def setUpClass(cls):
# load observations
import pyerna.datasets
obs = pyerna.datasets.load_2well_discrete().dtraj_T100K_dt10
obs -= np.min(obs) # remove empty states
# hidden states
nstates = 2
# run with lag 1 and 10
cls.msm_lag1 = msm.estimate_markov_model([obs], 1, reversible=True, connectivity='largest')
cls.hmsm_lag1 = msm.estimate_hidden_markov_model([obs], nstates, 1, reversible=True, observe_nonempty=True)
cls.msm_lag10 = msm.estimate_markov_model([obs], 10, reversible=True, connectivity='largest')
cls.hmsm_lag10 = msm.estimate_hidden_markov_model([obs], nstates, 10, reversible=True, observe_nonempty=True)
def test_separate_states(self):
dtrajs = [np.array([0, 1, 1, 1, 1, 1, 0, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1]),
np.array([2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2]),]
hmm = msm.estimate_hidden_markov_model(dtrajs, 3, lag=1, separate=[0])
# we expect zeros in all samples at the following indexes:
pobs_zeros = [[0, 1, 2, 2, 2], [0, 0, 1, 2, 3]]
assert np.allclose(hmm.observation_probabilities[pobs_zeros], 0)
def test_failfast_false(self):
""" test, that no exception is raised during estimation"""
from pyerna._base.estimator import _estimate_param_scan_worker
failfast = False
@wraps(_estimate_param_scan_worker)
def worker_wrapper(*args):
args = list(args)
args[5] = failfast
return _estimate_param_scan_worker(*args)
with mock.patch('pyerna._base.estimator._estimate_param_scan_worker',
worker_wrapper):
hmm = msm.estimate_hidden_markov_model(
[0, 0, 0, 1, 1, 1, 0, 0],
2,
1,
)
hmm.cktest()
def test_failfast_true(self):
""" test that exception is thrown for failfast=True"""
from pyerna._base.estimator import _estimate_param_scan_worker
failfast = True
@wraps(_estimate_param_scan_worker)
def worker_wrapper(*args):
args = list(args)
args[5] = failfast
return _estimate_param_scan_worker(*args)
with self.assertRaises(NotImplementedError):
with mock.patch(
'pyerna._base.estimator._estimate_param_scan_worker',
worker_wrapper):
hmm = msm.estimate_hidden_markov_model(
[0, 0, 0, 1, 1, 1, 0, 0],
2,
1,
)
hmm.cktest()
def test_its_hmsm(self):
MLHMM = msm.estimate_hidden_markov_model([self.double_well_data.dtraj_T100K_dt10_n6good], 2, 10)
self.ck = MLHMM.cktest(mlags=[0,1,10])
estref = np.array([[[ 1., 0. ],
[ 0., 1. ]],
[[ 0.98515058, 0.01484942],
[ 0.01442843, 0.98557157]],
[[ 0.88172685, 0.11827315],
[ 0.11878823, 0.88121177]]])
predref = np.array([[[ 1., 0. ],
[ 0., 1. ]],
[[ 0.98515058, 0.01484942],
[ 0.01442843, 0.98557157]],
[[ 0.86961812, 0.13038188],
[ 0.12668553, 0.87331447]]])
# rough agreement with MLE
assert np.allclose(self.ck.estimates, estref, rtol=0.1, atol=10.0)
assert self.ck.estimates_conf[0] is None
assert self.ck.estimates_conf[1] is None
assert np.allclose(self.ck.predictions, predref, rtol=0.1, atol=10.0)
assert self.ck.predictions_conf[0] is None
assert self.ck.predictions_conf[1] is None
def test_submodel_simple(self):
# sanity check for submodel;
# call should not alter self
from copy import deepcopy
# dtrj = np.random.randint(0, 2, size=100)
# dtrj[np.random.randint(0, dtrj.shape[0], 3)] = 2
# hard-coded due to stochastic failures
dtrj = [1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,
0, 2, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,
1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 2, 0, 0, 1, 1, 2, 0, 1, 1, 1,
0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0]
h = msm.estimate_hidden_markov_model(dtrj, 3, 2)
h_original = deepcopy(h)
hs = h.submodel_largest(mincount_connectivity=5)
self.assertTrue(h == h_original)
self.assertEqual(hs.timescales().shape[0], 1)
self.assertEqual(hs.pi.shape[0], 2)
self.assertEqual(hs.transition_matrix.shape, (2, 2))