def test_simulate_stats(msm):
# test statistics of starting state
N = 5000
trajs = [msm.simulate(1, seed=i + 1) for i in range(N)]
ss = np.concatenate(trajs).astype(int)
pi = stationary_distribution(msm.transition_matrix)
piest = count_states(ss) / float(N)
np.testing.assert_allclose(piest, pi, atol=0.025)
assert_(msm.stationary)
def active_count_fraction(self):
"""The fraction of counts in the largest connected set.
"""
self._check_is_estimated()
from pyemma.util.discrete_trajectories import count_states
hist = count_states(self._dtrajs_full)
hist_active = hist[self.active_set]
return float(_np.sum(hist_active)) / float(_np.sum(hist))
def test_active_state_indices(oom_msm_scenario):
for msm in oom_msm_scenario.msms:
dtrajs_proj = msm.count_model.transform_discrete_trajectories_to_submodel(
oom_msm_scenario.dtrajs)
indices = sample.compute_index_states(dtrajs_proj)
np.testing.assert_equal(len(indices), msm.n_states)
hist = count_states(oom_msm_scenario.dtrajs)
for state in range(msm.n_states):
np.testing.assert_equal(indices[state].shape[0],
hist[msm.count_model.state_symbols[state]])
np.testing.assert_equal(indices[state].shape[1], 2)
def test_active_state_indices(self, setting):
scenario = make_double_well(setting)
from deeptime.markov import sample
I = sample.compute_index_states(
scenario.data.dtraj, subset=scenario.msm.count_model.state_symbols)
assert (len(I) == scenario.msm.n_states)
# compare to histogram
from deeptime.markov import count_states
hist = count_states(scenario.data.dtraj)
# number of frames should match on active subset
A = scenario.msm.count_model.state_symbols
for i in range(A.shape[0]):
assert I[i].shape[0] == hist[A[i]]
assert I[i].shape[1] == 2
def test_observable_state_indices(self):
from deeptime.markov import sample
hmsm = self.hmm_lag10_largest
I = sample.compute_index_states(self.dtrajs, subset=hmsm.observation_symbols)
# I = hmsm.observable_state_indexes
np.testing.assert_equal(len(I), hmsm.n_observation_states)
# compare to histogram
hist = count_states(self.dtrajs)
# number of frames should match on active subset
A = hmsm.observation_symbols
for i in range(A.shape[0]):
np.testing.assert_equal(I[i].shape[0], hist[A[i]])
np.testing.assert_equal(I[i].shape[1], 2)
def fit(self, data, *args, **kw):
r""" Counts transitions at given lag time according to configuration of the estimator.
Parameters
----------
data : array_like or list of array_like
discretized trajectories
"""
from deeptime.markov import count_states
dtrajs = ensure_dtraj_list(data)
# basic count statistics
histogram = count_states(dtrajs, ignore_negative=True)
# Compute count matrix
count_mode = self.count_mode
lagtime = self.lagtime
count_matrix = TransitionCountEstimator.count(count_mode,
dtrajs,
lagtime,
sparse=self.sparse,
n_jobs=kw.pop(
'n_jobs', None))
if self.n_states is not None and self.n_states > count_matrix.shape[0]:
histogram = np.pad(histogram,
pad_width=[
(0, self.n_states - count_matrix.shape[0])
])
if issparse(count_matrix):
count_matrix = scipy.sparse.csr_matrix(
(count_matrix.data, count_matrix.indices,
count_matrix.indptr),
shape=(self.n_states, self.n_states))
else:
n_pad = self.n_states - count_matrix.shape[0]
count_matrix = np.pad(count_matrix,
pad_width=[(0, n_pad), (0, n_pad)])
# initially state symbols, full count matrix, and full histogram can be left None because they coincide
# with the input arguments
self._model = TransitionCountModel(count_matrix=count_matrix,
counting_mode=count_mode,
lagtime=lagtime,
state_histogram=histogram)
return self
def __init__(self, dtrajs):
from pyemma.util.types import ensure_dtraj_list
# discrete trajectories
self._dtrajs = ensure_dtraj_list(dtrajs)
# TODO: extensive input checking!
if any([np.any(d < -1) for d in self._dtrajs]):
raise ValueError('Discrete trajectory contains elements < -1.')
## basic count statistics
# histogram
self._hist = count_states(self._dtrajs, ignore_negative=True)
# total counts
self._total_count = np.sum(self._hist)
# number of states
self._nstates = number_of_states(dtrajs)
# not yet estimated
self._counted_at_lag = False
def nonempty_obs(self, dtrajs) -> np.ndarray:
r"""
Computes the set of visited observable states given a set of discrete trajectories.
Parameters
----------
dtrajs : array_like
observable trajectory
Returns
-------
symbols : np.ndarray
The observation symbols which are visited.
"""
from deeptime.markov import compute_dtrajs_effective, count_states
if dtrajs is None:
raise ValueError("Needs nonempty dtrajs to evaluate nonempty obs.")
dtrajs = ensure_dtraj_list(dtrajs)
dtrajs_lagged_strided = compute_dtrajs_effective(
dtrajs, self.transition_model.lagtime, self.transition_model.count_model.n_states_full, self.stride
)
obs = np.where(count_states(dtrajs_lagged_strided) > 0)[0]
return obs
def trajectory_weights(self):
r"""Uses the MSM to assign a probability weight to each trajectory frame.
This is a powerful function for the calculation of arbitrary observables in the trajectories one has
started the analysis with. The stationary probability of the MSM will be used to reweigh all states.
Returns a list of weight arrays, one for each trajectory, and with a number of elements equal to
trajectory frames. Given :math:`N` trajectories of lengths :math:`T_1` to :math:`T_N`, this function
returns corresponding weights:
.. math::
(w_{1,1}, ..., w_{1,T_1}), (w_{N,1}, ..., w_{N,T_N})
that are normalized to one:
.. math::
\sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} = 1
Suppose you are interested in computing the expectation value of a function :math:`a(x)`, where :math:`x`
are your input configurations. Use this function to compute the weights of all input configurations and
obtain the estimated expectation by:
.. math::
\langle a \rangle = \sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} a(x_{i,t})
Or if you are interested in computing the time-lagged correlation between functions :math:`a(x)` and
:math:`b(x)` you could do:
.. math::
\langle a(t) b(t+\tau) \rangle_t = \sum_{i=1}^N \sum_{t=1}^{T_i} w_{i,t} a(x_{i,t}) a(x_{i,t+\tau})
Returns
-------
weights : list of ndarray
The normalized trajectory weights. Given :math:`N` trajectories of lengths :math:`T_1` to :math:`T_N`,
returns the corresponding weights:
.. math::
(w_{1,1}, ..., w_{1,T_1}), (w_{N,1}, ..., w_{N,T_N})
"""
self._check_is_estimated()
# compute stationary distribution, expanded to full set
statdist_full = _np.zeros([self._nstates_full])
statdist_full[self.active_set] = self.stationary_distribution
# histogram observed states
hist = 1.0 * count_states(self.discrete_trajectories_full)
# simply read off stationary distribution and accumulate total weight
W = []
wtot = 0.0
for dtraj in self.discrete_trajectories_full:
w = statdist_full[dtraj] / hist[dtraj]
W.append(w)
wtot += _np.sum(w)
# normalize
for w in W:
w /= wtot
# done
return W
def submodel(self,
states=None,
obs=None,
mincount_connectivity='1/n',
inplace=False):
"""Returns a HMM with restricted state space
Parameters
----------
states : None, str or int-array
Hidden states to restrict the model to. In addition to specifying
the subset, possible options are:
* None : all states - don't restrict
* 'populous-strong' : strongly connected subset with maximum counts
* 'populous-weak' : weakly connected subset with maximum counts
* 'largest-strong' : strongly connected subset with maximum size
* 'largest-weak' : weakly connected subset with maximum size
obs : None, str or int-array
Observed states to restrict the model to. In addition to specifying
an array with the state labels to be observed, possible options are:
* None : all states - don't restrict
* 'nonempty' : all states with at least one observation in the estimator
mincount_connectivity : float or '1/n'
minimum number of counts to consider a connection between two states.
Counts lower than that will count zero in the connectivity check and
may thus separate the resulting transition matrix. Default value:
1/nstates.
inplace : Bool
if True, submodel is estimated in-place, overwriting the original
estimator and possibly discarding information. Default value: False
Returns
-------
hmm : HMM
The restricted HMM.
"""
if states is None and obs is None and mincount_connectivity == 0:
return self
if states is None:
states = _np.arange(self.nstates)
if obs is None:
obs = _np.arange(self.nstates_obs)
if str(mincount_connectivity) == '1/n':
mincount_connectivity = 1.0 / float(self.nstates)
# handle new connectivity
cm = TransitionCountModel(self.count_matrix)
S = cm.connected_sets(connectivity_threshold=mincount_connectivity,
directed=True)
if inplace:
submodel_estimator = self
else:
from copy import deepcopy
submodel_estimator = deepcopy(self)
from deeptime.markov._transition_matrix import stationary_distribution
if len(S) > 1:
# keep only non-negligible transitions
C = _np.zeros(self.count_matrix.shape)
large = _np.where(self.count_matrix >= mincount_connectivity)
C[large] = self.count_matrix[large]
for s in S: # keep all (also small) transition counts within strongly connected subsets
C[_np.ix_(s, s)] = self.count_matrix[_np.ix_(s, s)]
# re-estimate transition matrix with disc.
from deeptime.markov.msm import MaximumLikelihoodMSM
msmest = MaximumLikelihoodMSM(allow_disconnected=True,
reversible=self.reversible,
connectivity_threshold=0)
msm = msmest.fit_fetch(C)
P = msm.transition_matrix
pi = stationary_distribution(P, C, mincount_connectivity=0)
else:
C = self.count_matrix
P = self.transition_matrix
pi = self.stationary_distribution
# determine substates
if isinstance(states, str):
strong = 'strong' in states
largest = 'largest' in states
S = cm.connected_sets(connectivity_threshold=mincount_connectivity,
directed=strong)
if largest:
score = [len(s) for s in S]
else:
score = [self.count_matrix[_np.ix_(s, s)].sum() for s in S]
states = _np.array(S[_np.argmax(score)])
if states is not None: # sub-transition matrix
submodel_estimator._active_set = states
C = C[_np.ix_(states, states)].copy()
P = P[_np.ix_(states, states)].copy()
P /= P.sum(axis=1)[:, None]
pi = stationary_distribution(P, C)
submodel_estimator.initial_count = self.initial_count[states]
submodel_estimator.initial_distribution = self.initial_distribution[
states] / self.initial_distribution[states].sum()
# determine observed states
if str(obs) == 'nonempty':
obs = _np.where(
count_states(self.discrete_trajectories_lagged) > 0)[0]
if obs is not None:
# set observable set
submodel_estimator._observable_set = obs
submodel_estimator._nstates_obs = obs.size
# full2active mapping
_full2obs = -1 * _np.ones(self._nstates_obs_full, dtype=int)
_full2obs[obs] = _np.arange(len(obs), dtype=int)
# observable trajectories
submodel_estimator._dtrajs_obs = []
for dtraj in self.discrete_trajectories_full:
submodel_estimator._dtrajs_obs.append(_full2obs[dtraj])
# observation matrix
B = self.observation_probabilities[_np.ix_(states, obs)].copy()
B /= B.sum(axis=1)[:, None]
else:
B = self.observation_probabilities
# set quantities back.
submodel_estimator.update_model_params(P=P, pobs=B, pi=pi)
submodel_estimator.count_matrix_EM = self.count_matrix[_np.ix_(
states, states)] # unchanged count matrix
submodel_estimator.count_matrix = C # count matrix consistent with P
return submodel_estimator
def __init__(self, complete: bool = True):
self.complete = complete
data = np.load(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'resources', 'TestData_OOM_MSM.npz'))
if complete:
self.dtrajs = [data['arr_%d' % k] for k in range(1000)]
else:
excluded = [
21, 25, 30, 40, 66, 72, 74, 91, 116, 158, 171, 175, 201, 239,
246, 280, 300, 301, 310, 318, 322, 323, 339, 352, 365, 368,
407, 412, 444, 475, 486, 494, 510, 529, 560, 617, 623, 637,
676, 689, 728, 731, 778, 780, 811, 828, 838, 845, 851, 859,
868, 874, 895, 933, 935, 938, 958, 961, 968, 974, 984, 990, 999
]
self.dtrajs = [
data['arr_%d' % k]
for k in np.setdiff1d(np.arange(1000), excluded)
]
# Number of states:
self.N = 5
# Lag time:
self.tau = 5
self.dtrajs_lag = [traj[:-self.tau] for traj in self.dtrajs]
# Rank:
if complete:
self.rank = 3
else:
self.rank = 2
# Build models:
self.msmrev = OOMReweightedMSM(lagtime=self.tau,
rank_mode='bootstrap_trajs').fit(
self.dtrajs)
self.msmrev_sparse = OOMReweightedMSM(lagtime=self.tau, sparse=True, rank_mode='bootstrap_trajs') \
.fit(self.dtrajs)
self.msm = OOMReweightedMSM(lagtime=self.tau,
reversible=False,
rank_mode='bootstrap_trajs').fit(
self.dtrajs)
self.msm_sparse = OOMReweightedMSM(lagtime=self.tau,
reversible=False,
sparse=True,
rank_mode='bootstrap_trajs').fit(
self.dtrajs)
self.estimators = [
self.msmrev, self.msm, self.msmrev_sparse, self.msm_sparse
]
self.msms = [est.fetch_model() for est in self.estimators]
# Reference count matrices at lag time tau and 2*tau:
if complete:
self.C2t = data['C2t']
else:
self.C2t = data['C2t_s']
self.Ct = np.sum(self.C2t, axis=1)
if complete:
self.Ct_active = self.Ct
self.C2t_active = self.C2t
self.active_faction = 1.
else:
lcc = msmest.largest_connected_set(self.Ct)
self.Ct_active = msmest.largest_connected_submatrix(self.Ct,
lcc=lcc)
self.C2t_active = self.C2t[:4, :4, :4]
self.active_fraction = np.sum(self.Ct_active) / np.sum(self.Ct)
# Compute OOM-components:
self.Xi, self.omega, self.sigma, self.l = oom_transformations(
self.Ct_active, self.C2t_active, self.rank)
# Compute corrected transition matrix:
Tt_rev = compute_transition_matrix(self.Xi,
self.omega,
self.sigma,
reversible=True)
Tt = compute_transition_matrix(self.Xi,
self.omega,
self.sigma,
reversible=False)
# Build reference models:
self.rmsmrev = MarkovStateModel(Tt_rev)
self.rmsm = MarkovStateModel(Tt)
# Active count fraction:
self.hist = count_states(self.dtrajs)
self.active_hist = self.hist[:-1] if not complete else self.hist
self.active_count_frac = float(np.sum(self.active_hist)) / np.sum(
self.hist) if not complete else 1.
self.active_state_frac = 0.8 if not complete else 1.
# Commitor and MFPT:
a = np.array([0, 1])
b = np.array([4]) if complete else np.array([3])
self.comm_forward = self.rmsm.committor_forward(a, b)
self.comm_forward_rev = self.rmsmrev.committor_forward(a, b)
self.comm_backward = self.rmsm.committor_backward(a, b)
self.comm_backward_rev = self.rmsmrev.committor_backward(a, b)
self.mfpt = self.tau * self.rmsm.mfpt(a, b)
self.mfpt_rev = self.tau * self.rmsmrev.mfpt(a, b)
# PCCA:
pcca = self.rmsmrev.pcca(3 if complete else 2)
self.pcca_ass = pcca.assignments
self.pcca_dist = pcca.metastable_distributions
self.pcca_mem = pcca.memberships
self.pcca_sets = pcca.sets
# Experimental quantities:
a = np.array([1, 2, 3, 4, 5])
b = np.array([1, -1, 0, -2, 4])
p0 = np.array([0.5, 0.2, 0.2, 0.1, 0.0])
if not complete:
a = a[:-1]
b = b[:-1]
p0 = p0[:-1]
pi = self.rmsm.stationary_distribution
pi_rev = self.rmsmrev.stationary_distribution
_, _, L_rev = ma.rdl_decomposition(Tt_rev)
self.exp = np.dot(self.rmsm.stationary_distribution, a)
self.exp_rev = np.dot(self.rmsmrev.stationary_distribution, a)
self.corr_rev = np.zeros(10)
self.rel = np.zeros(10)
self.rel_rev = np.zeros(10)
for k in range(10):
Ck_rev = np.dot(np.diag(pi_rev), np.linalg.matrix_power(Tt_rev, k))
self.corr_rev[k] = np.dot(a.T, np.dot(Ck_rev, b))
self.rel[k] = np.dot(p0.T, np.dot(np.linalg.matrix_power(Tt, k),
a))
self.rel_rev[k] = np.dot(
p0.T, np.dot(np.linalg.matrix_power(Tt_rev, k), a))
self.fing_cor = np.dot(a.T, L_rev.T) * np.dot(b.T, L_rev.T)
self.fing_rel = np.dot(a.T, L_rev.T) * np.dot((p0 / pi_rev).T, L_rev.T)
