def test_fluxes_2():
# depends on tpt.committors
bmsm = BayesianMarkovStateModel(lag_time=1)
assignments = np.random.randint(3, size=(10, 1000))
bmsm.fit(assignments)
# forward committors
qplus = tpt.committors(0, 2, bmsm)
ref_fluxes = np.zeros((3, 3))
ref_net_fluxes = np.zeros((3, 3))
for el in zip(bmsm.all_populations_, bmsm.all_transmats_):
pop = el[0]
tprob = el[1]
for i in range(3):
for j in range(3):
if i != j:
# Eq. 2.24 in Metzner et al. Transition Path Theory.
# Multiscale Model. Simul. 2009, 7, 1192-1219.
ref_fluxes[i, j] += (pop[i] * tprob[i, j] *
(1 - qplus[i]) * qplus[j])
ref_fluxes /= 100.
for i in range(3):
for j in range(3):
ref_net_fluxes[i, j] = np.max([0, ref_fluxes[i, j] -
ref_fluxes[j, i]])
fluxes = tpt.fluxes(0, 2, bmsm)
net_fluxes = tpt.net_fluxes(0, 2, bmsm)
npt.assert_array_almost_equal(ref_fluxes, fluxes, decimal=2)
npt.assert_array_almost_equal(ref_net_fluxes, net_fluxes, decimal=2)
def fit_bayes_msms(yaml_file):
mdl_params = yaml_file["mdl_params"]
msm__lag_time = mdl_params["msm__lag_time"]
if "bayesmsm__n_samples" in mdl_params.keys():
bayesmsm__n_samples = mdl_params["bayesmsm__n_samples"]
else:
bayesmsm__n_samples = 800
if "bayesmsm__n_steps" in mdl_params.keys():
bayesmsm__n_steps = mdl_params["bayesmsm__n_steps"]
else:
bayesmsm__n_steps = 1000000
for protein in yaml_file["protein_list"]:
with enter_protein_mdl_dir(yaml_file, protein):
print(protein)
assignments = verboseload("assignments.pkl")
msm_mdl = BayesianMarkovStateModel(n_samples=bayesmsm__n_samples,
n_steps=bayesmsm__n_steps,
lag_time=msm__lag_time,
ergodic_cutoff=1.0/msm__lag_time,
verbose=True).fit(
[assignments[i] for i in assignments.keys()])
_ = msm_mdl.all_eigenvalues_
verbosedump(msm_mdl, "bayesmsm_mdl.pkl")
fixed_assignments = {}
for i in assignments.keys():
fixed_assignments[i] = msm_mdl.transform(
assignments[i], mode='fill')[0]
verbosedump(fixed_assignments, 'fixed_assignments.pkl')
return
def test_ergodic_cutoff():
assert (MarkovStateModel(lag_time=10).ergodic_cutoff ==
BayesianMarkovStateModel(lag_time=10).ergodic_cutoff)
assert (MarkovStateModel(lag_time=10)._parse_ergodic_cutoff() ==
BayesianMarkovStateModel(lag_time=10)._parse_ergodic_cutoff())
for cut_off in [0.01, 'on', 'off']:
assert (MarkovStateModel(ergodic_cutoff=cut_off).ergodic_cutoff ==
BayesianMarkovStateModel(ergodic_cutoff=cut_off).ergodic_cutoff)
def test_committors_2():
bmsm = BayesianMarkovStateModel(lag_time=1)
assignments = np.random.randint(3, size=(10, 1000))
bmsm.fit(assignments)
committors = tpt.committors([0], [2], bmsm)
ref = 0
for tprob in bmsm.all_transmats_:
ref += np.power(tprob[1, 1], np.arange(1000)).sum() * tprob[1, 2]
ref = np.array([0, ref / 100., 1])
npt.assert_array_almost_equal(ref, committors, decimal=2)
def test_3():
trajectory = [
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0,
0, 2, 2, 2, 0, 0, 0
]
msm1 = BayesianMarkovStateModel(sampler='metzner',
n_steps=1,
n_samples=100,
n_chains=1,
random_state=0)
msm1.fit([trajectory])
msm2 = BayesianMarkovStateModel(sampler='metzner_py',
n_steps=1,
n_samples=100,
n_chains=1,
random_state=0)
msm2.fit([trajectory])
np.testing.assert_array_almost_equal(msm1.all_transmats_,
msm2.all_transmats_)
assert msm1.all_timescales_.shape == (100, 2)
assert msm1.all_eigenvalues_.shape == (100, 3)
assert msm1.all_left_eigenvectors_.shape == (100, 3, 3)
assert msm1.all_right_eigenvectors_.shape == (100, 3, 3)
assert msm1.all_populations_.shape == (100, 3)
np.testing.assert_array_almost_equal(msm1.all_populations_.sum(axis=1),
np.ones(100))
def test_5():
trjs = DoubleWell(random_state=0).get_cached().trajectories
clusterer = NDGrid(n_bins_per_feature=5)
mle_msm = MarkovStateModel(lag_time=100, verbose=False)
b_msm = BayesianMarkovStateModel(lag_time=100, n_samples=1000, n_chains=8, n_steps=1000, random_state=0)
states = clusterer.fit_transform(trjs)
b_msm.fit(states)
mle_msm.fit(states)
# this is a pretty silly test. it checks that the mean transition
# matrix is not so dissimilar from the MLE transition matrix.
# This shouldn't necessarily be the case anyways -- the likelihood is
# not "symmetric". And the cutoff chosen is just heuristic.
assert np.linalg.norm(b_msm.all_transmats_.mean(axis=0) - mle_msm.transmat_) < 1e-2
def test_4():
trajectory = [
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0,
0, 2, 2, 2, 0, 0, 0
]
msm1 = BayesianMarkovStateModel(n_steps=3,
n_samples=10,
n_chains=1,
random_state=0).fit([trajectory])
assert msm1.all_transmats_.shape[0] == 10
msm2 = BayesianMarkovStateModel(n_steps=4,
n_samples=10,
n_chains=3,
random_state=0).fit([trajectory])
assert msm2.all_transmats_.shape[0] == 10
def test_cond_committors_2():
# depends on tpt.committors
bmsm = BayesianMarkovStateModel(lag_time=1)
assignments = np.random.randint(4, size=(10, 1000))
bmsm.fit(assignments)
for_committors = tpt.committors(0, 3, bmsm)
cond_committors = tpt.conditional_committors(0, 3, 2, bmsm)
ref = 0
for tprob in bmsm.all_transmats_:
ref += (for_committors[1] -
np.power(tprob[1, 1], np.arange(5000)).sum() *
tprob[1, 3])
ref = [0, ref / 100., for_committors[2], 0]
npt.assert_array_almost_equal(ref, cond_committors, decimal=2)
def test_5():
trjs = DoubleWell(random_state=0).get_cached().trajectories
clusterer = NDGrid(n_bins_per_feature=5)
mle_msm = MarkovStateModel(lag_time=100, verbose=False)
b_msm = BayesianMarkovStateModel(lag_time=100,
n_samples=1000,
n_chains=8,
n_steps=1000,
random_state=0)
states = clusterer.fit_transform(trjs)
b_msm.fit(states)
mle_msm.fit(states)
# this is a pretty silly test. it checks that the mean transition
# matrix is not so dissimilar from the MLE transition matrix.
# This shouldn't necessarily be the case anyways -- the likelihood is
# not "symmetric". And the cutoff chosen is just heuristic.
assert np.linalg.norm(
b_msm.all_transmats_.mean(axis=0) - mle_msm.transmat_) < 1e-2
def test_3():
trajectory = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0]
msm1 = BayesianMarkovStateModel(sampler="metzner", n_steps=1, n_samples=100, n_chains=1, random_state=0)
msm1.fit([trajectory])
msm2 = BayesianMarkovStateModel(sampler="metzner_py", n_steps=1, n_samples=100, n_chains=1, random_state=0)
msm2.fit([trajectory])
np.testing.assert_array_almost_equal(msm1.all_transmats_, msm2.all_transmats_)
assert msm1.all_timescales_.shape == (100, 2)
assert msm1.all_eigenvalues_.shape == (100, 3)
assert msm1.all_left_eigenvectors_.shape == (100, 3, 3)
assert msm1.all_right_eigenvectors_.shape == (100, 3, 3)
assert msm1.all_populations_.shape == (100, 3)
np.testing.assert_array_almost_equal(msm1.all_populations_.sum(axis=1), np.ones(100))
from nose.plugins.skip import SkipTest
import numpy as np
from msmbuilder.msm import MarkovStateModel, BayesianMarkovStateModel
from matplotlib.axes import SubplotBase
from ..plots import plot_tpaths
from . import PlotTestCase
rs = np.random.RandomState(42)
data = rs.randint(low=0, high=10, size=100000)
msm = MarkovStateModel()
msm.fit(data)
bmsm = BayesianMarkovStateModel()
bmsm.fit(data)
class TestTPTPlot(PlotTestCase):
"""Test the function(s) that visualize TPTs."""
def test_plot_tpaths_msm(self):
ax = plot_tpaths(msm, 0, 9)
assert isinstance(ax, SubplotBase)
@SkipTest
def test_plot_tpaths_bmsm(self):
ax = plot_tpaths(bmsm, 0, 9)
assert isinstance(ax, SubplotBase)
import numpy as np
from msmbuilder.msm import MarkovStateModel, BayesianMarkovStateModel
from matplotlib.axes import SubplotBase
from seaborn.apionly import JointGrid
from ..plots import (plot_pop_resids, plot_msm_network, plot_timescales,
plot_implied_timescales)
from . import PlotTestCase
rs = np.random.RandomState(42)
data = rs.randint(low=0, high=10, size=100000)
msm = MarkovStateModel()
msm.fit(data)
bmsm = BayesianMarkovStateModel()
bmsm.fit(data)
class TestMSMPlot(PlotTestCase):
"""Test the function(s) that visualize MSMs."""
def test_plot_pop_resids(self):
ax = plot_pop_resids(msm)
assert isinstance(ax, JointGrid)
def test_plot_msm_network(self):
ax = plot_msm_network(msm)
assert isinstance(ax, SubplotBase)
def test_plot_timescales_msm(self):
