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)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
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_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 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_that_all_featurizers_run():
# TODO: include all featurizers, perhaps with generator tests
trajectories = AlanineDipeptide().get_cached().trajectories
trj0 = trajectories[0][0]
atom_indices, pair_indices = get_atompair_indices(trj0)
featurizer = AtomPairsFeaturizer(pair_indices)
X_all = featurizer.transform(trajectories)
featurizer = SuperposeFeaturizer(np.arange(15), trj0)
X_all = featurizer.transform(trajectories)
featurizer = DihedralFeaturizer(["phi", "psi"])
X_all = featurizer.transform(trajectories)
featurizer = VonMisesFeaturizer(["phi", "psi"])
X_all = featurizer.transform(trajectories)
# Below doesn't work on ALA dipeptide
# featurizer = msmbuilder.featurizer.ContactFeaturizer()
# X_all = featurizer.transform(trajectories)
featurizer = RMSDFeaturizer(trj0)
X_all = featurizer.transform(trajectories)
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)
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)
def test_1():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
dataset = AlanineDipeptide().get()
trajectories = dataset.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 = GaussianFusionHMM(n_states=4,
n_features=sequences[0].shape[1],
n_init=1)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
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()
plt.style.use("ggplot")
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = [
atom.index for atom in topology.atoms
if atom.element.symbol in ['C', 'O', 'N']
]
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~ 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 = [
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
from msmbuilder.dataset import dataset
import mdtraj as md
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [
atom.index for atom in topology.atoms
if atom.element.symbol in ['C', 'O', 'N']
]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [n for n in range(2, 11, 2)]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
from msmbuilder.dataset import dataset
import mdtraj as md
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk = 100, top = topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [n for n in range(2,11,2)]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~ 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)]
