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################################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
"""
Unit tests for `categorical` module.
"""
import warnings
import numpy as np
import scipy
from bayespy.nodes import (Categorical,
Dirichlet,
Mixture,
Gamma)
from bayespy.utils import random
from bayespy.utils import misc
from bayespy.utils.misc import TestCase
class TestCategorical(TestCase):
"""
Unit tests for Categorical node
"""
def test_init(self):
"""
Test the creation of categorical nodes.
"""
# Some simple initializations
X = Categorical([0.1, 0.3, 0.6])
X = Categorical(Dirichlet([5,4,3]))
# Check that plates are correct
X = Categorical([0.1, 0.3, 0.6], plates=(3,4))
self.assertEqual(X.plates,
(3,4))
X = Categorical(0.25*np.ones((2,3,4)))
self.assertEqual(X.plates,
(2,3))
X = Categorical(Dirichlet([2,1,9], plates=(3,4)))
self.assertEqual(X.plates,
(3,4))
# Probabilities not a vector
self.assertRaises(ValueError,
Categorical,
0.5)
# Invalid probability
self.assertRaises(ValueError,
Categorical,
[-0.5, 1.5],
n=10)
self.assertRaises(ValueError,
Categorical,
[0.5, 1.5],
n=10)
# Inconsistent plates
self.assertRaises(ValueError,
Categorical,
0.25*np.ones((2,4)),
plates=(3,),
n=10)
# Explicit plates too small
self.assertRaises(ValueError,
Categorical,
0.25*np.ones((2,4)),
plates=(1,),
n=10)
pass
def test_moments(self):
"""
Test the moments of categorical nodes.
"""
# Simple test
X = Categorical([0.7,0.2,0.1])
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
[0.7,0.2,0.1])
# Test plates in p
p = np.random.dirichlet([1,1], size=3)
X = Categorical(p)
u = X._message_to_child()
self.assertAllClose(u[0],
p)
# Test with Dirichlet prior
P = Dirichlet([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
p1 = np.exp(logp[1]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Categorical(P)
u = X._message_to_child()
p = np.array([p0, p1])
self.assertAllClose(u[0],
p)
# Test with broadcasted plates
P = Dirichlet([7, 3], plates=(10,))
X = Categorical(P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
p*np.ones((10,1)))
pass
def test_observed(self):
"""
Test observed categorical nodes
"""
# Single observation
X = Categorical([0.7,0.2,0.1])
X.observe(2)
u = X._message_to_child()
self.assertAllClose(u[0],
[0,0,1])
# One plate axis
X = Categorical([0.7,0.2,0.1], plates=(2,))
X.observe([2,1])
u = X._message_to_child()
self.assertAllClose(u[0],
[[0,0,1],
[0,1,0]])
# Several plate axes
X = Categorical([0.7,0.1,0.1,0.1], plates=(2,3,))
X.observe([[2,1,1],
[0,2,3]])
u = X._message_to_child()
self.assertAllClose(u[0],
[ [[0,0,1,0],
[0,1,0,0],
[0,1,0,0]],
[[1,0,0,0],
[0,0,1,0],
[0,0,0,1]] ])
# Check invalid observations
X = Categorical([0.7,0.2,0.1])
self.assertRaises(ValueError,
X.observe,
-1)
self.assertRaises(ValueError,
X.observe,
3)
self.assertRaises(ValueError,
X.observe,
1.5)
pass
def test_constant(self):
"""
Test constant categorical nodes
"""
# Basic test
Y = Mixture(2, Gamma, [1, 2, 3], [1, 1, 1])
u = Y._message_to_child()
self.assertAllClose(u[0],
3/1)
# Test with one plate axis
alpha = [[1, 2, 3],
[4, 5, 6]]
Y = Mixture([2, 1], Gamma, alpha, 1)
u = Y._message_to_child()
self.assertAllClose(u[0],
[3, 5])
# Test with two plate axes
alpha = [ [[1, 2, 3],
[4, 5, 6]],
[[7, 8, 9],
[10, 11, 12]] ]
Y = Mixture([[2, 1], [0, 2]], Gamma, alpha, 1)
u = Y._message_to_child()
self.assertAllClose(u[0],
[[3, 5],
[7, 12]])
pass
def test_initialization(self):
"""
Test initialization of categorical nodes
"""
# Test initialization from random
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
Z = Categorical([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
Z.initialize_from_random()
u = Z._message_to_child()
self.assertAllClose(u[0],
[[0, 1, 0],
[0, 0, 1]])
pass
def test_gradient(self):
"""
Check the Euclidean gradient of the categorical node
"""
Z = Categorical([[0.3, 0.5, 0.2], [0.1, 0.6, 0.3]])
Y = Mixture(Z, Gamma, [2, 3, 4], [5, 6, 7])
Y.observe([4.2, 0.2])
def f(x):
Z.set_parameters([np.reshape(x, Z.get_shape(0))])
return Z.lower_bound_contribution() + Y.lower_bound_contribution()
def df(x):
Z.set_parameters([np.reshape(x, Z.get_shape(0))])
g = Z.get_riemannian_gradient()
return Z.get_gradient(g)[0]
x0 = np.ravel(np.log([[2, 3, 7], [0.1, 3, 1]]))
self.assertAllClose(
misc.gradient(f, x0),
np.ravel(df(x0))
)
pass
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