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It runs +the fair single- and multi-objective harnesses (:mod:`benchmarks.harness`) across +every problem (:mod:`benchmarks.problems`), every library (ctrl-freak, pymoo, +DEAP), and the committed 30-seed list (:data:`benchmarks.config.SEEDS`), applies +the metrics (:mod:`benchmarks.metrics`), aggregates them, and adjudicates the +**parity** claim with the overlapping-variance statistic +(:func:`benchmarks.stats.equivalence`). The result is written to +``benchmarks/results/benchmark_results.json`` -- the committed artifact the +pydrology manuscript's numbers trace back to. + +It is **not** a test: it lives in ``benchmarks/`` (outside ``testpaths=["tests"]``) +and is never named ``test_*``, so pytest never collects it. Run it explicitly:: + + uv run python benchmarks/run.py + +The committed JSON embeds the seeds, the pinned library versions (via +:mod:`importlib.metadata`), the budgets and operator settings, and a UTC +timestamp, so the artifact is self-describing and reproducible. + +Convergence history (R6 decision) +--------------------------------- +Persisting every per-seed per-generation Pareto front across all 1080 runs would +be tens of megabytes. Instead each run's harness ``history`` is reduced to a +single **scalar convergence metric per generation** -- best objective error for +SO, IGD+ for MO -- and then aggregated to a **mean and std curve per +(problem, library)** across the 30 seeds (not stored per-seed). For the MO +Pareto-front scatter figures, the final non-dominated front of a single +representative seed (the first committed seed) is stored verbatim per +(problem, library). Both feed s6's figures while keeping the JSON ~1-2 MB. + +Examples +-------- +>>> import numpy as np +>>> _aggregate(np.array([1.0, 2.0, 3.0]))["mean"] +2.0 +>>> curve = _aggregate_curve([[2.0, 1.0], [4.0, 1.0]]) +>>> (curve["mean"], curve["std"]) +([3.0, 1.0], [1.0, 0.0]) +""" + +# ruff: noqa: E402 (sys.path bootstrap must precede the first-party imports) + +from __future__ import annotations + +import json +import sys +from datetime import UTC, datetime +from importlib.metadata import version +from pathlib import Path + +import numpy as np + +# Make ``import benchmarks`` work under a plain ``python benchmarks/run.py`` +# invocation (where sys.path[0] is benchmarks/, not the repo root). +_REPO_ROOT = Path(__file__).resolve().parent.parent +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + +from benchmarks import config, stats +from benchmarks.harness.multi_objective import run_mo +from benchmarks.harness.single_objective import run_so +from benchmarks.metrics import gd, hypervolume, igd_plus, objective_error, solution_distance, success_rate +from benchmarks.problems.multi_objective import MO_PROBLEMS +from benchmarks.problems.single_objective import SO_PROBLEMS + +LIBRARIES: tuple[str, ...] = ("ctrl-freak", "pymoo", "deap") +"""The reference library first, then the two baselines.""" +BASELINES: tuple[str, ...] = ("pymoo", "deap") +"""Libraries ctrl-freak is adjudicated against.""" +SO_METRICS: tuple[str, ...] = ("objective_error", "solution_distance") +"""Per-seed SO metrics carried into the equivalence verdict.""" +MO_METRICS: tuple[str, ...] = ("igd_plus", "gd", "hypervolume") +"""Per-seed MO metrics carried into the equivalence verdict (HV secondary).""" +_PINNED: tuple[str, ...] = ("ctrl_freak", "pymoo", "deap", "numpy") +RESULTS_PATH = Path(__file__).resolve().parent / "results" / "benchmark_results.json" +"""Committed artifact path.""" +_STATISTIC = "overlapping_variance: equivalent iff |mean_cf - mean_lib| < max(std_cf, std_lib)" + + +def _aggregate(values: np.ndarray) -> dict[str, float]: + """Population mean and std of a per-seed metric array. + + Parameters + ---------- + values : numpy.ndarray + Per-seed metric values, 1-D. + + Returns + ------- + dict + ``{"mean": float, "std": float}`` (``ddof=0``). + + Examples + -------- + >>> r = _aggregate(np.array([1.0, 2.0, 3.0])) + >>> (r["mean"], round(r["std"], 6)) + (2.0, 0.816497) + """ + arr = np.asarray(values, dtype=float) + return {"mean": float(np.mean(arr)), "std": float(np.std(arr))} + + +def _metric_array(records: list[dict], problem: str, library: str, metric: str) -> np.ndarray: + """Per-seed values of one metric for one (problem, library) from raw records. + + Parameters + ---------- + records : list of dict + Raw per-run records (each carries ``problem``, ``library`` and the metric + keys). + problem, library, metric : str + Selectors. + + Returns + ------- + numpy.ndarray + The metric's per-seed values, ordered as in `records`. + + Examples + -------- + >>> recs = [ + ... {"problem": "sphere", "library": "ctrl-freak", "objective_error": 0.1}, + ... {"problem": "sphere", "library": "pymoo", "objective_error": 0.2}, + ... ] + >>> _metric_array(recs, "sphere", "ctrl-freak", "objective_error").tolist() + [0.1] + """ + return np.array( + [r[metric] for r in records if r["problem"] == problem and r["library"] == library], + dtype=float, + ) + + +def _aggregate_curve(curves: list[list[float]]) -> dict[str, list[float]]: + """Mean and std (across seeds) of a stack of equal-length scalar curves. + + Parameters + ---------- + curves : list of list of float + One scalar convergence curve per seed; every curve has length + ``n_generations + 1``. + + Returns + ------- + dict + ``{"mean": [...], "std": [...]}`` -- the per-generation mean and std. + + Examples + -------- + >>> c = _aggregate_curve([[2.0, 1.0], [4.0, 1.0]]) + >>> (c["mean"], c["std"]) + ([3.0, 1.0], [1.0, 0.0]) + """ + mat = np.asarray(curves, dtype=float) + return {"mean": np.mean(mat, axis=0).tolist(), "std": np.std(mat, axis=0).tolist()} + + +def _equivalence_section( + records: list[dict], problems: tuple[str, ...], metrics: tuple[str, ...] +) -> dict[str, dict[str, dict[str, dict[str, float | bool]]]]: + """Overlapping-variance verdicts for every problem x metric x baseline. + + Parameters + ---------- + records : list of dict + Raw per-run records for one modality (SO or MO). + problems : tuple of str + Problem names to adjudicate. + metrics : tuple of str + Metric keys to adjudicate. + + Returns + ------- + dict + ``section[problem][metric][baseline]`` -> the + :func:`benchmarks.stats.equivalence` verdict (ctrl-freak as reference). + + Examples + -------- + >>> recs = [ + ... {"problem": "p", "library": "ctrl-freak", "m": 1.0}, + ... {"problem": "p", "library": "ctrl-freak", "m": 1.2}, + ... {"problem": "p", "library": "pymoo", "m": 1.0}, + ... {"problem": "p", "library": "pymoo", "m": 1.4}, + ... {"problem": "p", "library": "deap", "m": 5.0}, + ... {"problem": "p", "library": "deap", "m": 5.2}, + ... ] + >>> sec = _equivalence_section(recs, ("p",), ("m",)) + >>> sec["p"]["m"]["pymoo"]["equivalent"] + True + >>> sec["p"]["m"]["deap"]["equivalent"] + False + """ + section: dict[str, dict[str, dict[str, dict[str, float | bool]]]] = {} + for problem in problems: + section[problem] = {} + for metric in metrics: + cf = _metric_array(records, problem, "ctrl-freak", metric) + section[problem][metric] = { + base: stats.equivalence(cf, _metric_array(records, problem, base, metric)) for base in BASELINES + } + return section + + +def run_so_sweep(seeds: list[int]) -> tuple[list[dict], dict, dict]: + """Run the single-objective sweep over all SO problems x libraries x seeds. + + Parameters + ---------- + seeds : list of int + Seeds to run (the committed sweep passes :data:`benchmarks.config.SEEDS`). + + Returns + ------- + tuple + ``(records, aggregated, convergence)`` where ``records`` is the flat + per-run list, ``aggregated[problem][library]`` holds per-metric mean/std + plus ``success_rate``, and ``convergence[problem][library]`` holds the + mean/std objective-error curve. + + Examples + -------- + >>> records, aggregated, convergence = run_so_sweep(config.SEEDS) # doctest: +SKIP + """ + records: list[dict] = [] + curves: dict[tuple[str, str], list[list[float]]] = {} + for name, problem in SO_PROBLEMS.items(): + for library in LIBRARIES: + seed_curves: list[list[float]] = [] + for seed in seeds: + result = run_so(problem, library, seed) + records.append( + { + "kind": "so", + "problem": name, + "library": library, + "seed": seed, + "objective_error": objective_error(result.best_f, problem.f_star), + "solution_distance": solution_distance(result.best_x, problem.x_star), + "best_f": result.best_f, + "n_evaluations": result.n_evaluations, + } + ) + seed_curves.append([abs(value - problem.f_star) for value in result.history]) + curves[(name, library)] = seed_curves + + aggregated: dict[str, dict[str, dict]] = {} + convergence: dict[str, dict[str, dict]] = {} + for name, problem in SO_PROBLEMS.items(): + aggregated[name] = {} + convergence[name] = {} + for library in LIBRARIES: + best_f = _metric_array(records, name, library, "best_f") + aggregated[name][library] = { + metric: _aggregate(_metric_array(records, name, library, metric)) for metric in SO_METRICS + } + aggregated[name][library]["success_rate"] = success_rate(best_f, problem.f_star, problem.epsilon) + convergence[name][library] = _aggregate_curve(curves[(name, library)]) + return records, aggregated, convergence + + +def run_mo_sweep(seeds: list[int]) -> tuple[list[dict], dict, dict, dict]: + """Run the multi-objective sweep over all MO problems x libraries x seeds. + + Parameters + ---------- + seeds : list of int + Seeds to run (the committed sweep passes :data:`benchmarks.config.SEEDS`). + + Returns + ------- + tuple + ``(records, aggregated, convergence, final_fronts)``. ``records`` is the + flat per-run list; ``aggregated[problem][library]`` holds per-metric + mean/std; ``convergence[problem][library]`` holds the mean/std IGD+ curve; + ``final_fronts[problem][library]`` is the first seed's non-dominated front + objectives (for s6's Pareto scatter). + + Examples + -------- + >>> records, aggregated, convergence, fronts = run_mo_sweep(config.SEEDS) # doctest: +SKIP + """ + records: list[dict] = [] + curves: dict[tuple[str, str], list[list[float]]] = {} + final_fronts: dict[str, dict[str, list]] = {} + representative = seeds[0] + for name, problem in MO_PROBLEMS.items(): + true_front = problem.true_front_sampler() + final_fronts[name] = {} + for library in LIBRARIES: + seed_curves: list[list[float]] = [] + for seed in seeds: + result = run_mo(problem, library, seed) + records.append( + { + "kind": "mo", + "problem": name, + "library": library, + "seed": seed, + "igd_plus": igd_plus(result.front_objectives, true_front), + "gd": gd(result.front_objectives, true_front), + "hypervolume": hypervolume(result.front_objectives, ref_point=problem.ref_point), + "n_front_points": int(result.front_objectives.shape[0]), + "n_evaluations": result.n_evaluations, + } + ) + seed_curves.append([igd_plus(snapshot, true_front) for snapshot in result.history]) + if seed == representative: + final_fronts[name][library] = result.front_objectives.tolist() + curves[(name, library)] = seed_curves + + aggregated: dict[str, dict[str, dict]] = {} + convergence: dict[str, dict[str, dict]] = {} + for name in MO_PROBLEMS: + aggregated[name] = {} + convergence[name] = {} + for library in LIBRARIES: + aggregated[name][library] = { + metric: _aggregate(_metric_array(records, name, library, metric)) for metric in MO_METRICS + } + convergence[name][library] = _aggregate_curve(curves[(name, library)]) + return records, aggregated, convergence, final_fronts + + +def build_results(seeds: list[int]) -> dict: + """Assemble the full results document (sweep + aggregates + verdicts + meta). + + Parameters + ---------- + seeds : list of int + The committed seed list. + + Returns + ------- + dict + The JSON-serialisable results document (see the module docstring schema). + + Examples + -------- + >>> doc = build_results(config.SEEDS) # doctest: +SKIP + """ + so_records, so_aggregated, so_convergence = run_so_sweep(seeds) + mo_records, mo_aggregated, mo_convergence, mo_fronts = run_mo_sweep(seeds) + + metadata = { + "timestamp": datetime.now(UTC).isoformat(), + "seeds": list(seeds), + "n_seeds": len(seeds), + "libraries": list(LIBRARIES), + "statistic": _STATISTIC, + "versions": {pkg: version(pkg) for pkg in _PINNED}, + "budgets": { + "so_pop_size": config.SO_POP_SIZE, + "so_n_generations": config.SO_N_GENERATIONS, + "mo_pop_size": config.MO_POP_SIZE, + "mo_n_generations": config.MO_N_GENERATIONS, + "sbx_eta": config.SBX_ETA, + "pm_eta": config.PM_ETA, + "crossover_prob": config.CROSSOVER_PROB, + }, + } + return { + "metadata": metadata, + "single_objective": { + "raw": so_records, + "aggregated": so_aggregated, + "equivalence": _equivalence_section(so_records, tuple(SO_PROBLEMS), SO_METRICS), + "convergence": so_convergence, + }, + "multi_objective": { + "raw": mo_records, + "aggregated": mo_aggregated, + "equivalence": _equivalence_section(mo_records, tuple(MO_PROBLEMS), MO_METRICS), + "convergence": mo_convergence, + "final_fronts": mo_fronts, + }, + } + + +def main() -> None: + """Run the committed 30-seed sweep and write the results JSON. + + Examples + -------- + >>> callable(main) + True + """ + document = build_results(config.SEEDS) + RESULTS_PATH.parent.mkdir(parents=True, exist_ok=True) + RESULTS_PATH.write_text(json.dumps(document, indent=2)) + print(f"wrote {RESULTS_PATH} ({RESULTS_PATH.stat().st_size} bytes)") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/stats.py b/benchmarks/stats.py new file mode 100644 index 0000000..aa8e5aa --- /dev/null +++ b/benchmarks/stats.py @@ -0,0 +1,101 @@ +"""Pure-numpy equivalence statistic for the validation benchmark suite. + +The milestone substantiates a **parity** ("just as good") claim, which is an +*equivalence* statement, not a difference test. A non-significant t-test is +explicitly insufficient (absence of evidence is not evidence of absence), so this +module implements the chosen, margin-free **overlapping-variance** rule from +``BENCHMARK_PLAN.md`` instead. + +For one ``problem x metric`` and a paired set of per-seed values from the +reference library (ctrl-freak) and one baseline (pymoo or DEAP), the two are +**equivalent** iff the gap between their means is smaller than the larger of the +two per-seed standard deviations:: + + |mean_reference - mean_candidate| < max(std_reference, std_candidate) + +The signed **margin** ``max(std_reference, std_candidate) - |delta_mean|`` is +reported alongside the boolean verdict: a positive margin means the spreads +overlap (equivalent); a negative margin quantifies how far apart the +distributions sit. The comparison is strict (``<``), matching the strict +``< epsilon`` convention used by :func:`benchmarks.metrics.is_success`, so the +degenerate case of two identical zero-variance constants (``delta = threshold = +0``) reports *not equivalent*. + +The standard deviation is the population estimate (``numpy`` default, +``ddof=0``), consistent with the rest of the suite. No ``scipy``; ``numpy`` only. + +Examples +-------- +>>> verdict = equivalence([1.0, 2.0, 3.0], [1.1, 2.0, 2.9]) +>>> verdict["equivalent"] +True +>>> round(verdict["margin"], 6) > 0 +True +>>> equivalence([0.0, 0.0, 0.0], [5.0, 5.0, 5.0])["equivalent"] +False +""" + +import numpy as np + +__all__ = ["equivalence"] + + +def equivalence(reference: np.ndarray, candidate: np.ndarray) -> dict[str, float | bool]: + """Overlapping-variance equivalence verdict for one metric distribution pair. + + Parameters + ---------- + reference : numpy.ndarray + Per-seed metric values from the reference library (ctrl-freak), 1-D, + non-empty. + candidate : numpy.ndarray + Per-seed metric values from the baseline library (pymoo or DEAP), 1-D, + non-empty. Need not be the same length as `reference`. + + Returns + ------- + dict + Keys ``mean_reference``, ``std_reference``, ``mean_candidate``, + ``std_candidate``, ``delta_mean`` (``|mean_reference - mean_candidate|``), + ``threshold`` (``max(std_reference, std_candidate)``), ``margin`` + (``threshold - delta_mean``; positive iff equivalent), and ``equivalent`` + (``bool``, ``delta_mean < threshold``). All numeric values are Python + floats so the dict is JSON-serialisable. + + Raises + ------ + ValueError + If either array is not 1-D or is empty. + + Examples + -------- + >>> r = equivalence(np.array([0.10, 0.12, 0.11]), np.array([0.10, 0.11, 0.12])) + >>> r["equivalent"] + True + >>> sorted(r) + ['delta_mean', 'equivalent', 'margin', 'mean_candidate', 'mean_reference', 'std_candidate', 'std_reference', 'threshold'] + """ + ref = np.asarray(reference, dtype=float) + cand = np.asarray(candidate, dtype=float) + if ref.ndim != 1 or cand.ndim != 1: + raise ValueError(f"reference and candidate must be 1-D, got {ref.ndim}-D and {cand.ndim}-D") + if ref.size == 0 or cand.size == 0: + raise ValueError("reference and candidate must be non-empty") + + mean_reference = float(np.mean(ref)) + std_reference = float(np.std(ref)) + mean_candidate = float(np.mean(cand)) + std_candidate = float(np.std(cand)) + delta_mean = abs(mean_reference - mean_candidate) + threshold = max(std_reference, std_candidate) + margin = threshold - delta_mean + return { + "mean_reference": mean_reference, + "std_reference": std_reference, + "mean_candidate": mean_candidate, + "std_candidate": std_candidate, + "delta_mean": delta_mean, + "threshold": threshold, + "margin": margin, + "equivalent": bool(delta_mean < threshold), + } diff --git a/tests/benchmarks/test_doctests.py b/tests/benchmarks/test_doctests.py new file mode 100644 index 0000000..4cad4a4 --- /dev/null +++ b/tests/benchmarks/test_doctests.py @@ -0,0 +1,69 @@ +"""Consolidated doctest gate for the whole ``benchmarks`` package (s5-owned). + +A single CI-collected test that discovers every benchmark module at runtime (a +recursive glob, never a frozen list) and runs its doctests, so the cross-cutting +"runnable doctests" discipline is enforced in CI for the entire suite -- including +modules later steps add (s6's ``render.py`` / ``reproduce.py``) without editing +this file. Modules whose doctests need an optional heavy dependency +(``pymoo`` / ``deap`` / ``matplotlib``) are skipped, not errored, when that +dependency is absent, so the numpy-floor CI job stays green. The legacy +``benchmarks/zdt/`` tree is excluded (s6 removes it). +""" + +import doctest +import importlib +import importlib.util +from pathlib import Path + +import pytest + +# Recursive runtime discovery root: /benchmarks. +_BENCHMARKS_DIR = Path(__file__).resolve().parents[2] / "benchmarks" + +# Heavy optional deps each module's *doctests* need to execute. Discovery is +# dynamic; this map only annotates the guard. Modules absent from the map +# (anything a future step adds) default to ``_DEFAULT_REQUIRES`` -- requiring all +# three -- so they run under the full-dev CI job and skip under numpy-floor, +# never erroring. +_REQUIRES: dict[str, tuple[str, ...]] = { + "benchmarks.config": (), + "benchmarks.stats": (), + "benchmarks.run": ("pymoo", "deap"), + "benchmarks.metrics": ("pymoo",), + "benchmarks.problems.single_objective": (), + "benchmarks.problems.multi_objective": ("pymoo",), + "benchmarks.harness.operators": ("pymoo", "deap"), + "benchmarks.harness.single_objective": ("pymoo", "deap"), + "benchmarks.harness.multi_objective": ("pymoo", "deap"), +} +_DEFAULT_REQUIRES: tuple[str, ...] = ("pymoo", "deap", "matplotlib") + + +def _discover_modules() -> list[str]: + """Return the dotted names of every benchmark module to doctest. + + Recursively globs ``benchmarks/**/*.py``, skipping the legacy ``zdt/`` tree + and ``__init__.py`` files. + """ + modules: list[str] = [] + for path in sorted(_BENCHMARKS_DIR.rglob("*.py")): + relative = path.relative_to(_BENCHMARKS_DIR.parent) + if "zdt" in relative.parts or path.name == "__init__.py": + continue + modules.append(".".join(relative.with_suffix("").parts)) + return modules + + +_MODULES = _discover_modules() + + +@pytest.mark.parametrize("module_name", _MODULES) +def test_benchmark_module_doctests(module_name: str) -> None: + """Run one benchmark module's doctests, skipping if a heavy dep is absent.""" + for dependency in _REQUIRES.get(module_name, _DEFAULT_REQUIRES): + if importlib.util.find_spec(dependency) is None: + pytest.skip(f"{module_name} doctests require {dependency!r}, which is not installed") + module = importlib.import_module(module_name) + results = doctest.testmod(module, verbose=False) + assert results.attempted > 0, f"{module_name} has no doctests" + assert results.failed == 0, f"{module_name}: {results.failed} of {results.attempted} doctests failed" diff --git a/tests/benchmarks/test_stats.py b/tests/benchmarks/test_stats.py new file mode 100644 index 0000000..5fa99cb --- /dev/null +++ b/tests/benchmarks/test_stats.py @@ -0,0 +1,78 @@ +"""Tests for the overlapping-variance equivalence statistic (pure numpy).""" + +import numpy as np +import pytest + +from benchmarks.stats import equivalence + + +def test_known_overlap_is_equivalent_with_positive_margin(): + # Same centre, generous spread -> spreads overlap -> equivalent, margin > 0. + reference = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) + candidate = np.array([1.2, 2.1, 3.3, 3.9, 5.1]) + verdict = equivalence(reference, candidate) + assert verdict["equivalent"] is True + assert verdict["margin"] > 0.0 + # margin == threshold - delta_mean, exactly. + np.testing.assert_allclose(verdict["margin"], verdict["threshold"] - verdict["delta_mean"]) + + +def test_known_non_overlap_is_not_equivalent_with_negative_margin(): + # Far-apart means, tiny spreads -> disjoint -> not equivalent, margin < 0. + reference = np.array([0.0010, 0.0020, 0.0015, 0.0012, 0.0018]) + candidate = np.array([10.0, 10.1, 9.9, 10.05, 9.95]) + verdict = equivalence(reference, candidate) + assert verdict["equivalent"] is False + assert verdict["margin"] < 0.0 + + +def test_boundary_delta_equals_threshold_is_strictly_not_equivalent(): + # Exactly delta_mean == threshold locks the strict-< rule: reference mean 0 + # std 1, candidate mean 1 std 1 -> delta 1 == threshold 1 -> margin 0. + reference = np.array([-1.0, 1.0]) + candidate = np.array([0.0, 2.0]) + verdict = equivalence(reference, candidate) + np.testing.assert_allclose(verdict["delta_mean"], verdict["threshold"]) + np.testing.assert_allclose(verdict["margin"], 0.0) + assert verdict["equivalent"] is False # strict < => boundary is NOT equivalent + + +def test_identical_zero_variance_constants_are_not_equivalent(): + # Degenerate edge: both libraries produce the same constant for every seed + # (e.g. ZDT4 hypervolume == 0 for all three). delta == threshold == 0; the + # strict-< rule reports NOT equivalent (documented degenerate case). + verdict = equivalence(np.full(5, 0.0), np.full(5, 0.0)) + assert verdict["equivalent"] is False + np.testing.assert_allclose(verdict["margin"], 0.0) + + +def test_threshold_uses_larger_std_and_verdict_is_order_symmetric(): + reference = np.array([0.0, 0.0, 0.0, 0.0]) # std 0 + candidate = np.array([-2.0, -1.0, 1.0, 2.0]) # larger std + forward = equivalence(reference, candidate) + backward = equivalence(candidate, reference) + np.testing.assert_allclose(forward["threshold"], np.std(candidate)) + # Swapping reference/candidate cannot change the verdict (threshold + |delta| + # are both order-independent). + assert forward["equivalent"] == backward["equivalent"] + np.testing.assert_allclose(forward["margin"], backward["margin"]) + + +def test_serialisable_python_scalars(): + verdict = equivalence(np.array([1.0, 2.0]), np.array([1.0, 2.0])) + assert isinstance(verdict["equivalent"], bool) + for key in ("mean_reference", "std_reference", "delta_mean", "threshold", "margin"): + assert isinstance(verdict[key], float) + + +@pytest.mark.parametrize( + ("reference", "candidate"), + [ + (np.array([]), np.array([1.0])), + (np.array([1.0]), np.array([])), + (np.array([[1.0, 2.0]]), np.array([1.0, 2.0])), + ], +) +def test_invalid_shapes_raise(reference, candidate): + with pytest.raises(ValueError): + equivalence(reference, candidate)