def __call__(self, *args, **kwargs):
model = GaussianHMM(n_states=2, init_algo=self.init_algo,
reversible_type=self.reversible_type,
thresh=1e-4, n_iter=30, random_state=rs)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.05)
assert abs(model.fit_logprob_[-1] - model.score(X)) < 0.5
def __call__(self, *args, **kwargs):
model = GaussianHMM(n_states=2,
init_algo=self.init_algo,
reversible_type=self.reversible_type,
thresh=1e-4,
n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.05)
assert abs(model.fit_logprob_[-1] - model.score(X)) < 0.5
def test_3():
transmat = np.array([[0.2, 0.3, 0.5], [0.4, 0.4, 0.2], [0.8, 0.2, 0.0]])
means = np.array([[0.0], [10.0], [5.0]])
vars = np.array([[1.0], [2.0], [0.3]])
X = [create_timeseries(means, vars, transmat) for i in range(20)]
# For each value of various options, create a 3 state HMM and see if it is correct.
for init_algo in ('kmeans', 'GMM'):
for reversible_type in ('mle', 'transpose'):
model = GaussianHMM(n_states=3, init_algo=init_algo, reversible_type=reversible_type, thresh=1e-4, n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.1)
assert abs(model.fit_logprob_[-1]-model.score(X)) < 0.5
def test_3():
transmat = np.array([[0.2, 0.3, 0.5], [0.4, 0.4, 0.2], [0.8, 0.2, 0.0]])
means = np.array([[0.0], [10.0], [5.0]])
vars = np.array([[1.0], [2.0], [0.3]])
X = [create_timeseries(means, vars, transmat) for i in range(20)]
# For each value of various options, create a 3 state HMM and see if it is correct.
for init_algo in ('kmeans', 'GMM'):
for reversible_type in ('mle', 'transpose'):
model = GaussianHMM(n_states=3, init_algo=init_algo, reversible_type=reversible_type, thresh=1e-4, n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.1)
assert abs(model.fit_logprob_[-1]-model.score(X)) < 0.5
def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
def test_pipeline():
trajs = AlanineDipeptide().get_cached().trajectories
topology = trajs[0].topology
indices = topology.select('backbone')
p = Pipeline([('diheds', SuperposeFeaturizer(indices, trajs[0][0])),
('hmm', GaussianHMM(n_states=4))])
predict = p.fit_predict(trajs)
p.named_steps['hmm'].summarize()
def hmm_arbitrary(ic_slice, n_dims, n_clusters, n_obs):
#
BIC_hmm = 0.0
print("Trying {:d} clusters...".format(n_clusters))
hmm = GHMM(n_states=n_clusters, reversible_type='transpose', thresh=1e-4, init_algo='GMM', timing=True)
print("Running fit...")
hmm.fit(ic_slice)
if n_obs > 10000:
#
BIC_hmm = (1.0 + 2.0 * float(n_dims)) * float(n_clusters)**2 * math.log(n_obs) - 2.0 * hmm.fit_logprob_[0]
#
else:
#
BIC_hmm = (1.0 + 2.0 * float(n_dims)) * float(n_clusters) * math.log(n_obs) - 2.0 * hmm.fit_logprob_[0]
#
return hmm, BIC_hmm
def test_ala2():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3, random_state=rs)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
def test_pickle():
"""Test pickling an HMM"""
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3, random_state=rs)
hmm.fit(sequences)
logprob, hidden = hmm.predict(sequences)
with tempfile.TemporaryFile() as savefile:
pickle.dump(hmm, savefile)
savefile.seek(0, 0)
hmm2 = pickle.load(savefile)
logprob2, hidden2 = hmm2.predict(sequences)
assert (logprob == logprob2)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~ HIDDEN MARKOV MODEL ~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
lag_times = [1, 10, 20, 30, 40]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [3, 5]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
strided_data = [
s[i::lag_time] for s in sequences for i in range(lag_time)
]
hmm = GaussianHMM(n_states=n, n_init=1).fit(strided_data)
timescales = hmm.timescales_ * lag_time
hmm_ts0[n].append(timescales[0])
hmm_ts1[n].append(timescales[1])
print('n_states=%d\tlag_time=%d\ttimescales=%s' %
(n, lag_time, timescales))
figure(figsize=(14, 3))
for i, n in enumerate(n_states):
subplot(1, len(n_states), 1 + i)
plot(lag_times, hmm_ts0[n])
plot(lag_times, hmm_ts1[n])
if i == 0:
ylabel('Relaxation Timescale')
xlabel('Lag Time')
def make_hmm_fullauto(ic_projs, equil_dists, n_comp, n_obs, resume):
#
all_hmms = [None]
all_BICs = [None]
all_BIC_mins = [None]
min_dim = 0
pos = 0
if equil_dists > n_comp:
#
temp = equil_dists
equil_dists = n_comp
n_comp = equil_dists
del temp
#
n_dims = equil_dists - 1
pos = 0
dim_strike = 1
dim_end = 0
while n_dims <= n_comp:
#
n_dims += 1
n_clusters = 0
if not (resume is None):
#
print("Resuming with {:d} dimensions and {:d} clusters.".format(int(resume[0]), int(resume[1])))
n_dims = int(resume[0])
n_clusters = int(resume[1]) - 1
resume = None
#
print("Trying with {:d} dimensions...\n".format(n_dims))
ic_slice = [ic_projs[0][:,:n_dims]]
for j in range(1, len(ic_projs)):
#
ic_slice.append(ic_projs[j][:,:n_dims])
#
best_hmm = None
BICs = np.zeros(1)
BIC_min = 0
strike_max = 0
strike_min = 0
while True:
#
n_clusters += 1
print("Trying {:d} clusters...".format(n_clusters))
hmm = GHMM(n_states=n_clusters, reversible_type='transpose', thresh=1e-4, init_algo='GMM', timing=True)
print("Running fit...")
hmm.fit(ic_slice)
print(hmm.fit_logprob_)
if n_obs > 10000:
#
BIC_hmm = (1.0 + 2.0 * float(n_dims)) * float(n_clusters)**2 * math.log(n_obs) - 2.0 * hmm.fit_logprob_[-1]
#
else:
#
BIC_hmm = (1.0 + 2.0 * float(n_dims)) * float(n_clusters) * math.log(n_obs) - 2.0 * hmm.fit_logprob_[-1]
#
if BICs[0] == 0.0:
#
BICs[0] = BIC_hmm
#
else:
#
BICs = np.append(BICs, [BIC_hmm])
#
if len(BICs) == 1:
#
best_hmm = deepcopy(hmm)
#
elif len(BICs) > 1:
#
if BICs[len(BICs)-1] >= BICs[len(BICs)-2]:
#
print("Strike max")
print("Current BIC:")
print(BIC_hmm)
print("Current min:")
print(BICs[BIC_min])
strike_max += 1
#
else:
#
print("Reset max")
strike_max = 0
if BICs[len(BICs)-1] >= BICs[BIC_min]:
#
if len(BICs) > 2 and BICs[len(BICs)-2] <= BICs[len(BICs)-3]:
#
print("Carry min")
print("Current BIC:")
print(BIC_hmm)
print("Current min:")
print(BICs[BIC_min])
#
else:
#
print("Strike min")
print("Current BIC:")
print(BIC_hmm)
print("Current min:")
print(BICs[BIC_min])
strike_min += 1
#
#
else:
#
print("Reset min")
print("Current BIC:")
print(BIC_hmm)
strike_min = 0
BIC_min = len(BICs)-1
best_hmm = deepcopy(hmm)
#
#
#
if n_clusters >= 10 and (strike_max >= 3 or strike_min >= 3):
#
print("Reached expected true minimum\n")
break
#
print("\n")
#
print("Pos :\n{:d}".format(pos))
if all_hmms[0] is None:
#
all_hmms[0] = deepcopy(best_hmm)
all_BICs[0] = BICs
all_BIC_mins[0] = BIC_min
min_dim = n_dims
#
else:
#
all_hmms.append(deepcopy(best_hmm))
all_BICs.append(BICs)
all_BIC_mins.append(BIC_min)
print("Current slowest timescale :")
print(all_hmms[-1].timescales_[0])
print("Current optimal slowest timescale :")
print(all_hmms[pos].timescales_[0])
if all_hmms[-1].timescales_[0] < all_hmms[pos].timescales_[0]:
#
print("Strike on dimension.")
dim_strike += 1
#
elif all_hmms[-1].timescales_[0] > all_hmms[pos].timescales_[0]:
#
print("New optimum found at {:d} dimensions".format(n_dims))
dim_strike = 1
min_dim = n_dims
pos = len(all_hmms) - 1
#
if n_dims == n_comp:
#
print("End of loop. Optimal model selected")
break
#
if dim_strike > 3:
#
print("Maximal strike reached.")
dim_end = n_dims + 1
break
#
#
#
all_dims = np.arange(equil_dists, dim_end)
if min_dim == 0:
#
raise Exception("Texte.")
#
return all_hmms, all_BICs, all_BIC_mins, min_dim, pos, all_dims
def make_hmm(ic_slice, dimension, dims):
#
#bad_clustering = True
#while bad_clustering:
#n_clusters += 1
populations, ranges, min_pop, tot_pop = md_populations(ic_slice, dimension)
n_clusters = 0
best_best_hmm = None
#best_best_KLD_H = None
best_best_hmm_popl = None
BICs = np.zeros(1)
BIC_min = 0
strike_max = 0
strike_min = 0
while True:
#
n_clusters += 1
print("Trying {:d} clusters...".format(n_clusters))
hmm = GHMM(n_states=n_clusters, reversible_type='transpose', thresh=1e-4, init_algo='GMM', timing=True)
best_hmm = None
#best_KLD_H = None
best_BIC = None
for tries in range(1):
#
print("Run {:d}...".format(tries + 1))
hmm.fit(ic_slice)
populations_hmm = hmm_populations(hmm, dimension, ranges, min_pop)
if tot_pop > 10000.0:
#
BIC_hmm = (1.0 + 2.0 * float(dims)) * float(n_clusters)**2 * math.log(tot_pop) - 2.0 * hmm.fit_logprob_[0]
#
else:
#
BIC_hmm = (1.0 + 2.0 * float(dims)) * float(n_clusters) * math.log(tot_pop) - 2.0 * hmm.fit_logprob_[0]
#
#KLD_H_hmm = hmm_KLD_H(populations_hmm, populations, min_pop)
if best_hmm is None:
#
print("Better fit hmm found with {:d} clusters".format(n_clusters))
#print(KLD_H_hmm)
#print(best_KLD_H)
print("BIC :")
print(BIC_hmm)
print("Penalty :")
print((1.0 + 2.0 * float(dims)) * float(n_clusters) * math.log(tot_pop))
print((1.0 + 2.0 * float(dims)) * float(n_clusters)**2 * math.log(tot_pop))
print((1.0 + 2.0 * float(dims)) * float(n_clusters)**3 * math.log(tot_pop))
best_hmm = deepcopy(hmm)
#best_KLD_H = KLD_H_hmm
best_hmm_popl = populations_hmm
best_BIC = BIC_hmm
#
elif BIC_hmm < best_BIC:
#
print("Better fit hmm found with {:d} clusters".format(n_clusters))
#print(KLD_H_hmm)
#print(best_KLD_H)
print("BIC :")
print(BIC_hmm)
print("Penalty :")
print((1.0 + 2.0 * float(dims)) * float(n_clusters) * math.log(tot_pop))
print((1.0 + 2.0 * float(dims)) * float(n_clusters)**2 * math.log(tot_pop))
print((1.0 + 2.0 * float(dims)) * float(n_clusters)**3 * math.log(tot_pop))
print("Previous minimum :")
print(best_BIC)
best_hmm = deepcopy(hmm)
#best_KLD_H = KLD_H_hmm
best_hmm_popl = populations_hmm
best_BIC = BIC_hmm
#
#
if BICs[0] == 0.0:
#
BICs[0] = best_BIC
#
else:
#
BICs = np.append(BICs, [best_BIC])
#
if len(BICs) == 1:
#
best_best_hmm = deepcopy(best_hmm)
best_best_hmm_popl = best_hmm_popl
#
elif len(BICs) > 1:
#
if BICs[len(BICs)-1] >= BICs[len(BICs)-2]:
#
print("Strike max")
strike_max += 1
#
else:
#
print("Reset max")
strike_max = 0
if BICs[len(BICs)-1] >= BICs[BIC_min]:
#
if len(BICs) > 2 and BICs[len(BICs)-2] <= BICs[len(BICs)-3]:
#
print("Carry min")
#
else:
#
print("Strike min")
strike_min += 1
#
#
else:
#
print("Reset min")
strike_min = 0
BIC_min = len(BICs)-1
best_best_hmm = deepcopy(best_hmm)
best_best_hmm_popl = best_hmm_popl
#
#
#
if n_clusters >= 10 and (strike_max >= 3 or strike_min >= 3):
#
print("Reached expected true minimum")
break
#
#print("Kullback-Leibler Divergence to posterior entropy ratio :")
#print(best_KLD_H)
#if best_KLD_H <= 0.005:
#
#print("Information loss is inferior to 0.75%")
#best_best_KLD_H = best_KLD_H
#best_best_hmm = deepcopy(best_hmm)
#best_best_hmm_popl = best_hmm_popl
#break
#
print("\n")
#
return best_best_hmm, ranges, populations, best_best_hmm_popl, BICs
