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################################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
"""
Unit tests for `multinomial` module.
"""
import numpy as np
import scipy
from bayespy.nodes import (Multinomial,
Dirichlet,
Mixture)
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestMultinomial(TestCase):
"""
Unit tests for Multinomial node
"""
def test_init(self):
"""
Test the creation of multinomial nodes.
"""
# Some simple initializations
X = Multinomial(10, [0.1, 0.3, 0.6])
X = Multinomial(10, Dirichlet([5,4,3]))
# Check that plates are correct
X = Multinomial(10, [0.1, 0.3, 0.6], plates=(3,4))
self.assertEqual(X.plates,
(3,4))
X = Multinomial(10, 0.25*np.ones((2,3,4)))
self.assertEqual(X.plates,
(2,3))
n = 10 * np.ones((3,4), dtype=np.int64)
X = Multinomial(n, [0.1, 0.3, 0.6])
self.assertEqual(X.plates,
(3,4))
X = Multinomial(n, Dirichlet([2,1,9], plates=(3,4)))
self.assertEqual(X.plates,
(3,4))
# Probabilities not a vector
self.assertRaises(ValueError,
Multinomial,
10,
0.5)
# Invalid probability
self.assertRaises(ValueError,
Multinomial,
10,
[-0.5, 1.5])
self.assertRaises(ValueError,
Multinomial,
10,
[0.5, 1.5])
# Invalid number of trials
self.assertRaises(ValueError,
Multinomial,
-1,
[0.5, 0.5])
self.assertRaises(ValueError,
Multinomial,
8.5,
[0.5, 0.5])
# Inconsistent plates
self.assertRaises(ValueError,
Multinomial,
10,
0.25*np.ones((2,4)),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Multinomial,
10,
0.25*np.ones((2,4)),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of multinomial nodes.
"""
# Simple test
X = Multinomial(1, [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 n
X = Multinomial(10, [0.7,0.2,0.1])
u = X._message_to_child()
self.assertAllClose(u[0],
[7,2,1])
# Test plates in p
n = np.random.randint(1, 10)
p = np.random.dirichlet([1,1], size=3)
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in n
n = np.random.randint(1, 10, size=(3,))
p = np.random.dirichlet([1,1,1,1])
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n[:,None])
# Test plates in p and n
n = np.random.randint(1, 10, size=(4,1))
p = np.random.dirichlet([1,1], size=3)
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n[...,None])
# 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 = Multinomial(1, 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 = Multinomial(5, P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
5*p*np.ones((10,1)))
pass
def test_lower_bound(self):
"""
Test lower bound for multinomial node.
"""
# Test for a bug found in multinomial
X = Multinomial(10, [0.3, 0.5, 0.2])
l = X.lower_bound_contribution()
self.assertAllClose(l, 0.0)
pass
def test_mixture(self):
"""
Test multinomial mixture
"""
p0 = [0.1, 0.5, 0.2, 0.2]
p1 = [0.5, 0.1, 0.1, 0.3]
p2 = [0.3, 0.2, 0.1, 0.4]
X = Mixture(2, Multinomial, 10, [p0, p1, p2])
u = X._message_to_child()
self.assertAllClose(u[0],
10*np.array(p2))
pass
def test_mixture_with_count_array(self):
"""
Test multinomial mixture
"""
p0 = [0.1, 0.5, 0.2, 0.2]
p1 = [0.5, 0.1, 0.1, 0.3]
p2 = [0.3, 0.2, 0.1, 0.4]
counts = [[10], [5], [3]]
X = Mixture(2, Multinomial, counts, [p0, p1, p2])
u = X._message_to_child()
self.assertAllClose(
u[0],
np.array(counts)*np.array(p2)
)
# Multi-mixture and count array
# Shape(p) = (2, 1, 3) + (4,)
p = [
[[
[0.1, 0.5, 0.2, 0.2],
[0.5, 0.1, 0.1, 0.3],
[0.3, 0.2, 0.1, 0.4],
]],
[[
[0.3, 0.2, 0.1, 0.4],
[0.5, 0.1, 0.2, 0.2],
[0.4, 0.1, 0.2, 0.3],
]],
]
# Shape(Z1) = (1, 3) + (2,) -> () + (2,)
Z1 = 1
# Shape(Z2) = (1,) + (3,) -> () + (3,)
Z2 = 2
# Shape(counts) = (5, 1)
counts = [[10], [5], [3], [2], [4]]
# Shape(X) = (5,) + (4,)
X = Mixture(
Z1,
Mixture,
Z2,
Multinomial,
counts,
p,
# NOTE: We mix over axes -3 and -1. But as we first mix over the
# default (-1), then the next mixing happens over -2 (because one
# axis was already dropped).
cluster_plate=-2,
)
self.assertAllClose(
X._message_to_child()[0],
np.array(counts)[:,0,None] * np.array(p)[Z1,:,Z2]
)
# Can't have non-singleton axis in counts over the mixed axis
p0 = [0.1, 0.5, 0.2, 0.2]
p1 = [0.5, 0.1, 0.1, 0.3]
p2 = [0.3, 0.2, 0.1, 0.4]
counts = [10, 5, 3]
self.assertRaises(
ValueError,
Mixture,
2,
Multinomial,
counts,
[p0, p1, p2],
)
return
|
