Datasets:
benchflow
/
frontierphysics-pr32-evidence

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"median_first_order_rms_residual": float(
np.median(arrays["first_order_rms_residual"])
),
"median_second_order_rms_residual": float(
np.median(arrays["second_order_rms_residual"])
),
"median_quadratic_signal_rms": float(
np.median(quadratic_signal)
),
"median_residual_reduction_fraction": float(
np.median(residual_reduction)
),
"fraction_residual_reduction_above_threshold": float(
np.mean(
residual_reduction > NONAFFINE_REDUCTION_THRESHOLD
)
),
"top_nonaffine_residue_keys": expected_top,
}
for key in (
"model_radius_angstrom",
"model_radius_multiplier",
"nonaffine_reduction_threshold",
"maximum_quadratic_design_condition",
"median_first_order_rms_residual",
"median_second_order_rms_residual",
"median_quadratic_signal_rms",
"median_residual_reduction_fraction",
"fraction_residual_reduction_above_threshold",
):
assert math.isclose(
float(summary[key]),
float(expected[key]),
rel_tol=2e-9,
abs_tol=2e-11,
)
for key in (
"residue_count",
"directed_edge_count",
"minimum_neighbor_count",
"maximum_neighbor_count",
"minimum_quadratic_design_rank",
):
assert int(summary[key]) == int(expected[key])
> assert summary["top_nonaffine_residue_keys"] == expected_top
E AssertionError: assert ['S001-R00362...-R00337', ...] == ['S024-R00004...-R00390', ...]
E
E At index 0 diff: 'S001-R00362' != 'S024-R00004'
E
E Full diff:
E [
E - 'S024-R00004',
E - 'S024-R00020',...
E
E ...Full output truncated (56 lines hidden), use '-vv' to show
test_outputs.py:1150: AssertionError
________ test_submitted_executable_handles_compact_full_rank_transition ________
def test_submitted_executable_handles_compact_full_rank_transition() -> None:
fixture, metadata = compact_full_rank_fixture()
public = input_data()
assert int(metadata["residue_count"]) == 80
assert int(metadata["residue_count"]) != np.asarray(public["residue_key"]).size
assert {str(value) for value in fixture["residue_key"]}.isdisjoint(str(value) for value in public["residue_key"])
assert {str(value) for value in fixture["segment_key"]}.isdisjoint(str(value) for value in public["segment_key"])
LOGS.mkdir(parents=True, exist_ok=True)
(LOGS / "compact-full-rank-fixture.json").write_text(
json.dumps(metadata, indent=2, sort_keys=True) + "\n",
encoding="utf-8",
)
> artifacts, hidden_reference, hidden_nonaffine = run_submitted_executable(
fixture,
COMPACT_FULL_RANK_RADIUS,
"compact-full-rank",
)
test_outputs.py:1881:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
test_outputs.py:1575: in run_submitted_executable
validate_nonaffine_summary_against_input(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
artifacts = {'arrays': {'compression': array([0.43653122, 0.52005674, 0.48332031, 0.26770511, 0.32860941,
0.60871512, 0.328...e_threshold': 0.5125, 'input_schema_version': 'neutral-protein-transition-v1', 'maximum_neighbor_count': 79, ...}, ...}
fixture = {'residue_key': array(['H-413DE2D906F3-R051', 'H-413DE2D906F3-R020',
'H-413DE2D906F3-R032', 'H-413DE2D906F3-R01...,
7042, 7133, 7035, 7154, 7504, 7490, 7217, 7014, 7420, 7273, 7539,
7063, 7301, 7336], dtype=int32), ...}
reference = {'deformation_hessian': array([[[[-1.61892042e-03, -1.08232234e-03, -1.85940772e-04],
[-1.08232234e-03, -2.61...4851384 , 0.43814921, 0.46335266, 0.44144179,
0.43479039, 0.46461473, 0.48350409, 0.4343621 , 0.44174635]), ...}
def validate_nonaffine_summary_against_input(
artifacts: dict[str, object],
fixture: dict[str, np.ndarray | str],
reference: dict[str, np.ndarray | float | int],
) -> None:
arrays = artifacts["nonaffine_arrays"]