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test_ml_hmm.py
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test_ml_hmm.py
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# encoding: utf-8
# pylint: skip-file
"""
This file contains tests for the madmom.ml.hmm module.
"""
from __future__ import absolute_import, division, print_function
import sys
import unittest
from madmom.ml.hmm import *
PRIOR = np.array([0.6, 0.2, 0.2])
TRANSITIONS = [(0, 0, 0.7),
(0, 1, 0.3),
(1, 0, 0.1),
(1, 1, 0.6),
(1, 2, 0.3),
(2, 1, 0.3),
(2, 2, 0.7)]
OBS_PROB = np.array([[0.7, 0.15, 0.15],
[0.3, 0.5, 0.2],
[0.2, 0.4, 0.4]])
OBS_SEQ = np.array([0, 2, 2, 0, 0, 1, 1, 2, 0, 2, 1, 1,
1, 2, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0])
CORRECT_FWD = np.array([[0.6754386, 0.23684211, 0.0877193],
[0.369291, 0.36798608, 0.26272292],
[0.18146746, 0.33625874, 0.4822738],
[0.35097423, 0.37533682, 0.27368895],
[0.51780506, 0.32329768, 0.15889725],
[0.17366244, 0.58209473, 0.24424283],
[0.06699296, 0.58957189, 0.34343515],
[0.05708114, 0.3428725, 0.60004636],
[0.18734426, 0.43567034, 0.3769854],
[0.09699435, 0.31882203, 0.58418362],
[0.03609747, 0.47711943, 0.4867831],
[0.02569311, 0.52002881, 0.45427808],
[0.02452257, 0.53259115, 0.44288628],
[0.03637171, 0.31660931, 0.64701899],
[0.02015006, 0.46444741, 0.51540253],
[0.02118133, 0.51228818, 0.46653049],
[0.16609052, 0.48889238, 0.3450171],
[0.06141349, 0.55365814, 0.38492837],
[0.2327641, 0.47273564, 0.29450026],
[0.42127593, 0.37947727, 0.1992468],
[0.57132392, 0.30444215, 0.12423393],
[0.66310201, 0.25840843, 0.07848956],
[0.23315472, 0.59876843, 0.16807684],
[0.43437318, 0.40024174, 0.16538507],
[0.58171672, 0.30436365, 0.11391962]])
class TestTransitionModelClass(unittest.TestCase):
def setUp(self):
frm, to, prob = list(zip(*TRANSITIONS))
self.tm = TransitionModel.from_dense(to, frm, prob)
def test_types(self):
self.assertIsInstance(self.tm.states, np.ndarray)
self.assertIsInstance(self.tm.pointers, np.ndarray)
self.assertIsInstance(self.tm.probabilities, np.ndarray)
self.assertIsInstance(self.tm.log_probabilities, np.ndarray)
self.assertIsInstance(self.tm.num_states, int)
self.assertIsInstance(self.tm.num_transitions, int)
self.assertTrue(self.tm.states.dtype == np.uint32)
self.assertTrue(self.tm.pointers.dtype == np.uint32)
self.assertTrue(self.tm.probabilities.dtype == float)
self.assertTrue(self.tm.log_probabilities.dtype == float)
def test_values(self):
self.assertTrue(np.allclose(self.tm.states, [0, 1, 0, 1, 2, 1, 2]))
self.assertTrue(np.allclose(self.tm.pointers, [0, 2, 5, 7]))
self.assertTrue(np.allclose(self.tm.probabilities,
[0.7, 0.1, 0.3, 0.6, 0.3, 0.3, 0.7]))
log_prob = [-0.35667494, -2.30258509, -1.2039728, -0.51082562,
-1.2039728, -1.2039728, -0.35667494]
self.assertTrue(np.allclose(self.tm.log_probabilities, log_prob))
self.assertTrue(self.tm.num_states == 3)
self.assertTrue(self.tm.num_transitions == 7)
def test_num_states_unreachable(self):
for r in range(3):
trans = np.array([[.5, .5, .0],
[.5, .5, .0],
[.5, .5, .0]])
trans = np.roll(trans, shift=r, axis=1)
frm, to = trans.nonzero()
tm = TransitionModel.from_dense(to, frm, trans[frm, to])
self.assertTrue(tm.num_states == 3)
class TestDiscreteObservationModelClass(unittest.TestCase):
def setUp(self):
self.om = DiscreteObservationModel(OBS_PROB)
def test_types(self):
self.assertIsInstance(self.om.pointers, np.ndarray)
self.assertIsInstance(self.om.densities(OBS_SEQ), np.ndarray)
self.assertIsInstance(self.om.log_densities(OBS_SEQ), np.ndarray)
self.assertTrue(self.om.pointers.dtype == np.uint32)
self.assertTrue(self.om.densities(OBS_SEQ).dtype == float)
self.assertTrue(self.om.log_densities(OBS_SEQ).dtype == float)
def test_values(self):
self.assertTrue(np.allclose(self.om.pointers, [0, 1, 2]))
self.assertTrue(np.allclose(self.om.observation_probabilities,
OBS_PROB))
self.assertTrue(np.allclose(self.om.densities(OBS_SEQ),
OBS_PROB[:, OBS_SEQ].T))
self.assertTrue(np.allclose(self.om.log_densities(OBS_SEQ),
np.log(OBS_PROB[:, OBS_SEQ].T)))
class TestHiddenMarkovModelClass(unittest.TestCase):
def setUp(self):
frm, to, prob = list(zip(*TRANSITIONS))
tm = TransitionModel.from_dense(to, frm, prob)
om = DiscreteObservationModel(OBS_PROB)
self.hmm = HiddenMarkovModel(tm, om, PRIOR)
def test_viterbi(self):
correct_state_seq = np.array([0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2,
2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])
correct_log_p = -35.2104311327
state_seq, log_p = self.hmm.viterbi(OBS_SEQ)
self.assertTrue((state_seq == correct_state_seq).all())
self.assertAlmostEqual(log_p, correct_log_p)
def test_forward(self):
fwd = self.hmm.forward(OBS_SEQ)
self.assertTrue(np.allclose(fwd, CORRECT_FWD))
# two runs must yield identical results
fwd = self.hmm.forward(OBS_SEQ)
self.assertTrue(np.allclose(fwd, CORRECT_FWD))
# after resetting the HMM, it must produce the same output as before
self.hmm.reset()
fwd = np.vstack([self.hmm.forward(np.atleast_1d(o), reset=False)
for o in OBS_SEQ])
self.assertTrue(np.allclose(fwd, CORRECT_FWD))
# without resetting it produces different results
fwd = np.vstack([self.hmm.forward(np.atleast_1d(o), reset=False)
for o in OBS_SEQ])
self.assertFalse(np.allclose(fwd, CORRECT_FWD))
# after resetting it must yield the correct result again
self.hmm.reset()
fwd = np.vstack([self.hmm.forward(np.atleast_1d(o), reset=False)
for o in OBS_SEQ])
self.assertTrue(np.allclose(fwd, CORRECT_FWD))
# initialisation must not change
self.assertTrue(np.allclose(self.hmm.initial_distribution, PRIOR))
def test_forward_generator(self):
fwd = np.vstack(list(self.hmm.forward_generator(OBS_SEQ,
block_size=5)))
self.assertTrue(np.allclose(fwd, CORRECT_FWD))
def test_invalid_sequence(self):
transitions = [(0, 0, 0.1), (0, 1, 0.9), (0, 2, 0),
(1, 0, 0), (1, 1, 1), (1, 2, 0),
(2, 0, 0), (2, 1, 0), (2, 2, 1)]
frm, to, prob = list(zip(*transitions))
tm = TransitionModel.from_dense(to, frm, prob)
obs_prob = np.array([[0.7, 0.3, 0],
[0.3, 0.7, 0],
[0, 0, 1]])
om = DiscreteObservationModel(obs_prob)
hmm = HiddenMarkovModel(tm, om)
state_seq, log_p = hmm.viterbi([0, 1, 0, 2])
self.assertTrue(np.allclose(state_seq, []))
self.assertAlmostEqual(log_p, -np.inf)
# TODO: assertWarns exist only for Python 3.2+, test in all versions
if sys.version_info >= (3, 2):
with self.assertWarns(RuntimeWarning):
hmm.viterbi([0, 1, 0, 2])
state_seq, log_p = hmm.viterbi([0, 0, 1, 1])
self.assertTrue((state_seq == [1, 1, 1, 1]).all())
self.assertAlmostEqual(log_p, -4.219907785197447)