def test_score_3():
ds = MullerPotential(random_state=0).get_cached().trajectories
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
train_indices = [9, 4, 3, 6, 2]
test_indices = [8, 0, 5, 7, 1]
temp = '1.0929e-02 5.4147e-02 9.8362e-02 0.1000e+00 6.0455e-02 2.8775e-02\
6.6456e-02 3.3957e-02 4.1484e-03 0.1000e+00 5.0847e-02 1.1516e-02\
3.5266e-02 1.2830e-02 0.1000e+00 2.1801e-02 1.6639e-02 9.4932e-03\
0.1000e+00 0.1000e+00 1.1050e-01 4.0076e-03 0.1000e+00 0.1000e+00\
1.8930e-02 -7.1060e+00 -4.5787e+00 -2.4950e+00 -4.0964e+00 -7.4127e+00\
-6.7574e+00 -4.7137e+00 -3.9530e+00 -4.5781e+00 -7.4585e+00 -6.4634e+00\
-5.8060e+00 -5.4783e+00 -5.3519e+00 -7.4653e+00 -6.5113e+00 -2.1477e+00\
-4.8138e+00 -9.7187e+00 -9.0358e+00 -1.4599e+00 -8.8985e-01 -8.3461e+00\
-7.0930e+00 -2.7618e+00 -6.7421e+00'
model = PESContinuousTimeMSM(lag_time=3, n_timescales=1, sliding_window=False,
ergodic_cutoff=1)
model.theta_ = list(map(np.float64, temp.split()))
train_data = [assignments[i] for i in train_indices]
test_data = [assignments[i] for i in test_indices]
model.fit(train_data)
print(model.summarize())
train = model.score_
test = model.score(test_data)
print(train, test)
def test_score_3():
import warnings
warnings.simplefilter('ignore')
from msmbuilder.example_datasets.muller import MULLER_PARAMETERS as PARAMS
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
ds = MullerPotential(random_state=0).get()['trajectories']
assignments = cluster.fit_transform(ds)
train_indices = [9, 4, 3, 6, 2]
test_indices = [8, 0, 5, 7, 1]
model = ContinuousTimeMSM(lag_time=3,
n_timescales=1,
sliding_window=False,
ergodic_cutoff=1)
train_data = [assignments[i] for i in train_indices]
test_data = [assignments[i] for i in test_indices]
model.fit(train_data)
train = model.score_
test = model.score(test_data)
print(train, test)
def test_score_2():
ds = MullerPotential(random_state=0).get_cached().trajectories
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
test_indices = [5, 0, 4, 1, 2]
train_indices = [3, 6, 7, 8, 9]
model = PESContinuousTimeMSM(lag_time=3, n_timescales=1)
temp='3.13268076e-02 1.45678356e-01 3.12558665e-01 0.10000000e+00\
2.72951862e-02 9.64773504e-02 1.26398091e-01 1.27775726e-01\
2.74208403e-03 9.57265955e-04 1.89498433e-01 0.10010000e+00\
9.73644477e-02 4.16877008e-02 0.10010000e+00 1.73803374e-01\
4.31281724e-02 0.10001000e+00 0.10010000e+00 4.03763450e-01\
0.10001000e+00 0.10001000e+00 0.10010000e+00 2.78156537e-01\
-7.75852152e+00 -4.77716045e+00 -2.67428479e+00 -4.33901900e+00\
-9.23925293e+00 -6.65216281e+00 -4.88309143e+00 -4.04247463e+00\
-4.67140081e+00 -7.95471679e+00 -6.26342874e+00 -6.02515423e+00\
-5.64532492e+00 -5.56770596e+00 -7.66164067e+00 -6.22050765e+00\
-2.12577068e+00 -4.84152585e+00 -9.21360166e+00 -1.43207874e+00\
-8.55459835e-01 -9.21329384e+00 -6.99418825e+00 -2.73060233e+00\
-6.60364249e+00'
model.theta_= list(map(np.float64, temp.split()))
model.fit([assignments[i] for i in train_indices])
print('Initial theta: \n')
print(model._initial_guess(model.countsmat_))
test = model.score([assignments[i] for i in test_indices])
train = model.score_
print('train', train, 'test', test)
print(model.optimizer_state_)
# print(model.summarize())
assert 1 <= test < 2
assert 1 <= train < 2
def test_guess():
ds = MullerPotential(random_state=0).get_cached().trajectories
cluster = NDGrid(n_bins_per_feature=5,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
model1 = ContinuousTimeMSM(guess='log')
model1.fit(assignments)
model2 = ContinuousTimeMSM(guess='pseudo')
model2.fit(assignments)
diff = model1.loglikelihoods_[-1] - model2.loglikelihoods_[-1]
assert np.abs(diff) < 1e-3
assert np.max(np.abs(model1.ratemat_ - model2.ratemat_)) < 1e-1
def test_score_2():
ds = MullerPotential(random_state=0).get_cached().trajectories
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
test_indices = [5, 0, 4, 1, 2]
train_indices = [3, 6, 7, 8, 9]
model = ContinuousTimeMSM(lag_time=3, n_timescales=1)
model.fit([assignments[i] for i in train_indices])
test = model.score([assignments[i] for i in test_indices])
train = model.score_
print('train', train, 'test', test)
assert 1 <= test < 2
assert 1 <= train < 2
def test_score_2():
from msmbuilder.example_datasets.muller import MULLER_PARAMETERS as PARAMS
ds = MullerPotential(random_state=0).get()['trajectories']
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
test_indices = [5, 0, 4, 1, 2]
train_indices = [3, 6, 7, 8, 9]
model = ContinuousTimeMSM(lag_time=3, n_timescales=1)
model.fit([assignments[i] for i in train_indices])
test = model.score([assignments[i] for i in test_indices])
train = model.score_
print('train', train, 'test', test)
assert 1 <= test < 2
assert 1 <= train < 2
def test_guess():
from msmbuilder.example_datasets.muller import MULLER_PARAMETERS as PARAMS
cluster = NDGrid(n_bins_per_feature=5,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
ds = MullerPotential(random_state=0).get()['trajectories']
assignments = cluster.fit_transform(ds)
model1 = ContinuousTimeMSM(guess='log')
model1.fit(assignments)
model2 = ContinuousTimeMSM(guess='pseudo')
model2.fit(assignments)
diff = model1.loglikelihoods_[-1] - model2.loglikelihoods_[-1]
assert np.abs(diff) < 1e-3
assert np.max(np.abs(model1.ratemat_ - model2.ratemat_)) < 1e-1
def test_score_3():
ds = MullerPotential(random_state=0).get_cached().trajectories
cluster = NDGrid(n_bins_per_feature=6,
min=[PARAMS['MIN_X'], PARAMS['MIN_Y']],
max=[PARAMS['MAX_X'], PARAMS['MAX_Y']])
assignments = cluster.fit_transform(ds)
train_indices = [9, 4, 3, 6, 2]
test_indices = [8, 0, 5, 7, 1]
model = ContinuousTimeMSM(lag_time=3, n_timescales=1, sliding_window=False,
ergodic_cutoff=1)
train_data = [assignments[i] for i in train_indices]
test_data = [assignments[i] for i in test_indices]
model.fit(train_data)
train = model.score_
test = model.score(test_data)
print(train, test)
def test_class():
xx = MullerPotential(random_state=123122).get()['trajectories']
assert len(xx) == 10
assert xx[0].ndim == 2
assert xx[0].shape[1] == 2
array2d(xx)
2-D Decomposition Grid Plot ============================ """ from msmbuilder.example_datasets import MullerPotential from msmbuilder.decomposition import tICA from msmbuilder.cluster import MiniBatchKMeans from msmbuilder.msm import MarkovStateModel import numpy as np import msmexplorer as msme rs = np.random.RandomState(42) # Load Fs Peptide Data trajs = MullerPotential().get().trajectories # Perform Dimensionality Reduction tica_model = tICA(lag_time=2, n_components=2) tica_trajs = tica_model.fit_transform(trajs) # Perform Clustering clusterer = MiniBatchKMeans(n_clusters=12, random_state=rs) clustered_trajs = clusterer.fit_transform(tica_trajs) # Construct MSM msm = MarkovStateModel(lag_time=2) assignments = msm.fit_transform(clustered_trajs) # Plot Free Energy data = np.concatenate(trajs, axis=0)