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import numpy as np
import pytest
from sklearn.utils import check_random_state
from hmmlearn import hmm, vhmm
from . import (
assert_log_likelihood_increasing, compare_variational_and_em_models,
vi_uniform_startprob_and_transmat)
class TestVariationalCategorical:
@pytest.fixture(autouse=True)
def setup(self):
# We fix the random state here to demonstrate that the model will
# successfully remove "unnecessary" states. In practice,
# one should not set the random_state, and perform multiple
# training steps, and take the model with the best lower-bound
self.n_components = 3
self.implementations = ["scaling", "log"]
@staticmethod
def get_beal_models():
m1 = hmm.CategoricalHMM(3, init_params="")
m1.n_features = 3
m1.startprob_ = np.array([1/3., 1/3., 1/3.])
m1.transmat_ = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
m1.emissionprob_ = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
m2 = hmm.CategoricalHMM(3)
m2.n_features = 3
m2.startprob_ = np.array([1/3., 1/3., 1/3.])
m2.transmat_ = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
m2.emissionprob_ = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
m3 = hmm.CategoricalHMM(1)
m3.n_features = 3
m3.startprob_ = np.array([1])
m3.transmat_ = np.array([[1]])
m3.emissionprob_ = np.array([[0.5, 0.5, 0]])
return m1, m2, m3
@classmethod
def get_from_one_beal(cls, N, length, rs=None):
# Just fit the first of the beal models
model = cls.get_beal_models()[0]
sequences = []
lengths = []
for i in range(N):
sequences.append(
model.sample(length, random_state=check_random_state(rs))[0])
lengths.append(len(sequences[-1]))
sequences = np.concatenate(sequences)
return sequences, lengths
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_init_priors(self, implementation):
sequences, lengths = self.get_from_one_beal(7, 100, None)
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="",
implementation=implementation)
model.pi_prior_ = np.full((4,), .25)
model.pi_posterior_ = np.full((4,), 7/4)
model.transmat_prior_ = np.full((4, 4), .25)
model.transmat_posterior_ = np.full((4, 4), 7/4)
model.emissionprob_prior_ = np.full((4, 3), 1/3)
model.emissionprob_posterior_ = np.asarray([[.3, .4, .3],
[.8, .1, .1],
[.2, .2, .6],
[.2, .6, .2]])
assert_log_likelihood_increasing(model, sequences, lengths, 10)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_n_features(self, implementation):
sequences, lengths = self.get_from_one_beal(7, 100, None)
# Learn n_Features
model = vhmm.VariationalCategoricalHMM(
4, implementation=implementation)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
assert model.n_features == 3
# Respect n_features
model = vhmm.VariationalCategoricalHMM(
4, implementation=implementation, n_features=5)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
assert model.n_features == 5
# Too few features
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984,
implementation=implementation)
model.n_features = 2
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# No Negative Values
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984,
implementation=implementation)
sequences[0] = -1
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Must be integers
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984,
implementation=implementation)
sequences = sequences.astype(float)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_init_incorrect_priors(self, implementation):
sequences, lengths = self.get_from_one_beal(7, 100, None)
# Test startprob shape
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="te",
implementation=implementation)
model.startprob_prior_ = np.full((3,), .25)
model.startprob_posterior_ = np.full((4,), 7/4)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="te",
implementation=implementation)
model.startprob_prior_ = np.full((4,), .25)
model.startprob_posterior_ = np.full((3,), 7/4)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Test transmat shape
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="se",
implementation=implementation)
model.transmat_prior_ = np.full((3, 3), .25)
model.transmat_posterior_ = np.full((4, 4), .25)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="se",
implementation=implementation)
model.transmat_prior_ = np.full((4, 4), .25)
model.transmat_posterior_ = np.full((3, 3), 7/4)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Test emission shape
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="st",
implementation=implementation)
model.emissionprob_prior_ = np.full((3, 3), 1/3)
model.emissionprob_posterior_ = np.asarray([[.3, .4, .3],
[.8, .1, .1],
[.2, .2, .6],
[.2, .6, .2]])
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Test too many n_features
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="se",
implementation=implementation)
model.emissionprob_prior_ = np.full((4, 4), 7/4)
model.emissionprob_posterior_ = np.full((4, 4), .25)
model.n_features_ = 10
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Too small n_features
with pytest.raises(ValueError):
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="se",
implementation=implementation)
model.emissionprob_prior_ = np.full((4, 4), 7/4)
model.emissionprob_posterior_ = np.full((4, 4), .25)
model.n_features_ = 1
assert_log_likelihood_increasing(model, sequences, lengths, 10)
# Test that setting the desired prior value works
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984, init_params="ste",
implementation=implementation,
startprob_prior=1, transmat_prior=2, emissionprob_prior=3)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
assert np.all(model.startprob_prior_ == 1)
assert np.all(model.transmat_prior_ == 2)
assert np.all(model.emissionprob_prior_ == 3)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_beal(self, implementation):
rs = check_random_state(1984)
m1, m2, m3 = self.get_beal_models()
sequences = []
lengths = []
for i in range(7):
for m in [m1, m2, m3]:
sequences.append(m.sample(39, random_state=rs)[0])
lengths.append(len(sequences[-1]))
sequences = np.concatenate(sequences)
model = vhmm.VariationalCategoricalHMM(12, n_iter=500,
implementation=implementation,
tol=1e-6,
random_state=rs,
verbose=False)
assert_log_likelihood_increasing(model, sequences, lengths, 100)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_and_compare_with_em(self, implementation):
# Explicitly setting Random State to test that certain
# model states will become "unused"
sequences, lengths = self.get_from_one_beal(7, 100, 1984)
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984,
init_params="e",
implementation=implementation)
vi_uniform_startprob_and_transmat(model, lengths)
model.fit(sequences, lengths)
# The 1st hidden state will be "unused"
assert (model.transmat_posterior_[1, :]
== pytest.approx(.25, rel=1e-3))
assert (model.emissionprob_posterior_[1, :]
== pytest.approx(.3333, rel=1e-3))
# An EM Model should behave the same behavior as a Variational Model,
# When initialized with the normalized probabilities of the mode of the
# Variational MOdel.
em_hmm = hmm.CategoricalHMM(n_components=4, init_params="")
em_hmm.startprob_ = model.startprob_
em_hmm.transmat_ = model.transmat_
em_hmm.emissionprob_ = model.emissionprob_
compare_variational_and_em_models(model, em_hmm, sequences, lengths)
@pytest.mark.parametrize("implementation", ["scaling", "log"])
def test_fit_length_1_sequences(self, implementation):
sequences1, lengths1 = self.get_from_one_beal(7, 100, 1984)
# Include some length 1 sequences
sequences2, lengths2 = self.get_from_one_beal(1, 1, 1984)
sequences = np.concatenate([sequences1, sequences2])
lengths = np.concatenate([lengths1, lengths2])
model = vhmm.VariationalCategoricalHMM(
4, n_iter=500, random_state=1984,
implementation=implementation)
assert_log_likelihood_increasing(model, sequences, lengths, 10)
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