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import numpy as np
from numpy.testing import assert_allclose
import pytest
from hmmlearn import hmm
from . import assert_log_likelihood_increasing, normalized
class TestMultinomialHMM:
n_components = 2
n_features = 4
n_trials = 5
def new_hmm(self, impl):
h = hmm.MultinomialHMM(
n_components=self.n_components,
n_trials=self.n_trials,
implementation=impl)
h.startprob_ = np.array([.6, .4])
h.transmat_ = np.array([[.8, .2], [.2, .8]])
h.emissionprob_ = np.array([[.5, .3, .1, .1], [.1, .1, .4, .4]])
return h
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_attributes(self, implementation):
with pytest.raises(ValueError):
h = self.new_hmm(implementation)
h.emissionprob_ = []
h._check()
with pytest.raises(ValueError):
h.emissionprob_ = np.zeros((self.n_components - 2,
self.n_features))
h._check()
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_score_samples(self, implementation):
X = np.array([
[1, 1, 3, 0],
[3, 1, 1, 0],
[3, 0, 2, 0],
[2, 2, 0, 1],
[2, 2, 0, 1],
[0, 1, 1, 3],
[1, 0, 3, 1],
[2, 0, 1, 2],
[0, 2, 1, 2],
[1, 0, 1, 3],
])
n_samples = X.shape[0]
h = self.new_hmm(implementation)
ll, posteriors = h.score_samples(X)
assert posteriors.shape == (n_samples, self.n_components)
assert_allclose(posteriors.sum(axis=1), np.ones(n_samples))
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_sample(self, implementation, n_samples=1000):
h = self.new_hmm(implementation)
X, state_sequence = h.sample(n_samples)
assert X.ndim == 2
assert len(X) == len(state_sequence) == n_samples
assert len(np.unique(X)) == self.n_trials + 1
assert (X.sum(axis=1) == self.n_trials).all()
h.n_trials = None
with pytest.raises(ValueError):
h.sample(n_samples)
h.n_trials = [1, 2, 3]
with pytest.raises(ValueError):
h.sample(n_samples)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit(self, implementation, params='ste', n_iter=5):
h = self.new_hmm(implementation)
h.params = params
lengths = np.array([10] * 10)
X, _state_sequence = h.sample(lengths.sum())
# Mess up the parameters and see if we can re-learn them.
h.startprob_ = normalized(np.random.random(self.n_components))
h.transmat_ = normalized(
np.random.random((self.n_components, self.n_components)),
axis=1)
h.emissionprob_ = normalized(
np.random.random((self.n_components, self.n_features)),
axis=1)
# Also mess up trial counts.
h.n_trials = None
X[::2] *= 2
assert_log_likelihood_increasing(h, X, lengths, n_iter)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_emissionprob(self, implementation):
self.test_fit(implementation, 'e')
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_with_init(self, implementation, params='ste', n_iter=5):
lengths = [10] * 10
h = self.new_hmm(implementation)
X, _state_sequence = h.sample(sum(lengths))
# use init_function to initialize paramerters
h = hmm.MultinomialHMM(
n_components=self.n_components, n_trials=self.n_trials,
params=params, init_params=params)
h._init(X, lengths)
assert_log_likelihood_increasing(h, X, lengths, n_iter)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test__check_and_set_multinomial_n_features_n_trials(
self, implementation):
h = hmm.MultinomialHMM(
n_components=2, n_trials=None, implementation=implementation)
h._check_and_set_n_features(
np.array([[0, 2, 3, 0], [1, 0, 2, 2]]))
assert (h.n_trials == 5).all()
with pytest.raises(ValueError): # wrong dimensions
h._check_and_set_n_features(
np.array([[0, 0, 2, 1, 3, 1, 1]]))
with pytest.raises(ValueError): # not added up to n_trials
h._check_and_set_n_features(
np.array([[0, 0, 1, 1], [3, 1, 1, 0]]))
with pytest.raises(ValueError): # non-integral
h._check_and_set_n_features(
np.array([[0., 2., 0., 3.], [0.0, 2.5, 2.5, 0.0]]))
with pytest.raises(ValueError): # negative integers
h._check_and_set_n_features(
np.array([[0, -2, 1, 6], [5, 6, -6, 0]]))
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_compare_with_categorical_hmm(self, implementation):
n_components = 2 # ['Rainy', 'Sunny']
n_features = 3 # ['walk', 'shop', 'clean']
n_trials = 1
startprob = np.array([0.6, 0.4])
transmat = np.array([[0.7, 0.3], [0.4, 0.6]])
emissionprob = np.array([[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]])
h1 = hmm.MultinomialHMM(
n_components=n_components, n_trials=n_trials,
implementation=implementation)
h2 = hmm.CategoricalHMM(
n_components=n_components, implementation=implementation)
h1.startprob_ = startprob
h2.startprob_ = startprob
h1.transmat_ = transmat
h2.transmat_ = transmat
h1.emissionprob_ = emissionprob
h2.emissionprob_ = emissionprob
X1 = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
X2 = [[0], [1], [2]] # different input format for CategoricalHMM
log_prob1, state_sequence1 = h1.decode(X1, algorithm="viterbi")
log_prob2, state_sequence2 = h2.decode(X2, algorithm="viterbi")
assert round(np.exp(log_prob1), 5) == 0.01344
assert round(np.exp(log_prob2), 5) == 0.01344
assert_allclose(state_sequence1, [1, 0, 0])
assert_allclose(state_sequence2, [1, 0, 0])
posteriors1 = h1.predict_proba(X1)
assert_allclose(posteriors1, [
[0.23170303, 0.76829697],
[0.62406281, 0.37593719],
[0.86397706, 0.13602294],
], rtol=0, atol=1e-6)
posteriors2 = h2.predict_proba(X2)
assert_allclose(posteriors2, [
[0.23170303, 0.76829697],
[0.62406281, 0.37593719],
[0.86397706, 0.13602294],
], rtol=0, atol=1e-6)
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