0)] check_state.assert_called_once_with(model, model_state) self.assertEqual(model.state, model_state)
def test_copy(self): model = MC(['a', 'b'], [2, 2], [State('a', 0), State('b', 1)]) model.add_transition_model('a', {0: {0: 0.1, 1: 0.9}, 1: {0: 0.2, 1: 0.8}}) model.add_transition_model('b', {0: {0: 0.3, 1: 0.7}, 1: {0: 0.4, 1: 0.6}}) copy = model.copy() self.assertIsInstance(copy, MC) self.assertEqual(sorted(model.variables), sorted(copy.variables)) self.assertEqual(model.cardinalities, copy.cardinalities) self.assertEqual(model.transition_models, copy.transition_models) self.assertEqual(model.state, copy.state) model.add_variable('p', 1) model.set_start_state([State('a', 0), State('b', 1), State('p', 0)]) model.add_transition_model('p', {0: {0: 1}}) self.assertNotEqual(sorted(model.variables), sorted(copy.variables)) self.assertEqual(sorted(['a', 'b']), sorted(copy.variables)) self.assertNotEqual(model.cardinalities, copy.cardinalities) self.assertEqual({'a': 2, 'b': 2}, copy.cardinalities) self.assertNotEqual(model.state, copy.state) self.assertEqual([State('a', 0), State('b', 1)], copy.state) self.assertNotEqual(model.transition_models, copy.transition_models) self.assertEqual(len(copy.transition_models), 2) self.assertEqual(copy.transition_models['a'], {0: {0: 0.1, 1: 0.9}, 1: {0: 0.2, 1: 0.8}}) self.assertEqual(copy.transition_models['b'], {0: {0: 0.3, 1: 0.7}, 1: {0: 0.4, 1: 0.6}})
def test_random_state(self): model = MC(['a', 'b'], [2, 3]) state = model.random_state() vars = [v for v, s in state] self.assertEqual(vars, ['a', 'b']) self.assertGreaterEqual(state[0].state, 0) self.assertGreaterEqual(state[1].state, 0) self.assertLessEqual(state[0].state, 1) self.assertLessEqual(state[1].state, 2)
def test_generate_sample(self, sample_discrete): model = MC(['a', 'b'], [2, 2]) model.transition_models['a'] = {0: {0: 0.1, 1: 0.9}, 1: {0: 0.2, 1: 0.8}} model.transition_models['b'] = {0: {0: 0.3, 1: 0.7}, 1: {0: 0.4, 1: 0.6}} sample_discrete.side_effect = [[1], [0]] * 2 gen = model.generate_sample(start_state=[State('a', 0), State('b', 1)], size=2) samples = [sample for sample in gen] expected_samples = [[State('a', 1), State('b', 0)]] * 2 self.assertEqual(samples, expected_samples)
def test_sample(self): model = MC(['a', 'b'], [2, 2]) model.transition_models['a'] = {0: {0: 0.1, 1: 0.9}, 1: {0: 0.2, 1: 0.8}} model.transition_models['b'] = {0: {0: 0.3, 1: 0.7}, 1: {0: 0.4, 1: 0.6}} sample = model.sample(start_state=[State('a', 0), State('b', 1)], size=2) self.assertEqual(len(sample), 2) self.assertEqual(list(sample.columns), ['a', 'b']) self.assertTrue(list(sample.loc[0]) in [[0, 0], [0, 1], [1, 0], [1, 1]]) self.assertTrue(list(sample.loc[1]) in [[0, 0], [0, 1], [1, 0], [1, 1]])
def test_sample_less_arg(self, random_state): model = MC(['a', 'b'], [2, 2]) random_state.return_value = [State('a', 0), State('b', 1)] sample = model.sample(size=1) random_state.assert_called_once_with(model) self.assertEqual(model.state, random_state.return_value) self.assertEqual(len(sample), 1) self.assertEqual(list(sample.columns), ['a', 'b']) self.assertEqual(list(sample.loc[0]), [0, 1])
def test_is_stationarity_failure(self): model = MC(['intel', 'diff'], [2, 3]) model.set_start_state([State('intel', 0), State('diff', 2)]) intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}} model.add_transition_model('intel', intel_tm) diff_tm = {0: {0: 0.1, 1: 0.5, 2: 0.4}, 1: {0: 0.2, 1: 0.2, 2: 0.6}, 2: {0: 0.7, 1: 0.15, 2: 0.15}} model.add_transition_model('diff', diff_tm) self.assertFalse(model.is_stationarity(0.002, None))
def test_is_stationarity_failure(self): model = MC(['intel', 'diff'], [2, 3]) model.set_start_state([State('intel', 0), State('diff', 2)]) intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}} model.add_transition_model('intel', intel_tm) diff_tm = { 0: { 0: 0.1, 1: 0.5, 2: 0.4 }, 1: { 0: 0.2, 1: 0.2, 2: 0.6 }, 2: { 0: 0.7, 1: 0.15, 2: 0.15 } } model.add_transition_model('diff', diff_tm) self.assertFalse(model.is_stationarity(0.002, None))
def test_add_variables_from(self, add_var): model = MC() model.add_variables_from(self.variables, self.card) calls = [call(model, *p) for p in zip(self.variables, self.card)] add_var.assert_has_calls(calls)
def test_set_start_state_none(self): model = MC() model.state = 'state' model.set_start_state(None) self.assertIsNone(model.state)
def test_add_transition_model_success(self): model = MC(['var'], [2]) transition_model = {0: {0: 0.3, 1: 0.7}, 1: {0: 0.5, 1: 0.5}} model.add_transition_model('var', transition_model) self.assertDictEqual(model.transition_models['var'], transition_model)
def test_transition_model_dict_to_matrix(self): model = MC(['var'], [2]) transition_model = {0: {0: 0.3, 1: 0.7}, 1: {0: 0.5, 1: 0.5}} transition_model_matrix = np.array([[0.3, 0.7], [0.5, 0.5]]) model.add_transition_model('var', transition_model_matrix) self.assertDictEqual(model.transition_models['var'], transition_model)
def test_add_variable_existing(self, warn): model = MC(['p'], [2]) model.add_variable('p', 3) self.assertEqual(warn.call_count, 1)
def test_prob_from_sample(self, sample): model = MC(['a', 'b'], [2, 2]) sample.return_value = self.sample probabilites = model.prob_from_sample([State('a', 1), State('b', 0)]) self.assertEqual(list(probabilites), [1] * 50 + [0] * 50)
def test_copy(self): model = MC(['a', 'b'], [2, 2], [State('a', 0), State('b', 1)]) model.add_transition_model('a', { 0: { 0: 0.1, 1: 0.9 }, 1: { 0: 0.2, 1: 0.8 } }) model.add_transition_model('b', { 0: { 0: 0.3, 1: 0.7 }, 1: { 0: 0.4, 1: 0.6 } }) copy = model.copy() self.assertIsInstance(copy, MC) self.assertEqual(sorted(model.variables), sorted(copy.variables)) self.assertEqual(model.cardinalities, copy.cardinalities) self.assertEqual(model.transition_models, copy.transition_models) self.assertEqual(model.state, copy.state) model.add_variable('p', 1) model.set_start_state([State('a', 0), State('b', 1), State('p', 0)]) model.add_transition_model('p', {0: {0: 1}}) self.assertNotEqual(sorted(model.variables), sorted(copy.variables)) self.assertEqual(sorted(['a', 'b']), sorted(copy.variables)) self.assertNotEqual(model.cardinalities, copy.cardinalities) self.assertEqual({'a': 2, 'b': 2}, copy.cardinalities) self.assertNotEqual(model.state, copy.state) self.assertEqual([State('a', 0), State('b', 1)], copy.state) self.assertNotEqual(model.transition_models, copy.transition_models) self.assertEqual(len(copy.transition_models), 2) self.assertEqual(copy.transition_models['a'], { 0: { 0: 0.1, 1: 0.9 }, 1: { 0: 0.2, 1: 0.8 } }) self.assertEqual(copy.transition_models['b'], { 0: { 0: 0.3, 1: 0.7 }, 1: { 0: 0.4, 1: 0.6 } })
def test_add_variable_new(self): model = MC(['a'], [2]) model.add_variable('p', 3) self.assertIn('p', model.variables) self.assertEqual(model.cardinalities['p'], 3) self.assertDictEqual(model.transition_models['p'], {})
def test_check_state_success(self): model = MC(['a'], [2]) self.assertTrue(model._check_state([State('a', 1)]))