def setUpClass(cls) -> None:
dtraj = DoubleWellDiscrete().dtraj
initial_hmm_10 = init.discrete.metastable_from_data(dtraj, n_hidden_states=2, lagtime=10)
cls.hmm_lag10 = MaximumLikelihoodHMM(initial_hmm_10, lagtime=10).fit(dtraj).fetch_model()
cls.hmm_lag10_largest = cls.hmm_lag10.submodel_largest(dtrajs=dtraj)
cls.msm_lag10 = estimate_markov_model(dtraj, 10, reversible=True)
initial_hmm_1 = init.discrete.metastable_from_data(dtraj, n_hidden_states=2, lagtime=1)
cls.hmm_lag1 = MaximumLikelihoodHMM(initial_hmm_1).fit(dtraj).fetch_model()
cls.hmm_lag1_largest = cls.hmm_lag1.submodel_largest(dtrajs=dtraj)
cls.msm_lag1 = estimate_markov_model(dtraj, 1, reversible=True)
cls.dtrajs = dtraj
def test_score_vs_MSM(self):
trajs_test, trajs_train = cvsplit_dtrajs(self.trajs, random_state=32)
dtrajs_test, dtrajs_train = cvsplit_dtrajs(self.dtrajs,
random_state=32)
methods = ('VAMP1', 'VAMP2', 'VAMPE')
for m in methods:
msm_train = estimate_markov_model(dtrajs=dtrajs_train,
lag=self.lag,
reversible=False)
score_msm = msm_train.score(dtrajs_test,
score_method=m,
score_k=None)
vamp_train = VAMP(
lagtime=self.lag,
var_cutoff=1.0).fit_from_timeseries(trajs_train).fetch_model()
vamp_test = VAMP(
lagtime=self.lag,
var_cutoff=1.0).fit_from_timeseries(trajs_test).fetch_model()
score_vamp = vamp_train.score(test_model=vamp_test, score_method=m)
self.assertAlmostEqual(score_msm,
score_vamp,
places=2 if m == 'VAMPE' else 3,
msg=m)
def setUpClass(cls):
N_steps = 10000
N_traj = 20
lag = 1
T = np.linalg.matrix_power(
np.array([[0.7, 0.2, 0.1], [0.1, 0.8, 0.1], [0.1, 0.1, 0.8]]), lag)
dtrajs = [generate(T, N_steps) for _ in range(N_traj)]
p0 = np.zeros(3)
p1 = np.zeros(3)
trajs = []
for dtraj in dtrajs:
traj = np.zeros((N_steps, T.shape[0]))
traj[np.arange(len(dtraj)), dtraj] = 1.0
trajs.append(traj)
p0 += traj[:-lag, :].sum(axis=0)
p1 += traj[lag:, :].sum(axis=0)
estimator = VAMP(scaling=None, var_cutoff=1.0)
cov = VAMP.covariance_estimator(lagtime=lag).fit(trajs).fetch_model()
vamp = estimator.fit(cov).fetch_model()
msm = estimate_markov_model(dtrajs, lag=lag, reversible=False)
cls.trajs = trajs
cls.dtrajs = dtrajs
cls.trajs_timeshifted = list(
timeshifted_split(cls.trajs, lagtime=lag, chunksize=5000))
cls.lag = lag
cls.msm = msm
cls.vamp = vamp
cls.estimator = estimator
cls.p0 = p0 / p0.sum()
cls.p1 = p1 / p1.sum()
cls.atol = np.finfo(np.float32).eps * 1000.0