text stringlengths 0 4.94k |
|---|
assert abs(float(committor[-1]) - 1.0) <= 1e-8 |
assert np.all(mean_exit >= -1e-8) |
assert abs(float(mean_exit[0])) <= 1e-8 |
assert abs(float(mean_exit[-1])) <= 1e-8 |
own_q, own_t = _backward_kinetics( |
potential[mask], |
mobility[mask], |
record_spec["temperature"], |
x_grid, |
) |
assert relative_l2_matrix(committor, own_q) <= 2e-3 |
assert relative_l2_matrix(mean_exit, own_t) <= 2e-3 |
spec = SPEC["campaigns"][campaign_id] |
truth_u, _ = _potential_truth(x_grid, spec["potential"]) |
truth_mu, _ = _mobility_truth(x_grid, spec["mobility"]) |
truth_q, truth_t = _backward_kinetics( |
truth_u, |
truth_mu, |
record_spec["temperature"], |
x_grid, |
) |
bands = KINETIC_BANDS[campaign_id] |
assert relative_l2_matrix(committor, truth_q) <= bands["committor"] |
> assert relative_l2_matrix(mean_exit, truth_t) <= bands["exit"] |
E assert 0.4583319972390812 <= 0.45 |
E + where 0.4583319972390812 = relative_l2_matrix(array([0. , 0.63240318, 0.89014784, 1.01120688, 1.08055327,\n 1.12861168, 1.16666852, 1.20032346, 1.23278171, 1.26613608,\n 1.30199652, 1.34049498, 1.37945075, 1.41153989, 1.42211625,\n 1.40254141, 1.36401969, 1.32110832, 1.28075273,... |
/verifier/test_outputs.py:1336: AssertionError |
_________ test_uncertainty_intervals_cover_truth_without_being_vacuous _________ |
def test_uncertainty_intervals_cover_truth_without_being_vacuous(): |
_, _, _, _, uncertainty = _submission() |
assert int(uncertainty.get("folds", -1)) == 8 |
assert uncertainty.get("method") == ( |
"eight-fold trajectory-index jackknife with finite-time " |
"model-discrepancy floors" |
) |
assert uncertainty.get("fold_rule") == ( |
"omit trajectory indices i with i % 8 == fold" |
) |
for campaign_id in CAMPAIGN_IDS: |
entry = _uncertainty_entry(campaign_id) |
campaign, x_grid, mobility, _, potential = _campaign(campaign_id) |
assert np.allclose( |
np.asarray(entry["x_grid"], float), x_grid, rtol=0, atol=1e-10 |
) |
arrays = { |
key: np.asarray(entry[key], float) |
for key in ( |
"mobility_lower", |
"mobility_upper", |
"potential_lower", |
"potential_upper", |
"mobility_jackknife_se", |
"potential_jackknife_se", |
) |
} |
assert all( |
array.shape == x_grid.shape and np.all(np.isfinite(array)) |
for array in arrays.values() |
) |
fold_mobility = np.asarray(entry["fold_mobility"], float) |
fold_potential = np.asarray(entry["fold_potential"], float) |
assert fold_mobility.shape == fold_potential.shape == ( |
8, |
len(x_grid), |
) |
assert np.all(np.isfinite(fold_mobility)) |
assert np.all(np.isfinite(fold_potential)) |
assert np.all(fold_mobility > 0) |
assert np.all( |
np.min(fold_potential, axis=1) |
<= 1.0e-6 |
+ 0.01 * np.maximum(np.ptp(fold_potential, axis=1), 1.0e-9) |
) |
mobility_se = np.sqrt( |
7.0 |
/ 8.0 |
* np.sum( |
( |
fold_mobility |
- np.mean(fold_mobility, axis=0, keepdims=True) |
) |
** 2, |
axis=0, |
) |
) |
potential_se = np.sqrt( |
7.0 |
/ 8.0 |
* np.sum( |
( |
fold_potential |
- np.mean(fold_potential, axis=0, keepdims=True) |
) |
** 2, |
axis=0, |
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