| """CPU environment and finite regression smoke checks, not research results.""" |
|
|
| import hashlib |
| import json |
| import os |
| from pathlib import Path |
| import platform |
| import subprocess |
| import sys |
|
|
| for key in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"): |
| os.environ[key] = "1" |
| os.environ["MPLBACKEND"] = "Agg" |
|
|
| import numpy as np |
| import scipy |
| from scipy.optimize import linprog |
| from scipy.stats import binom |
| import matplotlib |
| matplotlib.use("Agg") |
| from matplotlib import pyplot as plt |
|
|
|
|
| def main(): |
| project = Path(__file__).resolve().parents[1] |
| pmf = binom.pmf(np.arange(129), 128, 0.25) |
| np.testing.assert_allclose(pmf.sum(), 1.0, atol=1e-12, rtol=0) |
| np.testing.assert_allclose(pmf @ np.arange(129), 32.0, atol=1e-11, rtol=0) |
|
|
| |
| |
| |
| lp = linprog( |
| c=[0.0, 0.0, 1.0], |
| A_ub=[[0.125, 0.5, -1.0], [-0.125, -0.5, -1.0]], |
| b_ub=[0.25, -0.25], |
| A_eq=[[1.0, 1.0, 0.0]], b_eq=[1.0], |
| bounds=[(0, None), (0, None), (0, None)], method="highs", |
| ) |
| if not lp.success: |
| raise RuntimeError(lp.message) |
| np.testing.assert_allclose(lp.fun, 0.0, atol=1e-9, rtol=0) |
| np.testing.assert_allclose(lp.x[:2], [2 / 3, 1 / 3], atol=1e-9, rtol=0) |
| fig, ax = plt.subplots() |
| ax.plot(np.arange(129), pmf) |
| fig.canvas.draw() |
| plt.close(fig) |
|
|
| checker = project / "src" / "verify_main_examples.py" |
| completed = subprocess.run( |
| [sys.executable, str(checker)], check=True, capture_output=True, |
| text=True, timeout=180, |
| ) |
| regression = json.loads(completed.stdout) |
| if regression.get("status") != "PASS": |
| raise RuntimeError(regression) |
| record = { |
| "status": "PASS", |
| "scope": "CPU environment and finite regression checks only; no main experiments run", |
| "python_executable": sys.executable, |
| "python_version": platform.python_version(), |
| "platform": platform.platform(), |
| "hostname": platform.node(), |
| "packages": {"numpy": np.__version__, "scipy": scipy.__version__, |
| "matplotlib": matplotlib.__version__}, |
| "environment_mode": "project venv sharing existing Conda base site-packages", |
| "visible_cpu_count": os.cpu_count(), |
| "gpu_used": False, |
| "library_thread_settings": {key: os.environ[key] for key in |
| ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS")}, |
| "binomial_mass_residual": float(abs(pmf.sum() - 1)), |
| "highs_randomization_smoke": { |
| "objective": float(lp.fun), "weights": lp.x[:2].tolist(), |
| "equality_residual": float(np.max(np.abs(lp.eqlin.residual))), |
| "scope": "two-action LP only, not a general policy optimizer", |
| }, |
| "headless_plotting": "PASS (canvas only; no publication figure generated)", |
| "finite_regression": regression, |
| "finite_regression_sha256": hashlib.sha256(checker.read_bytes()).hexdigest(), |
| } |
| output = project / "logs" / "environment_check.json" |
| output.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8") |
| print(json.dumps(record, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
