def test_both():
model = MarkovStateModel(reversible_type="mle", lag_time=1, n_timescales=1)
# note this might break it if we ask for more than 1 timescale
sequences = np.random.randint(20, size=(10, 1000))
lag_times = [1, 5, 10]
models_ref = []
for tau in lag_times:
msm = MarkovStateModel(reversible_type="mle", lag_time=tau, n_timescales=10)
msm.fit(sequences)
models_ref.append(msm)
timescales_ref = [m.timescales_ for m in models_ref]
models = param_sweep(msm, sequences, {"lag_time": lag_times}, n_jobs=2)
timescales = implied_timescales(sequences, lag_times, msm=msm, n_timescales=10, n_jobs=2)
print(timescales)
print(timescales_ref)
if np.abs(models[0].transmat_ - models[1].transmat_).sum() < 1e-6:
raise Exception("you wrote a bad test.")
for i in range(len(lag_times)):
models[i].lag_time = lag_times[i]
npt.assert_array_almost_equal(models[i].transmat_, models_ref[i].transmat_)
npt.assert_array_almost_equal(timescales_ref[i], timescales[i])
def test_both():
model = MarkovStateModel(
reversible_type='mle', lag_time=1, n_timescales=1)
# note this might break it if we ask for more than 1 timescale
sequences = np.random.randint(20, size=(10, 1000))
lag_times = [1, 5, 10]
models_ref = []
for tau in lag_times:
msm = MarkovStateModel(
reversible_type='mle', lag_time=tau, n_timescales=10)
msm.fit(sequences)
models_ref.append(msm)
timescales_ref = [m.timescales_ for m in models_ref]
models = param_sweep(msm, sequences, {'lag_time' : lag_times}, n_jobs=2)
timescales = implied_timescales(sequences, lag_times, msm=msm,
n_timescales=10, n_jobs=2)
print(timescales)
print(timescales_ref)
if np.abs(models[0].transmat_ - models[1].transmat_).sum() < 1E-6:
raise Exception("you wrote a bad test.")
for i in range(len(lag_times)):
models[i].lag_time = lag_times[i]
npt.assert_array_almost_equal(models[i].transmat_, models_ref[i].transmat_)
npt.assert_array_almost_equal(timescales_ref[i], timescales[i])
def test_both():
sequences = [np.random.randint(20, size=1000) for _ in range(10)]
lag_times = [1, 5, 10]
models_ref = []
for tau in lag_times:
msm = MarkovStateModel(reversible_type='mle', lag_time=tau,
n_timescales=10)
msm.fit(sequences)
models_ref.append(msm)
timescales_ref = [m.timescales_ for m in models_ref]
model = MarkovStateModel(reversible_type='mle', lag_time=1, n_timescales=10)
models = param_sweep(model, sequences, {'lag_time': lag_times}, n_jobs=2)
timescales = implied_timescales(sequences, lag_times, msm=model,
n_timescales=10, n_jobs=2)
print(timescales)
print(timescales_ref)
if np.abs(models[0].transmat_ - models[1].transmat_).sum() < 1E-6:
raise Exception("you wrote a bad test.")
for i in range(len(lag_times)):
npt.assert_array_almost_equal(models[i].transmat_,
models_ref[i].transmat_)
npt.assert_array_almost_equal(timescales_ref[i], timescales[i])
def test_multi_params():
msm = MarkovStateModel()
param_grid = {"lag_time": [1, 2, 3], "reversible_type": ["mle", "transpose"]}
sequences = np.random.randint(20, size=(10, 1000))
models = param_sweep(msm, sequences, param_grid, n_jobs=2)
assert len(models) == 6
# I don't know what the order should be, so I'm just going
# to check that there are no duplicates
params = []
for m in models:
params.append("%s%d" % (m.reversible_type, m.lag_time))
for l in param_grid["lag_time"]:
for s in param_grid["reversible_type"]:
assert ("%s%d" % (s, l)) in params
# this is redundant, but w/e
assert len(set(params)) == 6
def test_multi_params():
msm = MarkovStateModel()
param_grid = {
'lag_time' : [1, 2, 3],
'reversible_type' : ['mle', 'transpose']
}
sequences = np.random.randint(20, size=(10, 1000))
models = param_sweep(msm, sequences, param_grid, n_jobs=2)
assert len(models) == 6
# I don't know what the order should be, so I'm just going
# to check that there are no duplicates
params = []
for m in models:
params.append('%s%d' % (m.reversible_type, m.lag_time))
for l in param_grid['lag_time']:
for s in param_grid['reversible_type']:
assert ('%s%d' % (s, l)) in params
# this is redundant, but w/e
assert len(set(params)) == 6
