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 = 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_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_score_3(): 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_1(): grid = NDGrid(n_bins_per_feature=5, min=-np.pi, max=np.pi) seqs = grid.fit_transform(load_doublewell(random_state=0)['trajectories']) model = ContinuousTimeMSM(verbose=False, lag_time=10, n_timescales=3).fit(seqs) np.testing.assert_approx_equal(model.score(seqs), model.score_)