Python BayesianModel.local_independencies方法代码示例

# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import local_independencies [as 别名]
class TestBayesianModelMethods(unittest.TestCase):
    def setUp(self):
        self.G = BayesianModel([('a', 'd'), ('b', 'd'),
                                ('d', 'e'), ('b', 'c')])

    def test_moral_graph(self):
        moral_graph = self.G.moralize()
        self.assertListEqual(sorted(moral_graph.nodes()), ['a', 'b', 'c', 'd', 'e'])
        for edge in moral_graph.edges():
            self.assertTrue(edge in [('a', 'b'), ('a', 'd'), ('b', 'c'), ('d', 'b'), ('e', 'd')] or
                            (edge[1], edge[0]) in [('a', 'b'), ('a', 'd'), ('b', 'c'), ('d', 'b'), ('e', 'd')])

    def test_moral_graph_with_edge_present_over_parents(self):
        G = BayesianModel([('a', 'd'), ('d', 'e'), ('b', 'd'), ('b', 'c'), ('a', 'b')])
        moral_graph = G.moralize()
        self.assertListEqual(sorted(moral_graph.nodes()), ['a', 'b', 'c', 'd', 'e'])
        for edge in moral_graph.edges():
            self.assertTrue(edge in [('a', 'b'), ('c', 'b'), ('d', 'a'), ('d', 'b'), ('d', 'e')] or
                            (edge[1], edge[0]) in [('a', 'b'), ('c', 'b'), ('d', 'a'), ('d', 'b'), ('d', 'e')])

    def test_local_independencies(self):
        self.assertEqual(self.G.local_independencies('a'), Independencies(['a', ['b', 'c']]))
        self.assertEqual(self.G.local_independencies('c'), Independencies(['c',['a','d','e'],'b']))
        self.assertEqual(self.G.local_independencies('d'), Independencies(['d','c',['b','a']]))
        self.assertEqual(self.G.local_independencies('e'), Independencies(['e',['c','b','a'],'d']))
        self.assertEqual(self.G.local_independencies('b'), Independencies(['b','a']))

    def tearDown(self):
        del self.G

# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import local_independencies [as 别名]

#.........这里部分代码省略.........
    import pgmpy
    import six  # NOQA

    def try_query(evidence):
        print('--------')
        query_vars = ut.setdiff_ordered(varnames, list(evidence.keys()))
        evidence_str = ', '.join(pretty_evidence(evidence))
        probs = name_belief.query(query_vars, evidence)
        factor_list = probs.values()
        joint_factor = pgmpy.factors.factor_product(*factor_list)
        print('P(' + ', '.join(query_vars) + ' | ' + evidence_str + ')')
        # print(six.text_type(joint_factor))
        factor = joint_factor  # NOQA
        # print_factor(factor)
        # import utool as ut
        print(ut.hz_str([(f._str(phi_or_p='phi')) for f in factor_list]))

    for evidence in evidence_dict:
        try_query(evidence)

    evidence = {'Aij': 1, 'Ajk': 1, 'Aki': 1, 'Ni': 0}
    try_query(evidence)

    evidence = {'Aij': 0, 'Ajk': 0, 'Aki': 0, 'Ni': 0}
    try_query(evidence)

    globals()['score_nice'] = score_nice
    globals()['name_nice'] = name_nice
    globals()['score_basis'] = score_basis
    globals()['nid_basis'] = nid_basis

    print('Independencies')
    print(name_model.get_independencies())
    print(name_model.local_independencies([Ni.variable]))

    # name_belief = BeliefPropagation(name_model)
    # # name_belief = VariableElimination(name_model)
    # for case in special_cases:
    #     test_data = case.drop('Lk', axis=1)
    #     test_data = test_data.reset_index(drop=True)
    #     print('----')
    #     for i in range(test_data.shape[0]):
    #         evidence = test_data.loc[i].to_dict()
    #         probs = name_belief.query(['Lk'], evidence)
    #         factor = probs['Lk']
    #         probs = factor.values
    #         evidence_ = evidence.copy()
    #         evidence_['Li'] = name_nice[evidence['Li']]
    #         evidence_['Lj'] = name_nice[evidence['Lj']]
    #         evidence_['Sij'] = score_nice[evidence['Sij']]
    #         evidence_['Sjk'] = score_nice[evidence['Sjk']]
    #         nice2_prob = ut.odict(zip(name_nice, probs.tolist()))
    #         ut.print_python_code('P(Lk | {evidence}) = {cpt}'.format(
    #             evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
    #             cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
    #         ))

    # for case in special_cases:
    #     test_data = case.drop('Lk', axis=1)
    #     test_data = test_data.drop('Lj', axis=1)
    #     test_data = test_data.reset_index(drop=True)
    #     print('----')
    #     for i in range(test_data.shape[0]):
    #         evidence = test_data.loc[i].to_dict()
    #         query_vars = ['Lk', 'Lj']
    #         probs = name_belief.query(query_vars, evidence)

# 需要导入模块: from pgmpy.models import BayesianModel [as 别名]
# 或者: from pgmpy.models.BayesianModel import local_independencies [as 别名]
class TestBayesianModelMethods(unittest.TestCase):

    def setUp(self):
        self.G = BayesianModel([('a', 'd'), ('b', 'd'),
                                ('d', 'e'), ('b', 'c')])
        self.G1 = BayesianModel([('diff', 'grade'), ('intel', 'grade')])
        diff_cpd = TabularCPD('diff', 2, values=[[0.2], [0.8]])
        intel_cpd = TabularCPD('intel', 3, values=[[0.5], [0.3], [0.2]])
        grade_cpd = TabularCPD('grade', 3, values=[[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
                                                   [0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
                                                   [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]],
                               evidence=['diff', 'intel'], evidence_card=[2, 3])
        self.G1.add_cpds(diff_cpd, intel_cpd, grade_cpd)
        self.G2 = BayesianModel([('d', 'g'), ('g', 'l'), ('i', 'g'), ('i', 'l')])

    def test_moral_graph(self):
        moral_graph = self.G.moralize()
        self.assertListEqual(sorted(moral_graph.nodes()), ['a', 'b', 'c', 'd', 'e'])
        for edge in moral_graph.edges():
            self.assertTrue(edge in [('a', 'b'), ('a', 'd'), ('b', 'c'), ('d', 'b'), ('e', 'd')] or
                            (edge[1], edge[0]) in [('a', 'b'), ('a', 'd'), ('b', 'c'), ('d', 'b'), ('e', 'd')])

    def test_moral_graph_with_edge_present_over_parents(self):
        G = BayesianModel([('a', 'd'), ('d', 'e'), ('b', 'd'), ('b', 'c'), ('a', 'b')])
        moral_graph = G.moralize()
        self.assertListEqual(sorted(moral_graph.nodes()), ['a', 'b', 'c', 'd', 'e'])
        for edge in moral_graph.edges():
            self.assertTrue(edge in [('a', 'b'), ('c', 'b'), ('d', 'a'), ('d', 'b'), ('d', 'e')] or
                            (edge[1], edge[0]) in [('a', 'b'), ('c', 'b'), ('d', 'a'), ('d', 'b'), ('d', 'e')])

    def test_get_ancestors_of_success(self):
        ancenstors1 = self.G2._get_ancestors_of('g')
        ancenstors2 = self.G2._get_ancestors_of('d')
        ancenstors3 = self.G2._get_ancestors_of(['i', 'l'])
        self.assertEqual(ancenstors1, {'d', 'i', 'g'})
        self.assertEqual(ancenstors2, {'d'})
        self.assertEqual(ancenstors3, {'g', 'i', 'l', 'd'})

    def test_get_ancestors_of_failure(self):
        self.assertRaises(ValueError, self.G2._get_ancestors_of, 'h')

    def test_local_independencies(self):
        self.assertEqual(self.G.local_independencies('a'), Independencies(['a', ['b', 'c']]))
        self.assertEqual(self.G.local_independencies('c'), Independencies(['c', ['a', 'd', 'e'], 'b']))
        self.assertEqual(self.G.local_independencies('d'), Independencies(['d', 'c', ['b', 'a']]))
        self.assertEqual(self.G.local_independencies('e'), Independencies(['e', ['c', 'b', 'a'], 'd']))
        self.assertEqual(self.G.local_independencies('b'), Independencies(['b', 'a']))
        self.assertEqual(self.G1.local_independencies('grade'), Independencies())

    def test_get_independencies(self):
        chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
        self.assertEqual(chain.get_independencies(), Independencies(('X', 'Z', 'Y'), ('Z', 'X', 'Y')))
        fork = BayesianModel([('Y', 'X'), ('Y', 'Z')])
        self.assertEqual(fork.get_independencies(), Independencies(('X', 'Z', 'Y'), ('Z', 'X', 'Y')))
        collider = BayesianModel([('X', 'Y'), ('Z', 'Y')])
        self.assertEqual(collider.get_independencies(), Independencies(('X', 'Z'), ('Z', 'X')))

    def test_is_imap(self):
        val = [0.01, 0.01, 0.08, 0.006, 0.006, 0.048, 0.004, 0.004, 0.032,
               0.04, 0.04, 0.32, 0.024, 0.024, 0.192, 0.016, 0.016, 0.128]
        JPD = JointProbabilityDistribution(['diff', 'intel', 'grade'], [2, 3, 3], val)
        fac = DiscreteFactor(['diff', 'intel', 'grade'], [2, 3, 3], val)
        self.assertTrue(self.G1.is_imap(JPD))
        self.assertRaises(TypeError, self.G1.is_imap, fac)

    def test_get_immoralities(self):
        G = BayesianModel([('x', 'y'), ('z', 'y'), ('x', 'z'), ('w', 'y')])
        self.assertEqual(G.get_immoralities(), {('w', 'x'), ('w', 'z')})
        G1 = BayesianModel([('x', 'y'), ('z', 'y'), ('z', 'x'), ('w', 'y')])
        self.assertEqual(G1.get_immoralities(), {('w', 'x'), ('w', 'z')})
        G2 = BayesianModel([('x', 'y'), ('z', 'y'), ('x', 'z'), ('w', 'y'), ('w', 'x')])
        self.assertEqual(G2.get_immoralities(), {('w', 'z')})

    def test_is_iequivalent(self):
        G = BayesianModel([('x', 'y'), ('z', 'y'), ('x', 'z'), ('w', 'y')])
        self.assertRaises(TypeError, G.is_iequivalent, MarkovModel())
        G1 = BayesianModel([('V', 'W'), ('W', 'X'), ('X', 'Y'), ('Z', 'Y')])
        G2 = BayesianModel([('W', 'V'), ('X', 'W'), ('X', 'Y'), ('Z', 'Y')])
        self.assertTrue(G1.is_iequivalent(G2))
        G3 = BayesianModel([('W', 'V'), ('W', 'X'), ('Y', 'X'), ('Z', 'Y')])
        self.assertFalse(G3.is_iequivalent(G2))

    def test_copy(self):
        model_copy = self.G1.copy()
        self.assertEqual(sorted(self.G1.nodes()), sorted(model_copy.nodes()))
        self.assertEqual(sorted(self.G1.edges()), sorted(model_copy.edges()))
        self.assertNotEqual(id(self.G1.get_cpds('diff')),
                            id(model_copy.get_cpds('diff')))

        self.G1.remove_cpds('diff')
        diff_cpd = TabularCPD('diff', 2, values=[[0.3], [0.7]])
        self.G1.add_cpds(diff_cpd)
        self.assertNotEqual(self.G1.get_cpds('diff'),
                            model_copy.get_cpds('diff'))

        self.G1.remove_node('intel')
        self.assertNotEqual(sorted(self.G1.nodes()), sorted(model_copy.nodes()))
        self.assertNotEqual(sorted(self.G1.edges()), sorted(model_copy.edges()))

    def test_remove_node(self):
#.........这里部分代码省略.........