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import numpy as np
from numpy.testing import assert_allclose
import pytest
from scipy import special
from hmmlearn.base import BaseHMM, ConvergenceMonitor
from hmmlearn import _hmmc
class TestMonitor:
def test_converged_by_iterations(self):
m = ConvergenceMonitor(tol=1e-3, n_iter=2, verbose=False)
assert not m.converged
m.report(-0.01)
assert not m.converged
m.report(-0.1)
assert m.converged
def test_converged_by_log_prob(self):
m = ConvergenceMonitor(tol=1e-3, n_iter=10, verbose=False)
for log_prob in [-0.03, -0.02, -0.01]:
m.report(log_prob)
assert not m.converged
m.report(-0.0101)
assert m.converged
def test_reset(self):
m = ConvergenceMonitor(tol=1e-3, n_iter=10, verbose=False)
m.iter = 1
m.history.append(-0.01)
m._reset()
assert m.iter == 0
assert not m.history
def test_report_first_iteration(self, capsys):
m = ConvergenceMonitor(tol=1e-3, n_iter=10, verbose=True)
m.report(-0.01)
out, err = capsys.readouterr()
assert not out
expected = m._template.format(iter=1, log_prob=-0.01, delta=np.nan)
assert err.splitlines() == [expected]
def test_report(self, capsys):
n_iter = 10
m = ConvergenceMonitor(tol=1e-3, n_iter=n_iter, verbose=True)
for i in reversed(range(n_iter)):
m.report(-0.01 * i)
out, err = capsys.readouterr()
assert not out
assert len(err.splitlines()) == n_iter
assert len(m.history) == n_iter
class StubHMM(BaseHMM):
"""An HMM with hardcoded observation probabilities."""
def _compute_log_likelihood(self, X):
return self.log_frameprob
class TestBaseAgainstWikipedia:
def setup_method(self, method):
# Example from http://en.wikipedia.org/wiki/Forward-backward_algorithm
self.frameprob = np.asarray([[0.9, 0.2],
[0.9, 0.2],
[0.1, 0.8],
[0.9, 0.2],
[0.9, 0.2]])
self.log_frameprob = np.log(self.frameprob)
h = StubHMM(2)
h.transmat_ = [[0.7, 0.3], [0.3, 0.7]]
h.startprob_ = [0.5, 0.5]
h.log_frameprob = self.log_frameprob
h.frameprob = self.frameprob
self.hmm = h
def test_do_forward_scaling_pass(self):
log_prob, fwdlattice, scaling_factors = _hmmc.forward_scaling(
self.hmm.startprob_, self.hmm.transmat_, self.frameprob)
ref_log_prob = -3.3725
assert round(log_prob, 4) == ref_log_prob
reffwdlattice = np.exp([[0.4500, 0.1000],
[0.3105, 0.0410],
[0.0230, 0.0975],
[0.0408, 0.0150],
[0.0298, 0.0046]])
assert_allclose(np.exp(fwdlattice), reffwdlattice, 4)
def test_do_forward_pass(self):
log_prob, fwdlattice = _hmmc.forward_log(
self.hmm.startprob_, self.hmm.transmat_, self.log_frameprob)
ref_log_prob = -3.3725
assert round(log_prob, 4) == ref_log_prob
reffwdlattice = np.array([[0.4500, 0.1000],
[0.3105, 0.0410],
[0.0230, 0.0975],
[0.0408, 0.0150],
[0.0298, 0.0046]])
assert_allclose(np.exp(fwdlattice), reffwdlattice, 4)
def test_do_backward_scaling_pass(self):
log_prob, fwdlattice, scaling_factors = _hmmc.forward_scaling(
self.hmm.startprob_, self.hmm.transmat_, self.frameprob)
bwdlattice = _hmmc.backward_scaling(self.hmm.startprob_,
self.hmm.transmat_, self.frameprob, scaling_factors)
refbwdlattice = np.array([[0.0661, 0.0455],
[0.0906, 0.1503],
[0.4593, 0.2437],
[0.6900, 0.4100],
[1.0000, 1.0000]])
scaling_factors = np.cumprod(scaling_factors[::-1])[::-1]
bwdlattice_scaled = bwdlattice / scaling_factors[:, None]
# Answer will be equivalent when the scaling factor is accounted for
assert_allclose(bwdlattice_scaled, refbwdlattice, 4)
def test_do_backward_log_pass(self):
bwdlattice = _hmmc.backward_log(
self.hmm.startprob_, self.hmm.transmat_, self.log_frameprob)
refbwdlattice = np.array([[0.0661, 0.0455],
[0.0906, 0.1503],
[0.4593, 0.2437],
[0.6900, 0.4100],
[1.0000, 1.0000]])
assert_allclose(np.exp(bwdlattice), refbwdlattice, 4)
def test_do_viterbi_pass(self):
log_prob, state_sequence = _hmmc.viterbi(
self.hmm.startprob_, self.hmm.transmat_, self.log_frameprob)
refstate_sequence = [0, 0, 1, 0, 0]
assert_allclose(state_sequence, refstate_sequence)
ref_log_prob = -4.4590
assert round(log_prob, 4) == ref_log_prob
def test_score_samples(self):
# ``StubHMM` ignores the values in ```X``, so we just pass in an
# array of the appropriate shape.
log_prob, posteriors = self.hmm.score_samples(self.log_frameprob)
assert_allclose(posteriors.sum(axis=1), np.ones(len(posteriors)))
ref_log_prob = -3.3725
assert round(log_prob, 4) == ref_log_prob
refposteriors = np.array([[0.8673, 0.1327],
[0.8204, 0.1796],
[0.3075, 0.6925],
[0.8204, 0.1796],
[0.8673, 0.1327]])
assert_allclose(posteriors, refposteriors, atol=1e-4)
def test_generate_samples(self):
X0, Z0 = self.hmm.sample(n_samples=10)
X, Z = self.hmm.sample(n_samples=10, currstate=Z0[-1])
assert len(Z0) == len(Z) == 10 and Z[0] == Z0[-1]
class TestBaseConsistentWithGMM:
def setup_method(self, method):
n_components = 8
n_samples = 10
self.log_frameprob = np.log(
np.random.random((n_samples, n_components)))
h = StubHMM(n_components)
h.log_frameprob = self.log_frameprob
# If startprob and transmat are uniform across all states (the
# default), the transitions are uninformative - the model
# reduces to a GMM with uniform mixing weights (in terms of
# posteriors, not likelihoods).
h.startprob_ = np.ones(n_components) / n_components
h.transmat_ = np.ones((n_components, n_components)) / n_components
self.hmm = h
def test_score_samples(self):
log_prob, hmmposteriors = self.hmm.score_samples(self.log_frameprob)
n_samples, n_components = self.log_frameprob.shape
assert_allclose(hmmposteriors.sum(axis=1), np.ones(n_samples))
norm = special.logsumexp(self.log_frameprob, axis=1)[:, np.newaxis]
gmmposteriors = np.exp(self.log_frameprob
- np.tile(norm, (1, n_components)))
assert_allclose(hmmposteriors, gmmposteriors)
def test_decode(self):
_log_prob, state_sequence = self.hmm.decode(self.log_frameprob)
n_samples, n_components = self.log_frameprob.shape
norm = special.logsumexp(self.log_frameprob, axis=1)[:, np.newaxis]
gmmposteriors = np.exp(self.log_frameprob
- np.tile(norm, (1, n_components)))
gmmstate_sequence = gmmposteriors.argmax(axis=1)
assert_allclose(state_sequence, gmmstate_sequence)
def test_base_hmm_attributes():
n_components = 20
startprob = np.random.random(n_components)
startprob /= startprob.sum()
transmat = np.random.random((n_components, n_components))
transmat /= np.tile(transmat.sum(axis=1)[:, np.newaxis], (1, n_components))
h = StubHMM(n_components)
assert h.n_components == n_components
h.startprob_ = startprob
assert_allclose(h.startprob_, startprob)
with pytest.raises(ValueError):
h.startprob_ = 2 * startprob
h._check()
with pytest.raises(ValueError):
h.startprob_ = []
h._check()
with pytest.raises(ValueError):
h.startprob_ = np.zeros((n_components - 2, 2))
h._check()
h.startprob_ = startprob
h.transmat_ = transmat
assert_allclose(h.transmat_, transmat)
with pytest.raises(ValueError):
h.transmat_ = 2 * transmat
h._check()
with pytest.raises(ValueError):
h.transmat_ = []
h._check()
with pytest.raises(ValueError):
h.transmat_ = np.zeros((n_components - 2, n_components))
h._check()
def test_stationary_distribution():
n_components = 10
h = StubHMM(n_components)
transmat = np.random.random((n_components, n_components))
transmat /= np.tile(transmat.sum(axis=1)[:, np.newaxis], (1, n_components))
h.transmat_ = transmat
stationary = h.get_stationary_distribution()
assert stationary.dtype == float
assert (h.get_stationary_distribution().T @ h.transmat_
== pytest.approx(stationary))
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