def test_alanine_dipeptide_mstep():
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)
# Fit reference model and initial MSLDS model
print "Starting Gaussian Model Fit"
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
print "Done with Gaussian Model Fit"
# Obtain sufficient statistics from refmodel
rlogprob, rstats = reference_estep(refmodel, data)
means = refmodel.means_
covars = refmodel.covars_
transmat = refmodel.transmat_
populations = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
Qs = refmodel.covars_
bs = refmodel.means_
means = refmodel.means_
covars = refmodel.covars_
# Test AQB solver for MSLDS
solver = MetastableSwitchingLDSSolver(n_components, n_features)
solver.do_mstep(As, Qs, bs, means, covars, rstats, N_iter=100)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
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)
def test_alanine_dipeptide():
import pdb, traceback, sys
warnings.filterwarnings("ignore",
category=DeprecationWarning)
try:
b = fetch_alanine_dipeptide()
trajs = b.trajectories
n_seq = len(trajs)
n_frames = trajs[0].n_frames
n_atoms = trajs[0].n_atoms
n_features = n_atoms * 3
sim_T = 1000
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, (len(Z), n_features), order='F')
data.append(Z)
# Fit MSLDS model
n_experiments = 1
n_em_iter = 1
tol = 1e-1
model = MetastableSwitchingLDS(n_components,
n_features, n_experiments=n_experiments,
n_em_iter=n_em_iter)
model.fit(data, gamma=.1, tol=tol, verbose=True)
mslds_score = model.score(data)
print("MSLDS Log-Likelihood = %f" % mslds_score)
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(n_components, n_features)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
print()
# Generate a trajectory from learned model.
sample_traj, hidden_states = model.sample(sim_T)
states = []
for k in range(n_components):
states.append([])
# Presort the data into the metastable wells
for k in range(n_components):
for i in range(len(trajs)):
traj = trajs[i]
Z = traj.xyz
Z = np.reshape(Z, (len(Z), n_features), order='F')
logprob = log_multivariate_normal_density(Z,
np.array(model.means_),
np.array(model.covars_), covariance_type='full')
assignments = np.argmax(logprob, axis=1)
#probs = np.max(logprob, axis=1)
# pick structures that have highest log probability in state
s = traj[assignments == k]
states[k].append(s)
# Pick frame from original trajectories closest to current sample
gen_traj = None
for t in range(sim_T):
h = hidden_states[t]
best_logprob = -np.inf
best_frame = None
for i in range(len(trajs)):
if t > 0:
states[h][i].superpose(gen_traj, t-1)
Z = states[h][i].xyz
Z = np.reshape(Z, (len(Z), n_features), order='F')
mean = sample_traj[t]
logprobs = log_multivariate_normal_density(Z,
mean, model.Qs_[h], covariance_type='full')
ind = np.argmax(logprobs, axis=0)
logprob = logprobs[ind]
if logprob > best_log_prob:
logprob = best_logprob
best_frame = states[h][i][ind]
if t == 0:
gen_traj = best_frame
else:
gen_traj = gen_traj.join(frame)
gen_traj.save('%s.xtc' % self.out)
gen_traj[0].save('%s.xtc.pdb' % self.out)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
