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test_bayesian_network.py
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test_bayesian_network.py
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# test_bayesian_network.py
# Contact: Jacob Schreiber <jmschreiber91@gmail.com>
import numpy
import torch
import pytest
from pomegranate.bayesian_network import BayesianNetwork
from pomegranate.distributions import Exponential
from pomegranate.distributions import Categorical
from pomegranate.distributions import ConditionalCategorical
from nose.tools import assert_raises
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
def assert_tuple_equal(x, y):
assert len(x) == len(y)
for i in range(len(x)):
assert len(x[i]) == len(y[i])
if len(x[i]) > 0:
for j in range(len(x[i])):
assert x[i][j] == y[i][j]
@pytest.fixture
def X():
return [[1, 2, 0, 0],
[0, 0, 1, 0],
[1, 1, 1, 0],
[1, 2, 1, 1],
[1, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 0],
[1, 0, 1, 1],
[1, 1, 0, 1],
[0, 2, 1, 0],
[0, 0, 0, 1]]
@pytest.fixture
def w():
return [0, 1.3, 0, 1.2, 2.5, 6.1, 0, 0, 2.1, 0.3, 3.3]
@pytest.fixture
def X_masked(X):
mask = torch.tensor(numpy.array([
[False, True, True, True ],
[True, True, False, False],
[False, False, False, True ],
[True, True, True, True ],
[False, True, False, False],
[True, True, True, False],
[False, False, False, True ],
[True, False, True, True ],
[True, True, True, False],
[True, True, True, True ],
[True, False, True, False]]))
X = torch.tensor(numpy.array(X))
return torch.masked.MaskedTensor(X, mask=mask)
@pytest.fixture
def distributions():
d1 = Categorical([[0.8, 0.1]])
d2 = Categorical([[0.3, 0.6, 0.1]])
d3 = Categorical([[0.9, 0.1]])
d4 = Categorical([[0.3, 0.7]])
d12 = ConditionalCategorical([[[0.4, 0.6], [0.7, 0.3]]])
d22 = ConditionalCategorical([[[0.7, 0.1, 0.2], [0.5, 0.4, 0.1]]])
d32 = ConditionalCategorical([[[0.5, 0.5], [0.1, 0.9]]])
d13 = ConditionalCategorical([[
[[0.6, 0.4], [0.3, 0.7]],
[[0.5, 0.5], [0.2, 0.8]],
[[0.3, 0.7], [0.9, 0.1]]]])
return d1, d2, d3, d4, d12, d22, d32, d13
###
def test_initialization(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork()
assert len(model.distributions) == 0
assert len(model.edges) == 0
assert len(model._marginal_mapping) == 0
assert len(model._factor_mapping) == 0
assert len(model._factor_graph.factors) == 0
assert len(model._factor_graph.marginals) == 0
assert len(model._factor_graph._factor_idxs) == 0
assert len(model._factor_graph._marginal_idxs) == 0
assert sum(map(len, model._factor_graph._factor_edges)) == 0
assert sum(map(len, model._factor_graph._marginal_edges)) == 0
model = BayesianNetwork([d1])
assert len(model.distributions) == 1
assert len(model.edges) == 0
assert len(model._marginal_mapping) == 1
assert len(model._factor_mapping) == 1
assert len(model._factor_graph.factors) == 1
assert len(model._factor_graph.marginals) == 1
assert len(model._factor_graph._factor_idxs) == 1
assert len(model._factor_graph._marginal_idxs) == 1
assert sum(map(len, model._factor_graph._factor_edges)) == 1
assert sum(map(len, model._factor_graph._marginal_edges)) == 1
model = BayesianNetwork([d1, d2])
assert len(model.distributions) == 2
assert len(model.edges) == 0
assert len(model._marginal_mapping) == 2
assert len(model._factor_mapping) == 2
assert len(model._factor_graph.factors) == 2
assert len(model._factor_graph.marginals) == 2
assert len(model._factor_graph._factor_idxs) == 2
assert len(model._factor_graph._marginal_idxs) == 2
assert sum(map(len, model._factor_graph._factor_edges)) == 2
assert sum(map(len, model._factor_graph._marginal_edges)) == 2
model = BayesianNetwork([d1, d12], [(d1, d12)])
assert len(model.distributions) == 2
assert len(model.edges) == 1
assert len(model._marginal_mapping) == 2
assert len(model._factor_mapping) == 2
assert len(model._factor_graph.factors) == 2
assert len(model._factor_graph.marginals) == 2
assert len(model._factor_graph._factor_idxs) == 2
assert len(model._factor_graph._marginal_idxs) == 2
assert sum(map(len, model._factor_graph._factor_edges)) == 3
assert sum(map(len, model._factor_graph._marginal_edges)) == 3
def test_initialization_raises(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
assert_raises(ValueError, BayesianNetwork, [d1, d12], [(d12, d1)])
assert_raises(ValueError, BayesianNetwork, None, [(d1, d12)])
assert_raises(ValueError, BayesianNetwork, [d1], [(d1, d12)])
assert_raises(ValueError, BayesianNetwork, [d12], [(d1, d12)])
assert_raises(ValueError, BayesianNetwork, [d1, d12], [(d1, d1)])
assert_raises(ValueError, BayesianNetwork, [d1, d12], [(d12, d12)])
def test_add_distribution(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork()
assert len(model.distributions) == 0
assert len(model.edges) == 0
assert model._marginal_mapping == {}
assert model._factor_mapping == {}
assert len(model._factor_graph.factors) == 0
assert len(model._factor_graph.marginals) == 0
assert model._factor_graph._factor_idxs == {}
assert model._factor_graph._factor_edges == []
assert model._factor_graph._marginal_idxs == {}
assert model._factor_graph._marginal_edges == []
model.add_distribution(d1)
assert len(model.distributions) == 1
assert len(model.edges) == 0
assert len(model._marginal_mapping) == 1
assert len(model._factor_mapping) == 1
assert len(model._factor_graph.factors) == 1
assert len(model._factor_graph.marginals) == 1
assert len(model._factor_graph._factor_idxs) == 1
assert model._factor_graph._factor_edges == [[0]]
assert len(model._factor_graph._marginal_idxs) == 1
assert model._factor_graph._marginal_edges == [[0]]
model.add_distribution(d12)
assert len(model.distributions) == 2
assert len(model.edges) == 0
assert len(model._marginal_mapping) == 2
assert len(model._factor_mapping) == 2
assert len(model._factor_graph.factors) == 2
assert len(model._factor_graph.marginals) == 2
assert len(model._factor_graph._factor_idxs) == 2
assert model._factor_graph._factor_edges == [[0], [1]]
assert len(model._factor_graph._marginal_idxs) == 2
assert model._factor_graph._marginal_edges == [[0], [1]]
def test_add_distribution_raises(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork()
assert_raises(ValueError, model.add_distribution, [d1])
assert_raises(ValueError, model.add_distribution, None)
assert_raises(ValueError, model.add_distribution, Exponential())
def test_add_edge(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d2, d12, d22, d13])
assert len(model.distributions) == 5
assert len(model.edges) == 0
assert model._factor_graph._factor_edges == [[0], [1], [2], [3], [4]]
assert model._factor_graph._marginal_edges == [[0], [1], [2], [3], [4]]
model.add_edge(d1, d12)
assert len(model.distributions) == 5
assert len(model.edges) == 1
assert model._factor_graph._factor_edges == [[0], [1], [0, 2], [3], [4]]
assert model._factor_graph._marginal_edges == [[2, 0], [1], [2], [3], [4]]
model.add_edge(d1, d22)
assert len(model.distributions) == 5
assert len(model.edges) == 2
assert model._factor_graph._factor_edges == [[0], [1], [0, 2], [0, 3], [4]]
assert model._factor_graph._marginal_edges == [[2, 3, 0], [1], [2], [3],
[4]]
model.add_edge(d2, d13)
model.add_edge(d1, d13)
assert len(model.distributions) == 5
assert len(model.edges) == 4
assert model._factor_graph._factor_edges == [[0], [1], [0, 2], [0, 3],
[1, 0, 4]]
assert model._factor_graph._marginal_edges == [[2, 3, 4, 0], [4, 1], [2],
[3], [4]]
def test_add_edge_raises(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d2, d12, d22, d13])
assert_raises(ValueError, model.add_edge, d1, d1)
assert_raises(ValueError, model.add_edge, d2, d1)
assert_raises(ValueError, model.add_edge, d12, d1)
assert_raises(ValueError, model.add_edge, None, d1)
assert_raises(ValueError, model.add_edge, d1, None)
assert_raises(ValueError, model.add_edge, d3, d1)
assert_raises(ValueError, model.add_edge, d1, d3)
###
@pytest.mark.sample
def test_sample(distributions):
torch.manual_seed(0)
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d22, d13, d4], [(d1, d22), (d22, d13),
(d4, d13)])
X = model.sample(1)
assert_array_equal(X, [[1, 1, 1, 1]])
X = model.sample(5)
assert_array_equal(X,
[[0, 0, 1, 1],
[0, 0, 0, 1],
[0, 2, 1, 0],
[0, 1, 1, 1],
[0, 0, 1, 1]])
###
def test_log_probability(X, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d22, d13, d4], [(d1, d22), (d22, d13),
(d4, d13)])
logps = model.log_probability(X)
assert_array_almost_equal(logps, [-7.013116, -2.700082, -5.115996,
-7.264431, -5.184989, -4.491842, -4.422849, -3.709082, -5.184989,
-3.393229, -2.140466])
def test_probability(X, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d22, d13, d4], [(d1, d22), (d22, d13),
(d4, d13)])
ps = model.probability(X)
assert_array_almost_equal(ps, numpy.exp([-7.013116, -2.700082, -5.115996,
-7.264431, -5.184989, -4.491842, -4.422849, -3.709082, -5.184989,
-3.393229, -2.140466]), 4)
def test_predict_proba_one_node():
d = Categorical([[0.23, 0.17, 0.60]])
X = torch.tensor([[0], [0], [1], [2]])
mask = torch.tensor([[False], [True], [True], [True]])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
model = BayesianNetwork([d])
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.23, 0.17, 0.60],
[1.00, 0.00, 0.00],
[0.00, 1.00, 0.00],
[0.00, 0.00, 1.00]])
def test_predict_proba_one_node_raises():
d = Categorical([[0.23, 0.17, 0.60]])
model = BayesianNetwork([d])
X = torch.tensor([[0, 0]])
mask = torch.tensor([[False, True]])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
assert_raises(ValueError, model.predict_proba, X_masked)
X = torch.tensor([[3]])
mask = torch.tensor([[True]])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
assert_raises(IndexError, model.predict_proba, X_masked)
mask = torch.tensor([[False]])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
model.predict_proba(X_masked)
def test_predict_proba_one_edge(distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d22], [(d1, d22)])
X = torch.tensor([[0, 0], [0, 1], [1, 1], [1, 2]])
mask = torch.tensor([
[False, False],
[True, False],
[True, True ],
[False, True ]
])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.8889, 0.1111],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.9412, 0.0588]], 4)
assert_array_almost_equal(y_hat[1],
[[0.6778, 0.1333, 0.1889],
[0.7000, 0.1000, 0.2000],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000]], 4)
def test_predict_proba_monty_hall():
p = numpy.array([[
[[0.0, 0.5, 0.5], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0]],
[[0.0, 0.0, 1.0], [0.5, 0.0, 0.5], [1.0, 0.0, 0.0]],
[[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.5, 0.5, 0.0]]
]])
d1 = Categorical([[1./3, 1./3, 1./3]])
d2 = Categorical([[1./3, 1./3, 1./3]])
d3 = ConditionalCategorical(p)
X = torch.tensor([
[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[1, 0, 0],
[2, 0, 0]
])
mask = torch.tensor([
[False, False, False],
[False, True, False],
[False, True, False],
[True, False, False],
[True, False, False]
])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
model = BayesianNetwork([d1, d2, d3], [(d1, d3), (d2, d3)])
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[1./3, 1./3, 1./3],
[1./3, 1./3, 1./3],
[1./3, 1./3, 1./3],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
assert_array_almost_equal(y_hat[1],
[[1./3, 1./3, 1./3],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1./3, 1./3, 1./3],
[1./3, 1./3, 1./3]])
assert_array_almost_equal(y_hat[2],
[[1./3, 1./3, 1./3],
[0.5000, 0.0000, 0.5000],
[0.5000, 0.5000, 0.0000],
[0.5000, 0.0000, 0.5000],
[0.5000, 0.5000, 0.0000]])
X = torch.tensor([
[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[1, 0, 0],
[2, 0, 0],
[0, 0, 1],
[0, 1, 0],
[2, 1, 0]
])
mask = torch.tensor([
[True, True, False],
[True, True, False],
[True, True, False],
[True, True, False],
[True, True, False],
[False, True, True],
[False, False, True],
[True , False, True]
])
X_masked = torch.masked.MaskedTensor(X, mask=mask)
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[1.0000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[1./3 , 0.0000, 2./3 ],
[0.0000, 0.5000, 0.5000],
[0.0000, 0.0000, 1.0000]])
assert_array_almost_equal(y_hat[1],
[[1.0000, 0.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000],
[0.0000, 0.5000, 0.5000],
[0.0000, 2./3 , 1./3 ]])
assert_array_almost_equal(y_hat[2],
[[0.0000, 0.5000, 0.5000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[1.0000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000]])
def test_predict_proba_simple(X_masked, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d1, d22, d13, d4], [(d1, d22), (d22, d13),
(d4, d13)])
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.9412, 0.0588],
[1.0000, 0.0000],
[0.8889, 0.1111],
[0.0000, 1.0000],
[0.6667, 0.3333],
[1.0000, 0.0000],
[0.8889, 0.1111],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[1],
[[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.6778, 0.1333, 0.1889],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.6778, 0.1333, 0.1889],
[0.5147, 0.4706, 0.0147],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.6121, 0.0650, 0.3229]], 4)
assert_array_almost_equal(y_hat[2],
[[1.0000, 0.0000],
[0.3900, 0.6100],
[0.5300, 0.4700],
[0.0000, 1.0000],
[0.2900, 0.7100],
[1.0000, 0.0000],
[0.5300, 0.4700],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[3],
[[1.0000, 0.0000],
[0.3000, 0.7000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.3000, 0.7000],
[0.5172, 0.4828],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.5172, 0.4828],
[1.0000, 0.0000],
[0.3565, 0.6435]], 4)
def test_predict_proba_diamond(X_masked, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.3000, 0.7000],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.2416, 0.7584],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[1],
[[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.5147, 0.4706, 0.0147],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.7204, 0.1844, 0.0952]], 4)
assert_array_almost_equal(y_hat[2],
[[1.0000, 0.0000],
[0.4000, 0.6000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[0.1387, 0.8613],
[1.0000, 0.0000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[3],
[[1.0000, 0.0000],
[0.4500, 0.5500],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.0968, 0.9032],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.6818, 0.3182]], 4)
def test_predict_proba_cycle(X, X_masked, distributions):
model = BayesianNetwork(structure=((1,), (2,), (3,), (0,))).fit(X)
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.4737, 0.5263],
[1.0000, 0.0000],
[0.6065, 0.3935],
[0.0000, 1.0000],
[0.3825, 0.6175],
[1.0000, 0.0000],
[0.6065, 0.3935],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[1],
[[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.3435, 0.3728, 0.2837],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.3435, 0.3728, 0.2837],
[0.2564, 0.2308, 0.5128],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.2564, 0.6154, 0.1282]], 4)
assert_array_almost_equal(y_hat[2],
[[1.0000, 0.0000],
[0.2997, 0.7003],
[0.3935, 0.6065],
[0.0000, 1.0000],
[0.8025, 0.1975],
[1.0000, 0.0000],
[0.3935, 0.6065],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[3],
[[1.0000, 0.0000],
[0.6530, 0.3470],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.3728, 0.6272],
[0.4737, 0.5263],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.2308, 0.7692],
[1.0000, 0.0000],
[0.4737, 0.5263]], 4)
def test_predict_log_proba_diamond(X_masked, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
y_hat = model.predict_log_proba(X_masked)
assert_array_almost_equal(y_hat[0],
numpy.log([[0.3000, 0.7000],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.2416, 0.7584],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]]), 3)
assert_array_almost_equal(y_hat[1],
numpy.log([[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.5147, 0.4706, 0.0147],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.7204, 0.1844, 0.0952]]), 3)
assert_array_almost_equal(y_hat[2],
numpy.log([[1.0000, 0.0000],
[0.4000, 0.6000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[0.1387, 0.8613],
[1.0000, 0.0000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]]), 3)
assert_array_almost_equal(y_hat[3],
numpy.log([[1.0000, 0.0000],
[0.4500, 0.5500],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.0968, 0.9032],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.6818, 0.3182]]), 3)
def test_predict_cycle(X, X_masked, distributions):
model = BayesianNetwork(structure=((1,), (2,), (3,), (0,))).fit(X)
y_hat = model.predict(X_masked)
assert_array_equal(y_hat,
[[1, 2, 0, 0],
[0, 0, 1, 0],
[0, 1, 1, 0],
[1, 2, 1, 1],
[1, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 1, 0],
[1, 2, 1, 1],
[1, 1, 0, 1],
[0, 2, 1, 0],
[0, 1, 0, 1]])
def test_predict_diamond(X_masked, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
y_hat = model.predict(X_masked)
assert_array_equal(y_hat,
[[1, 2, 0, 0],
[0, 0, 1, 1],
[1, 0, 0, 0],
[1, 2, 1, 1],
[1, 1, 1, 1],
[0, 1, 0, 1],
[1, 0, 0, 0],
[1, 0, 1, 1],
[1, 1, 0, 1],
[0, 2, 1, 0],
[0, 0, 0, 0]])
def test_fit(X, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
model.fit(X)
assert_array_almost_equal(model.distributions[0].probs[0],
[[[1.0000, 0.0000],
[0.5000, 0.5000]],
[[0.5000, 0.5000],
[0.0000, 1.0000]],
[[0.0000, 1.0000],
[0.5000, 0.5000]]])
assert_array_almost_equal(model.distributions[1].probs[0],
[[0.2000, 0.4000, 0.4000],
[0.3333, 0.5000, 0.1667]], 4)
assert_array_almost_equal(model.distributions[2].probs[0],
[[0.4000, 0.6000],
[0.6667, 0.3333]], 4)
assert_array_almost_equal(model.distributions[3].probs,
[[0.4545, 0.5455]], 4)
def test_fit_weighted(X, w, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
model.fit(X, sample_weight=w)
assert_array_almost_equal(model.distributions[0].probs[0],
[[[1.0000, 0.0000],
[1.0000, 0.0000]],
[[0.5701, 0.4299],
[0.5000, 0.5000]],
[[0.5000, 0.5000],
[0.2000, 0.8000]]], 4)
assert_array_almost_equal(model.distributions[1].probs[0],
[[0.8125, 0.0000, 0.1875],
[0.2171, 0.7039, 0.0789]], 4)
assert_array_almost_equal(model.distributions[2].probs[0],
[[0.0000, 1.0000],
[0.9211, 0.0789]], 4)
assert_array_almost_equal(model.distributions[3].probs,
[[0.0952, 0.9048]], 4)
def test_fit_structure(X):
model = BayesianNetwork(structure=((1, 2), (3,), (3,), ()))
model.fit(X)
assert_array_almost_equal(model.distributions[0].probs[0],
[[[1.0000, 0.0000],
[0.5000, 0.5000]],
[[0.5000, 0.5000],
[0.0000, 1.0000]],
[[0.0000, 1.0000],
[0.5000, 0.5000]]])
assert_array_almost_equal(model.distributions[1].probs[0],
[[0.2000, 0.4000, 0.4000],
[0.3333, 0.5000, 0.1667]], 4)
assert_array_almost_equal(model.distributions[2].probs[0],
[[0.4000, 0.6000],
[0.6667, 0.3333]], 4)
assert_array_almost_equal(model.distributions[3].probs,
[[0.4545, 0.5455]], 4)
def test_fit_fg(X, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
model.fit(X)
assert_array_almost_equal(model._factor_graph.factors[0].probs,
[[[0.1667, 0.0000],
[0.0833, 0.0833]],
[[0.0833, 0.0833],
[0.0000, 0.1667]],
[[0.0000, 0.1667],
[0.0833, 0.0833]]], 4)
assert_array_almost_equal(model._factor_graph.factors[1].probs,
[[0.1000, 0.2000, 0.2000],
[0.1667, 0.2500, 0.0833]], 4)
assert_array_almost_equal(model._factor_graph.factors[2].probs,
[[0.2000, 0.3000],
[0.3333, 0.1667]], 4)
assert_array_almost_equal(model._factor_graph.factors[3].probs,
[[0.4545, 0.5455]], 4)
def test_fit_weighted_fg(X, w, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
model.fit(X, sample_weight=w)
assert_array_almost_equal(model._factor_graph.factors[0].probs,
[[[0.1667, 0.0000],
[0.1667, 0.0000]],
[[0.0950, 0.0717],
[0.0833, 0.0833]],
[[0.0833, 0.0833],
[0.0333, 0.1333]]], 4)
assert_array_almost_equal(model._factor_graph.factors[1].probs,
[[0.4062, 0.0000, 0.0938],
[0.1086, 0.3520, 0.0395]], 4)
assert_array_almost_equal(model._factor_graph.factors[2].probs,
[[0.0000, 0.5000],
[0.4605, 0.0395]], 4)
assert_array_almost_equal(model._factor_graph.factors[3].probs,
[[0.0952, 0.9048]], 4)
def test_fit_predict_proba(X, X_masked, distributions):
d1, d2, d3, d4, d12, d22, d32, d13 = distributions
model = BayesianNetwork([d13, d22, d32, d4], [(d4, d22), (d4, d32),
(d22, d13), (d32, d13)])
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.3000, 0.7000],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.2416, 0.7584],
[1.0000, 0.0000],
[0.4700, 0.5300],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[1],
[[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.7000, 0.1000, 0.2000],
[0.5147, 0.4706, 0.0147],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.7204, 0.1844, 0.0952]], 4)
assert_array_almost_equal(y_hat[2],
[[1.0000, 0.0000],
[0.4000, 0.6000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[0.1387, 0.8613],
[1.0000, 0.0000],
[0.5000, 0.5000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[3],
[[1.0000, 0.0000],
[0.4500, 0.5500],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.0968, 0.9032],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.3488, 0.6512],
[1.0000, 0.0000],
[0.6818, 0.3182]], 4)
model.fit(X)
y_hat = model.predict_proba(X_masked)
assert_array_almost_equal(y_hat[0],
[[0.0000, 1.0000],
[1.0000, 0.0000],
[0.3400, 0.6600],
[0.0000, 1.0000],
[0.2800, 0.7200],
[1.0000, 0.0000],
[0.3400, 0.6600],
[0.0000, 1.0000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[1],
[[0.0000, 0.0000, 1.0000],
[1.0000, 0.0000, 0.0000],
[0.2000, 0.4000, 0.4000],
[0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.0000],
[0.0000, 1.0000, 0.0000],
[0.2000, 0.4000, 0.4000],
[0.2222, 0.6667, 0.1111],
[0.0000, 1.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.5532, 0.4468, 0.0000]], 4)
assert_array_almost_equal(y_hat[2],
[[1.0000, 0.0000],
[0.7324, 0.2676],
[0.4000, 0.6000],
[0.0000, 1.0000],
[0.5600, 0.4400],
[1.0000, 0.0000],
[0.4000, 0.6000],
[0.0000, 1.0000],
[1.0000, 0.0000],
[0.0000, 1.0000],
[1.0000, 0.0000]], 4)
assert_array_almost_equal(y_hat[3],
[[1.0000, 0.0000],
[0.2958, 0.7042],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.4000, 0.6000],
[0.2857, 0.7143],
[1.0000, 0.0000],
[0.0000, 1.0000],
[0.2857, 0.7143],
[1.0000, 0.0000],
[0.2553, 0.7447]], 4)
def test_learn_structure_chow_liu(X):
X = numpy.array(X)
X[:,1] = X[:,3]