def test_train_sequences_of_different_length(self, tr_params="stmc"):
h = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
covariance_type=self.covariance_type,
)
h.A = self.A
h.pi = self.pi
h.B = self.B
h.means = self.means
h.covars = self.covars
h.tr_params = tr_params
# Generate observation sequences
lengths = [30, 40, 50]
X = [
h.sample(n_sequences=1, n_samples=n_samples)[0]
for n_samples in lengths
]
h, log_likelihoods = h._train(X,
n_iter=10,
conv_thresh=0.01,
return_log_likelihoods=True)
# we consider learning if the log_likelihood increases (the first one is discarded, because sometimes it drops
# after the first iteration and increases for the rest
assert np.all(np.round(np.diff(log_likelihoods[1:]), 10) >= 0)
def test_train(self, n_samples=100, n_sequences=30, tr_params="stmc"):
h = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
init_type='kmeans',
covariance_type=self.covariance_type,
tr_params=tr_params,
verbose=True,
)
h.pi = self.pi
h.A = self.A
h.B = self.B
h.means = 20 * self.means
h.covars = np.maximum(self.covars, 0.1)
# Generate observation sequences
X = h.sample(n_sequences=n_sequences, n_samples=n_samples)
# Mess up the parameters and see if we can re-learn them.
h, log_likelihoods = h._train(X,
n_iter=10,
conv_thresh=0.01,
return_log_likelihoods=True,
n_processes=4)
# we consider learning if the log_likelihood increases
assert np.all(np.round(np.diff(log_likelihoods), 10) >= 0)
def test_non_trainable_emission(self,
n_samples=100,
n_sequences=30,
tr_params="ste"):
h = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
covariance_type=self.covariance_type,
)
h.A = self.A
h.pi = self.pi
h.B = self.B
h.means = self.means
h.covars = self.covars
h.tr_params = tr_params
# Generate observation sequences
X = h.sample(n_sequences=n_sequences, n_samples=n_samples)
h_tst = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
covariance_type=self.covariance_type,
nr_no_train_de=1,
)
# Set up the emission probabilities and see if we can re-learn them.
B_fix = np.eye(self.n_states, self.n_d_features[-1])
with pytest.raises(AttributeError):
h_tst, log_likelihoods = h_tst._train(X,
n_iter=100,
conv_thresh=0.01,
return_log_likelihoods=True,
no_init=False)
# we consider learning if the log_likelihood increases
assert np.all(np.round(np.diff(log_likelihoods), 10) >= 0)
# we want that the emissions haven't changed
assert np.allclose(B_fix, h_tst.B[-1])
def test_train_without_init(self,
n_samples=100,
n_sequences=30,
tr_params="ste"):
h = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
covariance_type=self.covariance_type,
tr_params=tr_params,
)
h.pi = self.pi
h.A = self.A
h.means = 20 * self.means
h.covars = np.maximum(self.covars, 0.1)
h.B = self.B
# Generate observation sequences
X = h.sample(n_sequences=n_sequences, n_samples=n_samples)
h_tst = HeterogeneousHMM(
n_states=self.n_states,
n_g_emissions=self.n_g_emissions,
n_d_emissions=self.n_d_emissions,
n_d_features=self.n_d_features,
covariance_type=self.covariance_type,
tr_params=tr_params,
)
with pytest.raises(AttributeError):
h_tst, _ = h_tst._train(X,
n_iter=100,
conv_thresh=0.01,
return_log_likelihoods=True,
no_init=True,
n_processes=4)