Datasets:
benchflow
/
frontierphysics-pr75-evidence

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actual = 0.9974282723997658, expected = 1.0
def assert_close(actual: float, expected: float, *, atol: float = 3e-3, rtol: float = 3e-3, message: str = "") -> None:
> assert math.isclose(actual, expected, abs_tol=atol, rel_tol=rtol), message or f"{actual} != {expected}"
E AssertionError: 0.9974282723997658 != 1.0
E assert False
E + where False = <built-in function isclose>(0.9974282723997658, 1.0, abs_tol=2e-06, rel_tol=0.0)
E + where <built-in function isclose> = math.isclose
/verifier/test_outputs.py:132: AssertionError
---------------------------- Captured stdout setup -----------------------------
Processing 4 runs...
Processing run: hidden-94a8233b14ca3446-adaptive-a
Processing run: hidden-94a8233b14ca3446-adaptive-b
Processing run: hidden-94a8233b14ca3446-resonance-a
Processing run: hidden-94a8233b14ca3446-resonance-b
Computing strategy statistics...
Computing standard MLE baseline...
Running convergence diagnostics...
Running phi bound sensitivity analysis...
Writing posterior_trajectories.csv...
Writing checkpoint_posteriors.npz...
Writing precision_gain.csv...
Writing summary.json...
Writing analysis_diagnostics.json...
Generating posterior_diagnostics.png...
Generating report.md...
Analysis complete!
Processing 4 runs...
Processing run: hidden-e9840e162a71d3cc-adaptive-a
Processing run: hidden-e9840e162a71d3cc-adaptive-b
Processing run: hidden-e9840e162a71d3cc-resonance-a
Processing run: hidden-e9840e162a71d3cc-resonance-b
Computing strategy statistics...
Computing standard MLE baseline...
Running convergence diagnostics...
Running phi bound sensitivity analysis...
Writing posterior_trajectories.csv...
Writing checkpoint_posteriors.npz...
Writing precision_gain.csv...
Writing summary.json...
Writing analysis_diagnostics.json...
Generating posterior_diagnostics.png...
Generating report.md...
Analysis complete!
________________ test_adversarial_regressions_reject_shortcuts _________________
hidden_outputs = [({'checkpoint_cases': [{'case_id': 'zero-prefix-94a8233b14ca3446', 'run_id': 'hidden-94a8233b14ca3446-adaptive-a', 's...pytest-3/hidden-cases0/hidden_input_1.json'), PosixPath('/tmp/pytest-of-root/pytest-3/hidden-cases0/hidden_output_1'))]
tmp_path = PosixPath('/tmp/pytest-of-root/pytest-3/test_adversarial_regressions_r0')
def test_adversarial_regressions_reject_shortcuts(
hidden_outputs: list[tuple[dict[str, Any], Path, Path]],
tmp_path: Path,
) -> None:
payload, _, valid_output = hidden_outputs[0]
dummy_trajectory = tmp_path / "dummy-trajectory"
shutil.copytree(valid_output, dummy_trajectory)
rows = load_trajectory(dummy_trajectory)
for row in rows[4:]:
row["theta_mean"] = "1.0"
row["theta_sd"] = "1.0"
row["phi_mean"] = "10.0"
row["phi_sd"] = "1.0"
with (dummy_trajectory / "posterior_trajectories.csv").open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=TRAJECTORY_COLUMNS)
writer.writeheader()
writer.writerows(rows)
with pytest.raises(AssertionError):
validate_trajectory(payload, dummy_trajectory)
fake_design = tmp_path / "fake-design"
shutil.copytree(valid_output, fake_design)
design_rows = load_design(fake_design)
for row in design_rows:
row["detuning_mhz"] = "999"
row["zeta"] = "999"
row["expected_variance_reduction"] = "999"
with (fake_design / "precision_gain.csv").open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=DESIGN_COLUMNS)
writer.writeheader()
writer.writerows(design_rows)
valid_rows = [numeric_trajectory_row(row) for row in load_trajectory(fake_design)]
> normalization_error = validate_checkpoints(payload, fake_design, valid_rows)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:715:
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/verifier/test_outputs.py:240: in validate_checkpoints
assert_close(normalization, 1.0, atol=2e-6, rtol=0.0)
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actual = 0.9974282723997658, expected = 1.0
def assert_close(actual: float, expected: float, *, atol: float = 3e-3, rtol: float = 3e-3, message: str = "") -> None:
> assert math.isclose(actual, expected, abs_tol=atol, rel_tol=rtol), message or f"{actual} != {expected}"
E AssertionError: 0.9974282723997658 != 1.0
E assert False