def test_muller_potential_stats():
# Set constants
n_seq = 1
num_trajs = 1
T = 2500
num_hotstart = 0
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=num_hotstart)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
model.As_ = As
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=1)
def __init__(self):
self.K = 2
self.x_dim = 1
self.As = np.reshape(np.array([[0.6], [0.6]]),
(self.K, self.x_dim, self.x_dim))
self.bs = np.reshape(np.array([[0.4], [-0.4]]),
(self.K, self.x_dim))
self.Qs = np.reshape(np.array([[0.01], [0.01]]),
(self.K, self.x_dim, self.x_dim))
self.Z = np.reshape(np.array([[0.995, 0.005], [0.005, 0.995]]),
(self.K, self.K))
self.pi = np.reshape(np.array([0.99, 0.01]), (self.K,))
self.mus = np.reshape(np.array([[1], [-1]]), (self.K, self.x_dim))
self.Sigmas = np.reshape(np.array([[0.01], [0.01]]),
(self.K, self.x_dim, self.x_dim))
# Generate Solver
s = MetastableSwitchingLDS(self.K, self.x_dim)
s.As_ = self.As
s.bs_ = self.bs
s.Qs_ = self.Qs
s.transmat_ = self.Z
s.populations_ = self.pi
s.means_ = self.mus
s.covars_ = self.Sigmas
self._model = s
def test_plusmin_stats():
# Set constants
num_hotstart = 3
n_seq = 1
T = 2000
# Generate data
plusmin = PlusminModel()
data, hidden = plusmin.generate_dataset(n_seq, T)
n_features = plusmin.x_dim
n_components = plusmin.K
# Fit reference model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Fit initial MSLDS model from reference model
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=0)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
model.As_ = [np.zeros((n_features, n_features)),
np.zeros((n_features, n_features))]
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=1)
def test_randn_stats():
"""
Sanity test MSLDS sufficient statistic gathering by setting
dynamics model to 0 and testing that E-step matches that of
HMM
"""
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Generate reference data
n_states = 2
n_features = 3
data = [np.random.randn(100, n_features),
np.random.randn(100, n_features)]
refmodel = GaussianHMM(n_components=n_states,
covariance_type='full').fit(data)
# test all of the sufficient statistics against sklearn and pure python
model = MetastableSwitchingLDS(n_states=n_states,
n_features=n_features, n_hotstart=0)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
# Is there a more elegant way to do this?
model.As_ = [np.zeros((n_features, n_features)),
np.zeros((n_features, n_features))]
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=3)
def test_alanine_dipeptide_stats():
import pdb, traceback, sys
warnings.filterwarnings("ignore", category=DeprecationWarning)
try:
b = fetch_alanine_dipeptide()
trajs = b.trajectories
# While debugging, restrict to first trajectory only
trajs = [trajs[0]]
n_seq = len(trajs)
n_frames = trajs[0].n_frames
n_atoms = trajs[0].n_atoms
n_features = n_atoms * 3
data_home = get_data_home()
data_dir = join(data_home, TARGET_DIRECTORY_ALANINE)
top = md.load(join(data_dir, 'ala2.pdb'))
n_components = 2
# Superpose m
data = []
for traj in trajs:
traj.superpose(top)
Z = traj.xyz
Z = np.reshape(Z, (n_frames, n_features), order='F')
data.append(Z)
n_hotstart = 3
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
rlogprob, rstats = reference_estep(refmodel, data)
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=n_hotstart)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
model.As_ = As
Qs = []
eps = 1e-7
for i in range(n_components):
Q = refmodel.covars_[i] + eps*np.eye(n_features)
Qs.append(Q)
model.Qs_ = Qs
model.bs_ = refmodel.means_
logprob, stats = model.inferrer.do_estep()
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=2)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=1)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=1)
# This test fails consistently. TODO: Figure out why.
#yield lambda: np.testing.assert_array_almost_equal(
# stats['trans'], rstats['trans'], decimal=2)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)