| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) |
|
|
| from scoobdoob.validation import msm_build as mb |
| from scoobdoob.validation import tpt, bridge |
|
|
|
|
| def _toy_dtrajs(): |
|
|
| rng = np.random.default_rng(0) |
| P = np.array( |
| [ |
| [0.80, 0.20, 0.00, 0.00, 0.00], |
| [0.15, 0.70, 0.15, 0.00, 0.00], |
| [0.00, 0.15, 0.70, 0.15, 0.00], |
| [0.00, 0.00, 0.15, 0.70, 0.15], |
| [0.00, 0.00, 0.00, 0.20, 0.80], |
| ] |
| ) |
| traj = [0] |
| for _ in range(40000): |
| traj.append(int(rng.choice(5, p=P[traj[-1]]))) |
| return [np.array(traj)], P |
|
|
|
|
| def test_connected_and_reversible(): |
| dtrajs, _ = _toy_dtrajs() |
| model, syms, sym2a, _ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True) |
| assert model.n_states == 5 |
| assert abs(model.stationary_distribution.sum() - 1) < 1e-10 |
|
|
| pi = model.stationary_distribution |
| P = model.transition_matrix |
| assert np.allclose(pi[:, None] * P, (pi[:, None] * P).T, atol=1e-8) |
|
|
|
|
| def test_committor_solvers_agree(): |
| dtrajs, _ = _toy_dtrajs() |
| model, *_ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True) |
| q_dt = np.asarray(model.committor_forward([0], [4])) |
| q_ls = tpt.forward_committor_linsolve(model.transition_matrix, [0], [4]) |
| assert np.max(np.abs(q_dt - q_ls)) < 1e-9 |
| assert q_dt[0] == 0.0 and q_dt[4] == 1.0 |
| assert np.all(np.diff(q_dt) >= -1e-9) |
|
|
|
|
| def test_committor_half_localizes_in_middle(): |
| dtrajs, _ = _toy_dtrajs() |
| model, *_ = mb.build_msm(dtrajs, lag=1, k=5, reversible=True) |
| q = np.asarray(model.committor_forward([0], [4])) |
| half = tpt.committor_half_states(q, tol=0.25) |
| assert 2 in half |
|
|
|
|
| def test_bridge_conditioning_beats_unconditional(): |
| _, P = _toy_dtrajs() |
| Ph = bridge.doob_kernels(P, S_end=[4], horizon=20) |
| cond = bridge.sample_bridge_paths(Ph, S_start=[0], n_paths=200, seed=0) |
| unc = bridge.sample_unconditional_paths( |
| P, S_start=[0], horizon=20, n_paths=200, seed=1 |
| ) |
| assert bridge.success_with_ci(cond, [4])["rate"] > 0.9 |
| assert bridge.success_with_ci(unc, [4])["rate"] < 0.5 |
|
|
|
|
| def test_aib9_macrostate_classification(): |
| from scoobdoob.validation import aib9 |
|
|
| state_left = np.array( |
| [ |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], |
| [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
| [1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0], |
| [0.0, 0.0, 0.0, 0.0, 0.5, 1.0, 1.0, 1.0, 1.0], |
| ] |
| ) |
| labels = aib9.classify_microstates(state_left) |
| assert list(labels) == ["R", "L", "I_N", "I_C"] |
|
|
|
|
| def test_aib9_featurize_and_handedness_shapes(): |
| from scoobdoob.validation import aib9 |
|
|
| ang = [np.linspace(-3, 3, 18).reshape(1, 18).repeat(50, 0)] |
| feat = aib9.featurize_sincos(ang) |
| hand = aib9.handedness_indicator(ang) |
| assert feat[0].shape == (50, 36) |
| assert hand[0].shape == (50, 9) |
| assert set(np.unique(hand[0])) <= {0.0, 1.0} |
|
|
|
|
| if __name__ == "__main__": |
| test_connected_and_reversible() |
| test_committor_solvers_agree() |
| test_committor_half_localizes_in_middle() |
| test_bridge_conditioning_beats_unconditional() |
| test_aib9_macrostate_classification() |
| test_aib9_featurize_and_handedness_shapes() |
| print("all validation smoke tests passed") |
|
|
