bp6 = belief_prop.query(variables=['y'],
evidence={
'marital': 1,
'loan': 1,
'contact': 1,
'month': 5
})
print(bp6['y'])
#-----------------Sampling using GibbsSampling--------------------------
gibbs_chain = GibbsSampling(mark)
gen = gibbs_chain.generate_sample(size=5)
[sample for sample in gen]
gibbs_chain.sample(size=4)
for fact in mark.get_factors():
print(fact)
data1 = data[[
'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month',
'poutcome', 'y'
]].copy()
df = data1[0:5]
#------------------Calculate mean and entropy using the samples generated above---------------------------
np.mean(df)
scipy.stats.entropy(df)
class TestGibbsSampling(unittest.TestCase):
def setUp(self):
# A test Bayesian model
diff_cpd = TabularCPD('diff', 2, [[0.6], [0.4]])
intel_cpd = TabularCPD('intel', 2, [[0.7], [0.3]])
grade_cpd = TabularCPD('grade',
3,
[[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2]],
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.bayesian_model = BayesianModel()
self.bayesian_model.add_nodes_from(['diff', 'intel', 'grade'])
self.bayesian_model.add_edges_from([('diff', 'grade'),
('intel', 'grade')])
self.bayesian_model.add_cpds(diff_cpd, intel_cpd, grade_cpd)
# A test Markov model
self.markov_model = MarkovModel([('A', 'B'), ('C', 'B'), ('B', 'D')])
factor_ab = DiscreteFactor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
factor_cb = DiscreteFactor(['C', 'B'], [4, 3],
[3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
factor_bd = DiscreteFactor(['B', 'D'], [3, 2], [5, 7, 2, 1, 9, 3])
self.markov_model.add_factors(factor_ab, factor_cb, factor_bd)
self.gibbs = GibbsSampling(self.bayesian_model)
def tearDown(self):
del self.bayesian_model
del self.markov_model
@patch('pgmpy.sampling.GibbsSampling._get_kernel_from_bayesian_model',
autospec=True)
@patch('pgmpy.models.MarkovChain.__init__', autospec=True)
def test_init_bayesian_model(self, init, get_kernel):
model = MagicMock(spec_set=BayesianModel)
gibbs = GibbsSampling(model)
init.assert_called_once_with(gibbs)
get_kernel.assert_called_once_with(gibbs, model)
@patch('pgmpy.sampling.GibbsSampling._get_kernel_from_markov_model',
autospec=True)
def test_init_markov_model(self, get_kernel):
model = MagicMock(spec_set=MarkovModel)
gibbs = GibbsSampling(model)
get_kernel.assert_called_once_with(gibbs, model)
def test_get_kernel_from_bayesian_model(self):
gibbs = GibbsSampling()
gibbs._get_kernel_from_bayesian_model(self.bayesian_model)
self.assertListEqual(list(gibbs.variables),
self.bayesian_model.nodes())
self.assertDictEqual(gibbs.cardinalities, {
'diff': 2,
'intel': 2,
'grade': 3
})
def test_get_kernel_from_markov_model(self):
gibbs = GibbsSampling()
gibbs._get_kernel_from_markov_model(self.markov_model)
self.assertListEqual(list(gibbs.variables), self.markov_model.nodes())
self.assertDictEqual(gibbs.cardinalities, {
'A': 2,
'B': 3,
'C': 4,
'D': 2
})
def test_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
sample = self.gibbs.sample(start_state, 2)
self.assertEquals(len(sample), 2)
self.assertEquals(len(sample.columns), 3)
self.assertIn('diff', sample.columns)
self.assertIn('intel', sample.columns)
self.assertIn('grade', sample.columns)
self.assertTrue(set(sample['diff']).issubset({0, 1}))
self.assertTrue(set(sample['intel']).issubset({0, 1}))
self.assertTrue(set(sample['grade']).issubset({0, 1, 2}))
@patch("pgmpy.sampling.GibbsSampling.random_state", autospec=True)
def test_sample_less_arg(self, random_state):
self.gibbs.state = None
random_state.return_value = [
State('diff', 0),
State('intel', 0),
State('grade', 0)
]
sample = self.gibbs.sample(size=2)
random_state.assert_called_once_with(self.gibbs)
self.assertEqual(len(sample), 2)
def test_generate_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
gen = self.gibbs.generate_sample(start_state, 2)
samples = [sample for sample in gen]
self.assertEqual(len(samples), 2)
self.assertEqual(
{samples[0][0].var, samples[0][1].var, samples[0][2].var},
{'diff', 'intel', 'grade'})
self.assertEqual(
{samples[1][0].var, samples[1][1].var, samples[1][2].var},
{'diff', 'intel', 'grade'})
@patch("pgmpy.sampling.GibbsSampling.random_state", autospec=True)
def test_generate_sample_less_arg(self, random_state):
self.gibbs.state = None
gen = self.gibbs.generate_sample(size=2)
samples = [sample for sample in gen]
random_state.assert_called_once_with(self.gibbs)
self.assertEqual(len(samples), 2)
class TestGibbsSampling(unittest.TestCase):
def setUp(self):
# A test Bayesian model
diff_cpd = TabularCPD('diff', 2, [[0.6], [0.4]])
intel_cpd = TabularCPD('intel', 2, [[0.7], [0.3]])
grade_cpd = TabularCPD('grade', 3, [[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3], [0.3, 0.7, 0.02, 0.2]],
evidence=['diff', 'intel'], evidence_card=[2, 2])
self.bayesian_model = BayesianModel()
self.bayesian_model.add_nodes_from(['diff', 'intel', 'grade'])
self.bayesian_model.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
self.bayesian_model.add_cpds(diff_cpd, intel_cpd, grade_cpd)
# A test Markov model
self.markov_model = MarkovModel([('A', 'B'), ('C', 'B'), ('B', 'D')])
factor_ab = DiscreteFactor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
factor_cb = DiscreteFactor(['C', 'B'], [4, 3], [3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
factor_bd = DiscreteFactor(['B', 'D'], [3, 2], [5, 7, 2, 1, 9, 3])
self.markov_model.add_factors(factor_ab, factor_cb, factor_bd)
self.gibbs = GibbsSampling(self.bayesian_model)
def tearDown(self):
del self.bayesian_model
del self.markov_model
@patch('pgmpy.sampling.GibbsSampling._get_kernel_from_bayesian_model', autospec=True)
@patch('pgmpy.models.MarkovChain.__init__', autospec=True)
def test_init_bayesian_model(self, init, get_kernel):
model = MagicMock(spec_set=BayesianModel)
gibbs = GibbsSampling(model)
init.assert_called_once_with(gibbs)
get_kernel.assert_called_once_with(gibbs, model)
@patch('pgmpy.sampling.GibbsSampling._get_kernel_from_markov_model', autospec=True)
def test_init_markov_model(self, get_kernel):
model = MagicMock(spec_set=MarkovModel)
gibbs = GibbsSampling(model)
get_kernel.assert_called_once_with(gibbs, model)
def test_get_kernel_from_bayesian_model(self):
gibbs = GibbsSampling()
gibbs._get_kernel_from_bayesian_model(self.bayesian_model)
self.assertListEqual(list(gibbs.variables), self.bayesian_model.nodes())
self.assertDictEqual(gibbs.cardinalities, {'diff': 2, 'intel': 2, 'grade': 3})
def test_get_kernel_from_markov_model(self):
gibbs = GibbsSampling()
gibbs._get_kernel_from_markov_model(self.markov_model)
self.assertListEqual(list(gibbs.variables), self.markov_model.nodes())
self.assertDictEqual(gibbs.cardinalities, {'A': 2, 'B': 3, 'C': 4, 'D': 2})
def test_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
sample = self.gibbs.sample(start_state, 2)
self.assertEquals(len(sample), 2)
self.assertEquals(len(sample.columns), 3)
self.assertIn('diff', sample.columns)
self.assertIn('intel', sample.columns)
self.assertIn('grade', sample.columns)
self.assertTrue(set(sample['diff']).issubset({0, 1}))
self.assertTrue(set(sample['intel']).issubset({0, 1}))
self.assertTrue(set(sample['grade']).issubset({0, 1, 2}))
@patch("pgmpy.sampling.GibbsSampling.random_state", autospec=True)
def test_sample_less_arg(self, random_state):
self.gibbs.state = None
random_state.return_value = [State('diff', 0), State('intel', 0), State('grade', 0)]
sample = self.gibbs.sample(size=2)
random_state.assert_called_once_with(self.gibbs)
self.assertEqual(len(sample), 2)
def test_generate_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
gen = self.gibbs.generate_sample(start_state, 2)
samples = [sample for sample in gen]
self.assertEqual(len(samples), 2)
self.assertEqual({samples[0][0].var, samples[0][1].var, samples[0][2].var}, {'diff', 'intel', 'grade'})
self.assertEqual({samples[1][0].var, samples[1][1].var, samples[1][2].var}, {'diff', 'intel', 'grade'})
@patch("pgmpy.sampling.GibbsSampling.random_state", autospec=True)
def test_generate_sample_less_arg(self, random_state):
self.gibbs.state = None
gen = self.gibbs.generate_sample(size=2)
samples = [sample for sample in gen]
random_state.assert_called_once_with(self.gibbs)
self.assertEqual(len(samples), 2)