class TestUAIWriter(unittest.TestCase): def setUp(self): self.maxDiff = None edges = [['family-out', 'dog-out'], ['bowel-problem', 'dog-out'], ['family-out', 'light-on'], ['dog-out', 'hear-bark']] cpds = {'bowel-problem': np.array([[0.01], [0.99]]), 'dog-out': np.array([[0.99, 0.01, 0.97, 0.03], [0.9, 0.1, 0.3, 0.7]]), 'family-out': np.array([[0.15], [0.85]]), 'hear-bark': np.array([[0.7, 0.3], [0.01, 0.99]]), 'light-on': np.array([[0.6, 0.4], [0.05, 0.95]])} states = {'bowel-problem': ['true', 'false'], 'dog-out': ['true', 'false'], 'family-out': ['true', 'false'], 'hear-bark': ['true', 'false'], 'light-on': ['true', 'false']} parents = {'bowel-problem': [], 'dog-out': ['bowel-problem', 'family-out'], 'family-out': [], 'hear-bark': ['dog-out'], 'light-on': ['family-out']} self.bayesmodel = BayesianModel(edges) tabular_cpds = [] for var, values in cpds.items(): cpd = TabularCPD(var, len(states[var]), values, evidence=parents[var], evidence_card=[len(states[evidence_var]) for evidence_var in parents[var]]) tabular_cpds.append(cpd) self.bayesmodel.add_cpds(*tabular_cpds) self.bayeswriter = UAIWriter(self.bayesmodel) edges = {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')} self.markovmodel = MarkovModel(edges) tables = [(['var_0', 'var_1'], ['4.000', '2.400', '1.000', '0.000']), (['var_0', 'var_1', 'var_2'], ['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000', '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])] domain = {'var_1': '2', 'var_2': '3', 'var_0': '2'} factors = [] for table in tables: variables = table[0] cardinality = [int(domain[var]) for var in variables] values = list(map(float, table[1])) factor = DiscreteFactor(variables, cardinality, values) factors.append(factor) self.markovmodel.add_factors(*factors) self.markovwriter = UAIWriter(self.markovmodel) def test_bayes_model(self): self.expected_bayes_file = """BAYES 5 2 2 2 2 2 5 1 0 3 2 0 1 1 2 2 1 3 2 2 4 2 0.01 0.99 8 0.99 0.01 0.97 0.03 0.9 0.1 0.3 0.7 2 0.15 0.85 4 0.7 0.3 0.01 0.99 4 0.6 0.4 0.05 0.95""" self.assertEqual(str(self.bayeswriter.__str__()), str(self.expected_bayes_file)) def test_markov_model(self): self.expected_markov_file = """MARKOV 3 2 2 3 2 2 0 1 3 0 1 2 4 4.0 2.4 1.0 0.0 12 2.25 3.25 3.75 0.0 0.0 10.0 1.875 4.0 3.333 2.0 2.0 3.4""" self.assertEqual(str(self.markovwriter.__str__()), str(self.expected_markov_file))
class TestUAIWriter(unittest.TestCase): def setUp(self): self.maxDiff = None edges = [['family-out', 'dog-out'], ['bowel-problem', 'dog-out'], ['family-out', 'light-on'], ['dog-out', 'hear-bark']] cpds = { 'bowel-problem': np.array([[0.01], [0.99]]), 'dog-out': np.array([[0.99, 0.01, 0.97, 0.03], [0.9, 0.1, 0.3, 0.7]]), 'family-out': np.array([[0.15], [0.85]]), 'hear-bark': np.array([[0.7, 0.3], [0.01, 0.99]]), 'light-on': np.array([[0.6, 0.4], [0.05, 0.95]]) } states = { 'bowel-problem': ['true', 'false'], 'dog-out': ['true', 'false'], 'family-out': ['true', 'false'], 'hear-bark': ['true', 'false'], 'light-on': ['true', 'false'] } parents = { 'bowel-problem': [], 'dog-out': ['bowel-problem', 'family-out'], 'family-out': [], 'hear-bark': ['dog-out'], 'light-on': ['family-out'] } self.bayesmodel = BayesianModel(edges) tabular_cpds = [] for var, values in cpds.items(): cpd = TabularCPD(var, len(states[var]), values, evidence=parents[var], evidence_card=[ len(states[evidence_var]) for evidence_var in parents[var] ]) tabular_cpds.append(cpd) self.bayesmodel.add_cpds(*tabular_cpds) self.bayeswriter = UAIWriter(self.bayesmodel) edges = {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')} self.markovmodel = MarkovModel(edges) tables = [(['var_0', 'var_1'], ['4.000', '2.400', '1.000', '0.000']), (['var_0', 'var_1', 'var_2'], [ '2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000', '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000' ])] domain = {'var_1': '2', 'var_2': '3', 'var_0': '2'} factors = [] for table in tables: variables = table[0] cardinality = [int(domain[var]) for var in variables] values = list(map(float, table[1])) factor = Factor(variables, cardinality, values) factors.append(factor) self.markovmodel.add_factors(*factors) self.markovwriter = UAIWriter(self.markovmodel) def test_bayes_model(self): self.expected_bayes_file = """BAYES 5 2 2 2 2 2 5 1 0 3 2 0 1 1 2 2 1 3 2 2 4 2 0.01 0.99 8 0.99 0.01 0.97 0.03 0.9 0.1 0.3 0.7 2 0.15 0.85 4 0.7 0.3 0.01 0.99 4 0.6 0.4 0.05 0.95""" self.assertEqual(str(self.bayeswriter.__str__()), str(self.expected_bayes_file)) def test_markov_model(self): self.expected_markov_file = """MARKOV 3 2 2 3 2 2 0 1 3 0 1 2 4 4.0 2.4 1.0 0.0 12 2.25 3.25 3.75 0.0 0.0 10.0 1.875 4.0 3.333 2.0 2.0 3.4""" self.assertEqual(str(self.markovwriter.__str__()), str(self.expected_markov_file))
class TestUAIWriter(unittest.TestCase): def setUp(self): self.maxDiff = None variables = [ "kid", "bowel-problem", "dog-out", "family-out", "hear-bark", "light-on", ] edges = [ ["family-out", "dog-out"], ["bowel-problem", "dog-out"], ["family-out", "light-on"], ["dog-out", "hear-bark"], ] cpds = { "kid": np.array([[0.3], [0.7]]), "bowel-problem": np.array([[0.01], [0.99]]), "dog-out": np.array([[0.99, 0.01, 0.97, 0.03], [0.9, 0.1, 0.3, 0.7]]), "family-out": np.array([[0.15], [0.85]]), "hear-bark": np.array([[0.7, 0.3], [0.01, 0.99]]), "light-on": np.array([[0.6, 0.4], [0.05, 0.95]]), } states = { "kid": ["true", "false"], "bowel-problem": ["true", "false"], "dog-out": ["true", "false"], "family-out": ["true", "false"], "hear-bark": ["true", "false"], "light-on": ["true", "false"], } parents = { "kid": [], "bowel-problem": [], "dog-out": ["bowel-problem", "family-out"], "family-out": [], "hear-bark": ["dog-out"], "light-on": ["family-out"], } self.bayesmodel = BayesianModel() self.bayesmodel.add_nodes_from(variables) self.bayesmodel.add_edges_from(edges) tabular_cpds = [] for var, values in cpds.items(): cpd = TabularCPD( var, len(states[var]), values, evidence=parents[var], evidence_card=[ len(states[evidence_var]) for evidence_var in parents[var] ], ) tabular_cpds.append(cpd) self.bayesmodel.add_cpds(*tabular_cpds) self.bayeswriter = UAIWriter(self.bayesmodel) edges = {("var_0", "var_1"), ("var_0", "var_2"), ("var_1", "var_2")} self.markovmodel = MarkovModel(edges) tables = [ (["var_0", "var_1"], ["4.000", "2.400", "1.000", "0.000"]), ( ["var_0", "var_1", "var_2"], [ "2.2500", "3.2500", "3.7500", "0.0000", "0.0000", "10.0000", "1.8750", "4.0000", "3.3330", "2.0000", "2.0000", "3.4000", ], ), ] domain = {"var_1": "2", "var_2": "3", "var_0": "2"} factors = [] for table in tables: variables = table[0] cardinality = [int(domain[var]) for var in variables] values = list(map(float, table[1])) factor = DiscreteFactor(variables, cardinality, values) factors.append(factor) self.markovmodel.add_factors(*factors) self.markovwriter = UAIWriter(self.markovmodel) def test_bayes_model(self): self.expected_bayes_file = """BAYES 6 2 2 2 2 2 2 6 1 0 3 2 0 1 1 2 2 1 3 1 4 2 2 5 2 0.01 0.99 8 0.99 0.01 0.97 0.03 0.9 0.1 0.3 0.7 2 0.15 0.85 4 0.7 0.3 0.01 0.99 2 0.3 0.7 4 0.6 0.4 0.05 0.95""" self.assertEqual(str(self.bayeswriter.__str__()), str(self.expected_bayes_file)) def test_markov_model(self): self.expected_markov_file = """MARKOV 3 2 2 3 2 2 0 1 3 0 1 2 4 4.0 2.4 1.0 0.0 12 2.25 3.25 3.75 0.0 0.0 10.0 1.875 4.0 3.333 2.0 2.0 3.4""" self.assertEqual( str(self.markovwriter.__str__()), str(self.expected_markov_file) )