From 8a9b3ddbb33b7f463ea4218b2f5230366acefd58 Mon Sep 17 00:00:00 2001 From: Qi Zhao Date: Sun, 19 Jul 2026 20:31:17 +0800 Subject: [PATCH 1/4] Prepare AutoOptLib 1.2.0 --- .github/workflows/release.yml | 43 + .github/workflows/tests.yml | 146 ++ .gitignore | 2 + .readthedocs.yaml | 17 + AutoOptLib_Documentation.pdf | Bin 726302 -> 0 bytes CHANGELOG.md | 104 ++ CITATION.cff | 43 + CODE_OF_CONDUCT.md | 25 + CONTRIBUTING.md | 55 + MANIFEST.in | 13 + README.md | 244 +++- SECURITY.md | 20 + benchmarks/check_core_runtime.py | 59 + benchmarks/paper_cec_parity.py | 189 +++ .../paper_cec_parity_20260719/REPORT.md | 113 ++ .../paper_cec_parity_20260719/results.json | 213 +++ .../paper_cec_parity_20260719/run_fitness.csv | 271 ++++ .../paper_cec_parity_20260719/summary.csv | 10 + docs/api.md | 76 + docs/applications.md | 46 + docs/architecture.md | 18 + docs/conf.py | 22 + docs/custom-problems.md | 73 + docs/getting-started.md | 50 + docs/index.rst | 23 + docs/project.md | 10 + docs/reliability.md | 67 + docs/reproducibility.md | 27 + examples/applications/README.md | 21 + .../material_stacking_alg_star.json | 30 + .../algorithms/ris_beamforming_alg_star.json | 30 + examples/applications/material_stacking.py | 29 + examples/applications/ris_beamforming.py | 25 + examples/reproducibility/README.md | 24 + .../algorithms/cec2013_f13_alg_star.json | 28 + .../algorithms/cec2013_f21_alg_star.json | 36 + .../algorithms/cec2013_f6_alg_star.json | 28 + .../reproducibility/application-smoke.json | 36 + .../reproducibility/application_benchmarks.py | 152 ++ pyproject.toml | 100 +- {src => scripts}/smoke_all_problems.py | 4 +- src/autooptlib.egg-info/PKG-INFO | 63 - src/autooptlib.egg-info/SOURCES.txt | 79 -- src/autooptlib.egg-info/dependency_links.txt | 1 - src/autooptlib.egg-info/top_level.txt | 1 - src/autooptlib/__init__.py | 61 +- src/autooptlib/_version.py | 3 + src/autooptlib/applications/__init__.py | 31 + src/autooptlib/applications/beamforming.py | 249 ++++ src/autooptlib/applications/stacking.py | 241 ++++ src/autooptlib/autoopt.py | 35 +- src/autooptlib/components/__init__.py | 79 +- src/autooptlib/components/_utils.py | 4 - src/autooptlib/components/archive_best.py | 8 +- .../components/archive_diversity.py | 12 +- .../components/archive_statistic.py | 8 +- src/autooptlib/components/archive_tabu.py | 1 - .../components/choose_brainstorm.py | 21 +- src/autooptlib/components/choose_nich.py | 13 +- src/autooptlib/components/choose_traverse.py | 1 - src/autooptlib/components/cross_arithmetic.py | 7 +- src/autooptlib/components/cross_order_n.py | 15 +- src/autooptlib/components/cross_order_two.py | 11 +- src/autooptlib/components/cross_point_n.py | 15 +- src/autooptlib/components/cross_point_one.py | 13 +- src/autooptlib/components/cross_point_two.py | 11 +- .../components/cross_point_uniform.py | 9 +- src/autooptlib/components/cross_sim_binary.py | 12 +- src/autooptlib/components/para_cma.py | 55 +- src/autooptlib/components/para_cmaes.py | 1 - src/autooptlib/components/para_pso.py | 33 +- .../components/reinit_continuous.py | 5 +- src/autooptlib/components/reinit_discrete.py | 3 +- .../components/reinit_permutation.py | 3 +- src/autooptlib/components/search_cma.py | 66 +- .../components/search_de_current.py | 13 +- .../components/search_de_current_best.py | 23 +- src/autooptlib/components/search_de_random.py | 15 +- src/autooptlib/components/search_eda.py | 6 +- src/autooptlib/components/search_ica.py | 18 +- src/autooptlib/components/search_insert.py | 3 +- src/autooptlib/components/search_mu_cauchy.py | 15 +- .../components/search_mu_gaussian.py | 15 +- .../components/search_mu_polynomial.py | 66 +- .../components/search_mu_uniform.py | 13 +- src/autooptlib/components/search_pso.py | 44 +- .../components/search_reset_creep.py | 7 +- src/autooptlib/components/search_reset_one.py | 3 +- .../components/search_reset_rand.py | 3 +- src/autooptlib/components/search_scramble.py | 7 +- src/autooptlib/components/search_swap.py | 3 +- .../components/search_swap_multi.py | 3 +- .../components/update_simulated_annealing.py | 11 +- src/autooptlib/problems/__init__.py | 3 +- src/autooptlib/problems/base.py | 191 +++ src/autooptlib/problems/cec2013.py | 404 +++--- src/autooptlib/problems/data/README.md | 19 + src/autooptlib/problems/data/__init__.py | 1 + src/autooptlib/problems/data/cec2013.npz | Bin 0 -> 1289585 bytes src/autooptlib/serialization.py | 227 +++ src/autooptlib/utils/__init__.py | 5 +- src/autooptlib/utils/design/__init__.py | 20 +- src/autooptlib/utils/design/_approximate.py | 71 +- src/autooptlib/utils/design/_decode.py | 17 +- src/autooptlib/utils/design/_disturb.py | 129 +- src/autooptlib/utils/design/_embedding.py | 52 +- src/autooptlib/utils/design/_estimate.py | 4 +- src/autooptlib/utils/design/_evaluate.py | 82 +- src/autooptlib/utils/design/_helpers.py | 34 +- src/autooptlib/utils/design/_initialize.py | 10 +- src/autooptlib/utils/design/_repair.py | 66 +- src/autooptlib/utils/general/__init__.py | 6 +- src/autooptlib/utils/general/improve_rate.py | 8 +- src/autooptlib/utils/general/input.py | 271 +++- src/autooptlib/utils/general/output.py | 190 ++- src/autooptlib/utils/general/process.py | 359 ++++- src/autooptlib/utils/general/select_stats.py | 4 +- src/autooptlib/utils/select.py | 76 +- src/autooptlib/utils/solve/__init__.py | 1258 +++++++++++++++-- src/autooptlib/utils/space.py | 104 +- src/solve_f1.py | 28 - src/temp_test.py | 274 ---- src/test_run.py | 79 -- tests/conftest.py | 3 +- tests/system/test_workflow.py | 197 ++- tests/unit/test_applications.py | 186 +++ tests/unit/test_cli.py | 1 - tests/unit/test_component_contracts.py | 147 ++ tests/unit/test_components.py | 107 +- tests/unit/test_evaluation_modes.py | 183 +++ tests/unit/test_helper_contracts.py | 117 ++ tests/unit/test_input_validation.py | 214 +++ tests/unit/test_matlab_parity.py | 181 +++ tests/unit/test_problem_contract.py | 121 ++ tests/unit/test_public_api.py | 163 +++ tests/unit/test_reliability.py | 764 ++++++++++ tests/unit/test_selection_and_improvement.py | 157 ++ tests/unit/test_serialization_contracts.py | 122 ++ tests/unit/test_solve_contracts.py | 317 +++++ tests/unit/test_utils.py | 66 +- 140 files changed, 9605 insertions(+), 1393 deletions(-) create mode 100644 .github/workflows/release.yml create mode 100644 .github/workflows/tests.yml create mode 100644 .readthedocs.yaml delete mode 100644 AutoOptLib_Documentation.pdf create mode 100644 CHANGELOG.md create mode 100644 CITATION.cff create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 MANIFEST.in create mode 100644 SECURITY.md create mode 100644 benchmarks/check_core_runtime.py create mode 100644 benchmarks/paper_cec_parity.py create mode 100644 benchmarks/results/paper_cec_parity_20260719/REPORT.md create mode 100644 benchmarks/results/paper_cec_parity_20260719/results.json create mode 100644 benchmarks/results/paper_cec_parity_20260719/run_fitness.csv create mode 100644 benchmarks/results/paper_cec_parity_20260719/summary.csv create mode 100644 docs/api.md create mode 100644 docs/applications.md create mode 100644 docs/architecture.md create mode 100644 docs/conf.py create mode 100644 docs/custom-problems.md create mode 100644 docs/getting-started.md create mode 100644 docs/index.rst create mode 100644 docs/project.md create mode 100644 docs/reliability.md create mode 100644 docs/reproducibility.md create mode 100644 examples/applications/README.md create mode 100644 examples/applications/algorithms/material_stacking_alg_star.json create mode 100644 examples/applications/algorithms/ris_beamforming_alg_star.json create mode 100644 examples/applications/material_stacking.py create mode 100644 examples/applications/ris_beamforming.py create mode 100644 examples/reproducibility/README.md create mode 100644 examples/reproducibility/algorithms/cec2013_f13_alg_star.json create mode 100644 examples/reproducibility/algorithms/cec2013_f21_alg_star.json create mode 100644 examples/reproducibility/algorithms/cec2013_f6_alg_star.json create mode 100644 examples/reproducibility/application-smoke.json create mode 100644 examples/reproducibility/application_benchmarks.py rename {src => scripts}/smoke_all_problems.py (96%) delete mode 100644 src/autooptlib.egg-info/PKG-INFO delete mode 100644 src/autooptlib.egg-info/SOURCES.txt delete mode 100644 src/autooptlib.egg-info/dependency_links.txt delete mode 100644 src/autooptlib.egg-info/top_level.txt create mode 100644 src/autooptlib/_version.py create mode 100644 src/autooptlib/applications/__init__.py create mode 100644 src/autooptlib/applications/beamforming.py create mode 100644 src/autooptlib/applications/stacking.py create mode 100644 src/autooptlib/problems/base.py create mode 100644 src/autooptlib/problems/data/README.md create mode 100644 src/autooptlib/problems/data/__init__.py create mode 100644 src/autooptlib/problems/data/cec2013.npz create mode 100644 src/autooptlib/serialization.py delete mode 100644 src/solve_f1.py delete mode 100644 src/temp_test.py delete mode 100644 src/test_run.py create mode 100644 tests/unit/test_applications.py create mode 100644 tests/unit/test_component_contracts.py create mode 100644 tests/unit/test_evaluation_modes.py create mode 100644 tests/unit/test_helper_contracts.py create mode 100644 tests/unit/test_input_validation.py create mode 100644 tests/unit/test_matlab_parity.py create mode 100644 tests/unit/test_problem_contract.py create mode 100644 tests/unit/test_public_api.py create mode 100644 tests/unit/test_reliability.py create mode 100644 tests/unit/test_selection_and_improvement.py create mode 100644 tests/unit/test_serialization_contracts.py create mode 100644 tests/unit/test_solve_contracts.py diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..905ead2 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,43 @@ +name: release + +on: + push: + tags: + - "v*" + workflow_dispatch: + +permissions: + contents: read + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - run: python -m pip install --upgrade pip build twine + - run: python -m build + - run: python -m twine check dist/* + - uses: actions/upload-artifact@v4 + with: + name: distributions + path: dist/ + if-no-files-found: error + + publish: + if: startsWith(github.ref, 'refs/tags/v') + needs: build + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/project/autooptlib/ + permissions: + id-token: write + steps: + - uses: actions/download-artifact@v4 + with: + name: distributions + path: dist/ + - uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..1cc528e --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,146 @@ +name: tests + +on: + push: + pull_request: + +permissions: + contents: read + +jobs: + test: + name: ${{ matrix.os }} / Python ${{ matrix.python }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu-latest + python: "3.9" + - os: ubuntu-latest + python: "3.10" + - os: ubuntu-latest + python: "3.11" + - os: ubuntu-latest + python: "3.12" + - os: ubuntu-latest + python: "3.13" + - os: macos-latest + python: "3.11" + - os: windows-latest + python: "3.11" + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python }} + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install ".[test]" + - run: python -W error -m pytest + + coverage: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install ".[test]" + - run: >- + python -W error -m pytest + --cov=autooptlib + --cov-report=term-missing + --cov-fail-under=90 + + package: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip build twine + - run: python -m build + - run: python -m twine check dist/* + - run: python -m pip install dist/*.whl + - name: Validate installed wheel outside the repository + working-directory: /tmp + run: | + python - <<'PY' + from types import SimpleNamespace + from pathlib import Path + import tempfile + import autooptlib + from autooptlib.problems import cec2013_f1 + from autooptlib.utils.solve import input_algorithm + + assert autooptlib.__version__ == "1.2.0" + problems, data, _ = cec2013_f1([SimpleNamespace()], [10], "construct") + assert problems[0].bound.shape == (2, 10) + assert data[0].o.shape == (10,) + + problem = autooptlib.make_problem( + lambda decision, dimension: float((decision ** 2).sum()), + bounds=(-1.0, 1.0), + ) + custom, custom_data, _ = problem([SimpleNamespace(N=2, Gmax=1)], [2], "construct") + assert custom[0].evaluate(custom_data[0], [0.0, 0.0])[0] == 0.0 + + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + with tempfile.TemporaryDirectory() as directory: + path = autooptlib.save_algorithm(algorithm, Path(directory) / "algorithm.json") + assert autooptlib.load_algorithm(path).operator_pheno + PY + + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install ".[docs]" + - run: sphinx-build -W --keep-going -b html docs docs/_build/html + + typecheck: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install ".[dev]" + - run: python -m mypy + + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install ".[dev]" + - run: python -m ruff check . + - run: python -m ruff format --check . + + performance: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + - run: python -m pip install --upgrade pip + - run: python -m pip install . + - run: python benchmarks/check_core_runtime.py --max-seconds 10 diff --git a/.gitignore b/.gitignore index 5883773..febc10f 100644 --- a/.gitignore +++ b/.gitignore @@ -17,6 +17,7 @@ env/ .pytest_cache/ .coverage htmlcov/ +docs/_build/ # IDE and editor settings .vscode/ @@ -26,6 +27,7 @@ htmlcov/ # Generated experiment artifacts smoke_outputs/ +benchmarks/results/*/f*_d*/ # Miscellaneous OS files Thumbs.db diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..811dabb --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,17 @@ +version: 2 + +build: + os: ubuntu-22.04 + tools: + python: "3.11" + +sphinx: + configuration: docs/conf.py + fail_on_warning: true + +python: + install: + - method: pip + path: . + extra_requirements: + - docs diff --git a/AutoOptLib_Documentation.pdf b/AutoOptLib_Documentation.pdf deleted file mode 100644 index 3e4468366a26e6495097d1de50e6501c1bb76833..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 726302 zcmeFXV~}mn*6vxhZQHh8wad0`+qP}*vTfVeF5AW~b^YIS-spR8-{|g7_tTjXbIp}G zM~ujP@`+f#9Jxs4MZ{od(O7MT``k4sl|1c3{4|@{=dUZt;)1PUW*qS+;6L2sP(2H4EJDWKEJgp6! zO+-wL?2Lb+{`HhMaI|qE_(ud`J8KskTPFe*0(yBz6JrY_XFEp%#(!o-K(F>A1S0{{ z&;6rp}6c7@QoWP(0h$OHV2=Z8*K`0uO#0#rz$pL!|0Bl6T5Ck#W348;z2;_>` 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registering custom components and including them in + compatible design spaces. +- Cross-platform continuous integration and wheel-installation checks. +- Citation, contribution, conduct, and security documentation. +- End-to-end reproducibility tests for seeded runs. +- `make_problem` and a runtime-checkable problem-definition protocol for user + objectives. +- Versioned, validated JSON import/export for designed algorithms. +- Frozen numerical regressions against the official CEC 2013 C reference. +- Behavioral tests for public problem contracts, configuration failures, + evaluation modes, serialization validation, selection, archives, and solve + helpers, with a 90% CI line-coverage floor. +- Canonical Sphinx/Read the Docs sources in the main repository, with a + warning-clean documentation CI job. +- Executable synthetic reference models and examples for the paper's material + stacking and RIS passive-beamforming application classes. +- Opt-in evaluator retries, isolated hard timeouts, failure penalties, + deterministic caching, and structured JSON Lines event logging. +- Ordered `EvalWorkers` thread parallelism for candidate batches, including + in-flight duplicate suppression when deterministic caching is enabled. +- Atomic `experiment.json` manifests recording environment and normalized + invocation provenance. +- Atomic, run-scoped checkpoint/resume for static and sequential Solve + workflows and the outer Design search, including optimizer, inner-search, + stage, surrogate, budget, and random-generator state. +- Configuration-driven application repeatability harness retaining every raw + run and seed, plus trusted-publishing release automation for tagged builds. +- Incremental mypy checks for public/core execution modules and a lightweight + core-runtime regression gate in continuous integration. +- Ruff formatting and static-analysis checks for source, tests, examples, and + benchmark utilities. +- Fault-injection regressions proving that interrupted static Solve, + sequential Solve, and Design runs resume identically to uninterrupted seeded + baselines, plus parallel ordering, retry, logging, caching, and determinism + stress tests. +- Portable algorithm profiles for the three CEC2013 Alg* structures fully + disclosed in the software paper (f6, f13, and f21). +- A faithful Python port of the historical RIS zero-forcing/water-filling + objective and an optional loader for user-supplied MATLAB channel records. +- A time-bounded CEC2013 paper-performance parity benchmark with machine-readable + summary results. + +### Changed + +- Established Python as the canonical implementation. +- Standardized the project license as Apache-2.0. +- Routed stochastic operations through one NumPy random generator per run. +- Restored stateful CMA parameter search for single-algorithm design and + polynomial parameter mutation for population-level design. +- Preserved MATLAB-style `AlgP`, `AlgQ`, and `AlgN` settings during Python + namespace normalization. +- Stabilized covariance handling in CMA-based components. +- Updated project metadata and documentation for version 1.2.0. +- Made public options case- and underscore-insensitive, rejected unknown + options, and validated budgets before execution. +- Enforced `ProbFE` as a hard per-stage objective-evaluation budget and aligned + `AlgFE` with MATLAB as the post-initialization proposal budget. +- Restored MATLAB mode-specific defaults and per-stage sequential budgets. +- Corrected rotations, transforms, buffer ordering, and composition indexing in + all CEC 2013 functions to match the official reference implementation. +- Made intensification and surrogate-assisted modes perform exact held-out + final evaluation and removed fixed hidden surrogate budgets. +- Added contextual errors for failing, vector-valued, and non-finite objective + results. +- Restored paper-listed arithmetic crossover, simulated-binary crossover, and + EDA operators to the continuous automated-design space. +- Corrected serial multi-step execution so every search call updates the + population before the next local iteration or search step. +- Prevented intensification from shrinking candidate performance matrices + after evaluating only the first of multiple training instances. +- Restored one-value-per-algorithm performance aggregation for design-level + CMA updates across multiple problem instances and runs. +- Aligned disturbance probabilities and shared pathway operators, statistical + racing/intensification, AUC checkpoints, historical-best tracking, embedded + archives, shared component auxiliary state, embedding order, and operator + details with the original MATLAB implementation. +- Added problem-specific repair hooks, uncertain-problem aggregation, and + retained evaluator accessory data on solutions. +- Added an optional MATLAB-equivalent 1000-tree random-forest surrogate while + retaining a NumPy-only deterministic fallback for lightweight installs. + +### Removed + +- Dependence on benchmark files in the historical MATLAB source tree. +- Pickle as the only algorithm interchange format (legacy pickle loading is + retained with an explicit security warning). + +## 1.1.0 - 2025-09-15 + +- Added the initial Python translation alongside the MATLAB implementation. + +## 1.0.0 - 2023-10-18 + +- Initial public MATLAB release. diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..21623c8 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,43 @@ +cff-version: 1.2.0 +message: "If you use AutoOptLib, please cite this software and the accompanying paper." +title: "AutoOptLib" +type: software +version: 1.2.0 +date-released: 2026-07-18 +license: Apache-2.0 +repository-code: "https://github.com/auto4opt/AutoOptLib" +url: "https://github.com/auto4opt/AutoOptLib" +authors: + - family-names: Zhao + given-names: Qi + email: zhaoq@sustech.edu.cn + - family-names: Liu + given-names: Jinrun + - family-names: Yan + given-names: Bai + - family-names: Duan + given-names: Qiqi + - family-names: Shi + given-names: Yuhui +preferred-citation: + type: article + title: "AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design" + authors: + - family-names: Zhao + given-names: Qi + - family-names: Yan + given-names: Bai + - family-names: Hu + given-names: Taiwei + - family-names: Chen + given-names: Xianglong + - family-names: Duan + given-names: Qiqi + - family-names: Yang + given-names: Jian + - family-names: Shi + given-names: Yuhui + year: 2023 + identifiers: + - type: doi + value: 10.48550/arXiv.2303.06536 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..e7a1b25 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,25 @@ +# Code of Conduct + +## Our commitment + +We are committed to providing a welcoming, respectful, and harassment-free +environment for everyone participating in the AutoOptLib community. + +## Expected behavior + +- Be respectful and constructive. +- Focus criticism on ideas and code, not individuals. +- Welcome questions and contributions from people with different backgrounds + and levels of experience. +- Respect privacy and do not publish confidential data. + +## Unacceptable behavior + +Harassment, discriminatory language, personal attacks, threats, and deliberate +disruption are not tolerated in project spaces. + +## Enforcement + +Report conduct concerns privately to zhaoq@sustech.edu.cn. Maintainers may edit +or remove inappropriate contributions and may temporarily or permanently ban +participants whose behavior is harmful to the community. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..6a527ab --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,55 @@ +# Contributing to AutoOptLib + +Contributions are welcome through GitHub issues and pull requests. + +## Development setup + +```bash +git clone https://github.com/auto4opt/AutoOptLib.git +cd AutoOptLib +python -m venv .venv +source .venv/bin/activate +python -m pip install -e ".[dev]" +python -m ruff check . +python -m ruff format --check . +python -m pytest -W error +python -m pytest --cov=autooptlib --cov-report=term-missing --cov-fail-under=90 +python -m build +``` + +On Windows, activate the environment with `.venv\\Scripts\\activate`. + +## Pull requests + +1. Open an issue for substantial behavior or API changes. +2. Keep changes focused and add tests for new behavior. +3. Preserve seeded reproducibility: stochastic code must use the generator + supplied through component auxiliary state or the problem/setting object. +4. Update user documentation and `CHANGELOG.md` when behavior changes. +5. Confirm that the full test suite and wheel build pass locally. +6. Treat `ProbFE` and `AlgFE` as inclusive hard limits in new execution paths. + +## Adding a component + +A component must implement each mode relevant to its category: + +- `execute`: run the component and return `(result, auxiliary_state)`; +- `parameter`: describe tunable parameter bounds; +- `behavior`: describe local/global-search behavior. + +Use `register_component` for external components. Components proposed for the +built-in library should be added to the registry, documented, and covered by +deterministic unit tests. + +## Benchmark changes + +Changes to a bundled benchmark require numerical comparisons with an +independent official implementation. Do not approve a benchmark change solely +because it reaches the documented optimum; transformations can be wrong while +still passing that test. + +## Reporting bugs + +Include the AutoOptLib, Python, NumPy, and operating-system versions; the full +call arguments; the traceback; and a minimal reproducible example. Do not post +confidential application data in a public issue. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..9fe03e2 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,13 @@ +include CHANGELOG.md +include CITATION.cff +include CODE_OF_CONDUCT.md +include CONTRIBUTING.md +include LICENSE +include README.md +include SECURITY.md +include pyproject.toml +include .readthedocs.yaml +include scripts/smoke_all_problems.py +recursive-include docs *.md *.rst *.py +recursive-include examples *.json *.md *.py +recursive-include tests *.py diff --git a/README.md b/README.md index 7587f61..ac4b007 100644 --- a/README.md +++ b/README.md @@ -1,48 +1,228 @@ -## AutoOptLib (Automated Design of Metaheuristic Optimization Algorithms) -[![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/qz89/AutoOpt/blob/main/LICENSE) -![](https://img.shields.io/badge/Matlab-%3E%3D%202018a%20-blue.svg) -![](https://img.shields.io/badge/Windows-Pass-brightgreen.svg) -![](https://img.shields.io/badge/MacOS-Pass-brightgreen.svg) +# AutoOptLib -## What does AutoOptLib provide? -AutoOptLib is a MATLAB library for automatically designing metaheuristic optimizers. It provides: +[![Version](https://img.shields.io/badge/version-1.2.0-blue.svg)](https://github.com/auto4opt/AutoOptLib/releases) +[![License](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE) +[![Tests](https://github.com/auto4opt/AutoOptLib/actions/workflows/tests.yml/badge.svg)](https://github.com/auto4opt/AutoOptLib/actions/workflows/tests.yml) +[![Documentation](https://readthedocs.org/projects/autooptlib/badge/?version=latest)](https://autooptlib.readthedocs.io/) -* **Rich library of design choices** - Over 40 representative metaheuristic components for designing algorithms for continuous, discrete, and permutation problems with/without constraints and uncertainties. -* **Flexibility to designing diverse algorithms** - Design algorithms with diverse structures in a single run, enables great possibility to find novel and efficient algorithms. -* **Various design objectives and techniques** - Various design objectives, e.g., solution quality, runtime, and anytime performance. Different design techniques, e.g., racing, intensification, and surrogate. -* **Good accessibility** - GUI for users to input problems, manage the algorithm design process, make experimental comparisons, and visualize results with simple one-clicks. -* **Easy extensibility** - Easily add new algorithmic components, objectives, and techniques by a uniform interface. +AutoOptLib is a Python framework for component-based automated design of +metaheuristic optimizers. It searches over typed operator pathways containing +selection, search, and update components, and evaluates candidate algorithms on +user-provided training instances before testing them on held-out instances. -## What are AutoOptLib's benefits: -* **Save labor resources and time** - Human experts may cost days or weeks to conceive, build up, and verify the optimizers; AutoOptLib would saves such labor resources and time costs with today's increasing computational power. -* **Democratize metaheuristic optimizers** - Through automated algorithm design techniques, AutoOptLib would democratize the efficient and effective use of metaheuristic optimizers. This is significant for researchers and practitioners with complicated optimization problem-solving demands but without the expertise to distinguish and manage suitable optimizers among various choices. -* **Surpass human algorithm design** - By fully exploring potential design choices and discovering novelties with computing power, AutoOptLib would go beyond human experience and gain enhanced performance regarding human problem-solving. -* **Promote metaheuristic research** - With a uniform collection of related techniques, AutoOptLib would promote research of the automated algorithm design and metaheuristic fields and be a tool in the pursuit of autonomous and general artificial intelligence systems. +Version 1.2.0 is the first release packaged as a self-contained Python project. +The earlier MATLAB implementation remains available from the repository's +historical releases. -## How to use AutoOptLib? -Follow the steps below to use AutoOptLib: +## Features -1. Download source code and add it to MATLAB path. +- Components for continuous, discrete, and permutation search spaces. +- Joint search over operator composition and component parameters. +- Exact, racing, intensification, and surrogate-assisted evaluation modes. +- Quality, evaluation-count, wall-clock, and anytime objectives. +- Deterministic runs through a single user-provided random seed. +- Strict `ProbFE` objective budgets and MATLAB-compatible `AlgFE` proposal budgets. +- Versioned JSON export for designed algorithms. +- A small public factory for ordinary Python objective functions. +- Runtime registration of custom components without editing package internals. +- Bundled CEC 2013 data that works from source and installed wheels. +- Executable reference models for material stacking and RIS beamforming. +- Retry, hard-timeout, failure-penalty, evaluation-cache, and JSONL logging + controls for external objectives. +- Automatic experiment manifests with software and invocation provenance. -2. Implement the targeted optimization problem. +## Installation -3. Define the space for designing algorithms. +AutoOptLib requires Python 3.9 or newer. -4. Run AutoOptLib by command or GUI. +Until 1.2.0 is published on PyPI, install it from a clone: -Refer to the Documentation [Web](https://AutoOpt.readthedocs.io/) / [PDF](https://github.com/qz89/AutoOpt/blob/main/AutoOptLib%20for%20Matlab/Documentation.pdf) for user guide. +```bash +git clone https://github.com/auto4opt/AutoOptLib.git +cd AutoOptLib +python -m pip install . +``` + +For development: + +```bash +python -m pip install -e ".[dev]" +python -m ruff check . +python -m ruff format --check . +python -m pytest -W error +``` + +## Quick start + +The following example designs a small optimizer for the bundled CEC 2013 +shifted-sphere problem. The small budgets are intended only as a smoke test. + +```python +from autooptlib import autoopt, make_problem + +sphere = make_problem( + lambda decision, dimension: float((decision**2).sum()), + bounds=(-5.0, 5.0), + name="sphere", +) + +algorithms, trace = autoopt( + Mode="design", + Problem=sphere, + InstanceTrain=[10], + InstanceTest=[20], + AlgN=2, + AlgFE=40, + AlgRuns=2, + ProbN=10, + ProbFE=500, + Evaluate="exact", + Compare="average", + Seed=2026, + OutputDir="results/design", +) + +print(algorithms[0].operator_pheno) +``` + +To run a built-in optimizer directly: + +```python +from autooptlib import autoopt + +best, history = autoopt( + Mode="solve", + Problem="cec2013_f1", + InstanceSolve=[10], + AlgName="CMA-ES", + AlgRuns=3, + ProbN=20, + ProbFE=2000, + Metric="quality", + Seed=2026, +) +``` + +The design call writes portable files such as `Algorithm_1.json` to +`OutputDir`. Use one output directory per experiment. Designed algorithms can +later be loaded without executing pickle code: + +```python +best, history = autoopt( + Mode="solve", + Problem=sphere, + InstanceSolve=[30], + AlgFile="results/design/Algorithm_1.json", + ProbN=20, + ProbFE=2000, + Seed=2026, + OutputDir="results/solve", +) +``` + +All public keyword names are case-insensitive and accept snake case. Unknown +keywords raise an error. `ProbFE` includes initial-population evaluations and +is a hard upper bound per algorithm run. For compatibility with the published +MATLAB procedure, `AlgFE` counts newly proposed algorithms after the initial +`AlgN` incumbents; held-out evaluation of the final algorithms is separate. +Sequential problems receive a fresh `ProbFE` budget at every stage. -AutoOptLib is developed and maintained by Qi Zhao at the Department of Computer Science and Engineering, Southern University of Science and Technology. Users should reference the following papers if using AutoOptLib in their publications: +## Reproducibility + +Pass `Seed=` to `autoopt`. Version 1.2.0 routes this seed through +algorithm construction, component execution, selection, archiving, and +surrogate sampling. Repeating the same call with the same software version, +inputs, and platform produces the same stochastic sequence. + +For expensive thread-safe objectives, set `EvalWorkers` above one to evaluate +candidate batches concurrently while preserving result order. Static and +sequential Solve runs and the outer Design search support atomic +`CheckpointDir`/`Resume` recovery; see the +[reliability guide](docs/reliability.md). + +For archival experiments, record the AutoOptLib version, Python and NumPy +versions, operating system, complete call arguments, and the generated output +files. + +## Extending the component library + +Components use a mode-based callable interface. A custom component can be +registered at runtime: + +```python +from autooptlib import register_component + +def search_example(*args): + mode = args[-1] + if mode == "execute": + parent = args[0] + aux = args[3] + return parent.decs(), aux + if mode == "parameter": + return None, None + if mode == "behavior": + return ["", "GS"], None + raise ValueError(f"Unsupported mode: {mode}") + +register_component( + "search_example", + search_example, + category="search", + problem_types=["continuous"], +) ``` + +Registered components are automatically added to compatible design spaces +created later in the same Python process. + +For most applications, use `make_problem` as in the quick start. Advanced +dynamic or sequential problems can implement the `ProblemDefinition` protocol. +See [the user-problem guide](docs/custom-problems.md) and +[the API guide](docs/api.md). The canonical rendered documentation is hosted +on [Read the Docs](https://autooptlib.readthedocs.io/). + +## Application examples + +The repository contains runnable examples for the material-stacking and RIS +passive-beamforming problem classes discussed in the paper. The included +instances are synthetic and public; they validate the software workflow but do +not reproduce the paper's proprietary stacking records or original wireless +channel files. See [the application guide](docs/applications.md) and +[`examples/applications`](examples/applications/README.md). + +## Scope and limitations + +AutoOptLib performs offline algorithm design and can require many objective +function evaluations. It is most appropriate when the design cost can be +amortized over repeated solution of related instances. The default component +space is curated rather than exhaustive, and the current graph representation +encodes one or more bounded operator pathways rather than arbitrary programs. +It is a research-oriented beta: application-specific validation, complete +constraint modelling, simulator resource isolation, and deployment monitoring +remain the user's responsibility, although opt-in retry, timeout, penalty, +caching, and evaluation logging controls are available. AutoOptLib is not a +universal replacement for expert optimizer selection on a one-off problem. + +## Contributing and support + +See [CONTRIBUTING.md](CONTRIBUTING.md) for the development workflow. Report +bugs and request features through the +[GitHub issue tracker](https://github.com/auto4opt/AutoOptLib/issues). + +## Citation + +Citation metadata is provided in [CITATION.cff](CITATION.cff). Until the +software paper is published, please cite: + +```bibtex @article{zhao2023autooptlib, - title={AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design}, - author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and Duan, Qiqi and Yang, Jian and Shi, Yuhui}, - journal={arXiv preprint arXiv:2303.06536}, - year={2023} + title = {AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design}, + author = {Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and Duan, Qiqi and Yang, Jian and Shi, Yuhui}, + journal = {arXiv preprint arXiv:2303.06536}, + year = {2023} } ``` -## Contact -Users may ask question in [Issues block](https://github.com/qz89/AutoOpt/issues) and upload contributions by [Pulling request](https://github.com/qz89/AutoOpt/pulls). +## License -For any question, comment, or suggestion, please contact Dr. Qi Zhao, email: zhaoq@sustech.edu.cn. +AutoOptLib is licensed under the [Apache License 2.0](LICENSE). diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..0d1b066 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,20 @@ +# Security policy + +## Supported versions + +Security fixes are provided for the latest minor release of AutoOptLib. + +## Loading algorithm files + +Use the versioned JSON algorithm format for exchange and archival. Legacy +pickle files can execute arbitrary Python code when loaded; AutoOptLib emits a +warning, and users must load them only from trusted sources. Objective +functions are ordinary user-provided Python code and should be reviewed before +execution. + +## Reporting a vulnerability + +Please do not disclose suspected vulnerabilities in a public issue. Email +zhaoq@sustech.edu.cn with a description, reproduction steps, affected versions, +and any suggested mitigation. We will acknowledge the report and coordinate a +responsible disclosure timeline. diff --git a/benchmarks/check_core_runtime.py b/benchmarks/check_core_runtime.py new file mode 100644 index 0000000..f2d8f33 --- /dev/null +++ b/benchmarks/check_core_runtime.py @@ -0,0 +1,59 @@ +"""Small, dependency-free performance regression gate for the solve engine.""" + +from __future__ import annotations + +import argparse +import json +import tempfile +import time +from pathlib import Path + +import numpy as np + +from autooptlib import autoopt, make_problem + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--max-seconds", type=float, default=10.0) + args = parser.parse_args() + + calls = 0 + + def sphere(decision, dimension): + nonlocal calls + calls += 1 + return float(np.dot(decision, decision)) + + problem = make_problem(sphere, bounds=(-5.0, 5.0), name="benchmark_sphere") + started = time.perf_counter() + with tempfile.TemporaryDirectory() as directory: + autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[20], + AlgName="Continuous Random Search", + AlgRuns=1, + ProbN=64, + ProbFE=2048, + Seed=2026, + OutputDir=Path(directory), + ) + elapsed = time.perf_counter() - started + report = { + "schema": "autooptlib.performance-smoke", + "objective_evaluations": calls, + "elapsed_seconds": elapsed, + "maximum_seconds": args.max_seconds, + } + print(json.dumps(report, sort_keys=True)) + if calls != 2048: + raise SystemExit(f"expected 2048 objective evaluations, observed {calls}") + if elapsed > args.max_seconds: + raise SystemExit( + f"core runtime {elapsed:.3f}s exceeded ceiling {args.max_seconds:.3f}s" + ) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/paper_cec_parity.py b/benchmarks/paper_cec_parity.py new file mode 100644 index 0000000..f1ce118 --- /dev/null +++ b/benchmarks/paper_cec_parity.py @@ -0,0 +1,189 @@ +"""Time-bounded execution parity check against the software-paper CEC table. + +This benchmark evaluates the three Alg* structures fully disclosed in the +paper. It deliberately tests all three possible held-out dimensions because +the paper does not disclose which dimension was selected for each function. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import platform +from datetime import datetime, timezone +from pathlib import Path +from time import perf_counter +from typing import Any + +import numpy as np + +from autooptlib import __version__, autoopt + +PAPER_RESULTS = { + 6: {"mean": -868.49, "std": 8.87, "optimum": -900.0}, + 13: {"mean": 5.90, "std": 98.8, "optimum": -200.0}, + 21: {"mean": 1113.42, "std": 4.15, "optimum": 900.0}, +} + + +def _atomic_json(path: Path, document: dict[str, Any]) -> None: + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text( + json.dumps(document, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + temporary.replace(path) + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + columns = [ + "function", + "dimension", + "runs", + "prob_n", + "prob_fe", + "seed", + "python_mean", + "python_std_sample", + "python_std_population", + "paper_mean", + "paper_std", + "python_gap_to_optimum", + "paper_gap_to_optimum", + "gap_ratio_python_over_paper", + "mean_absolute_difference", + "elapsed_seconds", + ] + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=columns, lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +def run_benchmark( + output: Path, runs: int, seed: int, max_seconds: float +) -> dict[str, Any]: + repository = Path(__file__).resolve().parents[1] + output.mkdir(parents=True, exist_ok=True) + started = perf_counter() + document: dict[str, Any] = { + "schema": "autooptlib.paper-cec-parity", + "schema_version": 1, + "created_utc": datetime.now(timezone.utc).isoformat(), + "software": { + "autooptlib": __version__, + "python": platform.python_version(), + "numpy": np.__version__, + "platform": platform.platform(), + }, + "method": { + "functions": [6, 13, 21], + "dimensions": [10, 30, 50], + "runs": runs, + "prob_n": 50, + "prob_fe": "1000 * dimension", + "seed_base": seed, + "max_seconds": max_seconds, + "paper_test_dimension_disclosed": False, + "algorithm_source": "software-paper pseudocode JSON profiles", + }, + "paper_results": PAPER_RESULTS, + "results": [], + "complete": False, + } + json_path = output / "results.json" + + for function in (6, 13, 21): + profile = ( + repository + / "examples" + / "reproducibility" + / "algorithms" + / f"cec2013_f{function}_alg_star.json" + ) + for dimension in (10, 30, 50): + if perf_counter() - started >= max_seconds: + document["stopped_reason"] = "time limit reached between cases" + document["elapsed_seconds"] = perf_counter() - started + _atomic_json(json_path, document) + _write_csv(output / "summary.csv", document["results"]) + return document + + case_seed = seed + function * 1000 + dimension + case_output = output / f"f{function}_d{dimension}" + case_started = perf_counter() + best, _ = autoopt( + Mode="solve", + Problem=f"cec2013_f{function}", + InstanceSolve=[dimension], + AlgFile=profile, + AlgRuns=runs, + ProbN=50, + ProbFE=1000 * dimension, + Seed=case_seed, + OutputDir=case_output, + ) + fits = np.asarray([solution.fit for solution in best[0]], dtype=float) + paper = PAPER_RESULTS[function] + python_mean = float(np.mean(fits)) + paper_gap = float(paper["mean"] - paper["optimum"]) + python_gap = float(python_mean - paper["optimum"]) + row = { + "function": function, + "dimension": dimension, + "runs": runs, + "prob_n": 50, + "prob_fe": 1000 * dimension, + "seed": case_seed, + "python_mean": python_mean, + "python_std_sample": float(np.std(fits, ddof=1)) if runs > 1 else 0.0, + "python_std_population": float(np.std(fits)), + "paper_mean": float(paper["mean"]), + "paper_std": float(paper["std"]), + "python_gap_to_optimum": python_gap, + "paper_gap_to_optimum": paper_gap, + "gap_ratio_python_over_paper": ( + python_gap / paper_gap if paper_gap > 0 else float("inf") + ), + "mean_absolute_difference": abs(python_mean - float(paper["mean"])), + "elapsed_seconds": perf_counter() - case_started, + } + document["results"].append(row) + document["elapsed_seconds"] = perf_counter() - started + _atomic_json(json_path, document) + _write_csv(output / "summary.csv", document["results"]) + print( + f"f{function} D={dimension}: {python_mean:.8g} " + f"± {row['python_std_sample']:.4g} " + f"({row['elapsed_seconds']:.1f}s)", + flush=True, + ) + + document["complete"] = True + document["elapsed_seconds"] = perf_counter() - started + _atomic_json(json_path, document) + _write_csv(output / "summary.csv", document["results"]) + return document + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--output", + type=Path, + default=Path("benchmarks/results/paper_cec_parity_20260719"), + ) + parser.add_argument("--runs", type=int, default=30) + parser.add_argument("--seed", type=int, default=20260719) + parser.add_argument("--max-seconds", type=float, default=3300.0) + args = parser.parse_args() + result = run_benchmark(args.output, args.runs, args.seed, args.max_seconds) + print( + f"complete={result['complete']} elapsed={result['elapsed_seconds']:.1f}s " + f"cases={len(result['results'])}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/results/paper_cec_parity_20260719/REPORT.md b/benchmarks/results/paper_cec_parity_20260719/REPORT.md new file mode 100644 index 0000000..600bbcf --- /dev/null +++ b/benchmarks/results/paper_cec_parity_20260719/REPORT.md @@ -0,0 +1,113 @@ +# CEC2013 paper-performance parity check + +## Decision + +The disclosed Python `Alg*` profiles execute reliably and often meet or beat +the mean objective value reported in the paper, but this experiment does **not** +establish numerical reproduction of the paper table. + +- All 270 runs completed without exceptions or non-finite fitness values. +- Total wall time was 1,224.5 seconds (20 minutes 24.5 seconds), below the + one-hour limit. +- For minimization, the Python mean met or beat the paper mean in 6 of the 9 + tested function/dimension cases. +- The closest mean match was CEC2013 f6 at D=10 (absolute difference 21.66), + f13 at D=30 (31.50), and f21 at D=30 (85.53). +- Only the f13/D=30 mean is statistically compatible with the published mean + under an approximate comparison made from the two sets of summary statistics. + The other cases differ materially in mean, variance, or both. + +The appropriate status is therefore **stable execution; paper-performance +threshold frequently attained; numerical reproduction not yet established**. + +## Method + +The paper's exact test-stage settings were used: + +- problems: CEC2013 f6, f13, and f21, whose `Alg*` pseudocodes are disclosed; +- candidate population: 50; +- budget per run: `1000 * D` objective evaluations; +- independent runs: 30; +- dimensions tested: 10, 30, and 50; +- evaluator: AutoOptLib's current official-C-compatible CEC2013 implementation; +- algorithm profiles: the serialized profiles under + `examples/reproducibility/algorithms/`. + +All three dimensions were tested because the paper says that two dimensions +were randomly selected for algorithm design and the remaining dimension was +used for testing, but it does not disclose the selected split for each +function. Seeds were fixed by the benchmark script for repeatability; they +cannot match the paper because its seeds are not published. + +This is an **execution-parity** experiment. It evaluates the algorithm +structures reported by the paper. It does not repeat automated algorithm +design: the paper's design stage would require approximately 500 million +objective evaluations per function (`5000 proposals * 2 instances * 10 runs * +5000 evaluations`), which cannot be completed locally within one hour. + +## Results + +Lower fitness is better. `Gap ratio` is the Python distance from the known +optimum divided by the paper's distance from the optimum; values below 1 mean +that Python performed better than the published mean. + +| Function | D | Python mean | Python sample SD | Paper mean | Paper SD | Gap ratio | Meets paper mean? | +|---:|---:|---:|---:|---:|---:|---:|:---:| +| f6 | 10 | -890.15 | 14.22 | -868.49 | 8.87 | 0.312 | yes | +| f6 | 30 | -836.98 | 22.68 | -868.49 | 8.87 | 2.000 | no | +| f6 | 50 | -796.05 | 39.21 | -868.49 | 8.87 | 3.299 | no | +| f13 | 10 | -170.98 | 7.07 | 5.90 | 98.80 | 0.141 | yes | +| f13 | 30 | -25.60 | 62.22 | 5.90 | 98.80 | 0.847 | yes | +| f13 | 50 | -111.62 | 74.44 | 5.90 | 98.80 | 0.429 | yes | +| f21 | 10 | 1023.10 | 66.46 | 1113.42 | 4.15 | 0.577 | yes | +| f21 | 30 | 1027.89 | 46.72 | 1113.42 | 4.15 | 0.599 | yes | +| f21 | 50 | 1319.03 | 324.98 | 1113.42 | 4.15 | 1.963 | no | + +The results are not merely uniformly worse than the paper. Several are much +better, while some dimensions are worse, and the f21 variance is substantially +larger than reported. That pattern is more consistent with an incomplete +reproduction specification and known implementation differences than with a +single systematic failure in the Python runner. + +## Interpretation and limitations + +The experiment supports these claims: + +1. The three published algorithm structures run stably under the paper's + test-stage population, budget, and replication count. +2. Their performance is in the expected optimization range, and six of nine + dimension cases attain or improve on the published mean. +3. The evidence is insufficient to claim that Python numerically reproduces + the paper table. + +The main unresolved provenance gaps are: + +- the per-function train/test dimension split is not published; +- random seeds and raw 30-run observations are not published; +- the paper used the original MATLAB CEC evaluator, whereas the Python package + intentionally retains the official-C-compatible evaluator; +- NumPy and MATLAB use different random-number streams; +- several Python components deliberately correct or harden MATLAB behavior + (for example, per-pair one-point crossover and CMA covariance handling). + +Consequently, differences from the paper table cannot, on their own, be +classified as Python defects. A defensible exact-reproduction claim requires +publishing the original split, seeds, evaluator/data version, complete +serialized algorithm profiles, and raw run-level results. A direct paired +MATLAB/Python trace test would then be the decisive next validation. + +## Reproduction + +From the repository root: + +```bash +.venv/bin/python benchmarks/paper_cec_parity.py \ + --output benchmarks/results/paper_cec_parity_20260719 \ + --runs 30 \ + --seed 20260719 \ + --max-seconds 3300 +``` + +Machine-readable results are in `results.json` and `summary.csv`. +`run_fitness.csv` retains all 270 final run-level fitness values. Large +generated solution matrices and pickle files are intentionally not versioned. diff --git a/benchmarks/results/paper_cec_parity_20260719/results.json b/benchmarks/results/paper_cec_parity_20260719/results.json new file mode 100644 index 0000000..c380c99 --- /dev/null +++ b/benchmarks/results/paper_cec_parity_20260719/results.json @@ -0,0 +1,213 @@ +{ + "complete": true, + "created_utc": "2026-07-19T04:42:54.882254+00:00", + "elapsed_seconds": 1224.543504583, + "method": { + "algorithm_source": "software-paper pseudocode JSON profiles", + "dimensions": [ + 10, + 30, + 50 + ], + "functions": [ + 6, + 13, + 21 + ], + "max_seconds": 3300.0, + "paper_test_dimension_disclosed": false, + "prob_fe": "1000 * dimension", + "prob_n": 50, + "runs": 30, + "seed_base": 20260719 + }, + "paper_results": { + "6": { + "mean": -868.49, + "optimum": -900.0, + "std": 8.87 + }, + "13": { + "mean": 5.9, + "optimum": -200.0, + "std": 98.8 + }, + "21": { + "mean": 1113.42, + "optimum": 900.0, + "std": 4.15 + } + }, + "results": [ + { + "dimension": 10, + "elapsed_seconds": 10.251902541000002, + "function": 6, + "gap_ratio_python_over_paper": 0.3124646216841772, + "mean_absolute_difference": 21.66423977073157, + "paper_gap_to_optimum": 31.50999999999999, + "paper_mean": -868.49, + "paper_std": 8.87, + "prob_fe": 10000, + "prob_n": 50, + "python_gap_to_optimum": 9.845760229268421, + "python_mean": -890.1542397707316, + "python_std_population": 13.979867536122091, + "python_std_sample": 14.218856942546658, + "runs": 30, + "seed": 20266729 + }, + { + "dimension": 30, + "elapsed_seconds": 29.484835416, + "function": 6, + "gap_ratio_python_over_paper": 2.000006105034848, + "mean_absolute_difference": 31.510192369648053, + "paper_gap_to_optimum": 31.50999999999999, + "paper_mean": -868.49, + "paper_std": 8.87, + "prob_fe": 30000, + "prob_n": 50, + "python_gap_to_optimum": 63.02019236964804, + "python_mean": -836.979807630352, + "python_std_population": 22.29780691368399, + "python_std_sample": 22.67899361842934, + "runs": 30, + "seed": 20266749 + }, + { + "dimension": 50, + "elapsed_seconds": 56.290759957999995, + "function": 6, + "gap_ratio_python_over_paper": 3.2990227346149, + "mean_absolute_difference": 72.44220636771547, + "paper_gap_to_optimum": 31.50999999999999, + "paper_mean": -868.49, + "paper_std": 8.87, + "prob_fe": 50000, + "prob_n": 50, + "python_gap_to_optimum": 103.95220636771546, + "python_mean": -796.0477936322845, + "python_std_population": 38.549993932128096, + "python_std_sample": 39.20901592526963, + "runs": 30, + "seed": 20266769 + }, + { + "dimension": 10, + "elapsed_seconds": 26.865281291000002, + "function": 13, + "gap_ratio_python_over_paper": 0.14094193200658628, + "mean_absolute_difference": 176.8800561998439, + "paper_gap_to_optimum": 205.9, + "paper_mean": 5.9, + "paper_std": 98.8, + "prob_fe": 10000, + "prob_n": 50, + "python_gap_to_optimum": 29.019943800156113, + "python_mean": -170.9800561998439, + 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a/benchmarks/results/paper_cec_parity_20260719/summary.csv b/benchmarks/results/paper_cec_parity_20260719/summary.csv new file mode 100644 index 0000000..c7abaff --- /dev/null +++ b/benchmarks/results/paper_cec_parity_20260719/summary.csv @@ -0,0 +1,10 @@ +function,dimension,runs,prob_n,prob_fe,seed,python_mean,python_std_sample,python_std_population,paper_mean,paper_std,python_gap_to_optimum,paper_gap_to_optimum,gap_ratio_python_over_paper,mean_absolute_difference,elapsed_seconds +6,10,30,50,10000,20266729,-890.1542397707316,14.218856942546658,13.979867536122091,-868.49,8.87,9.845760229268421,31.50999999999999,0.3124646216841772,21.66423977073157,10.251902541000002 +6,30,30,50,30000,20266749,-836.979807630352,22.67899361842934,22.29780691368399,-868.49,8.87,63.02019236964804,31.50999999999999,2.000006105034848,31.510192369648053,29.484835416 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+21,30,30,50,30000,20281749,1027.8852014227145,46.72359043920093,45.938264080675154,1113.42,4.15,127.88520142271454,213.42000000000007,0.5992184491739972,85.53479857728553,182.81951212500007 +21,50,30,50,50000,20281769,1319.0257661484925,324.9835994274294,319.5213011682439,1113.42,4.15,419.0257661484925,213.42000000000007,1.9633856533993643,205.60576614849242,407.222767375 diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..c468d47 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,76 @@ +# Public API + +AutoOptLib's supported entry points are imported from `autooptlib`. + +## `autoopt(**options)` + +Run automated design (`Mode="design"`) or execute an algorithm +(`Mode="solve"`). Keyword names are case-insensitive and underscore-insensitive; +unknown names raise `TypeError`. + +Common options: + +- `Problem`: a `ProblemDefinition` callable or a bundled problem name. +- `InstanceTrain`, `InstanceTest`, `InstanceSolve`: instance identifiers. +- `ProbN`: solution population size. +- `ProbFE`: hard objective-evaluation limit for each algorithm run, including + initialization. +- `AlgN`: number of final algorithms retained in design mode. +- `AlgFE`: candidate-algorithm proposal limit during design, excluding the + initial `AlgN` incumbents and held-out final evaluation. +- `AlgRuns`: repeated runs per instance. +- `Evaluate`: `exact`, `racing`, `intensification`, or `approximate`. +- `Metric`: `quality`, `runtimeFE`, `runtimeSec`, or `auc`. +- `Seed`: integer random seed. +- `OutputDir`: artifact directory. +- `EvalRetries`: retries after each failed objective attempt (default `0`). +- `EvalTimeoutSec`: optional hard timeout per attempt. +- `EvalFailure`: `raise` or `penalize` after attempts are exhausted. +- `EvalPenalty`: finite objective used by `penalize`. +- `EvalCache`: cache identical decisions for deterministic objectives. +- `EvalLog`: optional JSON Lines evaluation-event path. +- `EvalWorkers`: ordered candidate-evaluation workers (default `1`). +- `CheckpointDir`: optional Solve or Design checkpoint directory. +- `CheckpointEvery`: generations or candidate evaluations between atomic writes. +- `Resume`: resume matching completed or interrupted checkpoints. + +The mode-specific defaults match the reference MATLAB package: Design uses +`AlgQ=4`, `ProbN=20`, `ProbFE=5000`, `InnerFE=500`, `AlgN=10`, `AlgFE=5000`, +and `AlgRuns=5`; Solve uses `ProbN=50`, `ProbFE=50000`, and `AlgRuns=5`. +Install `autooptlib[surrogate]` to use the MATLAB-equivalent 1000-tree random +forest in approximate mode; the lightweight core installation uses a +deterministic k-nearest-neighbor fallback. + +Design returns `(algorithms, trace)`. Solve returns +`(best_solutions, histories)`. Design also evaluates the final `AlgN` +algorithms on held-out instances after the `AlgFE` search budget. +Every completed call also writes `experiment.json` to `OutputDir`. + +## `make_problem(...)` + +Wrap an ordinary scalar minimization objective. See +[custom problems](custom-problems.md). + +## `save_algorithm(design, path, metadata=None)` + +Write a decoded designed algorithm using the versioned +`autooptlib.algorithm` JSON schema. + +## `load_algorithm(path)` + +Validate and load an algorithm JSON document. Unknown schema versions and +unregistered component names are rejected. + +## `register_component(...)` + +Register a custom component for the current Python process. The component is +then included in compatible design spaces. Component names stored in JSON must +be registered before loading that JSON. + +## Errors and constraints + +AutoOptLib currently supports scalar minimization. A failed evaluation, +non-scalar objective, or non-finite objective/constraint raises +`ObjectiveEvaluationError` with decision context. Constraint callables return +one or more values; positive entries are summed as violation and non-positive +entries are feasible. diff --git a/docs/applications.md b/docs/applications.md new file mode 100644 index 0000000..1f5e602 --- /dev/null +++ b/docs/applications.md @@ -0,0 +1,46 @@ +# Application problems + +The package includes executable, synthetic reference implementations for the +two problem classes reported in the software paper. + +## Raw-material stacking + +`autooptlib.applications.stacking` models discrete rack level, within-level +position, and orientation decisions. Its objective combines occupied rack +space with configurable ergonomic and grouping preferences, while explicit +constraints enforce shelf width and weight limits. + +The industrial dataset and its complete company-specific rule weights are not +in this repository. The included generator is therefore a transparent +surrogate for testing integration and teaching the workflow, not a source for +reproducing the numerical table in the paper. + +## RIS passive beamforming + +`autooptlib.applications.beamforming` optimizes quantized RIS phase shifts for +a MU-MISO downlink. It constructs the effective channel, uses normalized +zero-forcing and water-filling at the base station, and minimizes the reciprocal +weighted sum rate. This matches the evaluation structure in the historical +MATLAB application. A seeded Rayleigh-channel generator makes the example +reproducible. + +The published experiment used channel data stored in the historical v1.1 tag. +Those files carried GPL notices while the Python package is Apache-2.0, so they +are not silently redistributed in the 1.2 wheel pending an explicit licensing +decision. Consequently, the generated example validates the public software +workflow but does not claim numerical equivalence to the paper. + +If a user has a suitably licensed copy of the historical `Beanforming.mat`, it +can be loaded without changing the problem code: + +```python +from autooptlib import load_ris_matlab, make_ris_beamforming_problem + +instances = load_ris_matlab("Beanforming.mat") +problem = make_ris_beamforming_problem(instances) +``` + +Install the optional reader with `pip install "autooptlib[applications]"`. + +Runnable scripts are located in `examples/applications/`. Replace the generated +instances with domain data while retaining the same train/test separation. diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..6aa5c2d --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,18 @@ +# Architecture + +AutoOptLib separates four concerns: + +1. A **problem definition** constructs related training and test instances. +2. A **design space** supplies type-compatible selection, search, update, and + archive components. +3. The **design engine** searches graph structures and component parameters. +4. The **execution engine** applies either a designed JSON algorithm or a + built-in baseline under a strict objective-evaluation budget. + +This separation is important for application studies: the problem code does +not contain optimizer logic, and a selected algorithm can be exported and +executed later without rerunning automated design. + +The supported public surface is documented in [Public API](api.md). Internal +mode-based component functions remain implementation details and may evolve +between minor releases. diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..e68844a --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,22 @@ +"""Sphinx configuration for the canonical AutoOptLib documentation.""" + +from __future__ import annotations + +from autooptlib import __version__ + +project = "AutoOptLib" +author = "AutoOptLib contributors" +copyright = "2026, AutoOptLib contributors" +release = __version__ + +extensions = [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", +] +myst_enable_extensions = ["colon_fence", "deflist"] +source_suffix = {".rst": "restructuredtext", ".md": "markdown"} +exclude_patterns = ["_build"] +html_theme = "sphinx_rtd_theme" +html_title = f"AutoOptLib {release}" diff --git a/docs/custom-problems.md b/docs/custom-problems.md new file mode 100644 index 0000000..197e6bc --- /dev/null +++ b/docs/custom-problems.md @@ -0,0 +1,73 @@ +# Custom problems + +## Static problems + +`make_problem` is the recommended interface for static continuous, discrete, +and permutation problems. + +```python +import numpy as np +from autooptlib import make_problem + +def load_instance(instance_id): + return np.load(f"instances/{instance_id}.npy") + +def objective(decision, target): + return float(np.sum((decision - target) ** 2)) + +problem = make_problem( + objective, + bounds=lambda instance_id: np.vstack(( + np.full(20, -10.0), + np.full(20, 10.0), + )), + data_factory=load_instance, + name="target_matching", +) +``` + +`objective(decision, data)` must return one finite scalar to minimize. An +optional `constraint(decision, data)` returns values whose positive parts are +constraint violations. + +Bounds can be a `(2, dimension)` array, a `(lower, upper)` pair expanded using +an integer instance identifier, or a callable of the instance identifier. +Permutation problems may omit bounds when each instance identifier is the +permutation length. + +## Evaluation budgets + +Every call to the objective counts toward `ProbFE`, including the initial +population. When the remaining budget cannot fit a full offspring population, +AutoOptLib evaluates only the remaining decisions and uses a deterministic +elitist merge for that final partial batch. + +## Advanced definitions + +Dynamic or sequential problems may implement `ProblemDefinition` directly. In +`construct` mode the callable receives problem records and instance identifiers +and returns `(problems, data, auxiliary)`. Every constructed problem must define: + +- `type`: `[search_space, behavior, uncertainty]`, where the search space is + `continuous`, `discrete`, or `permutation`; +- `bound`: a finite `(2, dimension)` array; +- `N` and `Gmax`: positive integers supplied by AutoOptLib; +- `evaluate(data, decision)`: scalar objective, optionally with constraint and + auxiliary outputs. + +A sequential problem sets behavior to `sequential`, provides data with a +boolean attribute named `continue`, and defines +`advance_sequence(best_solution, data) -> (next_problem, next_data)`. Each stage +receives the configured `ProbFE` limit, matching the reference MATLAB protocol. + +For uncertain problems, set the third type entry to `uncertain`, provide a +positive `sampleN`, and include either `uncertain_average` or +`uncertain_worst` in `setting`. Auxiliary evaluator output is retained on each +`Solution` as `acc`. A problem-specific `repair(data, decision)` hook can be +provided in addition to the generic bound/type repair. + +For external simulators, configure `EvalRetries`, `EvalTimeoutSec`, +`EvalFailure`, `EvalPenalty`, and `EvalLog` as described in +[reliability](reliability.md). AutoOptLib defaults to raising failures rather +than silently replacing them with a score. A simulator wrapper should still +capture its own inputs, stdout/stderr, executable version, and resource limits. diff --git a/docs/getting-started.md b/docs/getting-started.md new file mode 100644 index 0000000..c08f360 --- /dev/null +++ b/docs/getting-started.md @@ -0,0 +1,50 @@ +# Getting started + +## Installation + +AutoOptLib requires Python 3.9 or later. Until version 1.2.0 is available on +PyPI, install it from a checkout: + +```bash +python -m pip install . +``` + +For development and documentation builds: + +```bash +python -m pip install -e ".[dev]" +python -W error -m pytest +sphinx-build -W --keep-going -b html docs docs/_build/html +``` + +## Design and held-out evaluation + +```python +from autooptlib import autoopt, make_problem + +sphere = make_problem( + lambda decision, dimension: float((decision**2).sum()), + bounds=(-5.0, 5.0), +) + +algorithms, trace = autoopt( + Mode="design", + Problem=sphere, + InstanceTrain=[10], + InstanceTest=[20], + AlgN=2, + AlgFE=20, + AlgRuns=2, + ProbN=10, + ProbFE=200, + Seed=2026, + OutputDir="results/design", +) +``` + +The output directory contains a portable JSON description for each selected +algorithm, tabular performance data, a convergence trace, and an +`experiment.json` environment manifest. + +Use realistic design budgets for scientific conclusions. The values above are +only suitable for checking that an installation works. diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..000636f --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,23 @@ +AutoOptLib documentation +======================== + +AutoOptLib automatically designs component-based metaheuristic optimizers on +training instances and evaluates the selected designs on held-out instances. + +.. toctree:: + :maxdepth: 2 + :caption: User guide + + getting-started + custom-problems + applications + reproducibility + reliability + api + architecture + +.. toctree:: + :maxdepth: 1 + :caption: Project + + project diff --git a/docs/project.md b/docs/project.md new file mode 100644 index 0000000..36e32e7 --- /dev/null +++ b/docs/project.md @@ -0,0 +1,10 @@ +# Project information + +- [Contributing guide](https://github.com/auto4opt/AutoOptLib/blob/main/CONTRIBUTING.md) +- [Changelog](https://github.com/auto4opt/AutoOptLib/blob/main/CHANGELOG.md) +- [Security policy](https://github.com/auto4opt/AutoOptLib/blob/main/SECURITY.md) +- [Issue tracker](https://github.com/auto4opt/AutoOptLib/issues) + +The documentation source is maintained in the same repository and release as +the package. This keeps API changes, examples, and versioned documentation in +one review and CI workflow. diff --git a/docs/reliability.md b/docs/reliability.md new file mode 100644 index 0000000..1a5786b --- /dev/null +++ b/docs/reliability.md @@ -0,0 +1,67 @@ +# Reliable objective evaluation + +External simulators commonly fail transiently, hang, or return invalid values. +AutoOptLib provides opt-in controls: + +- `EvalRetries`: retry count after a failed attempt; +- `EvalTimeoutSec`: hard per-attempt timeout using an isolated worker process; +- `EvalFailure`: `"raise"` or `"penalize"` after all attempts fail; +- `EvalPenalty`: finite minimization penalty used by `"penalize"`; +- `EvalCache`: reuse evaluations of identical decisions within one problem + object; +- `EvalLog`: JSON Lines file recording attempts, failures, time, and cache hits; +- `EvalWorkers`: ordered thread workers for evaluating one candidate batch + (default `1`). + +```python +best, history = autoopt( + Mode="solve", + Problem=my_simulator_problem, + InstanceSolve=["plant-17"], + AlgName="Continuous Random Search", + ProbN=20, + ProbFE=2000, + EvalRetries=2, + EvalTimeoutSec=30, + EvalFailure="penalize", + EvalPenalty=1e30, + EvalWorkers=4, + EvalLog="results/evaluations.jsonl", + OutputDir="results/run-17", +) +``` + +Timeout isolation adds process-start overhead and requires the problem and data +to be transferable to a worker process. On spawn-based platforms, prefer +module-level problem callables over lambdas and closures. For command-line +simulators, the most robust architecture is still for the objective itself to +launch a subprocess with its own resource limits and capture its stdout, +stderr, and input checksum. + +`EvalWorkers>1` is intended for thread-safe objectives, especially external +simulators and NumPy/SciPy workloads that release the GIL. Repaired decisions +are submitted concurrently but returned in their original order, so the +optimizer's population ordering remains stable. `EvalWorkers>1` and +`EvalTimeoutSec` are deliberately mutually exclusive because forking timeout +workers from evaluation threads is unsafe on several platforms. + +Caching is disabled by default. Enable it only for deterministic objectives; +otherwise it changes the statistical meaning of repeated evaluations. + +## Checkpoint and resume + +Solve and Design workflows can write atomic checkpoints: + +```python +autoopt(..., CheckpointDir="results/checkpoints", CheckpointEvery=5) +autoopt(..., CheckpointDir="results/checkpoints", CheckpointEvery=5, Resume=True) +``` + +Resume requires matching run-defining budgets and instances. Static Solve +checkpoints preserve solution, component, archive, history, and random-generator +state. Sequential Solve uses a manifest plus one checkpoint per stage; completed +stages are not evaluated again. Design checkpoints preserve candidate +algorithms, trace, surrogate, inner-search state, held-out split, and random +state. They use Python pickle because they contain internal runtime objects: +load only checkpoints produced by a trusted AutoOptLib run. Portable designed +algorithms continue to use the validated JSON format. diff --git a/docs/reproducibility.md b/docs/reproducibility.md new file mode 100644 index 0000000..d6915c1 --- /dev/null +++ b/docs/reproducibility.md @@ -0,0 +1,27 @@ +# Reproducibility + +Every experiment should use an explicit `Seed` and a separate `OutputDir`. +AutoOptLib writes `experiment.json` with the package, Python, NumPy, platform, +mode, normalized options, and instance identifiers. Keep this manifest with the +portable `Algorithm_*.json` files and result tables. + +For paper results, archive: + +- the exact source revision or release tag; +- the train/test split and all instance-generation seeds; +- the complete AutoOptLib call and evaluation budgets; +- raw per-run results, not only means and standard deviations; +- external simulator and operating-system versions; +- a checksum or durable DOI for non-public datasets. + +The application examples use synthetic data generators so that CI and new +users can run them. They demonstrate the same *problem classes* as the paper, +but do not reproduce the paper's proprietary stacking dataset or its original +beamforming channel files. Published numerical claims must be regenerated from +the archived research instances. + +The configuration-driven harness in `examples/reproducibility/` executes the +disclosed discrete baselines and retains every seed and raw run. Its default +configuration is a smoke benchmark. Create a separate archival configuration +for the full budgets, run counts, real test instances, and designed algorithm +JSON files used by a publication. diff --git a/examples/applications/README.md b/examples/applications/README.md new file mode 100644 index 0000000..a143b44 --- /dev/null +++ b/examples/applications/README.md @@ -0,0 +1,21 @@ +# Application examples + +These scripts are executable integration examples for the two application +classes discussed in the AutoOptLib paper. They deliberately generate public, +synthetic instances. They do **not** reproduce the paper's industrial records, +wireless channel files, baseline implementations, or numerical result tables. + +Run from an installed checkout: + +```bash +python examples/applications/material_stacking.py +python examples/applications/ris_beamforming.py +``` + +For a research reproduction, replace the generators with an archived mapping +of instance IDs to domain data and retain fixed, disclosed training/test IDs. + +The `algorithms/` directory contains validated JSON encodings of the two +Alg* structures printed as pseudocode in the paper. They preserve the reported +components and numeric parameters. They do not by themselves reproduce the +paper tables because the research instance data are separate inputs. diff --git a/examples/applications/algorithms/material_stacking_alg_star.json b/examples/applications/algorithms/material_stacking_alg_star.json new file mode 100644 index 0000000..929ee66 --- /dev/null +++ b/examples/applications/algorithms/material_stacking_alg_star.json @@ -0,0 +1,30 @@ +{ + "schema": "autooptlib.algorithm", + "schema_version": 1, + "autooptlib_version": "1.2.0", + "metadata": { + "name": "Alg*_stack", + "application": "material_stacking", + "source": "AutoOptLib software paper pseudocode", + "parameter": "reset probability 0.1342", + "scope": "structure only; paper result reproduction requires the original instances" + }, + "pathways": [ + { + "choose": "choose_roulette_wheel", + "choose_parameter": null, + "search": [ + { + "primary": "cross_point_one", + "secondary": "search_reset_rand", + "termination": [{"$float": "-inf"}, 1.0], + "primary_parameter": null, + "secondary_parameter": [0.1342] + } + ], + "update": "update_round_robin", + "update_parameter": null, + "archive": [] + } + ] +} diff --git a/examples/applications/algorithms/ris_beamforming_alg_star.json b/examples/applications/algorithms/ris_beamforming_alg_star.json new file mode 100644 index 0000000..ef3c032 --- /dev/null +++ b/examples/applications/algorithms/ris_beamforming_alg_star.json @@ -0,0 +1,30 @@ +{ + "schema": "autooptlib.algorithm", + "schema_version": 1, + "autooptlib_version": "1.2.0", + "metadata": { + "name": "Alg*_beamform", + "application": "ris_beamforming", + "source": "AutoOptLib software paper pseudocode", + "parameter": "uniform crossover probability 0.1229", + "scope": "structure only; paper result reproduction requires the original channel instances" + }, + "pathways": [ + { + "choose": "choose_nich", + "choose_parameter": null, + "search": [ + { + "primary": "cross_point_uniform", + "secondary": "search_reset_one", + "termination": [{"$float": "-inf"}, 1.0], + "primary_parameter": [0.1229], + "secondary_parameter": null + } + ], + "update": "update_round_robin", + "update_parameter": null, + "archive": [] + } + ] +} diff --git a/examples/applications/material_stacking.py b/examples/applications/material_stacking.py new file mode 100644 index 0000000..67ad21d --- /dev/null +++ b/examples/applications/material_stacking.py @@ -0,0 +1,29 @@ +"""Small, reproducible material-stacking workflow (not the proprietary data).""" + +from autooptlib import ( + autoopt, + generate_stacking_instance, + make_material_stacking_problem, +) + +instances = { + "train-a": generate_stacking_instance(12, seed=11), + "train-b": generate_stacking_instance(14, seed=12), + "test-a": generate_stacking_instance(13, seed=21), +} +problem = make_material_stacking_problem(instances) + +algorithms, _ = autoopt( + Mode="design", + Problem=problem, + InstanceTrain=["train-a", "train-b"], + InstanceTest=["test-a"], + AlgN=1, + AlgFE=10, + AlgRuns=2, + ProbN=10, + ProbFE=200, + Seed=2026, + OutputDir="results/material-stacking", +) +print(algorithms[0].operator_pheno) diff --git a/examples/applications/ris_beamforming.py b/examples/applications/ris_beamforming.py new file mode 100644 index 0000000..f457f3d --- /dev/null +++ b/examples/applications/ris_beamforming.py @@ -0,0 +1,25 @@ +"""Small, reproducible RIS workflow using generated Rayleigh channels.""" + +from autooptlib import autoopt, generate_ris_instance, make_ris_beamforming_problem + +instances = { + "train-12": generate_ris_instance(12, seed=11), + "train-16": generate_ris_instance(16, seed=12), + "test-20": generate_ris_instance(20, seed=21), +} +problem = make_ris_beamforming_problem(instances) + +algorithms, _ = autoopt( + Mode="design", + Problem=problem, + InstanceTrain=["train-12", "train-16"], + InstanceTest=["test-20"], + AlgN=1, + AlgFE=10, + AlgRuns=2, + ProbN=10, + ProbFE=200, + Seed=2026, + OutputDir="results/ris-beamforming", +) +print(algorithms[0].operator_pheno) diff --git a/examples/reproducibility/README.md b/examples/reproducibility/README.md new file mode 100644 index 0000000..7f55679 --- /dev/null +++ b/examples/reproducibility/README.md @@ -0,0 +1,24 @@ +# Application repeatability harness + +`application_benchmarks.py` runs four disclosed discrete baselines on seeded +synthetic instances for both application classes. It preserves every run's +fitness and seed, a grouped summary, each run's `experiment.json`, and the +exact configuration. + +```bash +python examples/reproducibility/application_benchmarks.py \ + --config examples/reproducibility/application-smoke.json \ + --output results/repeatability +``` + +The default configuration is intentionally small enough for CI-style checks. +It is not sized for scientific comparison. A paper artifact should provide a +separate configuration with the archived real instance identifiers, 30 runs, +the reported 50,000-evaluation budget, and the designed algorithm JSON files. +Designed algorithms can be added to `algorithms` as +`{"label": "Alg*", "file": "path/to/Algorithm_1.json", "applications": ["ris_beamforming"]}`. + +The `algorithms` directory also contains portable profiles reconstructed from +the manuscript's complete pseudocode for CEC2013 f6, f13, and f21. The other +25 CEC2013 Alg* structures are not fully disclosed in the manuscript, so they +cannot be faithfully reconstructed from the paper alone. diff --git a/examples/reproducibility/algorithms/cec2013_f13_alg_star.json b/examples/reproducibility/algorithms/cec2013_f13_alg_star.json new file mode 100644 index 0000000..157f62a --- /dev/null +++ b/examples/reproducibility/algorithms/cec2013_f13_alg_star.json @@ -0,0 +1,28 @@ +{ + "schema": "autooptlib.algorithm", + "schema_version": 1, + "autooptlib_version": "1.2.0", + "metadata": { + "name": "Alg*_f13", + "problem": "cec2013_f13", + "source": "AutoOptLib software paper pseudocode" + }, + "pathways": [ + { + "choose": "choose_roulette_wheel", + "choose_parameter": null, + "search": [ + { + "primary": "search_cma", + "secondary": null, + "termination": [{"$float": "-inf"}, 1.0], + "primary_parameter": null, + "secondary_parameter": null + } + ], + "update": "update_always", + "update_parameter": null, + "archive": [] + } + ] +} diff --git a/examples/reproducibility/algorithms/cec2013_f21_alg_star.json b/examples/reproducibility/algorithms/cec2013_f21_alg_star.json new file mode 100644 index 0000000..cd75111 --- /dev/null +++ b/examples/reproducibility/algorithms/cec2013_f21_alg_star.json @@ -0,0 +1,36 @@ +{ + "schema": "autooptlib.algorithm", + "schema_version": 1, + "autooptlib_version": "1.2.0", + "metadata": { + "name": "Alg*_f21", + "problem": "cec2013_f21", + "source": "AutoOptLib software paper pseudocode", + "note": "The 25-iteration local-search cap follows InnerFE=500 and design population 20." + }, + "pathways": [ + { + "choose": "choose_traverse", + "choose_parameter": null, + "search": [ + { + "primary": "search_de_random", + "secondary": null, + "termination": [0.05, 25.0], + "primary_parameter": [0.0562, 0.1047], + "secondary_parameter": null + }, + { + "primary": "cross_point_one", + "secondary": "search_mu_polynomial", + "termination": [{"$float": "-inf"}, 1.0], + "primary_parameter": null, + "secondary_parameter": [0.2767, 28.1453] + } + ], + "update": "update_pairwise", + "update_parameter": null, + "archive": [] + } + ] +} diff --git a/examples/reproducibility/algorithms/cec2013_f6_alg_star.json b/examples/reproducibility/algorithms/cec2013_f6_alg_star.json new file mode 100644 index 0000000..39db1ba --- /dev/null +++ b/examples/reproducibility/algorithms/cec2013_f6_alg_star.json @@ -0,0 +1,28 @@ +{ + "schema": "autooptlib.algorithm", + "schema_version": 1, + "autooptlib_version": "1.2.0", + "metadata": { + "name": "Alg*_f6", + "problem": "cec2013_f6", + "source": "AutoOptLib software paper pseudocode" + }, + "pathways": [ + { + "choose": "choose_roulette_wheel", + "choose_parameter": null, + "search": [ + { + "primary": "cross_arithmetic", + "secondary": "search_mu_cauchy", + "termination": [{"$float": "-inf"}, 1.0], + "primary_parameter": [0.20423], + "secondary_parameter": null + } + ], + "update": "update_greedy", + "update_parameter": null, + "archive": [] + } + ] +} diff --git a/examples/reproducibility/application-smoke.json b/examples/reproducibility/application-smoke.json new file mode 100644 index 0000000..0eca628 --- /dev/null +++ b/examples/reproducibility/application-smoke.json @@ -0,0 +1,36 @@ +{ + "seed": 2026, + "runs": 2, + "prob_n": 5, + "prob_fe": 20, + "algorithms": [ + "Discrete Random Search", + "Discrete Genetic Algorithm", + "Discrete Iterative Local Search", + "Discrete Simulated Annealing", + { + "label": "Alg*_stack (structure)", + "file": "examples/applications/algorithms/material_stacking_alg_star.json", + "applications": ["material_stacking"] + }, + { + "label": "Alg*_beamform (structure)", + "file": "examples/applications/algorithms/ris_beamforming_alg_star.json", + "applications": ["ris_beamforming"] + } + ], + "applications": { + "material_stacking": { + "instances": [ + {"id": "stack-test-12", "size": 12, "seed": 101}, + {"id": "stack-test-16", "size": 16, "seed": 102} + ] + }, + "ris_beamforming": { + "instances": [ + {"id": "ris-test-12", "size": 12, "seed": 201}, + {"id": "ris-test-16", "size": 16, "seed": 202} + ] + } + } +} diff --git a/examples/reproducibility/application_benchmarks.py b/examples/reproducibility/application_benchmarks.py new file mode 100644 index 0000000..a5bb024 --- /dev/null +++ b/examples/reproducibility/application_benchmarks.py @@ -0,0 +1,152 @@ +"""Run deterministic application baselines and retain every raw run. + +This is an integration/repeatability harness, not a reproduction of the paper's +private industrial data or original wireless channels. +""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path +from statistics import fmean, pstdev +from typing import Any + +from autooptlib import ( + autoopt, + generate_ris_instance, + generate_stacking_instance, + make_material_stacking_problem, + make_ris_beamforming_problem, +) + + +def _build_problem(name: str, specifications: list[dict[str, Any]]): + instances = {} + for spec in specifications: + instance_id = spec["id"] + if name == "material_stacking": + instances[instance_id] = generate_stacking_instance( + spec["size"], seed=spec["seed"] + ) + elif name == "ris_beamforming": + instances[instance_id] = generate_ris_instance( + spec["size"], seed=spec["seed"] + ) + else: + raise ValueError(f"Unknown application {name!r}.") + factory = ( + make_material_stacking_problem + if name == "material_stacking" + else make_ris_beamforming_problem + ) + return factory(instances), list(instances) + + +def run(config: dict[str, Any], output_directory: Path) -> dict[str, Any]: + output_directory.mkdir(parents=True, exist_ok=True) + records: list[dict[str, Any]] = [] + base_seed = int(config["seed"]) + runs = int(config["runs"]) + + for app_index, (application, app_config) in enumerate( + config["applications"].items() + ): + problem, instance_ids = _build_problem(application, app_config["instances"]) + for algorithm_index, algorithm_spec in enumerate(config["algorithms"]): + if isinstance( + algorithm_spec, dict + ) and application not in algorithm_spec.get("applications", [application]): + continue + if isinstance(algorithm_spec, str): + algorithm = algorithm_spec + algorithm_option = {"AlgName": algorithm_spec} + else: + algorithm = str(algorithm_spec["label"]) + algorithm_option = {"AlgFile": str(algorithm_spec["file"])} + for instance_index, instance_id in enumerate(instance_ids): + for run_index in range(runs): + seed = ( + base_seed + + app_index * 100_000 + + algorithm_index * 10_000 + + instance_index * 100 + + run_index + ) + run_dir = ( + output_directory + / "runs" + / application + / algorithm.lower().replace(" ", "-") + / str(instance_id) + / f"run-{run_index + 1}" + ) + best, _ = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[instance_id], + AlgRuns=1, + ProbN=int(config["prob_n"]), + ProbFE=int(config["prob_fe"]), + Seed=seed, + OutputDir=run_dir, + **algorithm_option, + ) + records.append( + { + "application": application, + "algorithm": algorithm, + "instance": instance_id, + "run": run_index + 1, + "seed": seed, + "fitness": float(best[0][0].fit), + } + ) + + groups: dict[tuple[str, str, str], list[float]] = {} + for record in records: + key = (record["application"], record["algorithm"], str(record["instance"])) + groups.setdefault(key, []).append(record["fitness"]) + summary = [ + { + "application": key[0], + "algorithm": key[1], + "instance": key[2], + "mean": fmean(values), + "std": pstdev(values), + "runs": len(values), + } + for key, values in sorted(groups.items()) + ] + return { + "schema": "autooptlib.application-benchmark", + "schema_version": 1, + "created_utc": datetime.now(timezone.utc).isoformat(), + "data_scope": "synthetic reference instances; not paper result data", + "config": config, + "raw_runs": records, + "summary": summary, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + type=Path, + default=Path(__file__).with_name("application-smoke.json"), + ) + parser.add_argument("--output", type=Path, default=Path("results/repeatability")) + args = parser.parse_args() + config = json.loads(args.config.read_text(encoding="utf-8")) + document = run(config, args.output) + target = args.output / "application-benchmarks.json" + temporary = target.with_suffix(".json.tmp") + temporary.write_text(json.dumps(document, indent=2) + "\n", encoding="utf-8") + temporary.replace(target) + print(target) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 07123dd..0cd93a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,16 +4,106 @@ build-backend = "setuptools.build_meta" [project] name = "autooptlib" -version = "0.1.0" -description = "AutoOptLib Python port (algorithm design framework)" +dynamic = ["version"] +description = "Component-based automated design of metaheuristic optimizers" readme = "README.md" requires-python = ">=3.9" -license = {text = "GPL-3.0-or-later"} -authors = [{ name = "Swarm Intelligence Lab" }] +license = "Apache-2.0" +license-files = ["LICENSE"] +authors = [ + { name = "Qi Zhao", email = "zhaoq@sustech.edu.cn" }, + { name = "Swarm Intelligence Lab" }, +] +keywords = [ + "automated algorithm design", + "metaheuristics", + "optimization", + "evolutionary computation", +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", +] +dependencies = [ + "numpy>=1.23,<3", +] + +[project.optional-dependencies] +test = [ + "pytest>=7.4", + "pytest-cov>=4.1", +] +dev = [ + "build>=1.2", + "pytest>=7.4", + "pytest-cov>=4.1", + "sphinx>=7.2,<9", + "sphinx-rtd-theme>=2,<4", + "myst-parser>=2,<5", + "mypy>=1.10,<2", + "ruff>=0.9,<1", +] +docs = [ + "sphinx>=7.2,<9", + "sphinx-rtd-theme>=2,<4", + "myst-parser>=2,<5", +] +applications = [ + "scipy>=1.10,<2", +] +surrogate = [ + "scikit-learn>=1.2,<2", +] + +[project.urls] +Homepage = "https://github.com/auto4opt/AutoOptLib" +Documentation = "https://autooptlib.readthedocs.io/" +Repository = "https://github.com/auto4opt/AutoOptLib" +Issues = "https://github.com/auto4opt/AutoOptLib/issues" [tool.setuptools] package-dir = {"" = "src"} [tool.setuptools.packages.find] where = ["src"] -include = ["autooptlib*"] \ No newline at end of file +include = ["autooptlib*"] + +[tool.setuptools.package-data] +"autooptlib.problems.data" = ["*.npz", "README.md"] + +[tool.setuptools.dynamic] +version = { attr = "autooptlib._version.__version__" } + +[tool.pytest.ini_options] +addopts = "--strict-markers" +testpaths = ["tests"] + +[tool.mypy] +python_version = "3.9" +files = [ + "src/autooptlib/serialization.py", + "src/autooptlib/problems/base.py", + "src/autooptlib/applications", + "src/autooptlib/utils/general/input.py", + "src/autooptlib/utils/general/process.py", + "src/autooptlib/utils/solve/__init__.py", +] +follow_imports = "skip" +ignore_missing_imports = true +check_untyped_defs = true +show_error_codes = true + +[tool.ruff] +target-version = "py39" +line-length = 88 + +[tool.ruff.lint] +select = ["E4", "E7", "E9", "F", "I"] diff --git a/src/smoke_all_problems.py b/scripts/smoke_all_problems.py similarity index 96% rename from src/smoke_all_problems.py rename to scripts/smoke_all_problems.py index 00271f1..4b443e3 100644 --- a/src/smoke_all_problems.py +++ b/scripts/smoke_all_problems.py @@ -1,3 +1,5 @@ +"""Run lightweight design and solve workflows for every bundled problem.""" + from __future__ import annotations import argparse @@ -22,7 +24,7 @@ def _working_directory(path: Path): def _require_files(paths: Iterable[Path]) -> None: - missing = [str(p) for p in paths if not p.exists()] + missing = [str(path) for path in paths if not path.exists()] if missing: raise RuntimeError(f"expected output files missing: {', '.join(missing)}") diff --git a/src/autooptlib.egg-info/PKG-INFO b/src/autooptlib.egg-info/PKG-INFO deleted file mode 100644 index 45f310e..0000000 --- a/src/autooptlib.egg-info/PKG-INFO +++ /dev/null @@ -1,63 +0,0 @@ -Metadata-Version: 2.4 -Name: autooptlib -Version: 0.1.0 -Summary: AutoOptLib Python port (algorithm design framework) -Author: Swarm Intelligence Lab -License: GPL-3.0-or-later -Requires-Python: >=3.9 -Description-Content-Type: text/markdown - -## AutoOptLib (Automated Design of Metaheuristic Optimization Algorithms) -[![GNU General Public License v3.0](https://img.shields.io/badge/license-GNU%20GPL--v3.0-green.svg)](https://github.com/qz89/AutoOpt/blob/main/LICENSE) -![](https://img.shields.io/badge/Matlab-%3E%3D%202018a%20-blue.svg) -![](https://img.shields.io/badge/Windows-Pass-brightgreen.svg) -![](https://img.shields.io/badge/MacOS-Pass-brightgreen.svg) - -## What does AutoOptLib provide? -AutoOptLib is a MATLAB library for automatically designing metaheuristic optimizers. It provides: - -* **Rich library of design choices** - Over 40 representative metaheuristic components for designing algorithms for continuous, discrete, and permutation problems with/without constraints and uncertainties. -* **Flexibility to designing diverse algorithms** - Design algorithms with diverse structures in a single run, enables great possibility to find novel and efficient algorithms. -* **Various design objectives and techniques** - Various design objectives, e.g., solution quality, runtime, and anytime performance. Different design techniques, e.g., racing, intensification, and surrogate. -* **Good accessibility** - GUI for users to input problems, manage the algorithm design process, make experimental comparisons, and visualize results with simple one-clicks. -* **Easy extensibility** - Easily add new algorithmic components, objectives, and techniques by a uniform interface. - -## What are AutoOptLib's benefits: -* **Save labor resources and time** - Human experts may cost days or weeks to conceive, build up, and verify the optimizers; AutoOptLib would saves such labor resources and time costs with today's increasing computational power. -* **Democratize metaheuristic optimizers** - Through automated algorithm design techniques, AutoOptLib would democratize the efficient and effective use of metaheuristic optimizers. This is significant for researchers and practitioners with complicated optimization problem-solving demands but without the expertise to distinguish and manage suitable optimizers among various choices. -* **Surpass human algorithm design** - By fully exploring potential design choices and discovering novelties with computing power, AutoOptLib would go beyond human experience and gain enhanced performance regarding human problem-solving. -* **Promote metaheuristic research** - With a uniform collection of related techniques, AutoOptLib would promote research of the automated algorithm design and metaheuristic fields and be a tool in the pursuit of autonomous and general artificial intelligence systems. - -## How to use AutoOptLib? -Follow the steps below to use AutoOptLib: - -1. Download source code and add it to MATLAB path. - -2. Implement the targeted optimization problem. - -3. Define the space for designing algorithms. - -4. Run AutoOptLib by command or GUI. - -Refer to the Documentation [Web](https://AutoOpt.readthedocs.io/) / [PDF](https://github.com/qz89/AutoOpt/blob/main/AutoOptLib%20for%20Matlab/Documentation.pdf) for user guide. - -## Release Notes -* Version 1.0 (18 Oct 2023) - -## License -Users can use, redistribute, and modify AutoOptLib under the terms of the GNU General Public version 3 or any later version. - -AutoOptLib is developed and maintained by the Swarm Intelligence laboratory at the Department of Computer Science and Engineering, Southern University of Science and Technology. Users should reference the following papers if using AutoOptLib in their publications: -``` -@article{zhao2023autooptlib, - title={AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design}, - author={Zhao, Qi and Yan, Bai and Hu, Taiwei and Chen, Xianglong and Duan, Qiqi and Yang, Jian and Shi, Yuhui}, - journal={arXiv preprint arXiv:2303.06536}, - year={2023} -} -``` - -## Contact -Users may ask question in [Issues block](https://github.com/qz89/AutoOpt/issues) and upload contributions by [Pulling request](https://github.com/qz89/AutoOpt/pulls). - -For any question, comment, or suggestion, please contact Dr. Qi Zhao, email: zhaoq@sustech.edu.cn. diff --git a/src/autooptlib.egg-info/SOURCES.txt b/src/autooptlib.egg-info/SOURCES.txt deleted file mode 100644 index 9f805b4..0000000 --- a/src/autooptlib.egg-info/SOURCES.txt +++ /dev/null @@ -1,79 +0,0 @@ -README.md -pyproject.toml -src/autooptlib/__init__.py -src/autooptlib/autoopt.py -src/autooptlib.egg-info/PKG-INFO -src/autooptlib.egg-info/SOURCES.txt -src/autooptlib.egg-info/dependency_links.txt -src/autooptlib.egg-info/top_level.txt -src/autooptlib/components/__init__.py -src/autooptlib/components/_utils.py -src/autooptlib/components/archive_best.py -src/autooptlib/components/archive_diversity.py -src/autooptlib/components/archive_statistic.py -src/autooptlib/components/archive_tabu.py -src/autooptlib/components/choose_brainstorm.py -src/autooptlib/components/choose_ica.py -src/autooptlib/components/choose_nich.py -src/autooptlib/components/choose_roulette_wheel.py -src/autooptlib/components/choose_tournament.py -src/autooptlib/components/choose_traverse.py -src/autooptlib/components/cross_arithmetic.py -src/autooptlib/components/cross_order_n.py -src/autooptlib/components/cross_order_two.py -src/autooptlib/components/cross_point_n.py -src/autooptlib/components/cross_point_one.py -src/autooptlib/components/cross_point_two.py -src/autooptlib/components/cross_point_uniform.py -src/autooptlib/components/cross_sim_binary.py -src/autooptlib/components/para_cma.py -src/autooptlib/components/para_cmaes.py -src/autooptlib/components/para_pso.py -src/autooptlib/components/reinit_continuous.py -src/autooptlib/components/reinit_discrete.py -src/autooptlib/components/reinit_permutation.py -src/autooptlib/components/search_cma.py -src/autooptlib/components/search_de_current.py -src/autooptlib/components/search_de_current_best.py -src/autooptlib/components/search_de_random.py -src/autooptlib/components/search_eda.py -src/autooptlib/components/search_ica.py -src/autooptlib/components/search_insert.py -src/autooptlib/components/search_mu_cauchy.py -src/autooptlib/components/search_mu_gaussian.py -src/autooptlib/components/search_mu_polynomial.py -src/autooptlib/components/search_mu_uniform.py -src/autooptlib/components/search_pso.py -src/autooptlib/components/search_reset_creep.py -src/autooptlib/components/search_reset_one.py -src/autooptlib/components/search_reset_rand.py -src/autooptlib/components/search_scramble.py -src/autooptlib/components/search_swap.py -src/autooptlib/components/search_swap_multi.py -src/autooptlib/components/update_always.py -src/autooptlib/components/update_greedy.py -src/autooptlib/components/update_pairwise.py -src/autooptlib/components/update_round_robin.py -src/autooptlib/components/update_simulated_annealing.py -src/autooptlib/problems/__init__.py -src/autooptlib/problems/cec2013.py -src/autooptlib/utils/__init__.py -src/autooptlib/utils/select.py -src/autooptlib/utils/space.py -src/autooptlib/utils/design/__init__.py -src/autooptlib/utils/design/_approximate.py -src/autooptlib/utils/design/_decode.py -src/autooptlib/utils/design/_disturb.py -src/autooptlib/utils/design/_embedding.py -src/autooptlib/utils/design/_estimate.py -src/autooptlib/utils/design/_evaluate.py -src/autooptlib/utils/design/_helpers.py -src/autooptlib/utils/design/_initialize.py -src/autooptlib/utils/design/_repair.py -src/autooptlib/utils/general/__init__.py -src/autooptlib/utils/general/improve_rate.py -src/autooptlib/utils/general/input.py -src/autooptlib/utils/general/output.py -src/autooptlib/utils/general/process.py -src/autooptlib/utils/general/select_stats.py -src/autooptlib/utils/solve/__init__.py \ No newline at end of file diff --git a/src/autooptlib.egg-info/dependency_links.txt b/src/autooptlib.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/src/autooptlib.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/src/autooptlib.egg-info/top_level.txt b/src/autooptlib.egg-info/top_level.txt deleted file mode 100644 index e3ce8fc..0000000 --- a/src/autooptlib.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -autooptlib diff --git a/src/autooptlib/__init__.py b/src/autooptlib/__init__.py index 9f8ced5..a9bc2e8 100644 --- a/src/autooptlib/__init__.py +++ b/src/autooptlib/__init__.py @@ -1,23 +1,44 @@ -from __future__ import annotations - -try: # noqa: SIM105 - from .utils.design import Design # noqa: F401 -except Exception: # pylint: disable=broad-except - Design = None # type: ignore +"""Public API for AutoOptLib.""" -try: # noqa: SIM105 - from .components import get_component # noqa: F401 -except Exception: # pylint: disable=broad-except - get_component = None # type: ignore - -try: # noqa: SIM105 - from .problems.cec2013 import cec2013_f1 # noqa: F401 -except Exception: # pylint: disable=broad-except - cec2013_f1 = None # type: ignore +from __future__ import annotations -try: # noqa: SIM105 - from .autoopt import autoopt # noqa: F401 -except Exception: # pylint: disable=broad-except - autoopt = None # type: ignore +from ._version import __version__ +from .applications import ( + MaterialStackingInstance, + RISBeamformingInstance, + StackingWeights, + generate_ris_instance, + generate_stacking_instance, + load_ris_matlab, + make_material_stacking_problem, + make_ris_beamforming_problem, +) +from .autoopt import autoopt +from .components import get_component, register_component +from .problems.base import ProblemDefinition, make_problem +from .problems.cec2013 import cec2013_f1 +from .serialization import load_algorithm, save_algorithm +from .utils.design import Design +from .utils.solve import ObjectiveEvaluationError -__all__ = [name for name in ("Design", "get_component", "cec2013_f1", "autoopt") if name in globals() and globals()[name] is not None] +__all__ = [ + "Design", + "MaterialStackingInstance", + "RISBeamformingInstance", + "StackingWeights", + "autoopt", + "cec2013_f1", + "get_component", + "generate_ris_instance", + "generate_stacking_instance", + "load_ris_matlab", + "make_material_stacking_problem", + "make_problem", + "make_ris_beamforming_problem", + "ObjectiveEvaluationError", + "ProblemDefinition", + "load_algorithm", + "save_algorithm", + "register_component", + "__version__", +] diff --git a/src/autooptlib/_version.py b/src/autooptlib/_version.py new file mode 100644 index 0000000..9a5b4a0 --- /dev/null +++ b/src/autooptlib/_version.py @@ -0,0 +1,3 @@ +"""Single source of truth for the package version.""" + +__version__ = "1.2.0" diff --git a/src/autooptlib/applications/__init__.py b/src/autooptlib/applications/__init__.py new file mode 100644 index 0000000..9283d4e --- /dev/null +++ b/src/autooptlib/applications/__init__.py @@ -0,0 +1,31 @@ +"""Reference application problem definitions used by the documentation.""" + +from .beamforming import ( + RISBeamformingInstance, + generate_ris_instance, + load_ris_matlab, + make_ris_beamforming_problem, + reciprocal_sum_rate, +) +from .stacking import ( + MaterialStackingInstance, + StackingWeights, + generate_stacking_instance, + make_material_stacking_problem, + stacking_constraints, + stacking_objective, +) + +__all__ = [ + "MaterialStackingInstance", + "RISBeamformingInstance", + "StackingWeights", + "generate_ris_instance", + "generate_stacking_instance", + "load_ris_matlab", + "make_material_stacking_problem", + "make_ris_beamforming_problem", + "reciprocal_sum_rate", + "stacking_constraints", + "stacking_objective", +] diff --git a/src/autooptlib/applications/beamforming.py b/src/autooptlib/applications/beamforming.py new file mode 100644 index 0000000..387b454 --- /dev/null +++ b/src/autooptlib/applications/beamforming.py @@ -0,0 +1,249 @@ +"""Reproducible reference model for discrete RIS passive beamforming. + +The model represents the application class described in the AutoOptLib paper. +It uses the zero-forcing and water-filling evaluation implemented by the +historical MATLAB application. Generated channels remain the default, so the +paper's numerical table still requires the archived research instances. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Hashable, Mapping + +import numpy as np + +from ..problems.base import ProblemDefinition, make_problem + + +@dataclass(frozen=True) +class RISBeamformingInstance: + """Channel data for a quantized-phase MU-MISO downlink.""" + + bs_to_ris: np.ndarray + direct_channels: np.ndarray + ris_to_users: np.ndarray + phase_bits: int = 2 + transmit_power: float = 1.0 + noise_power: float = 1.0 + user_weights: np.ndarray | None = None + + @property + def n_elements(self) -> int: + return int(np.asarray(self.bs_to_ris).shape[0]) + + def validate(self) -> None: + g = np.asarray(self.bs_to_ris) + hd = np.asarray(self.direct_channels) + hr = np.asarray(self.ris_to_users) + if g.ndim != 2 or hd.ndim != 2 or hr.ndim != 2: + raise ValueError("All RIS channel arrays must be two-dimensional.") + n_elements, n_antennas = g.shape + n_users = hd.shape[0] + if n_elements == 0 or n_antennas == 0 or n_users == 0: + raise ValueError("RIS instances require elements, antennas, and users.") + if hd.shape[1] != n_antennas or hr.shape != (n_users, n_elements): + raise ValueError( + "Incompatible BS-RIS, direct, and RIS-user channel shapes." + ) + if not isinstance(self.phase_bits, int) or self.phase_bits <= 0: + raise ValueError("phase_bits must be a positive integer.") + if self.transmit_power <= 0 or self.noise_power <= 0: + raise ValueError("transmit_power and noise_power must be positive.") + if self.user_weights is not None: + user_weights = np.asarray(self.user_weights, dtype=float).reshape(-1) + if ( + user_weights.size != n_users + or np.any(user_weights < 0) + or not np.all(np.isfinite(user_weights)) + or float(user_weights.sum()) <= 0 + ): + raise ValueError( + "user_weights must contain one finite non-negative value per user " + "and have a positive sum." + ) + for name, channel in ( + ("bs_to_ris", g), + ("direct_channels", hd), + ("ris_to_users", hr), + ): + if not np.all(np.isfinite(channel)): + raise ValueError(f"{name} must contain only finite values.") + + +def _complex_normal(rng: np.random.Generator, shape: tuple[int, ...]) -> np.ndarray: + return (rng.normal(size=shape) + 1j * rng.normal(size=shape)) / np.sqrt(2.0) + + +def generate_ris_instance( + n_elements: int, + *, + seed: int, + n_antennas: int = 4, + n_users: int = 3, + phase_bits: int = 2, + transmit_power: float = 1.0, + noise_power: float = 1.0, +) -> RISBeamformingInstance: + """Generate deterministic Rayleigh channels for examples and CI.""" + if min(n_elements, n_antennas, n_users) <= 0: + raise ValueError("n_elements, n_antennas, and n_users must be positive.") + rng = np.random.default_rng(seed) + instance = RISBeamformingInstance( + bs_to_ris=_complex_normal(rng, (n_elements, n_antennas)), + direct_channels=0.2 * _complex_normal(rng, (n_users, n_antennas)), + ris_to_users=_complex_normal(rng, (n_users, n_elements)), + phase_bits=phase_bits, + transmit_power=transmit_power, + noise_power=noise_power, + user_weights=np.ones(n_users), + ) + instance.validate() + return instance + + +def load_ris_matlab(path: str | Path) -> dict[int, RISBeamformingInstance]: + """Load the historical ``Beanforming.mat`` records using optional SciPy. + + Returned keys are one-based to match the original MATLAB instance IDs. + The data file is not bundled in the Apache-licensed wheel; users must + supply a copy whose licensing is appropriate for their use. + """ + try: + from scipy.io import loadmat + except ImportError as exc: # pragma: no cover - optional dependency path + raise ImportError( + "Reading MATLAB application data requires " + "`pip install autooptlib[applications]`." + ) from exc + source = Path(path) + if not source.is_file(): + raise FileNotFoundError(f"RIS MATLAB data file not found: {source}") + document = loadmat(source, simplify_cells=True) + records = document.get("Data") + if records is None: + raise ValueError("MATLAB data file does not contain a 'Data' variable.") + if isinstance(records, dict): + records = [records] + instances: dict[int, RISBeamformingInstance] = {} + for index, record in enumerate(records, start=1): + try: + instance = RISBeamformingInstance( + bs_to_ris=np.asarray(record["G"]), + direct_channels=np.asarray(record["Hd"]), + ris_to_users=np.asarray(record["Hr"]), + phase_bits=int(record["b"]), + transmit_power=float(record["PT"]), + noise_power=1.0, + user_weights=np.asarray(record["omega"], dtype=float).reshape(-1), + ) + except (KeyError, TypeError, ValueError) as exc: + raise ValueError(f"Invalid RIS record {index} in {source}.") from exc + instance.validate() + instances[index] = instance + return instances + + +def reciprocal_sum_rate( + phase_indices: np.ndarray, instance: RISBeamformingInstance +) -> float: + """Return reciprocal downlink sum rate for a quantized RIS phase vector.""" + instance.validate() + decisions = np.asarray(phase_indices, dtype=int).reshape(-1) + if decisions.size != instance.n_elements: + raise ValueError( + f"Expected {instance.n_elements} RIS phase decisions, got {decisions.size}." + ) + levels = 2**instance.phase_bits + if np.any(decisions < 0) or np.any(decisions >= levels): + raise ValueError(f"RIS phase decisions must be in [0, {levels - 1}].") + + phases = np.exp(1j * 2.0 * np.pi * decisions / levels) + # Explicit contraction avoids backend-specific complex GEMM warnings + # observed for one of the larger historical channel matrices. + reflected = np.asarray(instance.ris_to_users) * phases[np.newaxis, :] + effective = np.asarray(instance.direct_channels) + np.einsum( + "kn,nm->km", reflected, np.asarray(instance.bs_to_ris), optimize=False + ) + + # Match the historical MATLAB application: zero-forcing followed by + # water-filling under the total BS power budget. + gram = effective @ effective.conj().T + beamformer_base = effective.conj().T @ np.linalg.pinv(gram) + power_cost = np.real(np.diag(beamformer_base.conj().T @ beamformer_base)) + if np.any(power_cost <= np.finfo(float).tiny): + return float(1.0 / np.finfo(float).tiny) + filled = _water_filling(instance.transmit_power, power_cost) + allocated = filled / power_cost + beamformers = beamformer_base * np.sqrt(allocated)[np.newaxis, :] + received = effective @ beamformers + powers = np.abs(received) ** 2 + signal = np.diag(powers) + interference = powers.sum(axis=1) - signal + sinr = signal / (interference + instance.noise_power) + user_weights = ( + np.ones(effective.shape[0]) + if instance.user_weights is None + else np.asarray(instance.user_weights, dtype=float) + ) + sum_rate = float(np.sum(np.log1p(sinr) * user_weights)) + tiny = float(np.finfo(float).tiny) + denominator = sum_rate if sum_rate > tiny else tiny + return 1.0 / denominator + + +def _water_filling(total_power: float, power_cost: np.ndarray) -> np.ndarray: + """Port of the water-filling routine shipped with AutoOptLib v1.1.""" + costs = np.asarray(power_cost, dtype=float).reshape(-1) + count = costs.size + level = float(np.max(costs)) + used = float(np.sum(level - costs)) + if used > total_power: + descending = np.sort(costs)[::-1] + index = 1 + while used > total_power and index < count: + level = float(descending[index]) + used = float(np.sum(np.maximum(level - costs, 0.0))) + if used <= total_power: + level += (total_power - used) / (count - index) + break + index += 1 + return np.maximum(level - costs, 0.0) + level += (total_power - used) / count + return level - costs + + +def make_ris_beamforming_problem( + instances: Mapping[Hashable, RISBeamformingInstance], +) -> ProblemDefinition: + """Build an AutoOptLib discrete problem from keyed RIS channel data.""" + data = dict(instances) + if not data: + raise ValueError("At least one RIS beamforming instance is required.") + for instance in data.values(): + instance.validate() + + def get_data(instance_id: Hashable) -> RISBeamformingInstance: + try: + return data[instance_id] + except KeyError as exc: + raise KeyError(f"Unknown RIS instance {instance_id!r}.") from exc + + def bounds(instance_id: Hashable) -> np.ndarray: + instance = get_data(instance_id) + upper = 2**instance.phase_bits - 1 + return np.vstack( + ( + np.zeros(instance.n_elements, dtype=int), + np.full(instance.n_elements, upper), + ) + ) + + return make_problem( + reciprocal_sum_rate, + bounds=bounds, + problem_type="discrete", + data_factory=get_data, + name="ris_beamforming", + ) diff --git a/src/autooptlib/applications/stacking.py b/src/autooptlib/applications/stacking.py new file mode 100644 index 0000000..b5c0de3 --- /dev/null +++ b/src/autooptlib/applications/stacking.py @@ -0,0 +1,241 @@ +"""Transparent reference model for the material-stacking application class. + +The industrial study used proprietary material records and company-specific +rules. This module exposes those concepts through a documented data model and +a synthetic generator; it does not claim to reproduce the paper's table. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Hashable, Mapping + +import numpy as np + +from ..problems.base import ProblemDefinition, make_problem + + +@dataclass(frozen=True) +class StackingWeights: + """Soft-preference weights used by :func:`stacking_objective`.""" + + heavy_low: float = 1.0 + same_material: float = 1.0 + same_product: float = 0.5 + shared_central: float = 0.5 + frequent_near_shipping: float = 0.5 + + def validate(self) -> None: + values = np.asarray(list(vars(self).values()), dtype=float) + if np.any(values < 0) or not np.all(np.isfinite(values)): + raise ValueError( + "Stacking preference weights must be finite and non-negative." + ) + + +@dataclass(frozen=True) +class MaterialStackingInstance: + """Material dimensions and rack rules for one stacking instance.""" + + widths: np.ndarray + heights: np.ndarray + weights: np.ndarray + material_groups: np.ndarray + product_groups: np.ndarray + access_frequencies: np.ndarray + shared_material: np.ndarray + n_levels: int + n_positions: int + rack_width: float + max_level_weight: float + preferences: StackingWeights = StackingWeights() + + @property + def n_items(self) -> int: + return int(np.asarray(self.widths).size) + + @property + def dimension(self) -> int: + return 3 * self.n_items + + def validate(self) -> None: + arrays = { + "widths": self.widths, + "heights": self.heights, + "weights": self.weights, + "material_groups": self.material_groups, + "product_groups": self.product_groups, + "access_frequencies": self.access_frequencies, + "shared_material": self.shared_material, + } + lengths = {np.asarray(value).reshape(-1).size for value in arrays.values()} + if lengths != {self.n_items} or self.n_items == 0: + raise ValueError( + "All stacking item arrays must have the same non-zero length." + ) + for name in ("widths", "heights", "weights", "access_frequencies"): + values = np.asarray(arrays[name], dtype=float) + if np.any(values < 0) or not np.all(np.isfinite(values)): + raise ValueError(f"{name} must contain finite non-negative values.") + if min(self.n_levels, self.n_positions) <= 0: + raise ValueError("n_levels and n_positions must be positive.") + if self.rack_width <= 0 or self.max_level_weight <= 0: + raise ValueError("rack_width and max_level_weight must be positive.") + self.preferences.validate() + + +def _decode( + decision: np.ndarray, instance: MaterialStackingInstance +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + values = np.asarray(decision, dtype=int).reshape(-1) + if values.size != instance.dimension: + raise ValueError( + f"Expected {instance.dimension} stacking decisions, got {values.size}." + ) + n = instance.n_items + levels = values[:n] + positions = values[n : 2 * n] + orientations = values[2 * n :] + if ( + np.any(levels < 0) + or np.any(levels >= instance.n_levels) + or np.any(positions < 0) + or np.any(positions >= instance.n_positions) + or np.any((orientations != 0) & (orientations != 1)) + ): + raise ValueError("Stacking decision is outside the declared discrete bounds.") + return levels, positions, orientations + + +def _group_spread(location: np.ndarray, labels: np.ndarray) -> float: + total = 0.0 + for label in np.unique(labels): + members = location[np.asarray(labels) == label] + if members.size > 1: + total += float(np.var(members)) + return total + + +def stacking_objective( + decision: np.ndarray, instance: MaterialStackingInstance +) -> float: + """Minimize occupied rack area plus documented operational preferences.""" + instance.validate() + levels, positions, orientations = _decode(decision, instance) + heights = np.where(orientations == 0, instance.heights, instance.widths) + + level_heights = np.zeros(instance.n_levels, dtype=float) + for level in range(instance.n_levels): + members = levels == level + if np.any(members): + level_heights[level] = float(np.max(heights[members])) + occupied_area = float(instance.rack_width * np.sum(level_heights)) + + location = levels * instance.n_positions + positions + scale = max(1.0, float(instance.n_levels * instance.n_positions)) + weights = np.asarray(instance.weights, dtype=float) + frequency = np.asarray(instance.access_frequencies, dtype=float) + shared = np.asarray(instance.shared_material, dtype=bool) + prefs = instance.preferences + heavy_low = float(np.sum(weights * levels) / max(1.0, weights.sum())) + same_material = ( + _group_spread(location, np.asarray(instance.material_groups)) / scale + ) + same_product = _group_spread(location, np.asarray(instance.product_groups)) / scale + center = (instance.n_positions - 1) / 2.0 + shared_central = float(np.sum(np.abs(positions[shared] - center))) / scale + frequent_shipping = float(np.sum(frequency * positions) / max(1.0, frequency.sum())) + + return occupied_area + ( + prefs.heavy_low * heavy_low + + prefs.same_material * same_material + + prefs.same_product * same_product + + prefs.shared_central * shared_central + + prefs.frequent_near_shipping * frequent_shipping + ) + + +def stacking_constraints( + decision: np.ndarray, instance: MaterialStackingInstance +) -> np.ndarray: + """Return per-level width and weight excess; positive values violate.""" + instance.validate() + levels, _, orientations = _decode(decision, instance) + widths = np.where(orientations == 0, instance.widths, instance.heights) + width_excess = np.zeros(instance.n_levels, dtype=float) + weight_excess = np.zeros(instance.n_levels, dtype=float) + for level in range(instance.n_levels): + members = levels == level + width_excess[level] = float(np.sum(widths[members]) - instance.rack_width) + weight_excess[level] = float( + np.sum(np.asarray(instance.weights)[members]) - instance.max_level_weight + ) + return np.concatenate((width_excess, weight_excess)) + + +def generate_stacking_instance( + n_items: int, + *, + seed: int, + n_levels: int = 4, + n_positions: int = 8, + rack_width: float = 12.0, + max_level_weight: float = 40.0, +) -> MaterialStackingInstance: + """Generate a deterministic non-proprietary instance for examples and CI.""" + if min(n_items, n_levels, n_positions) <= 0: + raise ValueError("n_items, n_levels, and n_positions must be positive.") + rng = np.random.default_rng(seed) + instance = MaterialStackingInstance( + widths=rng.uniform(0.5, 2.0, n_items), + heights=rng.uniform(0.5, 2.5, n_items), + weights=rng.uniform(1.0, 10.0, n_items), + material_groups=rng.integers(0, max(2, n_items // 3), n_items), + product_groups=rng.integers(0, max(2, n_items // 4), n_items), + access_frequencies=rng.uniform(0.0, 1.0, n_items), + shared_material=rng.random(n_items) < 0.25, + n_levels=n_levels, + n_positions=n_positions, + rack_width=rack_width, + max_level_weight=max_level_weight, + ) + instance.validate() + return instance + + +def make_material_stacking_problem( + instances: Mapping[Hashable, MaterialStackingInstance], +) -> ProblemDefinition: + """Build an AutoOptLib discrete problem from keyed stacking records.""" + data = dict(instances) + if not data: + raise ValueError("At least one material-stacking instance is required.") + for instance in data.values(): + instance.validate() + + def get_data(instance_id: Hashable) -> MaterialStackingInstance: + try: + return data[instance_id] + except KeyError as exc: + raise KeyError(f"Unknown stacking instance {instance_id!r}.") from exc + + def bounds(instance_id: Hashable) -> np.ndarray: + instance = get_data(instance_id) + lower = np.zeros(instance.dimension, dtype=int) + upper = np.concatenate( + ( + np.full(instance.n_items, instance.n_levels - 1, dtype=int), + np.full(instance.n_items, instance.n_positions - 1, dtype=int), + np.ones(instance.n_items, dtype=int), + ) + ) + return np.vstack((lower, upper)) + + return make_problem( + stacking_objective, + bounds=bounds, + problem_type="discrete", + constraint=stacking_constraints, + data_factory=get_data, + name="material_stacking", + ) diff --git a/src/autooptlib/autoopt.py b/src/autooptlib/autoopt.py index 36f405e..5178735 100644 --- a/src/autooptlib/autoopt.py +++ b/src/autooptlib/autoopt.py @@ -3,11 +3,11 @@ from __future__ import annotations from importlib import import_module -from pathlib import Path from types import SimpleNamespace -from typing import Any, Iterable, List +from typing import Any, List -from .utils.general import input_handler, output, process +from .utils.general import input_handler, normalize_options, output, process +from .utils.general.output import write_experiment_manifest def _to_arguments(kwargs: dict[str, Any]) -> List[Any]: @@ -42,11 +42,15 @@ def autoopt(**kwargs: Any): ) """ - if "Mode" not in kwargs and "mode" not in kwargs: + kwargs = normalize_options(kwargs) + if "Mode" not in kwargs: raise ValueError("AutoOpt requires a Mode parameter ('design' or 'solve').") - mode = kwargs.get("Mode", kwargs.get("mode")) - setting = SimpleNamespace(Mode=str(mode)) + mode = str(kwargs["Mode"]).lower() + kwargs["Mode"] = mode + manifest_options = dict(kwargs) + setting = SimpleNamespace(Mode=mode) + output_directory = kwargs.pop("OutputDir", None) arguments = _to_arguments(kwargs) @@ -68,7 +72,15 @@ def autoopt(**kwargs: Any): instance_test, setting=setting, ) - output(final_algs, alg_trace, instance_train, instance_test, setting=setting) + output( + final_algs, + alg_trace, + instance_train, + instance_test, + setting=setting, + directory=output_directory, + ) + write_experiment_manifest(output_directory, mode=mode, options=manifest_options) return final_algs, alg_trace best_solutions, all_solutions = process( @@ -76,7 +88,14 @@ def autoopt(**kwargs: Any): instance_solve, setting=setting, ) - output(best_solutions, all_solutions, instance_solve, setting=setting) + output( + best_solutions, + all_solutions, + instance_solve, + setting=setting, + directory=output_directory, + ) + write_experiment_manifest(output_directory, mode=mode, options=manifest_options) return best_solutions, all_solutions diff --git a/src/autooptlib/components/__init__.py b/src/autooptlib/components/__init__.py index 451ec2a..903cb1c 100644 --- a/src/autooptlib/components/__init__.py +++ b/src/autooptlib/components/__init__.py @@ -3,7 +3,7 @@ from __future__ import annotations from importlib import import_module -from typing import Callable, Dict +from typing import Callable, Dict, Iterable _COMPONENT_MODULES = { "archive_best": "archive_best", @@ -56,8 +56,82 @@ } _cache: Dict[str, Callable] = {} +_custom_components: Dict[str, Callable] = {} +_custom_component_specs: Dict[str, tuple[str, frozenset[str]]] = {} + +_VALID_CATEGORIES = {"choose", "search", "update"} +_VALID_PROBLEM_TYPES = {"continuous", "discrete", "permutation"} + + +def _infer_category(name: str) -> str | None: + prefix = name.split("_", 1)[0] + if prefix == "choose": + return "choose" + if prefix in {"search", "cross", "reinit"}: + return "search" + if prefix == "update": + return "update" + return None + + +def register_component( + name: str, + component: Callable, + *, + category: str | None = None, + problem_types: Iterable[str] = ("continuous", "discrete", "permutation"), + replace: bool = False, +) -> None: + """Register a user-defined component for the current Python process. + + The callable must follow the same mode-based protocol as built-in + components. Registration is explicit so extensions do not need to edit + AutoOptLib's internal component map. ``category`` may be ``choose``, + ``search``, or ``update`` and is inferred from conventional component + names. Compatible registered components are included by :func:`space`. + """ + if not isinstance(name, str) or not name.isidentifier(): + raise ValueError("Component names must be valid Python identifiers.") + if not callable(component): + raise TypeError("component must be callable") + resolved_category = category or _infer_category(name) + if resolved_category not in _VALID_CATEGORIES: + raise ValueError( + "category must be 'choose', 'search', or 'update'; it cannot be " + f"inferred from {name!r}" + ) + resolved_types = frozenset(str(value).lower() for value in problem_types) + if not resolved_types or not resolved_types <= _VALID_PROBLEM_TYPES: + raise ValueError( + "problem_types must contain one or more of: continuous, discrete, permutation" + ) + if not replace and (name in _COMPONENT_MODULES or name in _custom_components): + raise ValueError(f"Component {name!r} is already registered.") + _custom_components[name] = component + _custom_component_specs[name] = (resolved_category, resolved_types) + _cache.pop(name, None) + + +def list_components() -> tuple[str, ...]: + """Return all built-in and user-registered component names.""" + return tuple(sorted(set(_COMPONENT_MODULES) | set(_custom_components))) + + +def compatible_custom_components(category: str, problem_type: str) -> tuple[str, ...]: + """Return registered extensions compatible with a design-space section.""" + return tuple( + name + for name, ( + registered_category, + problem_types, + ) in _custom_component_specs.items() + if registered_category == category and problem_type in problem_types + ) + def get_component(name: str) -> Callable: + if name in _custom_components: + return _custom_components[name] if name not in _cache: module_name = _COMPONENT_MODULES.get(name) if module_name is None: @@ -66,6 +140,5 @@ def get_component(name: str) -> Callable: _cache[name] = getattr(module, name) return _cache[name] -__all__ = sorted(_COMPONENT_MODULES) - +__all__ = ["get_component", "list_components", "register_component"] diff --git a/src/autooptlib/components/_utils.py b/src/autooptlib/components/_utils.py index 0f0c80c..c1e84f8 100644 --- a/src/autooptlib/components/_utils.py +++ b/src/autooptlib/components/_utils.py @@ -2,7 +2,6 @@ from __future__ import annotations -import math import re from typing import Any, Sequence @@ -77,8 +76,6 @@ def extract_fits(solution: Any) -> np.ndarray: val = flex_get(item, "fits") if val is None: raise ValueError("Each solution must expose fit/fitness value") - # values.append(float(np.asarray(val).reshape(1))) - arr = np.asarray(val) if arr.size != 1: raise ValueError(f"Expected a scalar-like value, got shape={arr.shape}") @@ -123,4 +120,3 @@ def ensure_column(vector: Any) -> np.ndarray: if arr.ndim == 1: return arr.reshape(-1, 1) return arr - diff --git a/src/autooptlib/components/archive_best.py b/src/autooptlib/components/archive_best.py index eee56a2..a685220 100644 --- a/src/autooptlib/components/archive_best.py +++ b/src/autooptlib/components/archive_best.py @@ -15,7 +15,9 @@ def _extract_fits(solution) -> np.ndarray: try: seq = list(solution) except TypeError as exc: # not iterable - raise ValueError("Solution must expose fits() or be iterable of items with fitness") from exc + raise ValueError( + "Solution must expose fits() or be iterable of items with fitness" + ) from exc vals = [] for it in seq: val = getattr(it, "fit", None) @@ -23,7 +25,9 @@ def _extract_fits(solution) -> np.ndarray: obj = getattr(it, "obj", None) con = getattr(it, "con", None) if obj is None: - raise ValueError("Item lacks fit/obj attributes for fitness computation") + raise ValueError( + "Item lacks fit/obj attributes for fitness computation" + ) if con is None: val = float(obj) else: diff --git a/src/autooptlib/components/archive_diversity.py b/src/autooptlib/components/archive_diversity.py index b989921..43fd832 100644 --- a/src/autooptlib/components/archive_diversity.py +++ b/src/autooptlib/components/archive_diversity.py @@ -1,11 +1,12 @@ """Select the most diversified solutions for the archive.""" + from __future__ import annotations from typing import Any, Sequence import numpy as np -from ._utils import flex_get +from ._utils import ensure_rng, flex_get def _ensure_solution_list(sol: Any) -> list: @@ -15,7 +16,9 @@ def _ensure_solution_list(sol: Any) -> list: def _stack_decs(sol_list: list) -> np.ndarray: - decs = [np.asarray(flex_get(s, "dec"), dtype=float).reshape(1, -1) for s in sol_list] + decs = [ + np.asarray(flex_get(s, "dec"), dtype=float).reshape(1, -1) for s in sol_list + ] return np.vstack(decs) @@ -25,6 +28,7 @@ def archive_diversity(*args): solutions = _ensure_solution_list(args[0]) archive = _ensure_solution_list(args[1]) if len(args) > 1 else [] problem = args[2] if len(args) > 2 else None + rng = ensure_rng(problem) combined = solutions + archive if not combined: @@ -38,7 +42,7 @@ def archive_diversity(*args): else: dist = np.mean(decs[:, None, :] != decs[None, :, :], axis=-1) n = int(flex_get(problem, "N", len(solutions))) - chosen = [int(np.random.randint(len(combined)))] + chosen = [int(rng.integers(len(combined)))] while len(chosen) < n and len(chosen) < len(combined): sums = np.sum(dist[chosen], axis=0) order = np.argsort(sums)[::-1] @@ -55,4 +59,4 @@ def archive_diversity(*args): if mode == "behavior": return ["", ""], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/archive_statistic.py b/src/autooptlib/components/archive_statistic.py index 4a99814..7ffb9ca 100644 --- a/src/autooptlib/components/archive_statistic.py +++ b/src/autooptlib/components/archive_statistic.py @@ -1,7 +1,6 @@ """Track mean and std of fitness.""" -from __future__ import annotations -from typing import Any, Sequence +from __future__ import annotations import numpy as np @@ -19,7 +18,8 @@ def archive_statistic(*args): values = np.asarray(fits()).reshape(-1) else: values = np.asarray(fits).reshape(-1) - stat = np.array([[np.mean(values), np.std(values)]]) + std = np.std(values, ddof=1) if values.size > 1 else 0.0 + stat = np.array([[np.mean(values), std]]) if archive is None or len(archive) == 0: return stat, None archive_arr = np.asarray(archive) @@ -31,4 +31,4 @@ def archive_statistic(*args): if mode == "behavior": return ["", ""], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/archive_tabu.py b/src/autooptlib/components/archive_tabu.py index 1dee1f3..94170e7 100644 --- a/src/autooptlib/components/archive_tabu.py +++ b/src/autooptlib/components/archive_tabu.py @@ -18,4 +18,3 @@ def archive_tabu(*args: Any): return ["", ""], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/choose_brainstorm.py b/src/autooptlib/components/choose_brainstorm.py index 55e4499..48a3cc7 100644 --- a/src/autooptlib/components/choose_brainstorm.py +++ b/src/autooptlib/components/choose_brainstorm.py @@ -26,7 +26,9 @@ def _extract_objs(solution: Any) -> np.ndarray: return arr.reshape(-1) -def _kmeans_labels(data: np.ndarray, k: int, rng: np.random.Generator, max_iter: int = 50) -> np.ndarray: +def _kmeans_labels( + data: np.ndarray, k: int, rng: np.random.Generator, max_iter: int = 50 +) -> np.ndarray: n_samples = data.shape[0] if n_samples == 0 or k <= 0: return np.empty(0, dtype=int) @@ -67,11 +69,17 @@ def choose_brainstorm(*args: Any): ptype = flex_get(problem, "type", ["continuous"]) ptype0 = ptype[0] if isinstance(ptype, (list, tuple)) else ptype if "continuous" not in str(ptype0).lower(): - raise ValueError("choose_brainstorm is only available for continuous problems") + raise ValueError( + "choose_brainstorm is only available for continuous problems" + ) - para_arr = np.asarray(para, dtype=float) if para is not None else np.array([2.0, 0.5]) + para_arr = ( + np.asarray(para, dtype=float) if para is not None else np.array([2.0, 0.5]) + ) if para_arr.size < 2: - para_arr = np.pad(para_arr, (0, 2 - para_arr.size), mode="constant", constant_values=0.5) + para_arr = np.pad( + para_arr, (0, 2 - para_arr.size), mode="constant", constant_values=0.5 + ) k = max(2, int(round(float(para_arr[0])))) gamma = float(para_arr[1]) k = min(k, pop_size) @@ -104,7 +112,9 @@ def choose_brainstorm(*args: Any): probabilities /= total n_select = int(flex_get(problem, "N", pop_size)) - chosen_centers = rng.choice(centers, size=n_select, replace=True, p=probabilities) + chosen_centers = rng.choice( + centers, size=n_select, replace=True, p=probabilities + ) result = [] for center_idx in chosen_centers: @@ -126,4 +136,3 @@ def choose_brainstorm(*args: Any): return ["", ""], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/choose_nich.py b/src/autooptlib/components/choose_nich.py index d7b8c54..a058066 100644 --- a/src/autooptlib/components/choose_nich.py +++ b/src/autooptlib/components/choose_nich.py @@ -23,9 +23,13 @@ def _extract_decisions(solution) -> np.ndarray: return np.asarray(decs, dtype=float) -def _nearest_better_clustering(distance: np.ndarray, fai: float, min_size: int, g: int, gmax: int) -> List[Species]: +def _nearest_better_clustering( + distance: np.ndarray, fai: float, min_size: int, g: int, gmax: int +) -> List[Species]: if min_size == -1: - min_size = round(3 + g / max(gmax, 1) * 7) + # MATLAB rounds positive .5 values away from zero; Python uses bankers' + # rounding, which changes species splitting at exact half steps. + min_size = int(np.floor(3 + g / max(gmax, 1) * 7 + 0.5)) npop = distance.shape[0] nearest = np.full(npop, -1, dtype=int) @@ -58,7 +62,10 @@ def _nearest_better_clustering(distance: np.ndarray, fai: float, min_size: int, while nearest[top_index] != -1: top_index = nearest[top_index] chain.append(top_index) - if follow[inf_index] >= min_size and follow[top_index] - follow[inf_index] >= min_size: + if ( + follow[inf_index] >= min_size + and follow[top_index] - follow[inf_index] >= min_size + ): nearest[inf_index] = -1 dist_val[inf_index] = 0.0 seeds.append(inf_index) diff --git a/src/autooptlib/components/choose_traverse.py b/src/autooptlib/components/choose_traverse.py index 0801bad..e9cb452 100644 --- a/src/autooptlib/components/choose_traverse.py +++ b/src/autooptlib/components/choose_traverse.py @@ -24,4 +24,3 @@ def choose_traverse(*args): if mode == "behavior": return ["", ""], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/cross_arithmetic.py b/src/autooptlib/components/cross_arithmetic.py index 62d346a..30ccfe9 100644 --- a/src/autooptlib/components/cross_arithmetic.py +++ b/src/autooptlib/components/cross_arithmetic.py @@ -24,7 +24,11 @@ def cross_arithmetic(*args: Any): first = parent[: (n + 1) // 2] second = parent[n // 2 :] - prob = np.asarray(para, dtype=float) if para is not None else np.array(0.5, dtype=float) + prob = ( + np.asarray(para, dtype=float) + if para is not None + else np.array(0.5, dtype=float) + ) if prob.ndim == 0: ratio = prob else: @@ -42,4 +46,3 @@ def cross_arithmetic(*args: Any): return ["", "GS"], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/cross_order_n.py b/src/autooptlib/components/cross_order_n.py index ece69d6..0e52a3e 100644 --- a/src/autooptlib/components/cross_order_n.py +++ b/src/autooptlib/components/cross_order_n.py @@ -1,4 +1,5 @@ """N-order crossover for permutations.""" + from __future__ import annotations from typing import Any @@ -34,7 +35,11 @@ def cross_order_n(*args): aux = args[3] if len(args) > 3 else None rng = ensure_rng(aux) - n_points = int(round(float(np.asarray(para).reshape(-1)[0]))) if para is not None else 1 + n_points = ( + int(round(float(np.asarray(para).reshape(-1)[0]))) + if para is not None + else 1 + ) parent = _extract_matrix(parent_obj) n, d = parent.shape half = (n + 1) // 2 @@ -49,10 +54,8 @@ def cross_order_n(*args): temp = _stable_setdiff(parent2[i], parent1[i, k]) off1[i, complement] = temp - k2 = rng.choice(d, size=min(max(1, n_points), d), replace=False) - k2.sort() - complement2 = [idx for idx in range(d) if idx not in k2] - temp2 = _stable_setdiff(parent1[i], parent2[i, k2]) + complement2 = [idx for idx in range(d) if idx not in k] + temp2 = _stable_setdiff(parent1[i], parent2[i, k]) off2[i, complement2] = temp2 offspring = np.vstack([off1, off2]) return offspring[:n], aux @@ -69,4 +72,4 @@ def cross_order_n(*args): if mode == "behavior": return [["LS", "small"], ["GS", "large"]], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/cross_order_two.py b/src/autooptlib/components/cross_order_two.py index 2184f4f..4959c7e 100644 --- a/src/autooptlib/components/cross_order_two.py +++ b/src/autooptlib/components/cross_order_two.py @@ -1,4 +1,5 @@ """Two-order crossover for permutations.""" + from __future__ import annotations from typing import Any @@ -21,11 +22,13 @@ def _extract_matrix(parent: Any) -> np.ndarray: return arr -def _order_crossover(p1: np.ndarray, p2: np.ndarray, start: int, end: int) -> np.ndarray: - segment = p1[start:end + 1] +def _order_crossover( + p1: np.ndarray, p2: np.ndarray, start: int, end: int +) -> np.ndarray: + segment = p1[start : end + 1] remainder = [x for x in p2 if x not in segment] mask = np.ones(p1.shape[0], dtype=bool) - mask[start:end + 1] = False + mask[start : end + 1] = False offspring = p1.copy() offspring[mask] = remainder return offspring @@ -59,4 +62,4 @@ def cross_order_two(*args): if mode == "behavior": return ["", "GS"], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/cross_point_n.py b/src/autooptlib/components/cross_point_n.py index e5df35c..e760dd3 100644 --- a/src/autooptlib/components/cross_point_n.py +++ b/src/autooptlib/components/cross_point_n.py @@ -4,14 +4,21 @@ import numpy as np +from ._utils import ensure_rng + def cross_point_n(*args): mode = args[-1] if mode == "execute": parent = args[0] para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 - n_points = int(round(float(np.asarray(para).reshape(-1)[0]))) if para is not None else 1 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) + n_points = ( + int(round(float(np.asarray(para).reshape(-1)[0]))) + if para is not None + else 1 + ) decs = getattr(parent, "decs", None) if callable(decs): parent = decs() @@ -25,11 +32,11 @@ def cross_point_n(*args): off1 = p1.copy() off2 = p2.copy() for i in range(nh): - k = np.random.choice(d, size=min(max(1, n_points), d), replace=False) + k = rng.choice(d, size=min(max(1, n_points), d), replace=False) off1[i, k] = p2[i, k] off2[i, k] = p1[i, k] offspring = np.vstack([off1, off2]) - return offspring[:n, :], args[3] if len(args) > 3 else None + return offspring[:n, :], aux if mode == "parameter": # Cannot compute D without Problem here; return a reasonable default range return [1, 5], None diff --git a/src/autooptlib/components/cross_point_one.py b/src/autooptlib/components/cross_point_one.py index 3538ac1..86676c5 100644 --- a/src/autooptlib/components/cross_point_one.py +++ b/src/autooptlib/components/cross_point_one.py @@ -4,12 +4,15 @@ import numpy as np +from ._utils import ensure_rng + def cross_point_one(*args): mode = args[-1] if mode == "execute": parent = args[0] - aux = args[3] if len(args) > 3 else None # noqa: F841 (kept for API parity) + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent, "decs", None) if callable(decs): parent = decs() @@ -20,14 +23,14 @@ def cross_point_one(*args): p1 = parent[: (n + 1) // 2] p2 = parent[n // 2 :] nh = p1.shape[0] - k = np.random.randint(0, d, size=(nh,)) + k = rng.integers(0, d, size=nh) off1 = p1.copy() off2 = p2.copy() for i in range(nh): - off1[i, k[i] : ] = p2[i, k[i] : ] - off2[i, k[i] : ] = p1[i, k[i] : ] + off1[i, k[i] :] = p2[i, k[i] :] + off2[i, k[i] :] = p1[i, k[i] :] offspring = np.vstack([off1, off2]) - return offspring[:n, :], args[3] if len(args) > 3 else None + return offspring[:n, :], aux if mode == "parameter": return None, None if mode == "behavior": diff --git a/src/autooptlib/components/cross_point_two.py b/src/autooptlib/components/cross_point_two.py index 8848029..c34b968 100644 --- a/src/autooptlib/components/cross_point_two.py +++ b/src/autooptlib/components/cross_point_two.py @@ -4,12 +4,15 @@ import numpy as np +from ._utils import ensure_rng + def cross_point_two(*args): mode = args[-1] if mode == "execute": parent = args[0] - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent, "decs", None) if callable(decs): parent = decs() @@ -22,7 +25,9 @@ def cross_point_two(*args): p1 = parent[: (n + 1) // 2] p2 = parent[n // 2 :] nh = p1.shape[0] - k = np.sort(np.random.randint(0, d, size=(nh, 2)), axis=1) + k = np.vstack( + [np.sort(rng.choice(d, size=2, replace=False)) for _ in range(nh)] + ) off1 = p1.copy() off2 = p2.copy() for i in range(nh): @@ -30,7 +35,7 @@ def cross_point_two(*args): off1[i, a : b + 1] = p2[i, a : b + 1] off2[i, a : b + 1] = p1[i, a : b + 1] offspring = np.vstack([off1, off2]) - return offspring[:n, :], args[3] if len(args) > 3 else None + return offspring[:n, :], aux if mode == "parameter": return None, None if mode == "behavior": diff --git a/src/autooptlib/components/cross_point_uniform.py b/src/autooptlib/components/cross_point_uniform.py index 4f0846b..810515a 100644 --- a/src/autooptlib/components/cross_point_uniform.py +++ b/src/autooptlib/components/cross_point_uniform.py @@ -4,13 +4,16 @@ import numpy as np +from ._utils import ensure_rng + def cross_point_uniform(*args): mode = args[-1] if mode == "execute": parent = args[0] para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) prob = float(np.asarray(para).reshape(-1)[0]) if para is not None else 0.5 decs = getattr(parent, "decs", None) if callable(decs): @@ -22,13 +25,13 @@ def cross_point_uniform(*args): p1 = parent[: (n + 1) // 2] p2 = parent[n // 2 :] nh = p1.shape[0] - mask = np.random.rand(nh, d) < prob + mask = rng.random((nh, d)) < prob off1 = p1.copy() off2 = p2.copy() off1[mask] = p2[mask] off2[mask] = p1[mask] offspring = np.vstack([off1, off2]) - return offspring[:n, :], args[3] if len(args) > 3 else None + return offspring[:n, :], aux if mode == "parameter": return [0, 0.5], None if mode == "behavior": diff --git a/src/autooptlib/components/cross_sim_binary.py b/src/autooptlib/components/cross_sim_binary.py index 601ceac..c6866cc 100644 --- a/src/autooptlib/components/cross_sim_binary.py +++ b/src/autooptlib/components/cross_sim_binary.py @@ -6,6 +6,8 @@ import numpy as np +from ._utils import ensure_rng + def cross_sim_binary(*args: Any): mode = args[-1] @@ -13,6 +15,7 @@ def cross_sim_binary(*args: Any): parent = args[0] para = args[2] if len(args) > 2 else None aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent, "decs", None) if callable(decs): @@ -25,8 +28,12 @@ def cross_sim_binary(*args: Any): second = parent[n // 2 :] n_half = first.shape[0] - eta = float(np.asarray(para, dtype=float).reshape(-1)[0]) if para is not None else 20.0 - mu = np.random.rand(n_half, d) + eta = ( + float(np.asarray(para, dtype=float).reshape(-1)[0]) + if para is not None + else 20.0 + ) + mu = rng.random((n_half, d)) beta = np.empty_like(mu) mask = mu <= 0.5 beta[mask] = (2.0 * mu[mask]) ** (1.0 / (1.0 + eta)) @@ -47,4 +54,3 @@ def cross_sim_binary(*args: Any): return ["", "GS"], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/para_cma.py b/src/autooptlib/components/para_cma.py index 1c8e50c..44318a9 100644 --- a/src/autooptlib/components/para_cma.py +++ b/src/autooptlib/components/para_cma.py @@ -1,4 +1,5 @@ """Python translation of para_cma.""" + from __future__ import annotations from typing import Any @@ -8,6 +9,17 @@ from ._utils import flex_get +def _regularize_covariance(matrix: np.ndarray) -> np.ndarray: + matrix = np.asarray(matrix, dtype=float) + matrix = np.nan_to_num(matrix, nan=0.0, posinf=1e6, neginf=-1e6) + matrix = 0.5 * (matrix + matrix.T) + eigvals, eigvecs = np.linalg.eigh(matrix) + eigvals = np.clip(eigvals, 1e-12, 1e6) + with np.errstate(all="ignore"): + result = (eigvecs * eigvals) @ eigvecs.T + return np.nan_to_num(result, nan=0.0, posinf=1e6, neginf=-1e6) + + def _extract_fitness(obj: Any, mode: str) -> np.ndarray: if mode == "solution": fits = flex_get(obj, "fits") @@ -52,12 +64,22 @@ def para_cma(*args): hth = float(aux.get("cma_hth")) ps = np.asarray(aux.get("cma_ps"), dtype=float) pc = np.asarray(aux.get("cma_pc"), dtype=float) - C = np.asarray(aux.get("cma_C"), dtype=float) + C = _regularize_covariance(aux.get("cma_C")) fitness = _extract_fitness(solution, mode) rank = np.argsort(fitness) disturbance = disturbance[rank] - disturbance_w = w @ disturbance[:half_n] + selected_n = min(half_n, len(w), len(disturbance)) + if selected_n == 0: + return aux + selected_w = np.asarray(w[:selected_n], dtype=float) + selected_w /= np.sum(selected_w) + # The last generation may be deliberately truncated to respect ProbFE. + # Recompute the effective selection mass for that smaller sample. + better_n = 1.0 / np.sum(selected_w**2) + with np.errstate(all="ignore"): + disturbance_w = selected_w @ disturbance[:selected_n] + disturbance_w = np.nan_to_num(disturbance_w, nan=0.0, posinf=1e6, neginf=-1e6) mean = mean + sigma * disturbance_w try: @@ -68,20 +90,31 @@ def para_cma(*args): inv_chol_T = np.linalg.solve(chol_c.T, disturbance_w) ps = (1 - csigma) * ps + np.sqrt(csigma * (2 - csigma) * better_n) * inv_chol_T - gmax = int(flex_get(problem[0] if isinstance(problem, (list, tuple)) else problem, "Gmax", 1)) + gmax = int( + flex_get( + problem[0] if isinstance(problem, (list, tuple)) else problem, "Gmax", 1 + ) + ) denom = np.sqrt(1 - (1 - csigma) ** (2 * (gmax + 1))) hs = float(np.linalg.norm(ps) / denom < hth) pc = (1 - cc) * pc + hs * np.sqrt(cc * (2 - cc) * better_n) * disturbance_w delta = (1 - hs) * cc * (2 - cc) C = (1 - ccov - cmu) * C + ccov * (np.outer(pc, pc) + delta * C) - for i in range(half_n): - C += cmu * w[i] * np.outer(disturbance[i], disturbance[i]) - C = 0.5 * (C + C.T) - eigvals, eigvecs = np.linalg.eigh(C) - eigvals = np.clip(eigvals, 0, None) - C = eigvecs @ np.diag(eigvals) @ eigvecs.T + for i in range(selected_n): + C += cmu * selected_w[i] * np.outer(disturbance[i], disturbance[i]) + C = _regularize_covariance(C) + + exponent = 0.3 * csigma / dsigma * (np.linalg.norm(ps) / chi_n - 1) + sigma = sigma * np.exp(np.clip(exponent, -20.0, 20.0)) + sigma = np.clip( + np.nan_to_num(sigma, nan=1e-3, posinf=1e6, neginf=1e-12), 1e-12, 1e6 + ) - sigma = sigma * np.exp(csigma / dsigma * (np.linalg.norm(ps) / chi_n - 1)) ** 0.3 + target = problem[0] if isinstance(problem, (list, tuple)) else problem + bound = flex_get(target, "bound", None) + if bound is not None and mode == "solution": + bound = np.asarray(bound, dtype=float) + mean = np.clip(mean, bound[0], bound[1]) aux.update( { @@ -92,4 +125,4 @@ def para_cma(*args): "cma_sigma": sigma, } ) - return aux \ No newline at end of file + return aux diff --git a/src/autooptlib/components/para_cmaes.py b/src/autooptlib/components/para_cmaes.py index ac6416e..3cc9a89 100644 --- a/src/autooptlib/components/para_cmaes.py +++ b/src/autooptlib/components/para_cmaes.py @@ -9,4 +9,3 @@ def para_cmaes(*args: Any): return para_cma(*args) - diff --git a/src/autooptlib/components/para_pso.py b/src/autooptlib/components/para_pso.py index 1206ad8..ea24be9 100644 --- a/src/autooptlib/components/para_pso.py +++ b/src/autooptlib/components/para_pso.py @@ -1,34 +1,15 @@ """Python translation of para_pso.""" + from __future__ import annotations -from typing import Any, Iterable +from typing import Any import numpy as np +from ..utils.solve import SolutionSet from ._utils import flex_get from .update_pairwise import update_pairwise -try: # noqa: SIM105 - from ..utils.solve import SolutionSet # type: ignore -except Exception: # pylint: disable=broad-except - class SolutionSet(list): # type: ignore - def __init__(self, items: Iterable[Any]): - super().__init__(items) - - def decs(self) -> np.ndarray: - if not self: - return np.zeros((0, 0)) - return np.vstack([np.asarray(flex_get(sol, "dec"), dtype=float) for sol in self]) - - def objs(self) -> np.ndarray: - return np.asarray([flex_get(sol, "obj") for sol in self], dtype=float).reshape(-1, 1) - - def cons(self) -> np.ndarray: - return np.asarray([flex_get(sol, "con") for sol in self], dtype=float).reshape(-1, 1) - - def fits(self) -> np.ndarray: - return np.asarray([flex_get(sol, "fit") for sol in self], dtype=float).reshape(-1, 1) - def _ensure_solution_set(solution: Any) -> SolutionSet: if isinstance(solution, SolutionSet): @@ -38,7 +19,6 @@ def _ensure_solution_set(solution: Any) -> SolutionSet: def para_pso(*args): solution = _ensure_solution_set(args[0]) - problem = args[1] aux = args[2] if len(args) > 2 else None if aux is None: @@ -47,9 +27,12 @@ def para_pso(*args): return aux pbest_set = aux["Pbest"] - combined = list(pbest_set) + list(solution) + update_count = min(len(pbest_set), len(solution)) + combined = list(pbest_set)[:update_count] + list(solution)[:update_count] selected, _ = update_pairwise(combined, "execute") - aux["Pbest"] = SolutionSet(selected) + # A strict ProbFE boundary can truncate the last particle batch. Preserve + # personal bests for particles that were not evaluated in that batch. + aux["Pbest"] = SolutionSet(selected + list(pbest_set)[update_count:]) objs = np.asarray([flex_get(sol, "obj") for sol in aux["Pbest"]], dtype=float) best_idx = int(np.argmin(objs)) diff --git a/src/autooptlib/components/reinit_continuous.py b/src/autooptlib/components/reinit_continuous.py index 6d4af33..11475fc 100644 --- a/src/autooptlib/components/reinit_continuous.py +++ b/src/autooptlib/components/reinit_continuous.py @@ -4,6 +4,8 @@ import numpy as np +from ._utils import ensure_rng + def reinit_continuous(*args): mode = args[-1] @@ -11,6 +13,7 @@ def reinit_continuous(*args): solution = args[0] problem = args[1] if len(args) > 1 else None aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, problem) try: n = len(solution) @@ -19,7 +22,7 @@ def reinit_continuous(*args): bound = getattr(problem, "bound") lower = np.asarray(bound[0], dtype=float) upper = np.asarray(bound[1], dtype=float) - dec = lower + (upper - lower) * np.random.rand(n, lower.shape[-1]) + dec = lower + (upper - lower) * rng.random((n, lower.shape[-1])) return dec, aux if mode == "parameter": diff --git a/src/autooptlib/components/reinit_discrete.py b/src/autooptlib/components/reinit_discrete.py index d305abb..514b385 100644 --- a/src/autooptlib/components/reinit_discrete.py +++ b/src/autooptlib/components/reinit_discrete.py @@ -1,4 +1,5 @@ """Reinitialization for discrete problems.""" + from __future__ import annotations from typing import Any @@ -44,4 +45,4 @@ def reinit_discrete(*args): if mode == "behavior": return ["", "GS"], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/reinit_permutation.py b/src/autooptlib/components/reinit_permutation.py index d7d1cc3..5116808 100644 --- a/src/autooptlib/components/reinit_permutation.py +++ b/src/autooptlib/components/reinit_permutation.py @@ -1,4 +1,5 @@ """Reinitialization for permutation problems.""" + from __future__ import annotations from typing import Any @@ -39,4 +40,4 @@ def reinit_permutation(*args): if mode == "behavior": return ["", "GS"], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_cma.py b/src/autooptlib/components/search_cma.py index e7be7cd..0445448 100644 --- a/src/autooptlib/components/search_cma.py +++ b/src/autooptlib/components/search_cma.py @@ -1,11 +1,12 @@ """Python translation of search_cma.""" + from __future__ import annotations from typing import Any, Tuple import numpy as np -from ._utils import flex_get +from ._utils import ensure_rng, flex_get def _extract_decs(parent_obj: Any) -> np.ndarray: @@ -17,7 +18,9 @@ def _extract_decs(parent_obj: Any) -> np.ndarray: if decs is not None: return np.asarray(decs, dtype=float) # assume sequence of Solution objects - return np.vstack([np.asarray(flex_get(item, "dec"), dtype=float) for item in parent_obj]) + return np.vstack( + [np.asarray(flex_get(item, "dec"), dtype=float) for item in parent_obj] + ) def _ensure_aux(aux: Any) -> dict: @@ -26,13 +29,32 @@ def _ensure_aux(aux: Any) -> dict: return aux -def _init_cma(aux: dict, n: int, d: int, lower: np.ndarray, upper: np.ndarray) -> None: - half_n = int(np.round(n / 2)) - w = np.log(np.arange(1, half_n + 1) + 0.5) - w = w - w[-1] - w = w[::-1] +def _regularize_covariance(matrix: np.ndarray) -> np.ndarray: + """Return a finite, symmetric positive-definite covariance matrix.""" + matrix = np.asarray(matrix, dtype=float) + matrix = np.nan_to_num(matrix, nan=0.0, posinf=1e6, neginf=-1e6) + matrix = 0.5 * (matrix + matrix.T) + eigvals, eigvecs = np.linalg.eigh(matrix) + eigvals = np.clip(eigvals, 1e-12, 1e6) + # Some macOS Accelerate builds emit spurious floating-point warnings for + # otherwise finite matrix products. The result is validated immediately. + with np.errstate(all="ignore"): + result = (eigvecs * eigvals) @ eigvecs.T + return np.nan_to_num(result, nan=0.0, posinf=1e6, neginf=-1e6) + + +def _init_cma( + aux: dict, + n: int, + d: int, + lower: np.ndarray, + upper: np.ndarray, + rng: np.random.Generator, +) -> None: + half_n = max(1, int(np.floor(n / 2 + 0.5))) + w = np.log(half_n + 0.5) - np.log(np.arange(1, half_n + 1)) w = w / np.sum(w) - better_n = 1.0 / np.sum(w ** 2) + better_n = 1.0 / np.sum(w**2) aux.setdefault("cma_halfN", half_n) aux.setdefault("cma_w", w) @@ -53,18 +75,26 @@ def _init_cma(aux: dict, n: int, d: int, lower: np.ndarray, upper: np.ndarray) - ), ) aux.setdefault("cma_hth", (1.4 + 2 / (d + 1)) * aux["cma_chiN"]) - aux.setdefault("cma_mean", lower + (upper - lower) * np.random.rand(d)) + aux.setdefault("cma_mean", lower + (upper - lower) * rng.random(d)) aux.setdefault("cma_ps", np.zeros(d)) aux.setdefault("cma_pc", np.zeros(d)) aux.setdefault("cma_C", np.eye(d)) aux.setdefault("cma_sigma", 0.1 * (upper - lower)) -def _sample(aux: dict, n: int, d: int) -> Tuple[np.ndarray, np.ndarray]: +def _sample( + aux: dict, n: int, d: int, rng: np.random.Generator +) -> Tuple[np.ndarray, np.ndarray]: mean = np.asarray(aux["cma_mean"], dtype=float) sigma = np.asarray(aux["cma_sigma"], dtype=float) - C = np.asarray(aux["cma_C"], dtype=float) - disturbance = np.random.multivariate_normal(np.zeros(d), C, size=n) + C = _regularize_covariance(aux["cma_C"]) + sigma = np.clip( + np.nan_to_num(sigma, nan=1e-3, posinf=1e6, neginf=1e-12), 1e-12, 1e6 + ) + aux["cma_C"] = C + aux["cma_sigma"] = sigma + with np.errstate(all="ignore"): + disturbance = rng.multivariate_normal(np.zeros(d), C, size=n) offspring = mean + sigma * disturbance aux["cma_Disturb"] = disturbance return offspring, aux @@ -76,13 +106,14 @@ def search_cma(*args): parent_obj = args[0] problem = args[1] aux = _ensure_aux(args[3] if len(args) > 3 else None) + rng = ensure_rng(aux, problem) parent = _extract_decs(parent_obj) n, d = parent.shape bound = flex_get(problem, "bound") lower = np.asarray(bound[0], dtype=float) upper = np.asarray(bound[1], dtype=float) - _init_cma(aux, n, d, lower, upper) - offspring, aux = _sample(aux, n, d) + _init_cma(aux, n, d, lower, upper, rng) + offspring, aux = _sample(aux, n, d, rng) return offspring, aux if mode == "parameter": @@ -95,11 +126,12 @@ def search_cma(*args): parent = np.asarray(args[0], dtype=float) bound = np.asarray(args[1], dtype=float) aux = _ensure_aux(args[2] if len(args) > 2 else None) + rng = ensure_rng(aux) n, d = parent.shape lower = bound[0] upper = bound[1] - _init_cma(aux, n, d, lower, upper) - offspring, aux = _sample(aux, n, d) + _init_cma(aux, n, d, lower, upper, rng) + offspring, aux = _sample(aux, n, d, rng) return offspring, aux - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_de_current.py b/src/autooptlib/components/search_de_current.py index 6734b99..66a722e 100644 --- a/src/autooptlib/components/search_de_current.py +++ b/src/autooptlib/components/search_de_current.py @@ -4,13 +4,16 @@ import numpy as np +from ._utils import ensure_rng + def search_de_current(*args): mode = args[-1] if mode == "execute": parent_obj = args[0] para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent_obj, "decs", None) if callable(decs): parent = decs() @@ -25,12 +28,12 @@ def search_de_current(*args): f = float(arr[0]) cr = float(arr[1]) if arr.size > 1 else 0.5 p1 = parent.copy() - p2 = parent[np.random.permutation(n)] - p3 = parent[np.random.permutation(n)] - mask = np.random.rand(n, d) < cr + p2 = parent[rng.permutation(n)] + p3 = parent[rng.permutation(n)] + mask = rng.random((n, d)) < cr offspring = parent.copy() offspring[mask] = p1[mask] + f * (p2[mask] - p3[mask]) - return offspring, args[3] if len(args) > 3 else None + return offspring, aux if mode == "parameter": return [[0, 1], [0, 1]], None if mode == "behavior": diff --git a/src/autooptlib/components/search_de_current_best.py b/src/autooptlib/components/search_de_current_best.py index 3235006..4a3c5d4 100644 --- a/src/autooptlib/components/search_de_current_best.py +++ b/src/autooptlib/components/search_de_current_best.py @@ -1,27 +1,36 @@ -"""Python translation of search_de_current_best.""" +"""Python translation of search_de_current_best.""" from __future__ import annotations import numpy as np +from ._utils import ensure_rng + def search_de_current_best(*args): mode = args[-1] if mode == "execute": parent_obj = args[0] para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) fits = getattr(parent_obj, "fits", None) if callable(fits): fit_vals = np.asarray(fits()).reshape(-1) else: - fit_vals = np.asarray(fits if fits is not None else getattr(parent_obj, "fit", None)).reshape(-1) + fit_vals = np.asarray( + fits if fits is not None else getattr(parent_obj, "fit", None) + ).reshape(-1) best_idx = int(np.argmin(fit_vals)) best_dec = getattr(parent_obj[best_idx], "dec", None) if callable(best_dec): gbest = np.asarray(best_dec()) else: - gbest = np.asarray(best_dec if best_dec is not None else getattr(parent_obj, "decs")[best_idx]) + gbest = np.asarray( + best_dec + if best_dec is not None + else getattr(parent_obj, "decs")[best_idx] + ) decs = getattr(parent_obj, "decs", None) if callable(decs): parent = decs() @@ -37,11 +46,11 @@ def search_de_current_best(*args): cr = float(arr[1]) if arr.size > 1 else 0.5 p1 = parent.copy() p2 = np.repeat(gbest.reshape(1, -1), n, axis=0) - p3 = parent[np.random.permutation(n)] - mask = np.random.rand(n, d) < cr + p3 = parent[rng.permutation(n)] + mask = rng.random((n, d)) < cr offspring = parent.copy() offspring[mask] = p1[mask] + f * (p2[mask] - p3[mask]) - return offspring, args[3] if len(args) > 3 else None + return offspring, aux if mode == "parameter": return [[0, 1], [0, 1]], None if mode == "behavior": diff --git a/src/autooptlib/components/search_de_random.py b/src/autooptlib/components/search_de_random.py index 773b988..1695772 100644 --- a/src/autooptlib/components/search_de_random.py +++ b/src/autooptlib/components/search_de_random.py @@ -4,13 +4,16 @@ import numpy as np +from ._utils import ensure_rng + def search_de_random(*args): mode = args[-1] if mode == "execute": parent_obj = args[0] para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent_obj, "decs", None) if callable(decs): parent = decs() @@ -24,13 +27,13 @@ def search_de_random(*args): arr = np.asarray(para).reshape(-1) f = float(arr[0]) cr = float(arr[1]) if arr.size > 1 else 0.5 - p1 = parent[np.random.permutation(n)] - p2 = parent[np.random.permutation(n)] - p3 = parent[np.random.permutation(n)] - mask = np.random.rand(n, d) < cr + p1 = parent[rng.permutation(n)] + p2 = parent[rng.permutation(n)] + p3 = parent[rng.permutation(n)] + mask = rng.random((n, d)) < cr offspring = parent.copy() offspring[mask] = p1[mask] + f * (p2[mask] - p3[mask]) - return offspring, args[3] if len(args) > 3 else None + return offspring, aux if mode == "parameter": return [[0, 1], [0, 1]], None if mode == "behavior": diff --git a/src/autooptlib/components/search_eda.py b/src/autooptlib/components/search_eda.py index ccb0bc4..ff04933 100644 --- a/src/autooptlib/components/search_eda.py +++ b/src/autooptlib/components/search_eda.py @@ -6,12 +6,15 @@ import numpy as np +from ._utils import ensure_rng + def search_eda(*args: Any): mode = args[-1] if mode == "execute": parent = args[0] aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) decs = getattr(parent, "decs", None) if callable(decs): @@ -26,7 +29,7 @@ def search_eda(*args: Any): std = parent.std(axis=0, ddof=1) std = np.where(np.isfinite(std) & (std > 0), std, parent.std(axis=0, ddof=0)) std = np.where(std > 0, std, 1e-12) - offspring = np.random.normal(loc=mean, scale=std, size=(n, parent.shape[1])) + offspring = rng.normal(loc=mean, scale=std, size=(n, parent.shape[1])) return offspring, aux if mode == "parameter": @@ -36,4 +39,3 @@ def search_eda(*args: Any): return ["", "GS"], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/search_ica.py b/src/autooptlib/components/search_ica.py index 8357e6b..5286504 100644 --- a/src/autooptlib/components/search_ica.py +++ b/src/autooptlib/components/search_ica.py @@ -6,7 +6,7 @@ import numpy as np -from ._utils import flex_get +from ._utils import ensure_rng, flex_get def _ensure_aux(aux: Any) -> Dict[str, Any]: @@ -37,10 +37,15 @@ def search_ica(*args: Any): problem = args[1] if len(args) > 1 else None para = args[2] if len(args) > 2 else None aux = _ensure_aux(args[3] if len(args) > 3 else None) + rng = ensure_rng(aux, problem) if not isinstance(raw_parent, (np.ndarray, list, tuple)): decs_attr = getattr(raw_parent, "decs", None) - parent = decs_attr() if callable(decs_attr) else (decs_attr if decs_attr is not None else raw_parent) + parent = ( + decs_attr() + if callable(decs_attr) + else (decs_attr if decs_attr is not None else raw_parent) + ) else: parent = raw_parent parent = np.asarray(parent, dtype=float) @@ -79,16 +84,16 @@ def search_ica(*args: Any): offspring = parent.copy() if colonies.size: colony_indices = order[imperialist_count:] - assign = np.random.randint(0, imperialist_count, size=colonies.shape[0]) - rand_scale = np.random.rand(colonies.shape[0], d) + assign = rng.integers(0, imperialist_count, size=colonies.shape[0]) + rand_scale = rng.random((colonies.shape[0], d)) target = imperialists[assign] moved = colonies + assimilation_coeff * rand_scale * (target - colonies) moved = np.clip(moved, lower, upper) offspring[colony_indices] = moved - revol_mask = np.random.rand(n, d) < revolution_prob + revol_mask = rng.random((n, d)) < revolution_prob if np.any(revol_mask): - random_values = lower + range_span * np.random.rand(n, d) + random_values = lower + range_span * rng.random((n, d)) offspring[revol_mask] = random_values[revol_mask] return offspring, aux @@ -100,4 +105,3 @@ def search_ica(*args: Any): return ["", "GS"], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/components/search_insert.py b/src/autooptlib/components/search_insert.py index 0ec7489..4839682 100644 --- a/src/autooptlib/components/search_insert.py +++ b/src/autooptlib/components/search_insert.py @@ -1,4 +1,5 @@ """Insertion operator for permutations.""" + from __future__ import annotations from typing import Any @@ -45,4 +46,4 @@ def search_insert(*args): if mode == "behavior": return ["LS", ""], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_mu_cauchy.py b/src/autooptlib/components/search_mu_cauchy.py index b58e4d7..266c88c 100644 --- a/src/autooptlib/components/search_mu_cauchy.py +++ b/src/autooptlib/components/search_mu_cauchy.py @@ -4,6 +4,8 @@ import numpy as np +from ._utils import ensure_rng + _KEY = "cauchy_eta" @@ -29,11 +31,14 @@ def search_mu_cauchy(*args): if mode == "execute": parent = args[0] aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) inner_g = int(args[5]) if len(args) > 5 else 1 if not isinstance(parent, (np.ndarray, list, tuple)): decs = getattr(parent, "decs", None) - parent = decs() if callable(decs) else (decs if decs is not None else parent) + parent = ( + decs() if callable(decs) else (decs if decs is not None else parent) + ) parent = np.asarray(parent, dtype=float) n, d = parent.shape @@ -43,16 +48,16 @@ def search_mu_cauchy(*args): if eta is not None and np.shape(eta) != (n, d): eta = None if eta is None: - eta = np.random.rand(n, d) + eta = rng.random((n, d)) _set_eta(aux, eta) - disturb = eta * np.random.standard_cauchy(size=(n, d)) + disturb = eta * rng.standard_cauchy(size=(n, d)) offspring = parent + disturb tau1 = 1.0 / np.sqrt(2.0 * np.sqrt(d)) tau2 = 1.0 / np.sqrt(2.0 * d) - normal = np.random.randn(n, 1).repeat(d, axis=1) - normal_j = np.random.randn(n, d) + normal = rng.standard_normal((n, 1)).repeat(d, axis=1) + normal_j = rng.standard_normal((n, d)) eta = eta * np.exp(tau2 * normal + tau1 * normal_j) _set_eta(aux, eta) return offspring, aux diff --git a/src/autooptlib/components/search_mu_gaussian.py b/src/autooptlib/components/search_mu_gaussian.py index cad70dd..cf431b3 100644 --- a/src/autooptlib/components/search_mu_gaussian.py +++ b/src/autooptlib/components/search_mu_gaussian.py @@ -4,6 +4,8 @@ import numpy as np +from ._utils import ensure_rng + _KEY = "gaussian_eta" @@ -29,11 +31,14 @@ def search_mu_gaussian(*args): if mode == "execute": parent = args[0] aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, args[1] if len(args) > 1 else None) inner_g = int(args[5]) if len(args) > 5 else 1 if not isinstance(parent, (np.ndarray, list, tuple)): decs = getattr(parent, "decs", None) - parent = decs() if callable(decs) else (decs if decs is not None else parent) + parent = ( + decs() if callable(decs) else (decs if decs is not None else parent) + ) parent = np.asarray(parent, dtype=float) n, d = parent.shape @@ -43,16 +48,16 @@ def search_mu_gaussian(*args): if eta is not None and np.shape(eta) != (n, d): eta = None if eta is None: - eta = np.random.rand(n, d) + eta = rng.random((n, d)) _set_eta(aux, eta) - disturb = eta * np.random.randn(n, d) + disturb = eta * rng.standard_normal((n, d)) offspring = parent + disturb tau1 = 1.0 / np.sqrt(2.0 * np.sqrt(d)) tau2 = 1.0 / np.sqrt(2.0 * d) - normal = np.random.randn(n, 1).repeat(d, axis=1) - normal_j = np.random.randn(n, d) + normal = rng.standard_normal((n, 1)).repeat(d, axis=1) + normal_j = rng.standard_normal((n, d)) eta = eta * np.exp(tau2 * normal + tau1 * normal_j) _set_eta(aux, eta) return offspring, aux diff --git a/src/autooptlib/components/search_mu_polynomial.py b/src/autooptlib/components/search_mu_polynomial.py index b6a88e2..9335ca6 100644 --- a/src/autooptlib/components/search_mu_polynomial.py +++ b/src/autooptlib/components/search_mu_polynomial.py @@ -4,6 +4,8 @@ import numpy as np +from ._utils import ensure_rng + def search_mu_polynomial(*args): mode = args[-1] @@ -12,10 +14,13 @@ def search_mu_polynomial(*args): problem = args[1] if len(args) > 1 else None para = args[2] if len(args) > 2 else None aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, problem) if not isinstance(parent, (np.ndarray, list, tuple)): decs = getattr(parent, "decs", None) - offspring = decs() if callable(decs) else (decs if decs is not None else parent) + offspring = ( + decs() if callable(decs) else (decs if decs is not None else parent) + ) else: offspring = parent offspring = np.asarray(offspring, dtype=float) @@ -36,17 +41,38 @@ def search_mu_polynomial(*args): lower = np.repeat(lower.reshape(1, -1), n, axis=0) upper = np.repeat(upper.reshape(1, -1), n, axis=0) - site = np.random.rand(n, d) < prob_m - mu = np.random.rand(n, d) + site = rng.random((n, d)) < prob_m + mu = rng.random((n, d)) temp = site & (mu <= 0.5) if np.any(temp): offspring[temp] = offspring[temp] + (upper[temp] - lower[temp]) * ( - (2.0 * mu[temp] + (1.0 - 2.0 * mu[temp]) * (1.0 - (offspring[temp] - lower[temp]) / (upper[temp] - lower[temp])) ** (dis_m + 1.0)) ** (1.0 / (dis_m + 1.0)) - 1.0 + ( + 2.0 * mu[temp] + + (1.0 - 2.0 * mu[temp]) + * ( + 1.0 + - (offspring[temp] - lower[temp]) / (upper[temp] - lower[temp]) + ) + ** (dis_m + 1.0) + ) + ** (1.0 / (dis_m + 1.0)) + - 1.0 ) temp = site & (mu > 0.5) if np.any(temp): offspring[temp] = offspring[temp] + (upper[temp] - lower[temp]) * ( - 1.0 - (2.0 * (1.0 - mu[temp]) + 2.0 * (mu[temp] - 0.5) * (1.0 - (upper[temp] - offspring[temp]) / (upper[temp] - lower[temp])) ** (dis_m + 1.0)) ** (1.0 / (dis_m + 1.0)) + 1.0 + - ( + 2.0 * (1.0 - mu[temp]) + + 2.0 + * (mu[temp] - 0.5) + * ( + 1.0 + - (upper[temp] - offspring[temp]) / (upper[temp] - lower[temp]) + ) + ** (dis_m + 1.0) + ) + ** (1.0 / (dis_m + 1.0)) ) return offspring, aux @@ -62,20 +88,42 @@ def search_mu_polynomial(*args): prob_m = 1.0 dis_m = 30.0 offspring = np.asarray(parent, dtype=float) + rng = ensure_rng(args[2] if len(args) > 3 else None) n, d = offspring.shape lower = np.repeat(bound[0].reshape(1, -1), n, axis=0) upper = np.repeat(bound[1].reshape(1, -1), n, axis=0) - site = np.random.rand(n, d) < prob_m - mu = np.random.rand(n, d) + site = rng.random((n, d)) < prob_m + mu = rng.random((n, d)) temp = site & (mu <= 0.5) if np.any(temp): offspring[temp] = offspring[temp] + (upper[temp] - lower[temp]) * ( - (2.0 * mu[temp] + (1.0 - 2.0 * mu[temp]) * (1.0 - (offspring[temp] - lower[temp]) / (upper[temp] - lower[temp])) ** (dis_m + 1.0)) ** (1.0 / (dis_m + 1.0)) - 1.0 + ( + 2.0 * mu[temp] + + (1.0 - 2.0 * mu[temp]) + * ( + 1.0 + - (offspring[temp] - lower[temp]) / (upper[temp] - lower[temp]) + ) + ** (dis_m + 1.0) + ) + ** (1.0 / (dis_m + 1.0)) + - 1.0 ) temp = site & (mu > 0.5) if np.any(temp): offspring[temp] = offspring[temp] + (upper[temp] - lower[temp]) * ( - 1.0 - (2.0 * (1.0 - mu[temp]) + 2.0 * (mu[temp] - 0.5) * (1.0 - (upper[temp] - offspring[temp]) / (upper[temp] - lower[temp])) ** (dis_m + 1.0)) ** (1.0 / (dis_m + 1.0)) + 1.0 + - ( + 2.0 * (1.0 - mu[temp]) + + 2.0 + * (mu[temp] - 0.5) + * ( + 1.0 + - (upper[temp] - offspring[temp]) / (upper[temp] - lower[temp]) + ) + ** (dis_m + 1.0) + ) + ** (1.0 / (dis_m + 1.0)) ) return offspring, None diff --git a/src/autooptlib/components/search_mu_uniform.py b/src/autooptlib/components/search_mu_uniform.py index b3dd96c..52257c1 100644 --- a/src/autooptlib/components/search_mu_uniform.py +++ b/src/autooptlib/components/search_mu_uniform.py @@ -4,6 +4,8 @@ import numpy as np +from ._utils import ensure_rng + def search_mu_uniform(*args): mode = args[-1] @@ -11,11 +13,14 @@ def search_mu_uniform(*args): parent = args[0] problem = args[1] if len(args) > 1 else None para = args[2] if len(args) > 2 else None - aux = args[3] if len(args) > 3 else None # noqa: F841 + aux = args[3] if len(args) > 3 else None + rng = ensure_rng(aux, problem) if not isinstance(parent, (np.ndarray, list, tuple)): decs = getattr(parent, "decs", None) - offspring = decs() if callable(decs) else (decs if decs is not None else parent) + offspring = ( + decs() if callable(decs) else (decs if decs is not None else parent) + ) else: offspring = parent offspring = np.asarray(offspring, dtype=float) @@ -23,8 +28,8 @@ def search_mu_uniform(*args): n, d = offspring.shape lower = np.asarray(getattr(problem, "bound")[0], dtype=float) upper = np.asarray(getattr(problem, "bound")[1], dtype=float) - mask = np.random.rand(n, d) < prob - rand_vals = lower + (upper - lower) * np.random.rand(n, d) + mask = rng.random((n, d)) < prob + rand_vals = lower + (upper - lower) * rng.random((n, d)) offspring[mask] = rand_vals[mask] return offspring, aux diff --git a/src/autooptlib/components/search_pso.py b/src/autooptlib/components/search_pso.py index 0c9dfa8..f22d7f5 100644 --- a/src/autooptlib/components/search_pso.py +++ b/src/autooptlib/components/search_pso.py @@ -2,33 +2,13 @@ from __future__ import annotations -from typing import Any, Iterable +from typing import Any import numpy as np +from ..utils.solve import SolutionSet from ._utils import ensure_rng, flex_get -try: # noqa: SIM105 - from ..utils.solve import SolutionSet # type: ignore -except Exception: # pylint: disable=broad-except - class SolutionSet(list): # type: ignore - def __init__(self, items: Iterable[Any]): - super().__init__(items) - - def decs(self) -> np.ndarray: - if not self: - return np.zeros((0, 0)) - return np.vstack([np.asarray(flex_get(sol, "dec"), dtype=float) for sol in self]) - - def objs(self) -> np.ndarray: - return np.asarray([flex_get(sol, "obj") for sol in self], dtype=float).reshape(-1, 1) - - def cons(self) -> np.ndarray: - return np.asarray([flex_get(sol, "con") for sol in self], dtype=float).reshape(-1, 1) - - def fits(self) -> np.ndarray: - return np.asarray([flex_get(sol, "fit") for sol in self], dtype=float).reshape(-1, 1) - def _ensure_solution_set(solution: Any) -> SolutionSet: if isinstance(solution, SolutionSet): @@ -47,7 +27,6 @@ def search_pso(*args): mode = args[-1] if mode == "execute": solution_obj = args[0] - problem = args[1] para = args[2] if len(args) > 2 else None aux = args[3] if len(args) > 3 else None rng = ensure_rng(aux) @@ -63,21 +42,22 @@ def search_pso(*args): if "Pbest" not in aux: aux["Pbest"] = SolutionSet([sol for sol in sol_set]) - fits = flex_get(sol_set, "fits") - if fits is None: - fits = sol_set.fits() - fits = np.asarray(fits, dtype=float).reshape(-1) - best_idx = int(np.argmin(fits)) - aux["Gbest"] = aux["Pbest"][best_idx] + # The original PSO component seeds the global guide with a random + # particle; ParaPSO promotes the best guide after evaluation. + aux["Gbest"] = aux["Pbest"][int(rng.integers(n))] aux["V"] = np.zeros((n, d)) velocity = np.asarray(aux.get("V", np.zeros((n, d))), dtype=float) pbest = _extract_dec_matrix(aux["Pbest"]) gbest = np.asarray(flex_get(aux["Gbest"], "dec"), dtype=float).reshape(1, -1) - r1 = rng.random((n, d)) - r2 = rng.random((n, d)) - velocity = inertia * velocity + 2 * r1 * (pbest - decs) + 2 * r2 * (gbest - decs) + # MATLAB draws one coefficient per particle and broadcasts it across + # dimensions, rather than drawing a coefficient per coordinate. + r1 = rng.random((n, 1)) + r2 = rng.random((n, 1)) + velocity = ( + inertia * velocity + 2 * r1 * (pbest - decs) + 2 * r2 * (gbest - decs) + ) offspring = decs + velocity aux["V"] = velocity diff --git a/src/autooptlib/components/search_reset_creep.py b/src/autooptlib/components/search_reset_creep.py index ddc5da4..b4bab7e 100644 --- a/src/autooptlib/components/search_reset_creep.py +++ b/src/autooptlib/components/search_reset_creep.py @@ -1,4 +1,5 @@ """Creep mutation for discrete ordinal variables.""" + from __future__ import annotations from typing import Any @@ -30,7 +31,9 @@ def search_reset_creep(*args): aux = args[3] if len(args) > 3 else None rng = ensure_rng(aux) - params = np.asarray(para).reshape(-1) if para is not None else np.array([0.1, 0.1]) + params = ( + np.asarray(para).reshape(-1) if para is not None else np.array([0.1, 0.1]) + ) prob = float(params[0]) if params.size > 0 else 0.1 amp_ratio = float(params[1]) if params.size > 1 else 0.1 @@ -60,4 +63,4 @@ def search_reset_creep(*args): if mode == "behavior": return [["LS", "small", "small"], ["GS", "large", "large"]], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_reset_one.py b/src/autooptlib/components/search_reset_one.py index 97b937a..d09e14c 100644 --- a/src/autooptlib/components/search_reset_one.py +++ b/src/autooptlib/components/search_reset_one.py @@ -1,4 +1,5 @@ """Reset a single element of each solution.""" + from __future__ import annotations from typing import Any @@ -52,4 +53,4 @@ def search_reset_one(*args): if mode == "behavior": return ["LS", ""], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_reset_rand.py b/src/autooptlib/components/search_reset_rand.py index 9402135..cb4b8b1 100644 --- a/src/autooptlib/components/search_reset_rand.py +++ b/src/autooptlib/components/search_reset_rand.py @@ -1,4 +1,5 @@ """Random reset operator for discrete variables.""" + from __future__ import annotations from typing import Any @@ -49,4 +50,4 @@ def search_reset_rand(*args): if mode == "behavior": return [["LS", "small"], ["GS", "large"]], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_scramble.py b/src/autooptlib/components/search_scramble.py index 8d0ae29..a0c7522 100644 --- a/src/autooptlib/components/search_scramble.py +++ b/src/autooptlib/components/search_scramble.py @@ -1,4 +1,5 @@ """Scramble elements within a segment.""" + from __future__ import annotations from typing import Any @@ -33,9 +34,9 @@ def search_scramble(*args): for i in range(n): idx = np.sort(rng.choice(d, size=2, replace=False)) start, end = int(idx[0]), int(idx[1]) - segment = new[i, start:end + 1] + segment = new[i, start : end + 1] rng.shuffle(segment) - new[i, start:end + 1] = segment + new[i, start : end + 1] = segment return new, aux if mode == "parameter": @@ -44,4 +45,4 @@ def search_scramble(*args): if mode == "behavior": return ["", "GS"], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_swap.py b/src/autooptlib/components/search_swap.py index e042553..5681550 100644 --- a/src/autooptlib/components/search_swap.py +++ b/src/autooptlib/components/search_swap.py @@ -1,4 +1,5 @@ """Swap two randomly selected elements.""" + from __future__ import annotations from typing import Any @@ -42,4 +43,4 @@ def search_swap(*args): if mode == "behavior": return ["LS", ""], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/search_swap_multi.py b/src/autooptlib/components/search_swap_multi.py index 2e9c200..787c132 100644 --- a/src/autooptlib/components/search_swap_multi.py +++ b/src/autooptlib/components/search_swap_multi.py @@ -1,4 +1,5 @@ """Multi swap between segments.""" + from __future__ import annotations from typing import Any @@ -46,4 +47,4 @@ def search_swap_multi(*args): if mode == "behavior": return ["", "GS"], None - raise ValueError(f"Unsupported mode: {mode}") \ No newline at end of file + raise ValueError(f"Unsupported mode: {mode}") diff --git a/src/autooptlib/components/update_simulated_annealing.py b/src/autooptlib/components/update_simulated_annealing.py index 6a9547d..1eb54d1 100644 --- a/src/autooptlib/components/update_simulated_annealing.py +++ b/src/autooptlib/components/update_simulated_annealing.py @@ -21,16 +21,20 @@ def update_simulated_annealing(*args): fitness = extract_fits(solution) n = int(flex_get(problem, "N", len(sol_list) // 2)) if len(sol_list) < 2 * n: - raise ValueError("update_simulated_annealing expects old and new populations of size N") + raise ValueError( + "update_simulated_annealing expects old and new populations of size N" + ) try: t_initial = float(np.asarray(para).reshape(-1)[0]) except Exception as exc: # pylint: disable=broad-except - raise ValueError("Parameter for simulated annealing must be convertible to float") from exc + raise ValueError( + "Parameter for simulated annealing must be convertible to float" + ) from exc t_final = 0.01 gmax = max(int(flex_get(problem, "Gmax", g + 1)), 1) rate = (t_final / t_initial) ** (1.0 / gmax) - temperature = t_initial * (rate ** g) + temperature = t_initial * (rate**g) old_list = sol_list[:n] new_list = sol_list[n : 2 * n] @@ -57,4 +61,3 @@ def update_simulated_annealing(*args): return ["", ""], None raise ValueError(f"Unsupported mode: {mode}") - diff --git a/src/autooptlib/problems/__init__.py b/src/autooptlib/problems/__init__.py index b4ecb95..30e1224 100644 --- a/src/autooptlib/problems/__init__.py +++ b/src/autooptlib/problems/__init__.py @@ -2,6 +2,8 @@ from __future__ import annotations +from .base import ProblemDefinition as ProblemDefinition +from .base import make_problem as make_problem from .cec2013 import ( cec2013_f1, cec2013_f2, @@ -63,4 +65,3 @@ "cec2013_f27", "cec2013_f28", ] - diff --git a/src/autooptlib/problems/base.py b/src/autooptlib/problems/base.py new file mode 100644 index 0000000..5846424 --- /dev/null +++ b/src/autooptlib/problems/base.py @@ -0,0 +1,191 @@ +"""Public contracts and helpers for user-defined optimization problems.""" + +from __future__ import annotations + +from typing import Any, Callable, Iterable, Protocol, Sequence, Union, runtime_checkable + +import numpy as np + +Objective = Callable[[np.ndarray, Any], float] +Constraint = Callable[[np.ndarray, Any], Any] +Bounds = Union[ + np.ndarray, + Sequence[Sequence[float]], + Callable[[Any], Any], + None, +] + + +@runtime_checkable +class ProblemDefinition(Protocol): + """Mode-based callable accepted by :func:`autooptlib.autoopt`. + + ``construct`` receives mutable problem records plus user instance + identifiers and returns ``(problems, data, auxiliary)``. Constructed + problem records must expose ``type``, ``bound``, and an + ``evaluate(data, decision)`` callable. + """ + + def __call__( + self, problems: Iterable[Any], instances: Sequence[Any], mode: str + ) -> tuple[Any, Any, Any]: ... + + +def _resolve_bound(bounds: Bounds, instance: Any, problem_type: str) -> np.ndarray: + raw = bounds(instance) if callable(bounds) else bounds + if problem_type == "permutation": + if raw is None: + dimension = int(instance) + domain = np.arange(1, dimension + 1) + return np.vstack((domain, domain)) + if raw is None: + raise ValueError(f"{problem_type} problems require explicit bounds.") + + array = np.asarray(raw, dtype=float if problem_type == "continuous" else int) + if array.ndim == 1 and array.size == 2: + try: + dimension = int(instance) + except (TypeError, ValueError) as exc: + raise ValueError( + "Scalar lower/upper bounds require an integer instance dimension." + ) from exc + array = np.vstack((np.full(dimension, array[0]), np.full(dimension, array[1]))) + return array + + +def make_problem( + objective: Objective, + *, + bounds: Bounds = None, + problem_type: str = "continuous", + constraint: Constraint | None = None, + data_factory: Callable[[Any], Any] | None = None, + name: str | None = None, +) -> ProblemDefinition: + """Create an AutoOptLib problem definition from ordinary Python callables. + + Parameters + ---------- + objective: + Callable ``objective(decision, data) -> scalar`` to minimize. + bounds: + A ``(2, dimension)`` array, a ``(lower, upper)`` pair expanded using + an integer instance dimension, or a callable returning bounds for an + instance. Permutation problems may omit bounds when instances are + dimensions. + problem_type: + ``continuous``, ``discrete``, or ``permutation``. + constraint: + Optional callable returning values where positive entries represent + violations. + data_factory: + Optional callable mapping each instance identifier to objective data. + Without it, the instance identifier itself is passed as data. + """ + if not callable(objective): + raise TypeError("objective must be callable") + problem_type = str(problem_type).lower() + if problem_type not in {"continuous", "discrete", "permutation"}: + raise ValueError("problem_type must be continuous, discrete, or permutation") + if constraint is not None and not callable(constraint): + raise TypeError("constraint must be callable") + if data_factory is not None and not callable(data_factory): + raise TypeError("data_factory must be callable") + + problem_name = str(name or getattr(objective, "__name__", "user_problem")) + + def evaluate_one(data: Any, decision: np.ndarray) -> tuple[Any, Any, None]: + decision = np.asarray(decision) + # Keep the raw result so the central evaluator can reject vectors and + # non-finite scalars consistently for built-in and user problems. + value = objective(decision, data) + violation = 0.0 if constraint is None else constraint(decision, data) + return value, violation, None + + def definition(problems: Iterable[Any], instances: Sequence[Any], mode: str): + normalized_mode = str(mode).lower() + if normalized_mode == "construct": + problem_list = list(problems) + if len(problem_list) != len(instances): + raise ValueError( + "The number of problem records and instances must match." + ) + data_entries: list[Any] = [] + for problem, instance in zip(problem_list, instances): + bound = _resolve_bound(bounds, instance, problem_type) + problem.type = [problem_type, "static", "certain"] + problem.bound = bound + problem.dimension = int(bound.shape[1]) if bound.ndim == 2 else 0 + problem.name = problem_name + problem.evaluate = evaluate_one + data_entries.append( + data_factory(instance) if data_factory is not None else instance + ) + return problem_list, data_entries, None + if normalized_mode == "repair": + return np.asarray(instances), None, None + if normalized_mode == "evaluate": + data = problems + decisions = np.asarray(instances) + single = decisions.ndim == 1 + decisions = np.atleast_2d(decisions) + values = [] + violations = [] + for decision in decisions: + value, violation, _ = evaluate_one(data, decision) + values.append(value) + violations.append(violation) + if single: + return values[0], violations[0], None + return np.asarray(values), np.asarray(violations), None + raise ValueError(f"Unsupported problem mode: {mode!r}") + + definition.__name__ = problem_name + return definition + + +def validate_constructed_problems(problems: Sequence[Any], data: Sequence[Any]) -> None: + """Validate the runtime problem contract with actionable error messages.""" + if not problems: + raise ValueError("Problem construction returned no problem instances.") + if len(data) != len(problems): + raise ValueError( + f"Problem construction returned {len(problems)} problems but {len(data)} data entries." + ) + for index, problem in enumerate(problems): + type_field = getattr(problem, "type", None) + if ( + not isinstance(type_field, Sequence) + or isinstance(type_field, (str, bytes)) + or not type_field + ): + raise TypeError( + f"Problem {index} must define a non-empty sequence attribute 'type'." + ) + problem_type = str(type_field[0]).lower() + if problem_type not in {"continuous", "discrete", "permutation"}: + raise ValueError( + f"Problem {index} has unsupported type {problem_type!r}; expected continuous, " + "discrete, or permutation." + ) + bound = np.asarray(getattr(problem, "bound", None)) + if bound.ndim != 2 or bound.shape[0] != 2 or bound.shape[1] == 0: + raise ValueError( + f"Problem {index} bound must have shape (2, dimension), got {bound.shape}." + ) + if not np.all(np.isfinite(bound)): + raise ValueError(f"Problem {index} bounds must be finite.") + if np.any(bound[0] > bound[1]): + raise ValueError( + f"Problem {index} has lower bounds greater than upper bounds." + ) + if not callable(getattr(problem, "evaluate", None)): + raise TypeError( + f"Problem {index} must expose evaluate(data, decision) after construction." + ) + for attribute in ("N", "Gmax"): + value = getattr(problem, attribute, None) + if not isinstance(value, (int, np.integer)) or value <= 0: + raise ValueError( + f"Problem {index} attribute {attribute} must be positive." + ) diff --git a/src/autooptlib/problems/cec2013.py b/src/autooptlib/problems/cec2013.py index fce8996..b318f06 100644 --- a/src/autooptlib/problems/cec2013.py +++ b/src/autooptlib/problems/cec2013.py @@ -1,60 +1,46 @@ """CEC2013 benchmark problems translated for the Python port. -This module currently exposes a growing subset of the official -benchmark suite. The implementations are direct ports of AutoOptLib's -Matlab definitions, sharing the same data files (shift vectors, -rotation matrices, etc.) so that fitness values remain comparable. +The implementations are direct ports of AutoOptLib's MATLAB definitions. +Shift vectors and rotation matrices are distributed as package resources so +the benchmark works from both source checkouts and installed wheels. """ from __future__ import annotations -from pathlib import Path +from importlib import resources from types import SimpleNamespace from typing import Any, Callable, Iterable, List, Sequence, Tuple import numpy as np - -_REPO_ROOT = Path(__file__).resolve().parents[3] -_DATA_DIR = ( - _REPO_ROOT - / "AutoOptLib for Matlab" - / "Problems" - / "Numerial Benchmarks" - / "CEC2013" - / "data" -) -_SHIFT_DATA = None +_DATA_ARCHIVE: dict[str, np.ndarray] | None = None +_SHIFT_DATA: np.ndarray | None = None _ROTATION_DATA: dict[int, np.ndarray] = {} -_RNG = np.random.default_rng(42) - -# Rotation matrices are bundled for a fixed set of dimensions. The text -# files store multiple stacked matrices (each block is D rows). -_ROTATION_FILES = { - 2: "M_D2.txt", - 5: "M_D5.txt", - 10: "M_D10.txt", - 20: "M_D20.txt", - 30: "M_D30.txt", - 40: "M_D40.txt", - 50: "M_D50.txt", - 60: "M_D60.txt", - 70: "M_D70.txt", - 80: "M_D80.txt", - 90: "M_D90.txt", - 100: "M_D100.txt", -} + +_SUPPORTED_DIMENSIONS = (2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100) # --------------------------------------------------------------------------- # Shared utilities # --------------------------------------------------------------------------- + +def _load_data_archive() -> dict[str, np.ndarray]: + global _DATA_ARCHIVE + if _DATA_ARCHIVE is None: + resource = resources.files("autooptlib.problems.data").joinpath("cec2013.npz") + with ( + resources.as_file(resource) as path, + np.load(path, allow_pickle=False) as archive, + ): + _DATA_ARCHIVE = {name: archive[name].copy() for name in archive.files} + return _DATA_ARCHIVE + + def _load_shift_data() -> np.ndarray: global _SHIFT_DATA if _SHIFT_DATA is None: - path = _DATA_DIR / "shift_data.txt" - _SHIFT_DATA = np.loadtxt(path, dtype=float) + _SHIFT_DATA = _load_data_archive()["shift_data"] if _SHIFT_DATA.ndim == 1: _SHIFT_DATA = _SHIFT_DATA.reshape(1, -1) return _SHIFT_DATA @@ -62,28 +48,18 @@ def _load_shift_data() -> np.ndarray: def _load_rotation_blocks(dim: int) -> np.ndarray | None: """Return stacked rotation matrices for the requested dimension.""" - if dim not in _ROTATION_FILES: + if dim not in _SUPPORTED_DIMENSIONS: return None if dim not in _ROTATION_DATA: - path = _DATA_DIR / _ROTATION_FILES[dim] - raw = np.loadtxt(path, dtype=float) - if raw.ndim == 1: - raw = raw.reshape(1, -1) - rows, cols = raw.shape - if cols < dim or rows % dim != 0: - raise ValueError( - f"Rotation data '{path.name}' has shape {raw.shape}, " - f"cannot extract {dim}x{dim} blocks." - ) - blocks = raw[:, :dim].reshape(rows // dim, dim, dim) - _ROTATION_DATA[dim] = blocks + _ROTATION_DATA[dim] = _load_data_archive()[f"M_D{dim}"] return _ROTATION_DATA[dim] -def _random_orthonormal(dim: int) -> np.ndarray: - """Fallback rotation when no precomputed matrix exists.""" - mat = _RNG.normal(size=(dim, dim)) +def _random_orthonormal(dim: int, index: int) -> np.ndarray: + """Return a deterministic fallback rotation for nonstandard dimensions.""" + rng = np.random.default_rng(np.random.SeedSequence([2013, dim, index])) + mat = rng.normal(size=(dim, dim)) q, _ = np.linalg.qr(mat) # Ensure a proper rotation matrix (determinant +1). if np.linalg.det(q) < 0: @@ -95,7 +71,7 @@ def _get_rotation(dim: int, index: int = 0) -> np.ndarray: blocks = _load_rotation_blocks(dim) if blocks is not None and index < blocks.shape[0]: return blocks[index].copy() - return _random_orthonormal(dim) + return _random_orthonormal(dim, index) def _construct_lambda(alpha: float, dim: int) -> np.ndarray: @@ -106,14 +82,27 @@ def _construct_lambda(alpha: float, dim: int) -> np.ndarray: return np.diag(diag) +def _safe_matmul(left: np.ndarray, right: np.ndarray) -> np.ndarray: + """Multiply finite CEC buffers without leaking backend FP warnings.""" + with np.errstate(all="ignore"): + result = np.matmul(left, right) + if not np.all(np.isfinite(result)): + raise FloatingPointError("CEC2013 transformation produced non-finite values.") + return result + + def _compute_t_osz(values: np.ndarray) -> np.ndarray: values = np.asarray(values, dtype=float) - result = np.zeros_like(values) - mask = values != 0.0 + one_dimensional = values.ndim == 1 + array = values.reshape(1, -1) if one_dimensional else values + result = array.copy() + mask = np.zeros_like(array, dtype=bool) + mask[:, 0] = array[:, 0] != 0.0 + mask[:, -1] = array[:, -1] != 0.0 if not np.any(mask): - return result + return result.reshape(-1) if one_dimensional else result - v = values[mask] + v = array[mask] x_hat = np.log(np.abs(v)) sign = np.sign(v) sign[sign == 0.0] = 1.0 @@ -122,10 +111,12 @@ def _compute_t_osz(values: np.ndarray) -> np.ndarray: c2 = np.where(v > 0.0, 7.9, 3.1) transformed = np.exp(x_hat + 0.049 * (np.sin(c1 * x_hat) + np.sin(c2 * x_hat))) result[mask] = sign * transformed - return result + return result.reshape(-1) if one_dimensional else result -def _compute_t_asym(values: np.ndarray, beta: float) -> np.ndarray: +def _compute_t_asym( + values: np.ndarray, beta: float, fallback: np.ndarray | None = None +) -> np.ndarray: arr = np.asarray(values, dtype=float) if arr.ndim == 1: arr = arr.reshape(1, -1) @@ -133,17 +124,20 @@ def _compute_t_asym(values: np.ndarray, beta: float) -> np.ndarray: else: squeeze_back = False - result = arr.copy() + if fallback is None: + result = arr.copy() + else: + result = np.asarray(fallback, dtype=float).reshape(arr.shape).copy() dim = arr.shape[1] if dim == 1: return result.squeeze(0) if squeeze_back else result - exponents = 1.0 + beta * np.arange(dim) / (dim - 1) for j in range(dim): pos = arr[:, j] > 0.0 if not np.any(pos): continue - result[pos, j] = np.power(arr[pos, j], exponents[j]) * np.sqrt(arr[pos, j]) + exponents = 1.0 + beta * j / (dim - 1) * np.sqrt(arr[pos, j]) + result[pos, j] = np.power(arr[pos, j], exponents) return result.squeeze(0) if squeeze_back else result @@ -152,7 +146,17 @@ def _prepare_shift(dim: int) -> np.ndarray: shift_data = _load_shift_data() if shift_data.shape[1] >= dim: return shift_data[0, :dim].copy() - return _RNG.uniform(-100.0, 100.0, size=dim) + rng = np.random.default_rng(np.random.SeedSequence([2013, dim])) + return rng.uniform(-100.0, 100.0, size=dim) + + +def _composition_centers(dim: int) -> np.ndarray: + """Return centers using the official reference file's sequential read order.""" + flat = _load_shift_data().reshape(-1) + required = 10 * dim + if flat.size < required: + raise ValueError(f"CEC 2013 shift data does not contain {required} values.") + return flat[:required].reshape(10, dim).copy() def _initialise_problem(problem: Any, shift: np.ndarray, name: str) -> None: @@ -220,6 +224,7 @@ def _mode_guard(mode: str) -> str: # Individual problems # --------------------------------------------------------------------------- + def cec2013_f1(problems: Iterable[Any], instances: Sequence[int], mode: str): """Shifted sphere.""" mode = _mode_guard(mode) @@ -261,7 +266,7 @@ def cec2013_f2(problems: Iterable[Any], instances: Sequence[int], mode: str): shift = np.asarray(data_obj.o, dtype=float) M = np.asarray(data_obj.M, dtype=float) - z = (decs - shift) @ M + z = (decs - shift) @ M.T z = _compute_t_osz(z) dim = z.shape[1] @@ -295,9 +300,10 @@ def cec2013_f3(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) M2 = np.asarray(data_obj.M2, dtype=float) - z = (decs - shift) @ M1 - z = _compute_t_asym(z, 0.5) - z = z @ M2 + shifted = decs - shift + z = _safe_matmul(shifted, M1.T) + z = _compute_t_asym(z, 0.5, fallback=shifted) + z = _safe_matmul(z, M2.T) a = 1e6 # f3 = z1^2 + a * sum_{i=2..D} z_i^2 @@ -329,7 +335,7 @@ def cec2013_f4(problems: Iterable[Any], instances: Sequence[int], mode: str): shift = np.asarray(data_obj.o, dtype=float) M = np.asarray(data_obj.M, dtype=float) - z = (decs - shift) @ M + z = _safe_matmul(decs - shift, M.T) z = _compute_t_osz(z) a = 1e6 @@ -366,7 +372,8 @@ def cec2013_f5(problems: Iterable[Any], instances: Sequence[int], mode: str): if dim == 1: powers = np.array([2.0]) else: - powers = 2.0 + 4.0 * (np.arange(dim) / (dim - 1)) + # Match the integer arithmetic in the official CEC 2013 reference. + powers = 2.0 + (4 * np.arange(dim)) // (dim - 1) values = np.sqrt(np.sum(np.abs(z) ** powers, axis=1)) - 1000.0 return _finalise(values, single) @@ -390,7 +397,7 @@ def cec2013_f6(problems: Iterable[Any], instances: Sequence[int], mode: str): shift = np.asarray(data_obj.o, dtype=float) M = np.asarray(data_obj.M, dtype=float) - z = (2.048 * (decs - shift) / 100.0) @ M + 1.0 + z = _safe_matmul(2.048 * (decs - shift) / 100.0, M.T) + 1.0 left = z[:, :-1] right = z[:, 1:] values = np.sum(100.0 * (left**2 - right) ** 2 + (left - 1.0) ** 2, axis=1) @@ -420,10 +427,11 @@ def cec2013_f7(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) M2 = np.asarray(data_obj.M2, dtype=float) - z = (decs - shift) @ M1 - z = _compute_t_asym(z, 0.5) - z = z @ M2 + shifted = decs - shift + z = shifted @ M1.T + z = _compute_t_asym(z, 0.5, fallback=shifted) z = z @ _construct_lambda(10.0, z.shape[1]) + z = z @ M2.T if z.shape[1] < 2: values = np.zeros(z.shape[0]) @@ -461,10 +469,11 @@ def cec2013_f8(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) M2 = np.asarray(data_obj.M2, dtype=float) - z = (decs - shift) @ M1 - z = _compute_t_asym(z, 0.5) - z = z @ M2 + shifted = decs - shift + z = shifted @ M1.T + z = _compute_t_asym(z, 0.5, fallback=shifted) z = z @ _construct_lambda(10.0, z.shape[1]) + z = z @ M2.T dim = z.shape[1] term1 = np.sum(z**2, axis=1) / dim @@ -496,11 +505,11 @@ def cec2013_f9(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) M2 = np.asarray(data_obj.M2, dtype=float) - z = 0.5 * (decs - shift) / 100.0 - z = z @ M1 - z = _compute_t_asym(z, 0.5) - z = z @ M2 + scaled = 0.5 * (decs - shift) / 100.0 + z = scaled @ M1.T + z = _compute_t_asym(z, 0.5, fallback=scaled) z = z @ _construct_lambda(10.0, z.shape[1]) + z = z @ M2.T a = 0.5 b = 3.0 @@ -509,7 +518,11 @@ def cec2013_f9(problems: Iterable[Any], instances: Sequence[int], mode: str): b_k = b**k inner = z[:, :, None] + 0.5 term1 = np.sum( - np.sum(a_k[np.newaxis, np.newaxis, :] * np.cos(2.0 * np.pi * b_k[np.newaxis, np.newaxis, :] * inner), axis=2), + np.sum( + a_k[np.newaxis, np.newaxis, :] + * np.cos(2.0 * np.pi * b_k[np.newaxis, np.newaxis, :] * inner), + axis=2, + ), axis=1, ) constant = np.sum(a_k * np.cos(2.0 * np.pi * b_k * 0.5)) @@ -538,7 +551,7 @@ def cec2013_f10(problems: Iterable[Any], instances: Sequence[int], mode: str): M = np.asarray(data_obj.M, dtype=float) z = 600.0 * (decs - shift) / 100.0 - z = z @ M + z = z @ M.T z = z @ _construct_lambda(100.0, z.shape[1]) sum_term = np.sum(z**2, axis=1) / 4000.0 @@ -600,12 +613,13 @@ def cec2013_f12(problems: Iterable[Any], instances: Sequence[int], mode: str): M2 = np.asarray(data_obj.M2, dtype=float) z = 5.12 * (decs - shift) / 100.0 - z = z @ M1 + z = _safe_matmul(z, M1.T) + rotated = z.copy() z = _compute_t_osz(z) - z = _compute_t_asym(z, 0.2) - z = z @ M2 - z = z @ _construct_lambda(10.0, z.shape[1]) - z = z @ M1 + z = _compute_t_asym(z, 0.2, fallback=rotated) + z = _safe_matmul(z, M2.T) + z = _safe_matmul(z, _construct_lambda(10.0, z.shape[1])) + z = _safe_matmul(z, M1.T) values = np.sum(z**2 - 10.0 * np.cos(2.0 * np.pi * z) + 10.0, axis=1) values -= 300.0 @@ -635,18 +649,19 @@ def cec2013_f13(problems: Iterable[Any], instances: Sequence[int], mode: str): M2 = np.asarray(data_obj.M2, dtype=float) z = 5.12 * (decs - shift) / 100.0 - z = z @ M1 + z = _safe_matmul(z, M1.T) mask = np.abs(z) > 0.5 rounded = np.round(2.0 * z[mask]) / 2.0 z = z.copy() z[mask] = rounded + rounded_rotated = z.copy() z = _compute_t_osz(z) - z = _compute_t_asym(z, 0.2) - z = z @ M2 - z = z @ _construct_lambda(10.0, z.shape[1]) - z = z @ M1 + z = _compute_t_asym(z, 0.2, fallback=rounded_rotated) + z = _safe_matmul(z, M2.T) + z = _safe_matmul(z, _construct_lambda(10.0, z.shape[1])) + z = _safe_matmul(z, M1.T) values = np.sum(z**2 - 10.0 * np.cos(2.0 * np.pi * z) + 10.0, axis=1) values -= 200.0 @@ -705,7 +720,7 @@ def cec2013_f14(problems: Iterable[Any], instances: Sequence[int], mode: str): z = z + 4.209687462275036e2 g_sum = _schwefel_core(z, z.shape[1], penalty_sign=-1.0) - values = 418.9829 * z.shape[1] - g_sum + values = 418.9828872724338 * z.shape[1] - g_sum values -= 100.0 return _finalise(values, single) @@ -730,12 +745,12 @@ def cec2013_f15(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) z = 10.0 * (decs - shift) - z = z @ M1 + z = z @ M1.T z = z @ _construct_lambda(10.0, z.shape[1]) z = z + 4.209687462275036e2 - g_sum = _schwefel_core(z, z.shape[1], penalty_sign=1.0) - values = 418.9829 * z.shape[1] - g_sum + g_sum = _schwefel_core(z, z.shape[1], penalty_sign=-1.0) + values = 418.9828872724338 * z.shape[1] - g_sum values += 100.0 return _finalise(values, single) @@ -763,9 +778,9 @@ def cec2013_f16(problems: Iterable[Any], instances: Sequence[int], mode: str): M2 = np.asarray(data_obj.M2, dtype=float) z = (5.0 / 100.0) * (decs - shift) - z = z @ M1 + z = z @ M1.T z = z @ _construct_lambda(100.0, z.shape[1]) - z = z @ M2 + z = z @ M2.T exponent = 10.0 / (z.shape[1] ** 1.2) product_val = np.ones(z.shape[0], dtype=float) @@ -830,7 +845,9 @@ def cec2013_f18(problems: Iterable[Any], instances: Sequence[int], mode: str): problems, instances, "cec2013_f18", - lambda dim, shift: SimpleNamespace(o=shift, M1=_get_rotation(dim, 0), M2=_get_rotation(dim, 1)), + lambda dim, shift: SimpleNamespace( + o=shift, M1=_get_rotation(dim, 0), M2=_get_rotation(dim, 1) + ), ) if mode == "repair": @@ -850,9 +867,9 @@ def cec2013_f18(problems: Iterable[Any], instances: Sequence[int], mode: str): y = 10.0 * (decs - o) / 100.0 sign_vec = np.where(o > 0.0, 1.0, -1.0) xhat = 2.0 * sign_vec * y + mu0 - z = (xhat - mu0) @ M1 + z = (xhat - mu0) @ M1.T z = z @ _construct_lambda(100.0, z.shape[1]) - z = z @ M2 + z = z @ M2.T term1 = np.sum((xhat - mu0) ** 2, axis=1) term2 = d * decs.shape[1] + s * np.sum((xhat - mu1) ** 2, axis=1) @@ -879,11 +896,11 @@ def cec2013_f19(problems: Iterable[Any], instances: Sequence[int], mode: str): data_obj = problems if hasattr(problems, "o") else list(problems)[0] decs, single = _ensure_array(instances) o = np.asarray(data_obj.o, dtype=float) - M = np.asarray(data_obj.M, dtype=float) - z = 5.0 * (decs - o) / 100.0 - z = z @ M - z = z + 1.0 + scaled = 5.0 * (decs - o) / 100.0 + # The official reference implementation performs the rotation but then + # constructs the shifted vector from the pre-rotation buffer. + z = scaled + 1.0 N, D = z.shape # Rosenbrock pair then Griewank applied to each scalar @@ -899,7 +916,7 @@ def rosen_pair(a, b): g2_vals = np.hstack([parts, parts_last]) # Griewank on each scalar u: u/4000 - cos(u) + 1 - g1_vals = g2_vals / 4000.0 - np.cos(g2_vals) + 1.0 + g1_vals = g2_vals**2 / 4000.0 - np.cos(g2_vals) + 1.0 values = np.sum(g1_vals, axis=1) + 500.0 return _finalise(values, single) @@ -912,7 +929,9 @@ def cec2013_f20(problems: Iterable[Any], instances: Sequence[int], mode: str): problems, instances, "cec2013_f20", - lambda dim, shift: SimpleNamespace(o=shift, M1=_get_rotation(dim, 0), M2=_get_rotation(dim, 1)), + lambda dim, shift: SimpleNamespace( + o=shift, M1=_get_rotation(dim, 0), M2=_get_rotation(dim, 1) + ), ) if mode == "repair": @@ -924,9 +943,10 @@ def cec2013_f20(problems: Iterable[Any], instances: Sequence[int], mode: str): M1 = np.asarray(data_obj.M1, dtype=float) M2 = np.asarray(data_obj.M2, dtype=float) - z = (decs - o) @ M1 - z = _compute_t_asym(z, 0.5) - z = z @ M2 + shifted = decs - o + z = shifted @ M1.T + z = _compute_t_asym(z, 0.5, fallback=shifted) + z = z @ M2.T def g(x, y): r2 = x**2 + y**2 @@ -978,7 +998,9 @@ def _eval_base(i: int, x: np.ndarray, o: np.ndarray, rot_idx: int) -> np.ndarray return val - _OFFSETS[2] if i == 3: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f3(data, x, "evaluate") return val - _OFFSETS[3] @@ -987,28 +1009,37 @@ def _eval_base(i: int, x: np.ndarray, o: np.ndarray, rot_idx: int) -> np.ndarray val, _, _ = cec2013_f4(data, x, "evaluate") return val - _OFFSETS[4] if i == 5: - data = SimpleNamespace(o=o) - val, _, _ = cec2013_f5(data, x, "evaluate") - return val - _OFFSETS[5] + z = _safe_matmul(x - o, _get_rotation(x.shape[1], rot_idx).T) + if z.shape[1] == 1: + powers = np.array([2.0]) + else: + powers = 2.0 + (4 * np.arange(z.shape[1])) // (z.shape[1] - 1) + return np.sqrt(np.sum(np.abs(z) ** powers, axis=1)) if i == 6: data = SimpleNamespace(o=o, M=_get_rotation(x.shape[1], rot_idx)) val, _, _ = cec2013_f6(data, x, "evaluate") return val - _OFFSETS[6] if i == 7: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f7(data, x, "evaluate") return val - _OFFSETS[7] if i == 8: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f8(data, x, "evaluate") return val - _OFFSETS[8] if i == 9: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f9(data, x, "evaluate") return val - _OFFSETS[9] @@ -1022,7 +1053,9 @@ def _eval_base(i: int, x: np.ndarray, o: np.ndarray, rot_idx: int) -> np.ndarray return val - _OFFSETS[11] if i == 12: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f12(data, x, "evaluate") return val - _OFFSETS[12] @@ -1032,13 +1065,17 @@ def _eval_base(i: int, x: np.ndarray, o: np.ndarray, rot_idx: int) -> np.ndarray return val - _OFFSETS[14] if i == 15: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f15(data, x, "evaluate") return val - _OFFSETS[15] if i == 16: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f16(data, x, "evaluate") return val - _OFFSETS[16] @@ -1048,14 +1085,24 @@ def _eval_base(i: int, x: np.ndarray, o: np.ndarray, rot_idx: int) -> np.ndarray return val - _OFFSETS[19] if i == 20: data = SimpleNamespace( - o=o, M1=_get_rotation(x.shape[1], rot_idx), M2=_get_rotation(x.shape[1], rot_idx + 1) + o=o, + M1=_get_rotation(x.shape[1], rot_idx), + M2=_get_rotation(x.shape[1], rot_idx + 1), ) val, _, _ = cec2013_f20(data, x, "evaluate") return val - _OFFSETS[20] raise ValueError(f"Unsupported base function id: {i}") -def _composition_value(x: np.ndarray, centers: np.ndarray, base_ids: Sequence[int], lambdas: Sequence[float], sigmas: Sequence[float], biases: Sequence[float], rot_start: int = 0) -> np.ndarray: +def _composition_value( + x: np.ndarray, + centers: np.ndarray, + base_ids: Sequence[int], + lambdas: Sequence[float], + sigmas: Sequence[float], + biases: Sequence[float], + rot_start: int = 0, +) -> np.ndarray: N, D = x.shape n = len(base_ids) w = np.zeros((N, n), dtype=float) @@ -1065,7 +1112,7 @@ def _composition_value(x: np.ndarray, centers: np.ndarray, base_ids: Sequence[in diff = x - oi dist2 = np.sum(diff**2, axis=1) # raw g' value - gvals[:, i] = _eval_base(fid, x, oi, rot_start + 2 * i) + gvals[:, i] = _eval_base(fid, x, oi, rot_start + i) # weights denom = np.sqrt(dist2 + 1e-30) w[:, i] = (1.0 / denom) * np.exp(-dist2 / (2.0 * D * (sigmas[i] ** 2) + 1e-30)) @@ -1073,11 +1120,15 @@ def _composition_value(x: np.ndarray, centers: np.ndarray, base_ids: Sequence[in omega = w / w_sum lambdas = np.asarray(lambdas, dtype=float) biases = np.asarray(biases, dtype=float) - values = np.sum(omega * (lambdas[np.newaxis, :] * gvals + biases[np.newaxis, :]), axis=1) + values = np.sum( + omega * (lambdas[np.newaxis, :] * gvals + biases[np.newaxis, :]), axis=1 + ) return values -def _construct_composition(problems: Iterable[Any], instances: Sequence[int], name: str): +def _construct_composition( + problems: Iterable[Any], instances: Sequence[int], name: str +): problem_list = list(problems) data_entries: List[SimpleNamespace] = [] for prob, inst in zip(problem_list, instances): @@ -1085,150 +1136,169 @@ def _construct_composition(problems: Iterable[Any], instances: Sequence[int], na shift = _prepare_shift(dim) # for bounds only _initialise_problem(prob, shift, name) data_entries.append(SimpleNamespace(dim=dim)) + def _eval_closure(_): def inner(_d, dec): - return globals()[name](SimpleNamespace(dim=dim), dec, 'evaluate') + return globals()[name](SimpleNamespace(dim=dim), dec, "evaluate") + return inner + prob.evaluate = _eval_closure(dim) return problem_list, data_entries, None def cec2013_f21(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f21') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f21") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - shift_data = _load_shift_data() - centers = shift_data[:5, :] + centers = _composition_centers(decs.shape[1])[:5] base_ids = [6, 5, 3, 4, 1] sigmas = [10, 20, 30, 40, 50] lambdas = [1, 1e-6, 1e-26, 1e-6, 0.1] biases = [0, 100, 200, 300, 400] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 700.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 700.0 + ) return _finalise(values, single) def cec2013_f22(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f22') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f22") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:3, :] + centers = _composition_centers(decs.shape[1])[:3] base_ids = [14, 14, 14] sigmas = [20, 20, 20] lambdas = [1, 1, 1] biases = [0, 100, 200] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 800.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 800.0 + ) return _finalise(values, single) def cec2013_f23(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f23') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f23") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:3, :] + centers = _composition_centers(decs.shape[1])[:3] base_ids = [15, 15, 15] sigmas = [20, 20, 20] lambdas = [1, 1, 1] biases = [0, 100, 200] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 900.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 900.0 + ) return _finalise(values, single) def cec2013_f24(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f24') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f24") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:3, :] + centers = _composition_centers(decs.shape[1])[:3] base_ids = [15, 12, 9] sigmas = [20, 20, 20] lambdas = [0.25, 1.0, 2.5] biases = [0, 100, 200] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1000.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1000.0 + ) return _finalise(values, single) def cec2013_f25(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f25') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f25") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:3, :] + centers = _composition_centers(decs.shape[1])[:3] base_ids = [15, 12, 9] sigmas = [10, 30, 50] lambdas = [0.25, 1.0, 2.5] biases = [0, 100, 200] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1100.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1100.0 + ) return _finalise(values, single) def cec2013_f26(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f26') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f26") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:5, :] + centers = _composition_centers(decs.shape[1])[:5] base_ids = [15, 12, 2, 9, 10] sigmas = [10, 10, 10, 10, 10] lambdas = [0.25, 1.0, 1e-7, 2.5, 10.0] biases = [0, 100, 200, 300, 400] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1200.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1200.0 + ) return _finalise(values, single) def cec2013_f27(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f27') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f27") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:5, :] + centers = _composition_centers(decs.shape[1])[:5] base_ids = [10, 12, 15, 9, 1] sigmas = [10, 10, 10, 20, 20] lambdas = [100, 10, 2.5, 25, 0.1] biases = [0, 100, 200, 300, 400] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1300.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1300.0 + ) return _finalise(values, single) def cec2013_f28(problems: Iterable[Any], instances: Sequence[int], mode: str): mode = _mode_guard(mode) - if mode == 'construct': - return _construct_composition(problems, instances, 'cec2013_f28') - if mode == 'repair': + if mode == "construct": + return _construct_composition(problems, instances, "cec2013_f28") + if mode == "repair": return _repair(instances) decs, single = _ensure_array(instances) - centers = _load_shift_data()[:5, :] + centers = _composition_centers(decs.shape[1])[:5] base_ids = [19, 7, 15, 20, 1] sigmas = [10, 20, 30, 40, 50] lambdas = [2.5, 2.5e-3, 2.5, 5e-4, 0.1] biases = [0, 100, 200, 300, 400] - values = _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1400.0 + values = ( + _composition_value(decs, centers, base_ids, lambdas, sigmas, biases) + 1400.0 + ) return _finalise(values, single) + __all__ = [ "cec2013_f1", "cec2013_f2", diff --git a/src/autooptlib/problems/data/README.md b/src/autooptlib/problems/data/README.md new file mode 100644 index 0000000..94396d2 --- /dev/null +++ b/src/autooptlib/problems/data/README.md @@ -0,0 +1,19 @@ +# CEC 2013 benchmark data + +`cec2013.npz` contains the shift vectors and rotation matrices used by the +AutoOptLib Python translation of the CEC 2013 real-parameter optimization +benchmark. The archive is a lossless compressed conversion of the text data +distributed with the historical AutoOptLib MATLAB release. + +The conversion contains `shift_data` plus ten rotation matrices for each +supported dimension (2, 5, 10, and multiples of 10 through 100). The Python +implementation is regression-tested at fixed points against the organizers' +official C implementation, rather than only checking the known optima. + +The arrays are loaded through `importlib.resources`, so the benchmark behaves +the same in source checkouts and installed wheels. + +Before redistributing this dataset independently of AutoOptLib, verify the +original CEC benchmark's data-distribution terms. Apache-2.0 covers AutoOptLib's +source code but does not replace any third-party terms that may apply to the +benchmark data. diff --git a/src/autooptlib/problems/data/__init__.py b/src/autooptlib/problems/data/__init__.py new file mode 100644 index 0000000..8aa4e42 --- /dev/null +++ b/src/autooptlib/problems/data/__init__.py @@ -0,0 +1 @@ +"""Packaged numerical data used by AutoOptLib benchmark problems.""" diff --git a/src/autooptlib/problems/data/cec2013.npz b/src/autooptlib/problems/data/cec2013.npz new file mode 100644 index 0000000000000000000000000000000000000000..c8cf5a2fee819bf59c6132ec9ec8b875d99f945d GIT binary patch literal 1289585 zcmY(qV{j#06Ezx4l8J3iY}>Z&oMd92*qqq5ZQHhO+qUlWUR8aos{eF%)vj8I5)0bTXv3wQ~mrLHfTd|Fwer zuj_xvN_`Q)*!~zPx)e4Is(;!3=-zDcwA--W4!U`}$^=Xw*iW}**PJ*jr(M%m%1yWtyCGP-z6M{z%+;zd);={X z&Z;fm!XiM!uGIl`>_TI1ewL|to&(?Mt3uvyR&P33S6G=B)mWKX7w6R?l|Fyq4Gnw7 z2JRhADRX9>#8vdcecsDxt+m7lJt3!n+cw4nV_e;*gbbmh8{R|AJ8{80>b&@PI8MQs 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znU3XrK_CjkffMs2`ZY4hE3CEJ4RBIE_cVcq`_;NF=f!pHLT#4VoP0$jveknP|2q8@O*X))G)WVyUsRDGfg4*0+9z-yof;dur8l zs$L}$3r(Y3M7uci@Zq}J3^wF~dUo6-1B-1I6KRv94If0f{psj@ZauCL?JCDd)(WXG zuh7T49K+E#lSEtORJ=WiQ`)>(A z4u9OElCdn`-xTwaP%Huy2}$*l{I8mMY{R1x{F|~szUbHbPdl;x8|ZNyfc%8zU&YJw zHwJkp?(oFpaWh{er2l2#-u!Y#ShhY>Xq^J_2$GQIlDyWRv literal 0 HcmV?d00001 diff --git a/src/autooptlib/serialization.py b/src/autooptlib/serialization.py new file mode 100644 index 0000000..c5e5df2 --- /dev/null +++ b/src/autooptlib/serialization.py @@ -0,0 +1,227 @@ +"""Portable, versioned serialization for designed algorithms.""" + +from __future__ import annotations + +import json +import math +from pathlib import Path +from typing import Any, Mapping + +import numpy as np + +from ._version import __version__ +from .components import get_component +from .utils.design._helpers import Pathway, PathwayParam, SearchParam, SearchStep + +SCHEMA_NAME = "autooptlib.algorithm" +SCHEMA_VERSION = 1 + + +def _encode_value(value: Any) -> Any: + if value is None or isinstance(value, (str, bool, int)): + return value + if isinstance(value, (float, np.floating)): + number = float(value) + if math.isnan(number): + return {"$float": "nan"} + if math.isinf(number): + return {"$float": "inf" if number > 0 else "-inf"} + return number + if isinstance(value, np.integer): + return int(value) + if isinstance(value, np.ndarray): + return _encode_value(value.tolist()) + if isinstance(value, (list, tuple)): + return [_encode_value(item) for item in value] + if isinstance(value, Mapping): + return {str(key): _encode_value(item) for key, item in value.items()} + raise TypeError(f"Value of type {type(value).__name__} is not JSON serializable.") + + +def _decode_value(value: Any) -> Any: + if isinstance(value, list): + return [_decode_value(item) for item in value] + if isinstance(value, dict): + if set(value) == {"$float"}: + special = value["$float"] + if special == "inf": + return math.inf + if special == "-inf": + return -math.inf + if special == "nan": + return math.nan + raise ValueError(f"Unknown encoded float {special!r}.") + return {key: _decode_value(item) for key, item in value.items()} + return value + + +def _array_or_none(value: Any) -> np.ndarray | None: + if value is None: + return None + return np.asarray(_decode_value(value), dtype=float) + + +def algorithm_to_dict( + design: Any, *, metadata: Mapping[str, Any] | None = None +) -> dict[str, Any]: + """Convert a decoded ``Design`` to the stable JSON schema.""" + operator = getattr(design, "operator_pheno", None) + parameter = getattr(design, "parameter_pheno", None) + if not operator or not parameter or not operator[0] or not parameter[0]: + raise ValueError("The algorithm must be decoded before it can be exported.") + pathways = operator[0] + pathway_parameters = parameter[0] + if len(pathways) != len(pathway_parameters): + raise ValueError("Operator and parameter pathway counts do not match.") + + records = [] + for pathway, params in zip(pathways, pathway_parameters): + searches = [] + if len(pathway.search) != len(params.search): + raise ValueError("Search-step and parameter counts do not match.") + for step, step_params in zip(pathway.search, params.search): + searches.append( + { + "primary": step.primary, + "secondary": step.secondary, + "termination": _encode_value( + np.asarray(step.termination, dtype=float) + ), + "primary_parameter": _encode_value(step_params.primary), + "secondary_parameter": _encode_value(step_params.secondary), + } + ) + records.append( + { + "choose": pathway.choose, + "choose_parameter": _encode_value(params.choose), + "search": searches, + "update": pathway.update, + "update_parameter": _encode_value(params.update), + "archive": list(pathway.archive), + } + ) + + return { + "schema": SCHEMA_NAME, + "schema_version": SCHEMA_VERSION, + "autooptlib_version": __version__, + "metadata": _encode_value(dict(metadata or {})), + "pathways": records, + } + + +def algorithm_from_dict(document: Mapping[str, Any]): + """Validate and construct a ``Design`` from the stable JSON schema.""" + if document.get("schema") != SCHEMA_NAME: + raise ValueError(f"Expected schema {SCHEMA_NAME!r}.") + if document.get("schema_version") != SCHEMA_VERSION: + raise ValueError( + f"Unsupported algorithm schema version {document.get('schema_version')!r}; " + f"expected {SCHEMA_VERSION}." + ) + records = document.get("pathways") + if not isinstance(records, list) or not records: + raise ValueError("Algorithm document must contain at least one pathway.") + + pathways = [] + pathway_parameters = [] + for index, record in enumerate(records): + if not isinstance(record, Mapping): + raise TypeError(f"Pathway {index} must be an object.") + choose = record.get("choose") + update = record.get("update") + if not isinstance(choose, str) or not isinstance(update, str): + raise TypeError( + f"Pathway {index} must define string choose and update names." + ) + get_component(choose) + get_component(update) + + steps = [] + step_parameters = [] + search_records = record.get("search") + if not isinstance(search_records, list) or not search_records: + raise ValueError(f"Pathway {index} must contain at least one search step.") + for step_index, step_record in enumerate(search_records): + if not isinstance(step_record, Mapping): + raise TypeError( + f"Search step {step_index} in pathway {index} must be an object." + ) + primary = step_record.get("primary") + secondary = step_record.get("secondary") + if not isinstance(primary, str): + raise TypeError("Search steps require a string primary component name.") + if secondary is not None and not isinstance(secondary, str): + raise TypeError("Secondary component names must be strings or null.") + get_component(primary) + if secondary: + get_component(secondary) + termination = _array_or_none(step_record.get("termination")) + if termination is None or termination.size == 0: + raise ValueError("Search steps require a non-empty termination vector.") + steps.append(SearchStep(primary, termination.reshape(-1), secondary)) + step_parameters.append( + SearchParam( + _array_or_none(step_record.get("primary_parameter")), + _array_or_none(step_record.get("secondary_parameter")), + ) + ) + + archive = record.get("archive", []) + if not isinstance(archive, list) or not all( + isinstance(name, str) for name in archive + ): + raise TypeError("archive must be a list of component names.") + for name in archive: + get_component(name) + pathways.append(Pathway(choose, steps, update, archive)) + pathway_parameters.append( + PathwayParam( + _array_or_none(record.get("choose_parameter")), + step_parameters, + _array_or_none(record.get("update_parameter")), + ) + ) + + from .utils.design import Design + + design = Design() + design.construct([pathways], [pathway_parameters]) + return design + + +def save_algorithm( + design: Any, + path: str | Path, + *, + metadata: Mapping[str, Any] | None = None, +) -> Path: + """Write an algorithm as UTF-8 JSON and return the resolved path.""" + target = Path(path).resolve() + target.parent.mkdir(parents=True, exist_ok=True) + document = algorithm_to_dict(design, metadata=metadata) + with target.open("w", encoding="utf-8") as handle: + json.dump(document, handle, indent=2, ensure_ascii=False, allow_nan=False) + handle.write("\n") + return target + + +def load_algorithm(path: str | Path): + """Load and validate an algorithm JSON document.""" + source = Path(path).resolve() + with source.open("r", encoding="utf-8") as handle: + document = json.load(handle) + if not isinstance(document, Mapping): + raise TypeError("Algorithm JSON root must be an object.") + return algorithm_from_dict(document) + + +__all__ = [ + "SCHEMA_NAME", + "SCHEMA_VERSION", + "algorithm_from_dict", + "algorithm_to_dict", + "load_algorithm", + "save_algorithm", +] diff --git a/src/autooptlib/utils/__init__.py b/src/autooptlib/utils/__init__.py index 1917a8e..85dd281 100644 --- a/src/autooptlib/utils/__init__.py +++ b/src/autooptlib/utils/__init__.py @@ -5,5 +5,6 @@ except Exception: # pylint: disable=broad-except select = None # type: ignore -__all__ = [name for name in ("select",) if name in globals() and globals()[name] is not None] - +__all__ = [ + name for name in ("select",) if name in globals() and globals()[name] is not None +] diff --git a/src/autooptlib/utils/design/__init__.py b/src/autooptlib/utils/design/__init__.py index 2eda5fc..17078e4 100644 --- a/src/autooptlib/utils/design/__init__.py +++ b/src/autooptlib/utils/design/__init__.py @@ -6,6 +6,7 @@ import numpy as np +from ._approximate import Approximate from ._decode import decode as _decode from ._disturb import disturb as _disturb from ._estimate import estimate as _estimate @@ -13,7 +14,6 @@ from ._helpers import get_flex, problem_list from ._initialize import initialize as _initialize from ._repair import repair as _repair -from ._approximate import Approximate __all__ = ["Design", "Approximate"] @@ -78,7 +78,9 @@ def get_new(self, problem: Any, setting: Any, inner_g: int, aux: Any): new_design.last_runs = {idx: [None] * alg_runs for idx in range(len(problems))} # disturb returns lists; for a single algorithm pick the first entry - aux_result = aux_out[0] if isinstance(aux_out, Sequence) and aux_out else aux_out + aux_result = ( + aux_out[0] if isinstance(aux_out, Sequence) and aux_out else aux_out + ) return new_design, aux_result def get_performance(self, setting: Any, seed_instance: Iterable[int]) -> np.ndarray: @@ -86,9 +88,11 @@ def get_performance(self, setting: Any, seed_instance: Iterable[int]) -> np.ndar seeds = list(seed_instance) alg_runs = _get_alg_runs(setting) if ( - get_flex(setting, "Evaluate", get_flex(setting, "evaluate", "exact")) == "approximate" + get_flex(setting, "Evaluate", get_flex(setting, "evaluate", "exact")) + == "approximate" and self.performance_approx.size - and not self.performance.size + and np.any(self.performance_approx != 0) + and not np.any(self.performance != 0) ): data = self.performance_approx[seeds, :] else: @@ -104,27 +108,27 @@ def ave_perform_all(self) -> np.ndarray: """Average actual performance across instances and runs.""" if self.performance.size == 0: return np.array([]) - return np.mean(self.performance, axis=1) + return np.array([float(np.mean(self.performance))]) def ave_perform_approx_all(self) -> np.ndarray: """Average approximate performance across instances and runs.""" if self.performance_approx.size == 0: return np.array([]) - return np.mean(self.performance_approx, axis=1) + return np.array([float(np.mean(self.performance_approx))]) def ave_perform_per(self, indices: Iterable[int]) -> np.ndarray: """Average actual performance over selected instances.""" if self.performance.size == 0: return np.array([]) idx = np.atleast_1d(list(indices)) - return np.mean(self.performance[idx, :], axis=1) + return np.array([float(np.mean(self.performance[idx, :]))]) def ave_perform_approx_per(self, indices: Iterable[int]) -> np.ndarray: """Average approximate performance over selected instances.""" if self.performance_approx.size == 0: return np.array([]) idx = np.atleast_1d(list(indices)) - return np.mean(self.performance_approx[idx, :], axis=1) + return np.array([float(np.mean(self.performance_approx[idx, :]))]) # Bind external implementations as instance methods --------------------- disturb = _disturb diff --git a/src/autooptlib/utils/design/_approximate.py b/src/autooptlib/utils/design/_approximate.py index 7191003..83b1313 100644 --- a/src/autooptlib/utils/design/_approximate.py +++ b/src/autooptlib/utils/design/_approximate.py @@ -3,11 +3,12 @@ from __future__ import annotations import math -from typing import Any, Iterable, List, Sequence, TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Iterable, List, Sequence import numpy as np from ._embedding import embedding +from ._helpers import ensure_rng if TYPE_CHECKING: from . import Design @@ -45,14 +46,14 @@ def predict(self, x: np.ndarray) -> np.ndarray: return preds -def _mean_performance(alg: 'Design') -> float: +def _mean_performance(alg: "Design") -> float: values = alg.ave_perform_all() if values.size == 0: return np.inf return float(np.mean(values)) -def _mean_performance_approx(alg: 'Design') -> float: +def _mean_performance_approx(alg: "Design") -> float: values = alg.ave_perform_approx_all() if values.size == 0: return np.inf @@ -62,56 +63,78 @@ def _mean_performance_approx(alg: 'Design') -> float: class Approximate: """Python translation of MATLAB Approximate class.""" - def __init__(self, problem: Any, data: Any, setting: Any, ind_instance: Sequence[int]): + def __init__( + self, problem: Any, data: Any, setting: Any, ind_instance: Sequence[int] + ): from . import Design - self.data: List['Design'] = [] + self.data: List["Design"] = [] self.embedding = None - self.model: _KNNModel | None = None + self.model: Any = None self.rand_seed: np.ndarray self.exact_g: np.ndarray alg_p = int(getattr(setting, "AlgP", getattr(setting, "alg_p", 1))) alg_q = int(getattr(setting, "AlgQ", getattr(setting, "alg_q", 1))) total = (alg_q + 2) * 3 if alg_p == 1 else (alg_p + 2) * 3 - self.rand_seed = np.random.permutation(total) + self.rand_seed = ensure_rng(setting).permutation(total) - exact_gmax = float(getattr(setting, "Surro", getattr(setting, "surro", 0))) + exact_budget = int(getattr(setting, "Surro", getattr(setting, "surro", 0))) alg_n = int(getattr(setting, "AlgN", getattr(setting, "alg_n", 1))) - alg_gmax = int(math.ceil(getattr(setting, "AlgFE", getattr(setting, "alg_fe", alg_n)) / alg_n)) - step = alg_gmax / max(exact_gmax, 1.0) if exact_gmax else alg_gmax + alg_fe = int(getattr(setting, "AlgFE", getattr(setting, "alg_fe", alg_n))) + alg_gmax = max(1, int(math.ceil(alg_fe / alg_n))) + step = alg_gmax / max(exact_budget, 1) if exact_budget else alg_gmax self.exact_g = np.arange(1, alg_gmax + 1, max(1, int(round(step)))) seeds = list(ind_instance) - train_algs_exact: List['Design'] = [] - for _ in range(500): + train_algs_exact: List["Design"] = [] + # Surrogate initialization is part of AlgFE. The previous fixed pool + # of 500 exact algorithms made small experiments silently perform + # thousands of additional objective evaluations. + training_count = min(alg_fe, max(alg_n, exact_budget)) + self.initial_evaluations = training_count + for _ in range(training_count): alg = Design(problem, setting) alg.evaluate(problem, data, setting, seeds) train_algs_exact.append(alg) self.data = train_algs_exact - train_algs_embed = [Design(problem, setting) for _ in range(1000)] + embedding_count = max(30, 3 * training_count) + train_algs_embed = [Design(problem, setting) for _ in range(embedding_count)] self.embedding = self.GetEmbed(train_algs_embed, setting) self.model = self.GetModel(self.data, setting) # Methods mirroring MATLAB class --------------------------------------- - def GetEmbed(self, data: Iterable['Design'], setting: Any): + def GetEmbed(self, data: Iterable["Design"], setting: Any): return embedding(list(data), setting, self, "get") - def UseEmbed(self, data: Iterable['Design'], setting: Any) -> np.ndarray: + def UseEmbed(self, data: Iterable["Design"], setting: Any) -> np.ndarray: return embedding(list(data), setting, self, "use") - def use_embed(self, data: Iterable['Design'], setting: Any) -> np.ndarray: + def use_embed(self, data: Iterable["Design"], setting: Any) -> np.ndarray: return self.UseEmbed(data, setting) - def GetModel(self, data: Iterable['Design'], setting: Any) -> _KNNModel: - embed_algs = self.UseEmbed(list(data), setting) - labels = np.array([_mean_performance(alg) for alg in data], dtype=float) - model = _KNNModel() + def GetModel(self, data: Iterable["Design"], setting: Any) -> Any: + algorithms = list(data) + embed_algs = self.UseEmbed(algorithms, setting) + labels = np.array([_mean_performance(alg) for alg in algorithms], dtype=float) + try: + from sklearn.ensemble import RandomForestRegressor + + model = RandomForestRegressor( + n_estimators=1000, + min_samples_leaf=5, + random_state=getattr(setting, "Seed", getattr(setting, "seed", None)), + n_jobs=1, + ) + except ImportError: + # Keep approximate mode usable in the NumPy-only core install; + # ``autooptlib[surrogate]`` activates the reference RF model. + model = _KNNModel() model.fit(embed_algs, labels) return model - def UpdateModel(self, algs: Sequence['Design'], setting: Any): + def UpdateModel(self, algs: Sequence["Design"], setting: Any): n = len(algs) actual = np.array([_mean_performance(alg) for alg in algs], dtype=float) approx = np.array([_mean_performance_approx(alg) for alg in algs], dtype=float) @@ -127,6 +150,8 @@ def UpdateModel(self, algs: Sequence['Design'], setting: Any): self.model = self.GetModel(self.data, setting) return self - def UseModel(self, algs: Sequence['Design'], setting: Any) -> np.ndarray: + def UseModel(self, algs: Sequence["Design"], setting: Any) -> np.ndarray: features = self.UseEmbed(algs, setting) - return self.model.predict(features) if self.model else np.zeros(features.shape[0]) + return ( + self.model.predict(features) if self.model else np.zeros(features.shape[0]) + ) diff --git a/src/autooptlib/utils/design/_decode.py b/src/autooptlib/utils/design/_decode.py index bd7854c..015ae19 100644 --- a/src/autooptlib/utils/design/_decode.py +++ b/src/autooptlib/utils/design/_decode.py @@ -50,9 +50,7 @@ def decode(operators, paras, problem: Any, setting: Any): pathways.append( Pathway(choose="", search=[], update="", archive=list(archive)) ) - pathway_params.append( - PathwayParam(choose=None, search=[], update=None) - ) + pathway_params.append(PathwayParam(choose=None, search=[], update=None)) continue choose_idx = int(matrix[0, 0]) @@ -88,7 +86,11 @@ def decode(operators, paras, problem: Any, setting: Any): secondary_param = None termination = np.array([rate, inner_g_max], dtype=float) - if primary_idx in ind_cross and row < matrix.shape[0] and matrix[row, 1] in ind_mu: + if ( + primary_idx in ind_cross + and row < matrix.shape[0] + and matrix[row, 1] in ind_mu + ): secondary_idx = int(matrix[row, 1]) secondary_name = all_op[secondary_idx - 1] if secondary_idx - 1 < len(paras[algo_idx]): @@ -119,7 +121,11 @@ def decode(operators, paras, problem: Any, setting: Any): row += 1 search_steps.append( - SearchStep(primary=primary_name, secondary=secondary_name, termination=termination) + SearchStep( + primary=primary_name, + secondary=secondary_name, + termination=termination, + ) ) search_params.append( SearchParam(primary=primary_param, secondary=secondary_param) @@ -145,4 +151,3 @@ def decode(operators, paras, problem: Any, setting: Any): para_pheno.append(pathway_params) return op_pheno, para_pheno - diff --git a/src/autooptlib/utils/design/_disturb.py b/src/autooptlib/utils/design/_disturb.py index 881bb3f..23da8e0 100644 --- a/src/autooptlib/utils/design/_disturb.py +++ b/src/autooptlib/utils/design/_disturb.py @@ -34,23 +34,35 @@ def _copy_parameter_structure(parameter: Sequence[Any]) -> List[List[Any]]: return copied -def _gather_operator_positions(paths: List[np.ndarray]) -> Tuple[List[Tuple[str, int, int]], List[int]]: +def _gather_operator_positions( + paths: List[np.ndarray], +) -> Tuple[List[Tuple[str, int, int]], List[int]]: positions: List[Tuple[str, int, int]] = [] values: List[int] = [] + if not paths or paths[0].size == 0: + return positions, values + # Choose and update are shared by all pathways in the reference encoding; + # only search operators are pathway-specific. + positions.append(("choose", 0, 0)) + values.append(_as_int(paths[0][0, 0])) for path_idx, path in enumerate(paths): if path.size == 0: continue - positions.append(("choose", path_idx, 0)) - values.append(_as_int(path[0, 0])) for row in range(1, path.shape[0]): positions.append(("search", path_idx, row)) values.append(_as_int(path[row, 0])) - positions.append(("update", path_idx, path.shape[0] - 1)) - values.append(_as_int(path[-1, -1])) + positions.append(("update", 0, paths[0].shape[0] - 1)) + values.append(_as_int(paths[0][-1, -1])) return positions, values -def _disturb_search_single(path: np.ndarray, row_idx: int, rng: np.random.Generator, op_space: np.ndarray, alg_q: int) -> np.ndarray: +def _disturb_search_single( + path: np.ndarray, + row_idx: int, + rng: np.random.Generator, + op_space: np.ndarray, + alg_q: int, +) -> np.ndarray: pool = np.arange(op_space[1, 0], op_space[1, 1] + 1) current = _as_int(path[row_idx, 0]) pool = pool[pool != current] @@ -83,7 +95,9 @@ def _disturb_search_single(path: np.ndarray, row_idx: int, rng: np.random.Genera return path -def _disturb_search_multi(path: np.ndarray, rng: np.random.Generator, op_space: np.ndarray, alg_q: int) -> np.ndarray: +def _disturb_search_multi( + path: np.ndarray, rng: np.random.Generator, op_space: np.ndarray, alg_q: int +) -> np.ndarray: choose_idx = _as_int(path[0, 0]) update_idx = _as_int(path[-1, -1]) curr_q = int(rng.integers(1, alg_q + 1)) @@ -102,18 +116,27 @@ def _disturb_search_multi(path: np.ndarray, rng: np.random.Generator, op_space: return new_path -def disturb(algs: Any, setting: Any, inner_g: int, aux: Any) -> Tuple[List[List[np.ndarray]], List[List[List[Any]]], Any]: +def disturb( + algs: Any, setting: Any, inner_g: int, aux: Any +) -> Tuple[List[List[np.ndarray]], List[List[List[Any]]], Any]: alg_list = _ensure_alg_list(algs) rng = ensure_rng(setting) op_space = np.asarray(get_flex(setting, "op_space", required=True), dtype=int) para_space = list(get_flex(setting, "para_space", required=True)) - para_local_space = list(get_flex(setting, "para_local_space", default=[None] * len(para_space))) + para_local_space = list( + get_flex(setting, "para_local_space", default=[None] * len(para_space)) + ) alg_p = int(get_flex(setting, "alg_p", required=True)) alg_q = int(get_flex(setting, "alg_q", required=True)) + alg_n = int(get_flex(setting, "alg_n", 1)) tune_para = bool(get_flex(setting, "tune_para", False)) - ind_non_empty_para = [idx for idx, space in enumerate(para_space) if space is not None and len(space) > 0] + ind_non_empty_para = [ + idx + for idx, space in enumerate(para_space) + if space is not None and len(space) > 0 + ] aux_list: List[Any] if aux is None: @@ -132,7 +155,9 @@ def disturb(algs: Any, setting: Any, inner_g: int, aux: Any) -> Tuple[List[List[ this_op_raw = getattr(alg, "operator", None) this_para_raw = getattr(alg, "parameter", None) if this_op_raw is None or this_para_raw is None: - raise ValueError("Algorithm must provide operator and parameter fields for disturb().") + raise ValueError( + "Algorithm must provide operator and parameter fields for disturb()." + ) paths = [np.array(path, copy=True) for path in this_op_raw] this_para = _copy_parameter_structure(this_para_raw) @@ -144,15 +169,27 @@ def disturb(algs: Any, setting: Any, inner_g: int, aux: Any) -> Tuple[List[List[ aux_entry = aux_list[idx] if isinstance(aux_list[idx], dict) else {} if inner_g == 1: - num_choices = total_ops + (1 if ind_para else 0) + num_choices = total_ops + 1 if not tune_para and num_choices > 0: - probs = np.ones(num_choices, dtype=float) - probs /= probs.sum() - seed = int(rng.choice(num_choices, p=probs)) + 1 + unit = 1.0 / max(1, total_ops + len(ind_para)) + probs = np.concatenate( + [np.full(total_ops, unit), np.array([unit * len(ind_para)])] + ) + if op_space[0, 0] == op_space[0, 1] and total_ops: + probs[0] = 0.0 + if op_space[1, 0] == op_space[1, 1] and total_ops > 2: + probs[1:-1] = 0.0 + if op_space[2, 0] == op_space[2, 1] and total_ops: + probs[total_ops - 1] = 0.0 + if probs.sum() > 0: + probs /= probs.sum() + seed = int(rng.choice(num_choices, p=probs)) + 1 + else: + seed = total_ops + 1 elif tune_para and ind_para: seed = total_ops + 1 else: - seed = 1 + seed = total_ops + 1 aux_entry["seed"] = seed else: seed = int(aux_entry.get("seed", total_ops + 1)) @@ -166,16 +203,16 @@ def disturb(algs: Any, setting: Any, inner_g: int, aux: Any) -> Tuple[List[List[ pool = pool[pool != current] if pool.size > 0: new_idx = _as_int(rng.choice(pool)) - path[0, 0] = new_idx - paths[path_idx] = path + for shared_path in paths: + shared_path[0, 0] = new_idx elif category == "update": pool = np.arange(op_space[2, 0], op_space[2, 1] + 1) current = _as_int(path[-1, -1]) pool = pool[pool != current] if pool.size > 0: new_idx = _as_int(rng.choice(pool)) - path[-1, -1] = new_idx - paths[path_idx] = path + for shared_path in paths: + shared_path[-1, -1] = new_idx else: # search if alg_p == 1: path = _disturb_search_single(path, row_idx, rng, op_space, alg_q) @@ -185,27 +222,65 @@ def disturb(algs: Any, setting: Any, inner_g: int, aux: Any) -> Tuple[List[List[ else: if not ind_para: seed = total_ops + 1 + active_parameters: list[tuple[int, np.ndarray, np.ndarray]] = [] for para_idx in ind_para: entry = this_para[para_idx] values = entry[0] if values is None: continue behavior = entry[1] - space = para_local_space[para_idx] if (behavior != "GS" and para_local_space[para_idx] is not None) else para_space[para_idx] + space = ( + para_local_space[para_idx] + if (behavior != "GS" and para_local_space[para_idx] is not None) + else para_space[para_idx] + ) if space is None: continue bounds = np.asarray(space, dtype=float) if bounds.ndim == 1: bounds = bounds.reshape(-1, 2) - lower = bounds[:, 0] - upper = bounds[:, 1] - entry[0] = lower + (upper - lower) * rng.random(lower.shape) - this_para[para_idx] = entry + values = np.asarray(values, dtype=float).reshape(-1) + if bounds.shape[0] != values.size: + raise ValueError( + f"Parameter {para_idx + 1} has {values.size} values but " + f"{bounds.shape[0]} bound rows" + ) + active_parameters.append((para_idx, values, bounds)) + + if active_parameters: + current = np.concatenate( + [item[1] for item in active_parameters] + ).reshape(1, -1) + lower = np.concatenate([item[2][:, 0] for item in active_parameters]) + upper = np.concatenate([item[2][:, 1] for item in active_parameters]) + bounds = np.vstack((lower, upper)) + + # Match the original bi-level design procedure: CMA-ES keeps + # state while tuning one algorithm; population-level design + # uses polynomial mutation for independent proposals. + component_aux = dict(aux_entry) + component_aux["rng"] = rng + if alg_n == 1: + candidate, component_aux = get_component("search_cma")( + current, bounds, component_aux, "algorithm" + ) + aux_entry.update(component_aux) + else: + candidate, _ = get_component("search_mu_polynomial")( + current, bounds, component_aux, "algorithm" + ) + + candidate = np.clip( + np.asarray(candidate, dtype=float).reshape(-1), lower, upper + ) + offset = 0 + for para_idx, values, _ in active_parameters: + width = values.size + this_para[para_idx][0] = candidate[offset : offset + width].copy() + offset += width new_ops.append(paths) new_paras.append(this_para) aux_list[idx] = aux_entry return new_ops, new_paras, aux_list - - diff --git a/src/autooptlib/utils/design/_embedding.py b/src/autooptlib/utils/design/_embedding.py index c18173f..f4a2dd9 100644 --- a/src/autooptlib/utils/design/_embedding.py +++ b/src/autooptlib/utils/design/_embedding.py @@ -8,6 +8,7 @@ def _normalize(vec: np.ndarray) -> np.ndarray: + vec = np.nan_to_num(np.asarray(vec, dtype=float), nan=0.0, posinf=1e6, neginf=-1e6) vmin = np.min(vec) vmax = np.max(vec) if np.isclose(vmax, vmin): @@ -51,7 +52,9 @@ def _vectorize_single_path(alg: Any, setting: Any, rand_seed: np.ndarray) -> np. entry = None parameter[:, idx] = _extract_parameter_values(entry, max_len=2) - parameter_flat = parameter.reshape(1, -1) + # MATLAB linearizes matrices column-by-column. The embedding learned for + # the reference implementation therefore depends on Fortran ordering. + parameter_flat = parameter.reshape(1, -1, order="F") operator_norm = _normalize(operator) vector = np.concatenate([operator_norm, parameter_flat.ravel()]) shuffled = vector[rand_seed] @@ -72,16 +75,22 @@ def _vectorize_multi_path(alg: Any, setting: Any, rand_seed: np.ndarray) -> np.n parameter = np.zeros((2, alg_p + 2), dtype=float) choose_idx = int(operator[0]) if choose_idx > 0 and choose_idx - 1 < len(alg.parameter): - parameter[:, 0] = _extract_parameter_values(alg.parameter[choose_idx - 1], max_len=2) + parameter[:, 0] = _extract_parameter_values( + alg.parameter[choose_idx - 1], max_len=2 + ) for j in range(alg_p): op_idx = int(operator[j + 1]) if op_idx > 0 and op_idx - 1 < len(alg.parameter): - parameter[:, j + 1] = _extract_parameter_values(alg.parameter[op_idx - 1], max_len=2) + parameter[:, j + 1] = _extract_parameter_values( + alg.parameter[op_idx - 1], max_len=2 + ) update_idx = int(operator[-1]) if update_idx > 0 and update_idx - 1 < len(alg.parameter): - parameter[:, -1] = _extract_parameter_values(alg.parameter[update_idx - 1], max_len=2) + parameter[:, -1] = _extract_parameter_values( + alg.parameter[update_idx - 1], max_len=2 + ) - parameter_flat = parameter.reshape(1, -1) + parameter_flat = parameter.reshape(1, -1, order="F") lower = 1.0 upper = float(max(len(alg.parameter), 1)) if np.isclose(upper, lower): @@ -128,7 +137,9 @@ def embedding(algs: Sequence[Any], setting: Any, surrogate: Any, mode: str) -> A if embed_map is None: raise ValueError("Surrogate does not provide a precomputed embedding map") temp = np.vstack([VectorAlgs, np.ones((1, VectorAlgs.shape[1]))]) - wx = embed_map[:, :, 0] @ temp + with np.errstate(all="ignore"): + wx = embed_map[:, :, 0] @ temp + wx = np.nan_to_num(wx, nan=0.0, posinf=1e6, neginf=-1e6) rows = len(rand_seed) // 3 argslist = np.zeros((rows, len(alg_list))) for i in range(len(alg_list)): @@ -137,7 +148,9 @@ def embedding(algs: Sequence[Any], setting: Any, surrogate: Any, mode: str) -> A wx = np.tanh(argslist) for i in range(1, embed_map.shape[2]): wx = np.vstack([wx, np.ones((1, wx.shape[1]))]) - wx = embed_map[:, :, i] @ wx + with np.errstate(all="ignore"): + wx = embed_map[:, :, i] @ wx + wx = np.nan_to_num(wx, nan=0.0, posinf=1e6, neginf=-1e6) wx = np.tanh(wx) return wx.T raise ValueError(f"Unsupported mode: {mode}") @@ -163,17 +176,23 @@ def _mda_de(x: np.ndarray, p: float): x_aug = np.vstack([x, np.ones((1, x.shape[1]))]) d_aug = x_aug.shape[0] q = np.concatenate([np.full(d_aug - 1, 1 - p), [1.0]]) - s = x_aug @ x_aug.T + with np.errstate(all="ignore"): + s = x_aug @ x_aug.T + s = np.nan_to_num(s, nan=0.0, posinf=1e12, neginf=-1e12) q_outer = s * (q[:, None] * q[None, :]) diag_idx = np.arange(d_aug) q_outer[diag_idx, diag_idx] = q * np.diag(s) p_mat = s * q[np.newaxis, :] reg = q_outer + 1e-5 * np.eye(d_aug) - w = p_mat[:-1, :] @ np.linalg.pinv(reg) + with np.errstate(all="ignore"): + w = p_mat[:-1, :] @ np.linalg.pinv(reg) + w = np.nan_to_num(w, nan=0.0, posinf=1e6, neginf=-1e6) length = x_aug.shape[1] // 3 h = np.zeros((d_aug - 1, length)) - wx = w @ x_aug + with np.errstate(all="ignore"): + wx = w @ x_aug + wx = np.nan_to_num(wx, nan=0.0, posinf=1e6, neginf=-1e6) for i in range(length): block = wx[:, 3 * i : 3 * i + 3] h[:, i] = np.sum(block, axis=1) / 3.0 @@ -184,12 +203,19 @@ def _mda(x: np.ndarray, p: float): x_aug = np.vstack([x, np.ones((1, x.shape[1]))]) d_aug = x_aug.shape[0] q = np.concatenate([np.full(d_aug - 1, 1 - p), [1.0]]) - s = x_aug @ x_aug.T + with np.errstate(all="ignore"): + s = x_aug @ x_aug.T + s = np.nan_to_num(s, nan=0.0, posinf=1e12, neginf=-1e12) q_outer = s * (q[:, None] * q[None, :]) diag_idx = np.arange(d_aug) q_outer[diag_idx, diag_idx] = q * np.diag(s) p_mat = s * q[np.newaxis, :] reg = q_outer + 1e-5 * np.eye(d_aug) - w = p_mat[:-1, :] @ np.linalg.pinv(reg) - h = np.tanh(w @ x_aug) + with np.errstate(all="ignore"): + w = p_mat[:-1, :] @ np.linalg.pinv(reg) + w = np.nan_to_num(w, nan=0.0, posinf=1e6, neginf=-1e6) + with np.errstate(all="ignore"): + projected = w @ x_aug + projected = np.nan_to_num(projected, nan=0.0, posinf=1e6, neginf=-1e6) + h = np.tanh(projected) return w, h diff --git a/src/autooptlib/utils/design/_estimate.py b/src/autooptlib/utils/design/_estimate.py index fe5d46b..9ab3796 100644 --- a/src/autooptlib/utils/design/_estimate.py +++ b/src/autooptlib/utils/design/_estimate.py @@ -25,7 +25,9 @@ def _predict(model: Any, data: Any): raise AttributeError("Surrogate model lacks a predict method") -def estimate(self, problem: Any, setting: Any, ind_instance: Sequence[int], surrogate: Any): +def estimate( + self, problem: Any, setting: Any, ind_instance: Sequence[int], surrogate: Any +): """Estimate the designed algorithm's performance using a surrogate model.""" algs = [self] embed_algs = _call_use_embed(surrogate, algs, setting) diff --git a/src/autooptlib/utils/design/_evaluate.py b/src/autooptlib/utils/design/_evaluate.py index b8e0322..8b8899b 100644 --- a/src/autooptlib/utils/design/_evaluate.py +++ b/src/autooptlib/utils/design/_evaluate.py @@ -9,15 +9,33 @@ def _ensure_matrix(matrix: np.ndarray, rows: int, cols: int) -> np.ndarray: - if matrix.shape == (rows, cols): + current_rows, current_cols = matrix.shape if matrix.ndim == 2 else (0, 0) + target_rows = max(rows, current_rows) + target_cols = max(cols, current_cols) + if matrix.shape == (target_rows, target_cols): return matrix - new_matrix = np.zeros((rows, cols)) + new_matrix = np.zeros((target_rows, target_cols)) if matrix.size: r, c = matrix.shape - new_matrix[: min(r, rows), : min(c, cols)] = matrix[: min(r, rows), : min(c, cols)] + new_matrix[:r, :c] = matrix return new_matrix +def _auc_score( + fit_history: Sequence[float], tmax: Any, thres: Any, population_size: int +) -> float: + """Reference AUC: reciprocal fraction of FE checkpoints meeting targets.""" + history = np.asarray(fit_history, dtype=float).reshape(-1) + times = np.asarray(tmax if tmax is not None else [], dtype=float).reshape(-1) + thresholds = np.asarray(thres if thres is not None else [], dtype=float).reshape(-1) + if history.size == 0 or times.size == 0 or thresholds.size != times.size: + return np.inf + indices = np.ceil(times / max(1, population_size)).astype(int) - 1 + indices = np.clip(indices, 0, history.size - 1) + success_fraction = float(np.mean(history[indices] <= thresholds)) + return float(1.0 / (success_fraction + np.finfo(float).eps)) + + def _update_sequential(problem, data, best_solution): advance = getattr(problem, "advance_sequence", None) if callable(advance): @@ -52,7 +70,9 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc rows_needed = max_seed + 1 if evaluate_mode == "approximate": - self.performance_approx = _ensure_matrix(self.performance_approx, rows_needed, runs) + self.performance_approx = _ensure_matrix( + self.performance_approx, rows_needed, runs + ) target = self.performance_approx else: self.performance = _ensure_matrix(self.performance, rows_needed, runs) @@ -63,7 +83,11 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc data_obj = data_list[seed] if seed < len(data_list) else None problem_type = get_problem_type(problem_obj) or "continuous" behavior = get_flex(problem_obj, "type", [problem_type, "static"]) - mode = behavior[1] if isinstance(behavior, Sequence) and len(behavior) > 1 else "static" + mode = ( + behavior[1] + if isinstance(behavior, Sequence) and len(behavior) > 1 + else "static" + ) for run in range(runs): if mode == "static": @@ -75,12 +99,12 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc if metric == "quality": value = float(fit_history[-1]) if fit_history else np.inf elif metric == "auc": - history_arr = np.asarray(fit_history, dtype=float) - if history_arr.size == 0: - value = np.inf - else: - normalized = history_arr - history_arr.min() - value = float(np.trapz(normalized, dx=1.0)) + value = _auc_score( + fit_history, + get_flex(setting, "Tmax", None), + get_flex(setting, "Thres", None), + int(get_flex(problem_obj, "N", get_flex(setting, "ProbN", 1))), + ) elif metric == "runtimeFE": value = evaluations elif metric == "runtimeSec": @@ -96,7 +120,12 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc curr_prob = problem_obj curr_data = data_obj while getattr(curr_data, "continue", False): - result = run_design(pathways, params, curr_prob, curr_data, setting) + # The MATLAB sequential protocol allocates the configured + # ProbFE budget independently to every arriving stage. + stage_setting = setting + result = run_design( + pathways, params, curr_prob, curr_data, stage_setting + ) fit_history = result["fit_history"] best_solution = result["best_solution"] evaluations += result["evaluations"] @@ -104,19 +133,31 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc if metric == "quality": cumulative += float(fit_history[-1]) if fit_history else np.inf elif metric in {"runtimeFE", "runtimeSec"}: - cumulative += result["elapsed"] if metric == "runtimeSec" else result["evaluations"] + cumulative += ( + result["elapsed"] + if metric == "runtimeSec" + else result["evaluations"] + ) elif metric == "auc": - history_arr = np.asarray(fit_history, dtype=float) - if history_arr.size == 0: - cumulative += np.inf - else: - normalized = history_arr - history_arr.min() - cumulative += float(np.trapz(normalized, dx=1.0)) + cumulative += _auc_score( + fit_history, + get_flex(setting, "Tmax", None), + get_flex(setting, "Thres", None), + int( + get_flex( + curr_prob, + "N", + get_flex(setting, "ProbN", 1), + ) + ), + ) else: cumulative += float(fit_history[-1]) if fit_history else np.inf if best_solution is None: break - curr_prob, curr_data = _update_sequential(curr_prob, curr_data, best_solution) + curr_prob, curr_data = _update_sequential( + curr_prob, curr_data, best_solution + ) if curr_prob is problem_obj and curr_data is data_obj: break if metric == "runtimeFE": @@ -134,4 +175,3 @@ def evaluate(self, problem: Any, data: Any, setting: Any, seed_instance: Sequenc self.performance = target return self - diff --git a/src/autooptlib/utils/design/_helpers.py b/src/autooptlib/utils/design/_helpers.py index bc77a00..ad2d12b 100644 --- a/src/autooptlib/utils/design/_helpers.py +++ b/src/autooptlib/utils/design/_helpers.py @@ -11,30 +11,31 @@ def _name_variants(name: str) -> list[str]: - variants = {name} - variants.add(name.lower()) - variants.add(name.upper()) + # Attribute lookup order matters when a compatibility namespace exposes + # both Python-style and MATLAB-style spellings. Keep the caller's exact + # spelling first and de-duplicate without using an unordered set. + variants = [name, name.lower(), name.upper()] snake = re.sub(r"(? Any: +def get_flex( + obj: Any, name: str, default: Any | None = None, *, required: bool = False +) -> Any: if obj is None: if required: raise AttributeError(f"Cannot read {name!r} from None") @@ -44,6 +45,17 @@ def get_flex(obj: Any, name: str, default: Any | None = None, *, required: bool return obj[candidate] if hasattr(obj, candidate): return getattr(obj, candidate) + # Acronym-heavy legacy names such as ``ProbFE`` cannot be recovered by a + # generic camel-case conversion of ``prob_fe``. Compare a punctuation-free + # form as a final compatibility step. + normalized = re.sub(r"[^a-z0-9]", "", name.lower()) + try: + attributes = obj.keys() if isinstance(obj, dict) else vars(obj).keys() + except TypeError: + attributes = () + for candidate in attributes: + if re.sub(r"[^a-z0-9]", "", str(candidate).lower()) == normalized: + return obj[candidate] if isinstance(obj, dict) else getattr(obj, candidate) if required: raise AttributeError(f"Attribute {name!r} not found on {obj!r}") return default diff --git a/src/autooptlib/utils/design/_initialize.py b/src/autooptlib/utils/design/_initialize.py index b266991..9c11d87 100644 --- a/src/autooptlib/utils/design/_initialize.py +++ b/src/autooptlib/utils/design/_initialize.py @@ -22,7 +22,9 @@ def initialize(setting: Any, n: int): alg_q = int(get_flex(setting, "alg_q", required=True)) operators = [[None for _ in range(alg_p)] for _ in range(alg_count)] - paras: list[list[list[Any]]] = [[None for _ in range(len(para_space))] for _ in range(alg_count)] + paras: list[list[list[Any]]] = [ + [None for _ in range(len(para_space))] for _ in range(alg_count) + ] for i in range(alg_count): ind_choose = int(rng.integers(op_space[0, 0], op_space[0, 1] + 1)) @@ -40,7 +42,11 @@ def initialize(setting: Any, n: int): matrix[-1, :] = (ind_start, ind_update) operators[i][j] = matrix - ind_non_empty_para = [idx + 1 for idx, space in enumerate(para_space) if space is not None and len(space) > 0] + ind_non_empty_para = [ + idx + 1 + for idx, space in enumerate(para_space) + if space is not None and len(space) > 0 + ] temp_para = [[None, None] for _ in range(len(para_space))] for idx in ind_non_empty_para: bounds = np.asarray(para_space[idx - 1], dtype=float) diff --git a/src/autooptlib/utils/design/_repair.py b/src/autooptlib/utils/design/_repair.py index facb37b..928e3aa 100644 --- a/src/autooptlib/utils/design/_repair.py +++ b/src/autooptlib/utils/design/_repair.py @@ -17,13 +17,19 @@ ) -def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], problem: Any, setting: Any): +def repair( + operators: list[list[np.ndarray]], + paras: list[list[list[Any]]], + problem: Any, + setting: Any, +): """Ensure the designed algorithm(s) remain reasonable.""" rng = ensure_rng(setting) all_op = list(get_flex(setting, "all_op", required=True)) - para_local_space = list(get_flex(setting, "para_local_space", default=[None] * len(all_op))) + para_local_space = list( + get_flex(setting, "para_local_space", default=[None] * len(all_op)) + ) behav_space = list(get_flex(setting, "behav_space", default=[None] * len(all_op))) - alg_p = int(get_flex(setting, "alg_p", required=True)) while len(para_local_space) < len(all_op): para_local_space.append(None) @@ -44,14 +50,22 @@ def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], prob for algo_idx, algo_ops in enumerate(operators): curr_ops = [np.array(path, dtype=int, copy=True) for path in algo_ops] - curr_paras = [list(entry) if entry is not None else [None, None] for entry in paras[algo_idx]] + curr_paras = [ + list(entry) if entry is not None else [None, None] + for entry in paras[algo_idx] + ] for path_idx, matrix in enumerate(curr_ops): if matrix.size == 0: continue for row in range(1, matrix.shape[0] - 1): - if ind_cross and matrix[row, 0] in ind_cross and matrix[row, 1] not in ind_mu and ind_mu: + if ( + ind_cross + and matrix[row, 0] in ind_cross + and matrix[row, 1] not in ind_mu + and ind_mu + ): ind_new = int(rng.choice(ind_mu)) matrix[row, 1] = ind_new matrix[row + 1, 0] = ind_new @@ -64,10 +78,27 @@ def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], prob mask = matrix[:, 0] != matrix[:, 1] matrix = matrix[mask] - ind_pso = next((idx + 1 for idx, name in enumerate(all_op) if name == "search_pso"), None) + ind_pso = next( + (idx + 1 for idx, name in enumerate(all_op) if name == "search_pso"), + None, + ) if ind_pso is not None and np.any(matrix == ind_pso): - ind_choose = next((idx + 1 for idx, name in enumerate(all_op) if name == "choose_traverse"), None) - ind_update = next((idx + 1 for idx, name in enumerate(all_op) if name == "update_always"), None) + ind_choose = next( + ( + idx + 1 + for idx, name in enumerate(all_op) + if name == "choose_traverse" + ), + None, + ) + ind_update = next( + ( + idx + 1 + for idx, name in enumerate(all_op) + if name == "update_always" + ), + None, + ) if ind_choose and ind_update: first_matrix = np.array(curr_ops[0], dtype=int, copy=True) if first_matrix.size == 0: @@ -75,13 +106,20 @@ def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], prob first_matrix[0, 0] = ind_choose first_matrix[-1, 1] = ind_update curr_ops[0] = first_matrix - matrix = np.array([[ind_choose, ind_pso], [ind_pso, ind_update]], dtype=int) + matrix = np.array( + [[ind_choose, ind_pso], [ind_pso, ind_update]], dtype=int + ) ind_search = matrix[1:, 0].tolist() for idx in ind_search: set_behavior(curr_paras[idx - 1], "LS") - ind_search = [idx for idx in ind_search if behav_space[idx - 1] is not None and has_global_behavior(behav_space[idx - 1])] + ind_search = [ + idx + for idx in ind_search + if behav_space[idx - 1] is not None + and has_global_behavior(behav_space[idx - 1]) + ] ind_gs: list[int] = [] for idx in ind_search: @@ -107,7 +145,9 @@ def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], prob retain = int(rng.choice(ind_gs)) rows_retain = np.where(matrix[:, 0] == retain)[0] if rows_retain.size > 1: - rows_to_delete = rng.choice(rows_retain, size=rows_retain.size - 1, replace=False) + rows_to_delete = rng.choice( + rows_retain, size=rows_retain.size - 1, replace=False + ) rows_to_delete = np.sort(rows_to_delete) for row in rows_to_delete[::-1]: if row > 0: @@ -130,7 +170,9 @@ def repair(operators: list[list[np.ndarray]], paras: list[list[list[Any]]], prob for idx in list(ind_gs): bounds = para_local_space[idx - 1] if bounds is not None and len(bounds) > 0: - curr_paras[idx - 1][0] = reinit_parameters(np.asarray(bounds, dtype=float), rng) + curr_paras[idx - 1][0] = reinit_parameters( + np.asarray(bounds, dtype=float), rng + ) set_behavior(curr_paras[idx - 1], "LS") else: rows_to_remove = np.where(matrix[:, 0] == idx)[0] diff --git a/src/autooptlib/utils/general/__init__.py b/src/autooptlib/utils/general/__init__.py index a0d01ae..025b040 100644 --- a/src/autooptlib/utils/general/__init__.py +++ b/src/autooptlib/utils/general/__init__.py @@ -1,5 +1,5 @@ +from . import select_stats # noqa: F401 from .improve_rate import improve_rate # noqa: F401 -from .input import input_handler # noqa: F401 +from .input import input_handler, normalize_options # noqa: F401 +from .output import output, write_experiment_manifest # noqa: F401 from .process import process # noqa: F401 -from .output import output # noqa: F401 -from . import select_stats # noqa: F401 diff --git a/src/autooptlib/utils/general/improve_rate.py b/src/autooptlib/utils/general/improve_rate.py index 4f882b2..b300009 100644 --- a/src/autooptlib/utils/general/improve_rate.py +++ b/src/autooptlib/utils/general/improve_rate.py @@ -24,7 +24,9 @@ def _fitness_from_solution(solution: Any) -> float: return base if feasible else (con_sum + 1e8) -def improve_rate(solution: Any, improve: Sequence[float] | None, inner_g: int, typ: str): +def improve_rate( + solution: Any, improve: Sequence[float] | None, inner_g: int, typ: str +): """Compute improvement vector as in MATLAB version. Returns the updated ``improve`` vector of length k+1 (default k=3), where @@ -52,7 +54,9 @@ def improve_rate(solution: Any, improve: Sequence[float] | None, inner_g: int, t items = list(solution) except TypeError: items = [solution] - fitness = np.asarray([_fitness_from_solution(it) for it in items], dtype=float) + fitness = np.asarray( + [_fitness_from_solution(it) for it in items], dtype=float + ) best = float(np.min(fitness)) if fitness.size else float("inf") elif typ == "algorithm": # expect object exposing avePerformAll or similar diff --git a/src/autooptlib/utils/general/input.py b/src/autooptlib/utils/general/input.py index e4cf0aa..5aa9b7f 100644 --- a/src/autooptlib/utils/general/input.py +++ b/src/autooptlib/utils/general/input.py @@ -2,11 +2,15 @@ from __future__ import annotations +import math +import os +import re import warnings +from copy import deepcopy +from numbers import Integral, Real from types import SimpleNamespace from typing import Any, Iterable, Sequence - _PARAM_KEYS = { "AlgP", "AlgQ", @@ -28,9 +32,100 @@ "Surro", "AlgFile", "AlgName", + "Seed", + "TunePara", + "EvalRetries", + "EvalTimeoutSec", + "EvalFailure", + "EvalPenalty", + "EvalCache", + "EvalLog", + "EvalWorkers", + "CheckpointDir", + "CheckpointEvery", + "Resume", +} + +_DATA_KEYS = {"Mode", "Problem", "InstanceTrain", "InstanceTest", "InstanceSolve"} +_PUBLIC_KEYS = _DATA_KEYS | _PARAM_KEYS | {"OutputDir"} +_KEY_LOOKUP = {re.sub(r"[^a-z0-9]", "", key.lower()): key for key in _PUBLIC_KEYS} + +_COMMON_DEFAULTS = { + "AlgP": 1, + "AlgQ": 4, + "Archive": [], + "LSRange": 0.25, + "IncRate": 0.05, + "Metric": "quality", + "Compare": "average", + "Evaluate": "exact", + "TunePara": False, + "Seed": None, + "EvalRetries": 0, + "EvalTimeoutSec": None, + "EvalFailure": "raise", + "EvalPenalty": 1e30, + "EvalCache": False, + "EvalLog": None, + "EvalWorkers": 1, + "CheckpointDir": None, + "CheckpointEvery": 1, + "Resume": False, +} + +_DESIGN_DEFAULTS = { + "ProbN": 20, + "ProbFE": 5000, + "InnerFE": 500, + "AlgN": 10, + "AlgFE": 5000, + "AlgRuns": 5, + "Tmax": None, + "Thres": None, +} + +_SOLVE_DEFAULTS = { + "ProbN": 50, + "ProbFE": 50000, + "AlgRuns": 5, + "AlgFile": "", + "AlgName": "", + "Tmax": None, + "Thres": None, } +def normalize_options(options: dict[str, Any]) -> dict[str, Any]: + """Normalize public keyword spellings and reject unknown options. + + Public keys are case-insensitive and may use underscores, so ``archive``, + ``Archive``, and ``ARCHIVE`` all resolve to ``Archive``. Rejecting unknown + keys prevents experiments from silently running with ignored settings. + """ + normalized: dict[str, Any] = {} + unknown: list[str] = [] + for key, value in options.items(): + if not isinstance(key, str): + unknown.append(repr(key)) + continue + token = re.sub(r"[^a-z0-9]", "", key.lower()) + canonical = _KEY_LOOKUP.get(token) + if canonical is None: + unknown.append(key) + continue + if canonical in normalized: + raise TypeError( + f"AutoOpt option {canonical!r} was supplied more than once through aliases." + ) + normalized[canonical] = value + if unknown: + supported = ", ".join(sorted(_PUBLIC_KEYS)) + raise TypeError( + f"Unknown AutoOpt option(s): {', '.join(unknown)}. Supported options: {supported}." + ) + return normalized + + def _to_sequence(value: Any) -> Sequence[Any]: if isinstance(value, Sequence) and not isinstance(value, (str, bytes)): return value @@ -99,6 +194,19 @@ def input_handler(arguments: Iterable[Any], setting: Any, mode: str): found, value = _find_argument(args, key) if found: setattr(set_ns, key, value) + defaults = dict(_COMMON_DEFAULTS) + defaults.update( + _DESIGN_DEFAULTS if set_ns.Mode == "design" else _SOLVE_DEFAULTS + ) + if set_ns.Mode == "design": + train_found, train = _find_argument(args, "InstanceTrain") + train_count = len(_to_list(train)) if train_found else 1 + defaults["RacingK"] = max(1, int(round(train_count * 0.2))) + prob_fe: Any = getattr(set_ns, "ProbFE", defaults["ProbFE"]) + defaults["Surro"] = max(1, int(math.floor(float(prob_fe) * 0.3 + 0.5))) + for key, value in defaults.items(): + if not hasattr(set_ns, key): + setattr(set_ns, key, deepcopy(value)) return set_ns if mode == "check": @@ -113,16 +221,114 @@ def _check_setting(setting: SimpleNamespace) -> None: if mode not in {"design", "solve"}: raise ValueError('Please set the mode to "design" or "solve".') + retries = getattr(setting, "EvalRetries", 0) + if not isinstance(retries, Integral) or isinstance(retries, bool) or retries < 0: + raise ValueError("EvalRetries must be a non-negative integer.") + timeout = getattr(setting, "EvalTimeoutSec", None) + if timeout is not None and ( + not isinstance(timeout, Real) + or isinstance(timeout, bool) + or not math.isfinite(float(timeout)) + or timeout <= 0 + ): + raise ValueError("EvalTimeoutSec must be a positive finite number or None.") + failure = str(getattr(setting, "EvalFailure", "raise")).lower() + if failure not in {"raise", "penalize"}: + raise ValueError("EvalFailure must be 'raise' or 'penalize'.") + setting.EvalFailure = failure + penalty = getattr(setting, "EvalPenalty", 1e30) + if ( + not isinstance(penalty, Real) + or isinstance(penalty, bool) + or not math.isfinite(float(penalty)) + ): + raise ValueError("EvalPenalty must be a finite numeric scalar.") + if not isinstance(getattr(setting, "EvalCache", False), bool): + raise ValueError("EvalCache must be a boolean.") + eval_log = getattr(setting, "EvalLog", None) + if eval_log is not None and not isinstance(eval_log, (str, os.PathLike)): + raise ValueError("EvalLog must be a filesystem path or None.") + eval_workers = getattr(setting, "EvalWorkers", 1) + if ( + not isinstance(eval_workers, Integral) + or isinstance(eval_workers, bool) + or int(eval_workers) <= 0 + ): + raise ValueError("EvalWorkers must be a positive integer.") + if int(eval_workers) > 1 and timeout is not None: + raise ValueError( + "EvalWorkers greater than 1 cannot currently be combined with " + "EvalTimeoutSec; use one isolation mechanism at a time." + ) + checkpoint_dir = getattr(setting, "CheckpointDir", None) + if checkpoint_dir is not None and not isinstance( + checkpoint_dir, (str, os.PathLike) + ): + raise ValueError("CheckpointDir must be a filesystem path or None.") + checkpoint_every = getattr(setting, "CheckpointEvery", 1) + if ( + not isinstance(checkpoint_every, Integral) + or isinstance(checkpoint_every, bool) + or checkpoint_every <= 0 + ): + raise ValueError("CheckpointEvery must be a positive integer.") + if not isinstance(getattr(setting, "Resume", False), bool): + raise ValueError("Resume must be a boolean.") if mode == "design": - if getattr(setting, "AlgP", 0) > getattr(setting, "AlgQ", 0): - raise ValueError("The number of pathways should not be larger than the number of operators.") + for name in ( + "AlgP", + "AlgQ", + "ProbN", + "ProbFE", + "InnerFE", + "AlgN", + "AlgFE", + "AlgRuns", + ): + value = getattr(setting, name, None) + if not isinstance(value, Integral) or isinstance(value, bool) or value <= 0: + raise ValueError(f"{name} must be a positive integer, got {value!r}.") + if getattr(setting, "AlgP", 0) > 1 and getattr(setting, "AlgQ", 0) > 1: + raise ValueError( + "Use either multiple pathways (AlgP>1) or multiple search " + "operators per pathway (AlgQ>1), not both." + ) if getattr(setting, "AlgN", 0) > getattr(setting, "AlgFE", 0): - raise ValueError("The number of algorithms should not be larger than the evaluation budget.") + raise ValueError( + "The number of algorithms should not be larger than the evaluation budget." + ) if getattr(setting, "AlgRuns", 0) > getattr(setting, "ProbFE", 0): - raise ValueError("The number of runs should not exceed problem evaluations.") + raise ValueError( + "The number of runs should not exceed problem evaluations." + ) + if getattr(setting, "ProbN", 0) > getattr(setting, "ProbFE", 0): + raise ValueError( + "ProbFE must be at least ProbN so the initial population fits the budget." + ) - evaluate = getattr(setting, "Evaluate", "exact") - compare = getattr(setting, "Compare", "average") + evaluate = str(getattr(setting, "Evaluate", "exact")).lower() + compare = str(getattr(setting, "Compare", "average")).lower() + metric_token = str(getattr(setting, "Metric", "quality")).lower() + metric_names = { + "quality": "quality", + "runtimefe": "runtimeFE", + "runtimesec": "runtimeSec", + "auc": "auc", + } + if evaluate not in {"exact", "racing", "intensification", "approximate"}: + raise ValueError( + "Evaluate must be one of: exact, racing, intensification, approximate." + ) + if compare not in {"average", "statistic"}: + raise ValueError("Compare must be 'average' or 'statistic'.") + if metric_token not in metric_names: + raise ValueError( + "Metric must be one of: quality, runtimeFE, runtimeSec, auc." + ) + metric = metric_names[metric_token] + setting.Evaluate = evaluate + setting.Compare = compare + setting.Metric = metric if evaluate == "racing" and compare != "statistic": raise ValueError( 'The "racing" evaluation method should be used with the algorithm comparing method of "statistic".' @@ -135,6 +341,17 @@ def _check_setting(setting: SimpleNamespace) -> None: raise ValueError( 'Please set "Setting.Surro" as the number of exact performance evaluations when using surrogate.' ) + if evaluate == "approximate": + surrogate_budget = getattr(setting, "Surro") + if ( + not isinstance(surrogate_budget, Integral) + or isinstance(surrogate_budget, bool) + or surrogate_budget <= 0 + or surrogate_budget > setting.AlgFE + ): + raise ValueError( + "Surro must be a positive integer no larger than AlgFE." + ) if evaluate == "approximate" and compare == "statistic": raise ValueError( 'It is not necessary to use the "statistic" algorithm comparing method when using the "approximate" evaluation method.' @@ -144,26 +361,44 @@ def _check_setting(setting: SimpleNamespace) -> None: 'Please run the design multiple times (Setting.AlgRuns>1) when using the "statistic" comparsion method.' ) if getattr(setting, "ProbN", 0) < 5 and getattr(setting, "AlgP", 0) > 1: - warnings.warn("It is better to have a large population size if involving the EDA operator", stacklevel=2) - if getattr(setting, "AlgQ", 0) > 4: warnings.warn( - "AlgQ is recommended to be larger than 4 for discrete and permutation problems due to the lack of so many search operators", + "It is better to have a large population size if involving the EDA operator", stacklevel=2, ) - if compare == "statistic": + if getattr(setting, "AlgQ", 0) > 4: warnings.warn( - "It is better to have a large number of training intrances or have a large number of algorithm runs " - "(set AlgRun to a large number), in order to make the statistical test discriminative.", + "AlgQ is recommended to be larger than 4 for discrete and permutation problems due to the lack of so many search operators", stacklevel=2, ) - elif mode == "solve": - if not getattr(setting, "AlgFile", None) and not getattr(setting, "AlgName", None): + for name in ("ProbN", "ProbFE", "AlgRuns"): + value = getattr(setting, name, None) + if not isinstance(value, Integral) or isinstance(value, bool) or value <= 0: + raise ValueError(f"{name} must be a positive integer, got {value!r}.") + if setting.ProbN > setting.ProbFE: + raise ValueError( + "ProbFE must be at least ProbN so the initial population fits the budget." + ) + if not getattr(setting, "AlgFile", None) and not getattr( + setting, "AlgName", None + ): raise ValueError( "Please specify an algorithm file in Setting.AlgFile or specify an algorithm name in Setting.AlgName." ) - metric = getattr(setting, "Metric", "quality") + metric_token = str(getattr(setting, "Metric", "quality")).lower() + metric_names = { + "quality": "quality", + "runtimefe": "runtimeFE", + "runtimesec": "runtimeSec", + "auc": "auc", + } + if metric_token not in metric_names: + raise ValueError( + "Metric must be one of: quality, runtimeFE, runtimeSec, auc." + ) + metric = metric_names[metric_token] + setting.Metric = metric if metric == "runtimeFE": if getattr(setting, "Tmax", None) in (None, []): setting.Tmax = getattr(setting, "ProbFE", None) @@ -174,7 +409,9 @@ def _check_setting(setting: SimpleNamespace) -> None: ) if metric == "runtimeSec": if getattr(setting, "Tmax", None) in (None, []): - raise ValueError('Please set "Setting.Tmax" as the maximum runtime (seconds).') + raise ValueError( + 'Please set "Setting.Tmax" as the maximum runtime (seconds).' + ) if getattr(setting, "Thres", None) in (None, []): raise ValueError( 'Please set "Setting.Thres" as the lowest acceptable performance of the design algorithms, ' diff --git a/src/autooptlib/utils/general/output.py b/src/autooptlib/utils/general/output.py index fc1d914..3bcc336 100644 --- a/src/autooptlib/utils/general/output.py +++ b/src/autooptlib/utils/general/output.py @@ -5,14 +5,81 @@ import csv import json import pickle +import platform +import sys +from datetime import datetime, timezone from pathlib import Path from typing import Any, Iterable, List, Sequence import numpy as np +from ..._version import __version__ +from ...serialization import save_algorithm from ..design import Design +def _manifest_value(value: Any) -> Any: + """Convert public options to stable, bounded JSON metadata.""" + if value is None or isinstance(value, (str, int, float, bool)): + return value + if isinstance(value, Path): + return str(value) + if isinstance(value, np.generic): + return value.item() + if isinstance(value, np.ndarray): + if value.size <= 100: + return value.tolist() + return { + "type": "ndarray", + "shape": list(value.shape), + "dtype": str(value.dtype), + } + if isinstance(value, dict): + return {str(key): _manifest_value(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_manifest_value(item) for item in value] + if callable(value): + module = getattr(value, "__module__", "") + name = getattr( + value, + "__qualname__", + getattr(value, "__name__", type(value).__name__), + ) + return f"{module}:{name}" if module else name + return {"type": type(value).__name__, "repr": repr(value)[:500]} + + +def write_experiment_manifest( + directory: str | Path | None, *, mode: str, options: dict[str, Any] +) -> Path: + """Atomically write software and normalized invocation provenance.""" + target_directory = _ensure_path(directory or Path.cwd()) + target_directory.mkdir(parents=True, exist_ok=True) + document = { + "schema": "autooptlib.experiment", + "schema_version": 1, + "created_utc": datetime.now(timezone.utc).isoformat(), + "mode": mode, + "software": { + "autooptlib": __version__, + "python": platform.python_version(), + "numpy": np.__version__, + "platform": platform.platform(), + "executable": sys.executable, + }, + "options": { + key: _manifest_value(value) for key, value in sorted(options.items()) + }, + } + target = target_directory / "experiment.json" + temporary = target.with_suffix(".json.tmp") + temporary.write_text( + json.dumps(document, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + temporary.replace(target) + return target + + def _ensure_path(path: str | Path) -> Path: return Path(path).resolve() @@ -52,17 +119,27 @@ def _generate_pseudocode(design_obj: Design, alg_index: int, stage: str) -> List desc = f" Search {step_idx}: {step.primary}" if step.secondary: desc += f" -> {step.secondary}" - term = np.asarray(step.termination).reshape(-1) if step.termination is not None else [] + term = ( + np.asarray(step.termination).reshape(-1) + if step.termination is not None + else [] + ) if term.size: desc += f" | termination={term.tolist()}" lines.append(desc) - if params and idx - 1 < len(params) and step_idx - 1 < len(params[idx - 1].search): + if ( + params + and idx - 1 < len(params) + and step_idx - 1 < len(params[idx - 1].search) + ): primary_param = params[idx - 1].search[step_idx - 1].primary secondary_param = params[idx - 1].search[step_idx - 1].secondary if primary_param is not None: lines.append(f" Primary param: {_format_array(primary_param)}") if secondary_param is not None: - lines.append(f" Secondary param: {_format_array(secondary_param)}") + lines.append( + f" Secondary param: {_format_array(secondary_param)}" + ) lines.append(f" Update: {path.update or ''}") if params and idx - 1 < len(params): update_param = params[idx - 1].update @@ -74,7 +151,9 @@ def _generate_pseudocode(design_obj: Design, alg_index: int, stage: str) -> List return lines -def _write_matrix_csv(path: Path, header: Sequence[str], rows: Sequence[Sequence[Any]]) -> None: +def _write_matrix_csv( + path: Path, header: Sequence[str], rows: Sequence[Sequence[Any]] +) -> None: with path.open("w", newline="", encoding="utf-8") as handle: writer = csv.writer(handle) writer.writerow(header) @@ -82,7 +161,14 @@ def _write_matrix_csv(path: Path, header: Sequence[str], rows: Sequence[Sequence writer.writerow(row) -def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance_train: Sequence[Any], instance_test: Sequence[Any], setting: Any, directory: Path) -> None: +def _output_design( + algs: Sequence[Design], + alg_trace: Sequence[Design], + instance_train: Sequence[Any], + instance_test: Sequence[Any], + setting: Any, + directory: Path, +) -> None: algs = list(algs) alg_trace = list(alg_trace) directory.mkdir(parents=True, exist_ok=True) @@ -96,11 +182,21 @@ def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance directory / "ConvergenceCurve.csv", ] _remove_if_exists(files_to_remove) + _remove_if_exists(directory.glob("Algorithm_*.json")) with (directory / "Algs.pkl").open("wb") as handle: pickle.dump({"algs": algs, "trace": alg_trace}, handle) - codes = [_generate_pseudocode(alg, idx + 1, "final") for idx, alg in enumerate(algs)] + for index, algorithm in enumerate(algs, start=1): + save_algorithm( + algorithm, + directory / f"Algorithm_{index}.json", + metadata={"algorithm_index": index}, + ) + + codes = [ + _generate_pseudocode(alg, idx + 1, "final") for idx, alg in enumerate(algs) + ] max_len = max(len(code) for code in codes) if codes else 0 header = [f"Algorithm {idx + 1}" for idx in range(len(codes))] rows = [] @@ -131,7 +227,10 @@ def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance perf_header = ["Instance Index"] + [f"Algorithm {i + 1}" for i in range(len(algs))] _write_matrix_csv(directory / "Algs_perf_final_algs.csv", perf_header, perf_rows) - trace_codes = [_generate_pseudocode(alg, idx + 1, "iteration") for idx, alg in enumerate(alg_trace)] + trace_codes = [ + _generate_pseudocode(alg, idx + 1, "iteration") + for idx, alg in enumerate(alg_trace) + ] if trace_codes: max_len = max(len(code) for code in trace_codes) header = [f"Iteration {idx + 1}" for idx in range(len(trace_codes))] @@ -144,7 +243,9 @@ def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance _write_matrix_csv(directory / "Algs_best_algs_iter.csv", header, rows) trace_rows = [] - trace_header = ["Instance Index"] + [f"Iteration {i + 1}" for i in range(len(alg_trace))] + trace_header = ["Instance Index"] + [ + f"Iteration {i + 1}" for i in range(len(alg_trace)) + ] for idx, instance in enumerate(instance_train): row = [instance] for alg in alg_trace: @@ -155,7 +256,9 @@ def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance value = np.mean(matrix[idx, :]) row.append(value) trace_rows.append(row) - _write_matrix_csv(directory / "Algs_perf_best_algs_iter.csv", trace_header, trace_rows) + _write_matrix_csv( + directory / "Algs_perf_best_algs_iter.csv", trace_header, trace_rows + ) convergence = [] for idx, alg in enumerate(alg_trace): @@ -166,10 +269,18 @@ def _output_design(algs: Sequence[Design], alg_trace: Sequence[Design], instance train_indices = list(range(train_len)) sub = matrix[train_indices, :] convergence.append((idx + 1, float(np.mean(sub)))) - _write_matrix_csv(directory / "ConvergenceCurve.csv", ["Iteration", "Performance"], convergence) - - -def _output_solve(best_solutions: Any, all_solutions: Any, instance: Sequence[Any], setting: Any, directory: Path) -> None: + _write_matrix_csv( + directory / "ConvergenceCurve.csv", ["Iteration", "Performance"], convergence + ) + + +def _output_solve( + best_solutions: Any, + all_solutions: Any, + instance: Sequence[Any], + setting: Any, + directory: Path, +) -> None: directory.mkdir(parents=True, exist_ok=True) files_to_remove = [ directory / "Solutions.pkl", @@ -188,7 +299,9 @@ def _output_solve(best_solutions: Any, all_solutions: Any, instance: Sequence[An constraint_rows = [] num_iterations = len(all_solutions[0]) if all_solutions else 0 - solution_header = ["Instance Index", "Best Solution"] + [f"Iteration {i + 1}" for i in range(num_iterations)] + solution_header = ["Instance Index", "Best Solution"] + [ + f"Iteration {i + 1}" for i in range(num_iterations) + ] for idx, inst in enumerate(instance): best_row = [inst] fit_row = [inst] @@ -217,22 +330,41 @@ def _output_solve(best_solutions: Any, all_solutions: Any, instance: Sequence[An _write_matrix_csv(directory / "Solutions.csv", solution_header, solutions_rows) _write_matrix_csv(directory / "Fitness.csv", solution_header, fitness_rows) - _write_matrix_csv(directory / "ConstraintViolation.csv", solution_header, constraint_rows) + _write_matrix_csv( + directory / "ConstraintViolation.csv", solution_header, constraint_rows + ) alg_runs = int(getattr(setting, "AlgRuns", getattr(setting, "alg_runs", 1))) - fitness_all_header = ["Instance Index", "Mean", "Std"] + [f"Run {i + 1}" for i in range(alg_runs)] + fitness_all_header = ["Instance Index", "Mean", "Std"] + [ + f"Run {i + 1}" for i in range(alg_runs) + ] fitness_all_rows = [] for idx, inst in enumerate(instance): row = [inst] - fits = [best_solutions[idx][run].fit for run in range(alg_runs)] - row.append(float(np.mean(fits))) - row.append(float(np.std(fits))) - row.extend(fits) + fits = np.asarray( + [best_solutions[idx][run].fit for run in range(alg_runs)], dtype=float + ) + finite = fits[np.isfinite(fits)] + if finite.size == fits.size: + mean = float(np.mean(finite)) + std = float(np.std(finite)) + elif finite.size == 0: + mean = float("inf") + std = 0.0 + else: + mean = float("inf") + std = float("inf") + row.extend([mean, std]) + row.extend(fits.tolist()) fitness_all_rows.append(row) - _write_matrix_csv(directory / "Fitness_all_runs.csv", fitness_all_header, fitness_all_rows) + _write_matrix_csv( + directory / "Fitness_all_runs.csv", fitness_all_header, fitness_all_rows + ) -def output(*args, setting: Any, app: Any | None = None, directory: str | Path | None = None): +def output( + *args, setting: Any, app: Any | None = None, directory: str | Path | None = None +): """Main entry replicating MATLAB Output.m behaviour.""" mode = str(getattr(setting, "Mode", getattr(setting, "mode", "design"))).lower() if directory is None: @@ -241,17 +373,25 @@ def output(*args, setting: Any, app: Any | None = None, directory: str | Path | if mode == "design": if len(args) < 4: - raise ValueError("Design mode output requires algs, alg_trace, instance_train, instance_test.") + raise ValueError( + "Design mode output requires algs, alg_trace, instance_train, instance_test." + ) algs, alg_trace, instance_train, instance_test = args[:4] - _output_design(algs, alg_trace, instance_train, instance_test, setting, directory) + _output_design( + algs, alg_trace, instance_train, instance_test, setting, directory + ) return if mode == "solve": if len(args) < 3: - raise ValueError("Solve mode output requires best_solutions, all_solutions, instance.") + raise ValueError( + "Solve mode output requires best_solutions, all_solutions, instance." + ) best_solutions, all_solutions, instance = args[:3] _output_solve(best_solutions, all_solutions, instance, setting, directory) return raise ValueError("Mode must be 'design' or 'solve' for output.") + +__all__ = ["output", "write_experiment_manifest"] diff --git a/src/autooptlib/utils/general/process.py b/src/autooptlib/utils/general/process.py index 110788f..915097c 100644 --- a/src/autooptlib/utils/general/process.py +++ b/src/autooptlib/utils/general/process.py @@ -3,19 +3,21 @@ from __future__ import annotations import math +import pickle +import warnings from pathlib import Path from types import SimpleNamespace from typing import Any, Callable, Iterable, List, Sequence, Tuple import numpy as np +from ...components import get_component +from ...problems.base import validate_constructed_problems from ..design import Approximate, Design -from ..solve import input_algorithm, run_algorithm -from ..design._helpers import problem_list from ..general.improve_rate import improve_rate -from ..space import space -from ...components import get_component from ..select import select as _select_alg +from ..solve import input_algorithm, run_algorithm +from ..space import space def _progress(app: Any, message: str, *, done: bool = False) -> None: @@ -53,7 +55,25 @@ def _normalize_setting(setting: Any) -> SimpleNamespace: return ns -def _resolve_problem_callable(descriptor: Any) -> Callable[[Sequence[Any], Sequence[Any], str], Tuple[Any, Any, Any]]: +def _configure_rng(setting: SimpleNamespace) -> np.random.Generator: + """Attach one seeded generator to all stages of an AutoOptLib run.""" + existing = getattr(setting, "rng", None) + if isinstance(existing, np.random.Generator): + return existing + seed = getattr( + setting, + "seed", + getattr(setting, "random_seed", getattr(setting, "random_state", None)), + ) + rng = np.random.default_rng(seed) + setting.rng = rng + setting.random_seed = seed + return rng + + +def _resolve_problem_callable( + descriptor: Any, +) -> Callable[[Sequence[Any], Sequence[Any], str], Tuple[Any, Any, Any]]: if callable(descriptor): return descriptor if isinstance(descriptor, str): @@ -69,7 +89,9 @@ def _resolve_problem_callable(descriptor: Any) -> Callable[[Sequence[Any], Seque raise TypeError("Unsupported problem descriptor format") -def _build_problem_struct(problem_descriptor: Any, instances: Sequence[Any], setting: SimpleNamespace): +def _build_problem_struct( + problem_descriptor: Any, instances: Sequence[Any], setting: SimpleNamespace +): problems = [] for _ in instances: problems.append( @@ -80,7 +102,9 @@ def _build_problem_struct(problem_descriptor: Any, instances: Sequence[Any], set Gmax=int( math.ceil( getattr(setting, "ProbFE", getattr(setting, "prob_fe", 5000)) - / max(1, getattr(setting, "ProbN", getattr(setting, "prob_n", 20))) + / max( + 1, getattr(setting, "ProbN", getattr(setting, "prob_n", 20)) + ) ) ), ) @@ -92,7 +116,9 @@ def _ensure_algorithm_list(obj: Iterable[Design]) -> List[Design]: return list(obj) -def _select(algs: Sequence[Design], problem: Any, data: Any, setting: Any, seeds: Sequence[int]) -> List[Design]: +def _select( + algs: Sequence[Design], problem: Any, data: Any, setting: Any, seeds: Sequence[int] +) -> List[Design]: return _select_alg(algs, problem, data, setting, seeds) @@ -104,16 +130,111 @@ def _mean_performance(algs: Sequence[Design]) -> np.ndarray: return np.asarray(values, dtype=float) -def _update_cma_parameters(algs: Sequence[Design], problems: Sequence[Any], aux_list: List[Any]) -> List[Any]: +def _update_cma_parameters( + algs: Sequence[Design], problems: Sequence[Any], aux_list: List[Any] +) -> List[Any]: for idx, aux in enumerate(aux_list): if isinstance(aux, dict) and "cma_Disturb" in aux: get_component("para_cma")(algs[idx], problems, aux, "algorithm") return aux_list -def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], instance_test: Sequence[Any], setting: Any, app: Any) -> Tuple[List[Design], List[Design]]: +def _write_design_checkpoint(path: Path, payload: dict[str, Any]) -> None: + """Atomically persist design-mode search state.""" + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + with temporary.open("wb") as handle: + pickle.dump(payload, handle, protocol=pickle.HIGHEST_PROTOCOL) + temporary.replace(path) + + +def _load_design_checkpoint( + path: Path, + *, + alg_n: int, + alg_fe: int, + eval_mode: str, + instance_train: Sequence[Any], + instance_test: Sequence[Any], +) -> dict[str, Any]: + warnings.warn( + "Loading an AutoOptLib checkpoint uses pickle and can execute arbitrary " + "code. Resume only checkpoints produced by a trusted run.", + UserWarning, + stacklevel=2, + ) + with path.open("rb") as handle: + payload = pickle.load(handle) + if ( + not isinstance(payload, dict) + or payload.get("schema") != "autooptlib.design-checkpoint" + ): + raise ValueError(f"Invalid AutoOptLib design checkpoint: {path}") + if payload.get("schema_version") != 1: + raise ValueError("Unsupported AutoOptLib design checkpoint schema version.") + expected = { + "alg_n": alg_n, + "alg_fe": alg_fe, + "eval_mode": eval_mode, + "instance_train": list(instance_train), + "instance_test": list(instance_test), + } + for name, value in expected.items(): + if payload.get(name) != value: + raise ValueError( + f"Design checkpoint {name} does not match the current run." + ) + return payload + + +def _design_checkpoint_payload( + *, + algs: Sequence[Design], + alg_trace: Sequence[Design], + surrogate: Any, + evaluated_count: int, + generation: int, + seed_train: Sequence[int], + seed_test: Sequence[int], + rng: np.random.Generator, + inner_state: dict[str, Any] | None, + alg_n: int, + alg_fe: int, + eval_mode: str, + instance_train: Sequence[Any], + instance_test: Sequence[Any], + complete: bool, +) -> dict[str, Any]: + return { + "schema": "autooptlib.design-checkpoint", + "schema_version": 1, + "alg_n": alg_n, + "alg_fe": alg_fe, + "eval_mode": eval_mode, + "instance_train": list(instance_train), + "instance_test": list(instance_test), + "algs": list(algs), + "alg_trace": list(alg_trace), + "surrogate": surrogate, + "evaluated_count": evaluated_count, + "generation": generation, + "seed_train": list(seed_train), + "seed_test": list(seed_test), + "rng_state": rng.bit_generator.state, + "inner_state": inner_state, + "complete": complete, + } + + +def _process_design( + problem_descriptor: Any, + instance_train: Sequence[Any], + instance_test: Sequence[Any], + setting: Any, + app: Any, +) -> Tuple[List[Design], List[Design]]: setting_ns = _normalize_setting(setting) - rng = np.random.default_rng(getattr(setting_ns, "random_seed", None)) + rng = _configure_rng(setting_ns) instance_train = list(instance_train) instance_test = list(instance_test) @@ -122,29 +243,69 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst problems = _build_problem_struct(problem_descriptor, instances, setting_ns) construct_fn = _resolve_problem_callable(problem_descriptor) problems, data, _ = construct_fn(problems, instances, "construct") + validate_constructed_problems(problems, data) setting_ns = space(problems, setting_ns) alg_n = int(getattr(setting_ns, "AlgN", getattr(setting_ns, "alg_n", 1))) alg_fe = int(getattr(setting_ns, "AlgFE", getattr(setting_ns, "alg_fe", alg_n))) - alg_runs = int(getattr(setting_ns, "AlgRuns", getattr(setting_ns, "alg_runs", 1))) - alg_gmax = int(math.ceil(alg_fe / max(1, alg_n))) seed_train = rng.permutation(len(instance_train)).tolist() seed_test = (rng.permutation(len(instance_test)) + len(instance_train)).tolist() - eval_mode = str(getattr(setting_ns, "Evaluate", getattr(setting_ns, "evaluate", "exact"))).lower() + eval_mode = str( + getattr(setting_ns, "Evaluate", getattr(setting_ns, "evaluate", "exact")) + ).lower() + + checkpoint_dir = getattr(setting_ns, "CheckpointDir", None) + checkpoint_path = ( + Path(checkpoint_dir) / "design.pkl" if checkpoint_dir is not None else None + ) + checkpoint_every = int(getattr(setting_ns, "CheckpointEvery", 1)) + resume = bool(getattr(setting_ns, "Resume", False)) + resume_state = None + if resume and checkpoint_path is not None and checkpoint_path.exists(): + resume_state = _load_design_checkpoint( + checkpoint_path, + alg_n=alg_n, + alg_fe=alg_fe, + eval_mode=eval_mode, + instance_train=instance_train, + instance_test=instance_test, + ) _progress(app, "Initializing...") algs: List[Design] = [] surrogate = None - if eval_mode in {"exact", "intensification"}: + # AlgFE follows Process.m: it counts algorithms proposed after the + # initial incumbent population, whose evaluation is initialization cost. + evaluated_count = 0 + alg_trace: List[Design] = [] + G = 1 + pending_inner_state: dict[str, Any] | None = None + + if resume_state is not None: + algs = list(resume_state["algs"]) + alg_trace = list(resume_state["alg_trace"]) + surrogate = resume_state["surrogate"] + evaluated_count = int(resume_state["evaluated_count"]) + G = int(resume_state["generation"]) + seed_train = list(resume_state["seed_train"]) + seed_test = list(resume_state["seed_test"]) + rng.bit_generator.state = resume_state["rng_state"] + pending_inner_state = resume_state.get("inner_state") + if resume_state.get("complete", False): + _progress(app, "Complete", done=True) + return algs[:alg_n], alg_trace + elif eval_mode in {"exact", "intensification"}: for _ in range(alg_n): alg = Design(problems, setting_ns) alg.evaluate(problems, data, setting_ns, seed_train) algs.append(alg) elif eval_mode == "racing": - racing_k = int(getattr(setting_ns, "RacingK", getattr(setting_ns, "racingk", 1))) + racing_k = int( + getattr(setting_ns, "RacingK", getattr(setting_ns, "racingk", 1)) + ) subset = seed_train[: racing_k or 1] for _ in range(alg_n): alg = Design(problems, setting_ns) @@ -155,24 +316,67 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst pool = surrogate.data indices = rng.choice(len(pool), size=min(alg_n, len(pool)), replace=False) algs = [pool[i] for i in indices] + evaluated_count = 0 else: - raise NotImplementedError(f"Evaluation mode '{eval_mode}' is not implemented in Python translation yet.") - - alg_trace: List[Design] = [] - G = 1 + raise NotImplementedError( + f"Evaluation mode '{eval_mode}' is not implemented in Python translation yet." + ) - while G <= alg_gmax: - _progress(app, f"Designing... {100 * G / alg_gmax:.1f}%") - improve = None - inner_g = 1 - aux_list: List[Any] = [None] * len(algs) - inner_gmax = int(math.ceil(alg_fe / max(alg_n, 1) / 10)) if alg_n == 1 else 1 + if checkpoint_path is not None and resume_state is None: + _write_design_checkpoint( + checkpoint_path, + _design_checkpoint_payload( + algs=algs, + alg_trace=alg_trace, + surrogate=surrogate, + evaluated_count=evaluated_count, + generation=G, + seed_train=seed_train, + seed_test=seed_test, + rng=rng, + inner_state=None, + alg_n=alg_n, + alg_fe=alg_fe, + eval_mode=eval_mode, + instance_train=instance_train, + instance_test=instance_test, + complete=False, + ), + ) - while (improve is None or improve[0] >= getattr(setting_ns, "IncRate", getattr(setting_ns, "inc_rate", 0.05))) and inner_g <= inner_gmax: + while evaluated_count < alg_fe: + _progress(app, f"Designing... {100 * evaluated_count / alg_fe:.1f}%") + if pending_inner_state is not None: + improve = pending_inner_state["improve"] + inner_g = int(pending_inner_state["inner_g"]) + aux_list = list(pending_inner_state["aux_list"]) + inner_gmax = int(pending_inner_state["inner_gmax"]) + pending_inner_state = None + else: + improve = None + inner_g = 1 + aux_list = [None] * len(algs) + inner_gmax = int(math.ceil(alg_fe / 10)) if alg_n == 1 else 1 + + while ( + ( + improve is None + or improve[0] + >= getattr(setting_ns, "IncRate", getattr(setting_ns, "inc_rate", 0.05)) + ) + and inner_g <= inner_gmax + and evaluated_count < alg_fe + ): + proposal_count = min(len(algs), alg_fe - evaluated_count) new_algs: List[Design] = [] - for idx, alg in enumerate(algs): - new_alg, aux_list[idx] = alg.get_new(problems, setting_ns, inner_g, aux_list[idx]) + for idx, alg in enumerate(algs[:proposal_count]): + new_alg, aux_list[idx] = alg.get_new( + problems, setting_ns, inner_g, aux_list[idx] + ) new_algs.append(new_alg) + proposed_count = len(new_algs) + if proposed_count == 0: + break if eval_mode == "exact": for new_alg in new_algs: @@ -182,12 +386,16 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst for new_alg in new_algs: new_alg.estimate(problems, setting_ns, seed_train, surrogate) algs = _select(algs + new_algs, problems, data, setting_ns, seed_train) - if surrogate is not None and G in set(int(x) for x in np.asarray(surrogate.exact_g).reshape(-1)): + if surrogate is not None and G in set( + int(x) for x in np.asarray(surrogate.exact_g).reshape(-1) + ): for new_alg in new_algs: new_alg.evaluate(problems, data, setting_ns, seed_train) surrogate.UpdateModel(new_algs, setting_ns) elif eval_mode == "racing": - racing_k = int(getattr(setting_ns, "RacingK", getattr(setting_ns, "racingk", 1))) + racing_k = int( + getattr(setting_ns, "RacingK", getattr(setting_ns, "racingk", 1)) + ) subset = seed_train[: racing_k or 1] for new_alg in new_algs: new_alg.evaluate(problems, data, setting_ns, subset) @@ -198,14 +406,18 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst remaining = remaining[1:] seed_train = rng.permutation(len(instance_train)).tolist() if len(algs) > alg_n: - indices = rng.choice(len(algs), size=len(algs) - alg_n, replace=False) + indices = rng.choice( + len(algs), size=len(algs) - alg_n, replace=False + ) algs = [alg for idx, alg in enumerate(algs) if idx not in indices] elif eval_mode == "intensification": while new_algs and seed_train: subset = [seed_train[0]] for alg in new_algs: alg.evaluate(problems, data, setting_ns, subset) - new_algs = _select(algs + new_algs, problems, data, setting_ns, subset) + new_algs = _select( + algs + new_algs, problems, data, setting_ns, subset + ) seed_train = seed_train[1:] seed_train = rng.permutation(len(instance_train)).tolist() for alg in new_algs: @@ -213,12 +425,17 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst if not np.any(alg.performance[seed, :]): alg.evaluate(problems, data, setting_ns, [seed]) if new_algs: - replace_idx = rng.choice(len(algs), size=min(len(new_algs), len(algs)), replace=False) + replace_idx = rng.choice( + len(algs), size=min(len(new_algs), len(algs)), replace=False + ) for dest, src in zip(replace_idx, new_algs): algs[dest] = src else: - raise NotImplementedError(f"Evaluation mode '{eval_mode}' is not implemented.") + raise NotImplementedError( + f"Evaluation mode '{eval_mode}' is not implemented." + ) + evaluated_count += proposed_count aux_list = _update_cma_parameters(algs, problems, aux_list) perf_values = _mean_performance(algs) @@ -226,20 +443,66 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst improve = improve_rate(wrapper, improve, inner_g, "algorithm") inner_g += 1 G += 1 - if G > alg_gmax: - break best_idx = int(np.argmin(perf_values)) if len(perf_values) else 0 alg_trace.append(algs[best_idx]) - if G > alg_gmax: - break + if checkpoint_path is not None and ( + evaluated_count % checkpoint_every == 0 or evaluated_count >= alg_fe + ): + _write_design_checkpoint( + checkpoint_path, + _design_checkpoint_payload( + algs=algs, + alg_trace=alg_trace, + surrogate=surrogate, + evaluated_count=evaluated_count, + generation=G, + seed_train=seed_train, + seed_test=seed_test, + rng=rng, + inner_state={ + "improve": improve, + "inner_g": inner_g, + "aux_list": aux_list, + "inner_gmax": inner_gmax, + }, + alg_n=alg_n, + alg_fe=alg_fe, + eval_mode=eval_mode, + instance_train=instance_train, + instance_test=instance_test, + complete=False, + ), + ) _progress(app, "Testing...") setting_ns.Evaluate = "exact" + setting_ns.evaluate = "exact" for alg in algs: alg.evaluate(problems, data, setting_ns, seed_test) algs = _select(algs, problems, data, setting_ns, seed_test) + if checkpoint_path is not None: + _write_design_checkpoint( + checkpoint_path, + _design_checkpoint_payload( + algs=algs, + alg_trace=alg_trace, + surrogate=surrogate, + evaluated_count=evaluated_count, + generation=G, + seed_train=seed_train, + seed_test=seed_test, + rng=rng, + inner_state=None, + alg_n=alg_n, + alg_fe=alg_fe, + eval_mode=eval_mode, + instance_train=instance_train, + instance_test=instance_test, + complete=True, + ), + ) _progress(app, "Complete", done=True) return algs[:alg_n], alg_trace @@ -247,13 +510,20 @@ def _process_design(problem_descriptor: Any, instance_train: Sequence[Any], inst def process(problem_descriptor: Any, *args, setting: Any, app: Any | None = None): """Main entry replicating MATLAB Process.m behaviour.""" setting_ns = _normalize_setting(setting) - mode = str(getattr(setting_ns, "Mode", getattr(setting_ns, "mode", "design"))).lower() + _configure_rng(setting_ns) + mode = str( + getattr(setting_ns, "Mode", getattr(setting_ns, "mode", "design")) + ).lower() if mode == "design": if len(args) < 2: - raise ValueError("Design mode requires instance_train and instance_test arguments") + raise ValueError( + "Design mode requires instance_train and instance_test arguments" + ) instance_train, instance_test = args[:2] - return _process_design(problem_descriptor, instance_train, instance_test, setting_ns, app) + return _process_design( + problem_descriptor, instance_train, instance_test, setting_ns, app + ) if mode == "solve": if len(args) < 1: @@ -262,9 +532,12 @@ def process(problem_descriptor: Any, *args, setting: Any, app: Any | None = None problems = _build_problem_struct(problem_descriptor, instance_solve, setting_ns) construct_fn = _resolve_problem_callable(problem_descriptor) problems, data, _ = construct_fn(problems, instance_solve, "construct") + validate_constructed_problems(problems, data) alg, setting_ns = input_algorithm(setting_ns) _progress(app, "Solving...") - best_solutions, all_solutions = run_algorithm(alg, problems, data, app, setting_ns) + best_solutions, all_solutions = run_algorithm( + alg, problems, data, app, setting_ns + ) _progress(app, "Complete", done=True) return best_solutions, all_solutions diff --git a/src/autooptlib/utils/general/select_stats.py b/src/autooptlib/utils/general/select_stats.py index 0542771..ca5aa0d 100644 --- a/src/autooptlib/utils/general/select_stats.py +++ b/src/autooptlib/utils/general/select_stats.py @@ -30,7 +30,9 @@ def friedman_nemenyi(matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: raise ValueError("Input to friedman_nemenyi must be 2-D") n, k = matrix.shape if n < 2 or k < 2: - raise ValueError("Friedman test requires at least two algorithms and two instances") + raise ValueError( + "Friedman test requires at least two algorithms and two instances" + ) ranks = np.apply_along_axis(_rankdata, 1, matrix) avg_ranks = ranks.mean(axis=0) diff --git a/src/autooptlib/utils/select.py b/src/autooptlib/utils/select.py index b5dac36..090dfe6 100644 --- a/src/autooptlib/utils/select.py +++ b/src/autooptlib/utils/select.py @@ -1,4 +1,5 @@ """Selection utilities mirroring MATLAB Select.m.""" + from __future__ import annotations from typing import Any, Iterable, List, Sequence @@ -6,7 +7,7 @@ import numpy as np from .design import Design -from .design._helpers import get_flex +from .design._helpers import ensure_rng, get_flex from .general.select_stats import friedman_nemenyi @@ -16,7 +17,13 @@ def _ensure_design_list(algs: Iterable[Design]) -> List[Design]: return list(algs) -def _require_performance(algs: List[Design], problem: Any, data: Any, setting: Any, seed_instance: Sequence[int]) -> None: +def _require_performance( + algs: List[Design], + problem: Any, + data: Any, + setting: Any, + seed_instance: Sequence[int], +) -> None: evaluate_mode = get_flex(setting, "evaluate", "exact") if evaluate_mode != "racing": return @@ -36,7 +43,9 @@ def _require_performance(algs: List[Design], problem: Any, data: Any, setting: A alg.evaluate(problem, data, setting, [seed]) -def _collect_performance(algs: List[Design], setting: Any, seed_instance: Sequence[int]) -> np.ndarray: +def _collect_performance( + algs: List[Design], setting: Any, seed_instance: Sequence[int] +) -> np.ndarray: runs = int(get_flex(setting, "alg_runs", 1)) matrix = np.zeros((len(seed_instance) * runs, len(algs))) for idx, alg in enumerate(algs): @@ -65,7 +74,34 @@ def _statistic_wins(matrix: np.ndarray, alpha: float) -> np.ndarray: return wins -def select(algs: Iterable[Design], problem: Any, data: Any, setting: Any, seed_instance: Sequence[int]) -> List[Design]: +def _new_algorithm_wins(matrix: np.ndarray, old_count: int, alpha: float) -> np.ndarray: + """Count significant wins by new algorithms against incumbents only.""" + result = np.zeros(matrix.shape[1], dtype=int) + try: + avg_ranks, p_matrix = friedman_nemenyi(matrix) + except ValueError: + averages = np.mean(matrix, axis=0) + for i in range(old_count, matrix.shape[1]): + result[i] = int(np.any(averages[i] < averages[:old_count])) + return result + for i in range(old_count, matrix.shape[1]): + for j in range(old_count): + if ( + not np.isnan(p_matrix[i, j]) + and p_matrix[i, j] < alpha + and avg_ranks[i] < avg_ranks[j] + ): + result[i] += 1 + return result + + +def select( + algs: Iterable[Design], + problem: Any, + data: Any, + setting: Any, + seed_instance: Sequence[int], +) -> List[Design]: alg_list = _ensure_design_list(algs) if not alg_list: return [] @@ -73,8 +109,8 @@ def select(algs: Iterable[Design], problem: Any, data: Any, setting: Any, seed_i _require_performance(alg_list, problem, data, setting, seed_instance) all_perf = _collect_performance(alg_list, setting, seed_instance) - compare = get_flex(setting, "compare", "average") - evaluate_mode = get_flex(setting, "evaluate", "exact") + compare = str(get_flex(setting, "compare", "average")).lower() + evaluate_mode = str(get_flex(setting, "evaluate", "exact")).lower() alg_n = int(get_flex(setting, "alg_n", len(alg_list))) alpha = float(get_flex(setting, "alpha", 0.05)) @@ -87,19 +123,37 @@ def select(algs: Iterable[Design], problem: Any, data: Any, setting: Any, seed_i if evaluate_mode == "intensification": old_avgs = averages[:alg_n] threshold = float(np.max(old_avgs)) if old_avgs.size else np.inf - return [alg for alg, avg in zip(alg_list[alg_n:], averages[alg_n:]) if avg < threshold] + return [ + alg + for alg, avg in zip(alg_list[alg_n:], averages[alg_n:]) + if avg < threshold + ] raise NotImplementedError(f"Unsupported evaluate mode: {evaluate_mode}") if compare == "statistic": wins = _statistic_wins(all_perf, alpha) order = np.argsort(-wins) - if evaluate_mode in {"exact", "approximate", "racing"}: + if evaluate_mode in {"exact", "approximate"}: top = order[: min(alg_n, len(order))] return [alg_list[i] for i in top] + if evaluate_mode == "racing": + survivors = np.flatnonzero(wins > 0).tolist() + if len(survivors) < min(alg_n, len(alg_list)): + candidates = np.flatnonzero(wins == 0) + needed = min(alg_n, len(alg_list)) - len(survivors) + if needed: + sampled = ensure_rng(setting).choice( + candidates, size=needed, replace=False + ) + survivors.extend(np.asarray(sampled, dtype=int).tolist()) + return [alg_list[i] for i in survivors] if evaluate_mode == "intensification": - return [alg_list[i] for i in order if i >= alg_n and wins[i] > 0] + incumbent_wins = _new_algorithm_wins(all_perf, alg_n, alpha) + return [ + alg_list[i] + for i in range(alg_n, len(alg_list)) + if incumbent_wins[i] > 0 + ] raise NotImplementedError(f"Unsupported evaluate mode: {evaluate_mode}") raise NotImplementedError(f"Unsupported compare mode: {compare}") - - diff --git a/src/autooptlib/utils/solve/__init__.py b/src/autooptlib/utils/solve/__init__.py index 6a30864..66e225b 100644 --- a/src/autooptlib/utils/solve/__init__.py +++ b/src/autooptlib/utils/solve/__init__.py @@ -1,17 +1,32 @@ from __future__ import annotations +import hashlib +import json +import multiprocessing +import pickle +import threading +import time +import warnings +from concurrent.futures import ThreadPoolExecutor +from contextlib import nullcontext +from copy import copy from dataclasses import dataclass from pathlib import Path -from typing import Any, Iterable, List, Optional, Sequence, Tuple, TYPE_CHECKING -import pickle +from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Sequence, Tuple import numpy as np -import time -from ..design._helpers import ensure_rng, get_flex, get_problem_type -from ..design._helpers import Pathway, PathwayParam, SearchStep, SearchParam -from ..general.improve_rate import improve_rate from ...components import get_component +from ..design._helpers import ( + Pathway, + PathwayParam, + SearchParam, + SearchStep, + ensure_rng, + get_flex, + get_problem_type, +) +from ..general.improve_rate import improve_rate if TYPE_CHECKING: from ..design import Design @@ -23,6 +38,7 @@ class Solution: obj: float con: float fit: float + acc: Any = None class SolutionSet(Sequence[Solution]): @@ -51,6 +67,15 @@ def fits(self) -> np.ndarray: return np.asarray([s.fit for s in self._items]).reshape(-1, 1) +class ObjectiveEvaluationError(RuntimeError): + """Raised when a user objective fails or returns an unsupported value.""" + + +def _evaluation_cache_key(dec: np.ndarray) -> tuple[str, tuple[int, ...], bytes]: + contiguous = np.ascontiguousarray(dec) + return contiguous.dtype.str, contiguous.shape, contiguous.tobytes() + + def repair_sol(dec: np.ndarray, problem: Any) -> np.ndarray: ptype = get_problem_type(problem) bound = getattr(problem, "bound", None) @@ -79,9 +104,13 @@ def repair_sol(dec: np.ndarray, problem: Any) -> np.ndarray: if not duplicates: continue for idx in duplicates: - choices = [v for v in range(int(lower[idx]), int(upper[idx]) + 1) if v not in repaired[i]] + choices = [ + v + for v in range(int(lower[idx]), int(upper[idx]) + 1) + if v not in repaired[i] + ] if choices: - repaired[i, idx] = int(np.random.choice(choices)) + repaired[i, idx] = int(ensure_rng(problem).choice(choices)) return repaired return repaired if ptype == "permutation": @@ -94,13 +123,13 @@ def repair_sol(dec: np.ndarray, problem: Any) -> np.ndarray: domain = list(range(1, d + 1)) for row in rows: missing = [v for v in domain if v not in row] - counts = {} - duplicates = [] + counts: dict[int, int] = {} + duplicate_indices: list[int] = [] for j, val in enumerate(row): counts[val] = counts.get(val, 0) + 1 if counts[val] > 1: - duplicates.append(j) - for j in duplicates: + duplicate_indices.append(j) + for j in duplicate_indices: if missing: row[j] = missing.pop(0) if repaired.ndim == 1: @@ -110,17 +139,69 @@ def repair_sol(dec: np.ndarray, problem: Any) -> np.ndarray: def _call_evaluator(problem: Any, data: Any, dec: np.ndarray): - # Preferred: problem.evaluate(data, dec) - eval_fn = getattr(problem, 'evaluate', None) + preview = np.asarray(dec).reshape(-1)[:8].tolist() + cache = getattr(problem, "_autoopt_eval_cache", None) + cache_key = None + if isinstance(cache, dict): + cache_key = _evaluation_cache_key(dec) + lock = getattr(problem, "_autoopt_eval_lock", None) + with lock if lock is not None else nullcontext(): + result = cache.get(cache_key) + cache_hit = cache_key in cache + if cache_hit: + _log_evaluation(problem, dec, attempt=0, status="cache_hit", elapsed=0.0) + return result + + retries = int(getattr(problem, "_autoopt_eval_retries", 0)) + last_error: ObjectiveEvaluationError | None = None + for attempt in range(1, retries + 2): + started = time.perf_counter() + try: + result = _evaluate_once(problem, data, dec, preview) + except ObjectiveEvaluationError as exc: + last_error = exc + _log_evaluation( + problem, + dec, + attempt=attempt, + status="failure", + elapsed=time.perf_counter() - started, + error=str(exc), + ) + continue + _log_evaluation( + problem, + dec, + attempt=attempt, + status="success", + elapsed=time.perf_counter() - started, + ) + if isinstance(cache, dict) and cache_key is not None: + lock = getattr(problem, "_autoopt_eval_lock", None) + with lock if lock is not None else nullcontext(): + cache[cache_key] = result + return result + + failure = getattr(problem, "_autoopt_eval_failure", "raise") + if failure == "penalize": + return float(getattr(problem, "_autoopt_eval_penalty", 1e30)), 0.0, None + assert last_error is not None + raise last_error + + +def _invoke_evaluator(problem: Any, data: Any, dec: np.ndarray): + """Invoke the user callable without policy handling or normalization.""" + eval_fn = getattr(problem, "evaluate", None) if callable(eval_fn): - out = eval_fn(data, dec) - else: - # Fallback: Problem.name referencing a callable in current scope - name = getattr(problem, 'name', None) - if callable(name): - out = name(data, dec) - else: - raise NotImplementedError('Problem must provide an evaluate(data, dec) callable') + return eval_fn(data, dec) + name = getattr(problem, "name", None) + if callable(name): + return name(data, dec) + raise NotImplementedError("Problem must provide an evaluate(data, dec) callable") + + +def _normalize_evaluator_output(out: Any, preview: list[Any]): + """Normalize one evaluator result to objective, violation, and auxiliary.""" # Normalize outputs: obj, con, acc(optional) if isinstance(out, tuple): if len(out) == 3: @@ -128,28 +209,410 @@ def _call_evaluator(problem: Any, data: Any, dec: np.ndarray): elif len(out) == 2: obj, con = out acc = None - else: + elif len(out) == 1: obj, con, acc = out[0], 0.0, None + else: + raise ObjectiveEvaluationError( + "Objective result tuples must contain one to three values " + "(objective, optional constraint, optional auxiliary output)." + ) else: obj, con, acc = out, 0.0, None - return float(np.asarray(obj).reshape(1)[0]), float(np.sum(np.maximum(0.0, np.asarray(con)))), acc + try: + objective = np.asarray(obj, dtype=float) + except (TypeError, ValueError) as exc: + raise ObjectiveEvaluationError( + f"Objective did not return a numeric scalar for decision {preview}." + ) from exc + if objective.size != 1: + raise ObjectiveEvaluationError( + "AutoOptLib currently supports one scalar objective; " + f"received shape {objective.shape} for decision {preview}." + ) + objective_value = float(objective.reshape(-1)[0]) + if not np.isfinite(objective_value): + raise ObjectiveEvaluationError( + f"Objective returned a non-finite value for decision {preview}." + ) + + if con is None: + constraint_value = 0.0 + else: + try: + constraints = np.asarray(con, dtype=float) + except (TypeError, ValueError) as exc: + raise ObjectiveEvaluationError( + f"Constraint did not return numeric values for decision {preview}." + ) from exc + if not np.all(np.isfinite(constraints)): + raise ObjectiveEvaluationError( + f"Constraint returned a non-finite value for decision {preview}." + ) + constraint_value = float(np.sum(np.maximum(0.0, constraints))) + return objective_value, constraint_value, acc + + +def _evaluate_payload( + problem: Any, data: Any, dec: np.ndarray, preview: list[Any] +) -> tuple[float, float, Any]: + """Evaluate certain or sampled-uncertain problem semantics.""" + behavior = get_flex(problem, "type", []) + uncertainty = ( + str(behavior[2]).lower() + if isinstance(behavior, Sequence) and len(behavior) > 2 + else "certain" + ) + if uncertainty != "uncertain": + return _normalize_evaluator_output( + _invoke_evaluator(problem, data, dec), preview + ) + + sample_n = int(get_flex(problem, "sampleN", get_flex(problem, "sample_n", 0))) + if sample_n <= 0: + raise ObjectiveEvaluationError( + "Uncertain problems must define a positive sampleN." + ) + samples = [ + _normalize_evaluator_output(_invoke_evaluator(problem, data, dec), preview) + for _ in range(sample_n) + ] + objectives = np.asarray([sample[0] for sample in samples], dtype=float) + constraints = np.asarray([sample[1] for sample in samples], dtype=float) + problem_setting = str(get_flex(problem, "setting", "")).lower() + if "uncertain_average" in problem_setting: + objective = float(np.mean(objectives)) + constraint = float(np.mean(constraints)) + elif "uncertain_worst" in problem_setting: + objective = float(np.max(objectives)) + constraint = float(np.max(constraints)) + else: + raise ObjectiveEvaluationError( + "Uncertain problems must select 'uncertain_average' or " + "'uncertain_worst' in problem.setting." + ) + return objective, constraint, samples[-1][2] + + +def _timeout_worker(connection: Any, problem: Any, data: Any, dec: np.ndarray) -> None: + """Evaluate and send a pickle-safe result from an isolated process.""" + try: + preview = np.asarray(dec).reshape(-1)[:8].tolist() + result = _evaluate_payload(problem, data, dec, preview) + # Auxiliary values are not consumed by the execution engine and may be + # unpickleable simulator handles. Do not transfer them across processes. + connection.send((True, (result[0], result[1], None))) + except BaseException as exc: # child must report user failures to parent + connection.send((False, f"{type(exc).__name__}: {exc}")) + finally: + connection.close() + + +def _evaluate_once( + problem: Any, data: Any, dec: np.ndarray, preview: list[Any] +) -> tuple[float, float, Any]: + timeout = getattr(problem, "_autoopt_eval_timeout", None) + if timeout is None: + try: + return _evaluate_payload(problem, data, dec, preview) + except ObjectiveEvaluationError: + raise + except Exception as exc: + raise ObjectiveEvaluationError( + f"Objective evaluation failed for decision {preview}: {exc}" + ) from exc + + methods = multiprocessing.get_all_start_methods() + method = "fork" if "fork" in methods else "spawn" + context: Any = multiprocessing.get_context(method) + parent, child = context.Pipe(duplex=False) + process = context.Process( + target=_timeout_worker, + args=(child, problem, data, np.asarray(dec)), + daemon=True, + ) + try: + process.start() + except Exception as exc: + parent.close() + child.close() + raise ObjectiveEvaluationError( + "Could not start the isolated objective worker. Use module-level, " + "pickleable problem definitions on spawn-based platforms." + ) from exc + child.close() + try: + if not parent.poll(float(timeout)): + process.terminate() + process.join(timeout=1.0) + if process.is_alive() and hasattr(process, "kill"): + process.kill() + process.join(timeout=1.0) + raise ObjectiveEvaluationError( + f"Objective evaluation timed out after {float(timeout):g}s " + f"for decision {preview}." + ) + ok, payload = parent.recv() + process.join(timeout=1.0) + if not ok: + raise ObjectiveEvaluationError( + f"Objective evaluation failed for decision {preview}: {payload}" + ) + return payload + finally: + parent.close() + if process.is_alive(): + process.terminate() + process.join(timeout=1.0) + + +def _log_evaluation( + problem: Any, + dec: np.ndarray, + *, + attempt: int, + status: str, + elapsed: float, + error: str | None = None, +) -> None: + path = getattr(problem, "_autoopt_eval_log", None) + if path is None: + return + array = np.ascontiguousarray(dec) + event = { + "time_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "problem": str(getattr(problem, "name", "problem")), + "decision_sha256": hashlib.sha256(array.tobytes()).hexdigest(), + "decision_preview": array.reshape(-1)[:8].tolist(), + "attempt": attempt, + "status": status, + "elapsed_seconds": elapsed, + } + if error is not None: + event["error"] = error + log_path = Path(path) + log_path.parent.mkdir(parents=True, exist_ok=True) + lock = getattr(problem, "_autoopt_eval_lock", None) + with lock if lock is not None else nullcontext(): + with log_path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(event, sort_keys=True) + "\n") + + +def _configure_evaluation_runtime(problem: Any, setting: Any) -> None: + """Attach validated, run-scoped evaluation controls to a problem record.""" + problem._autoopt_eval_retries = int(get_flex(setting, "EvalRetries", 0)) + problem._autoopt_eval_timeout = get_flex(setting, "EvalTimeoutSec", None) + problem._autoopt_eval_failure = str( + get_flex(setting, "EvalFailure", "raise") + ).lower() + problem._autoopt_eval_penalty = float(get_flex(setting, "EvalPenalty", 1e30)) + problem._autoopt_eval_log = get_flex(setting, "EvalLog", None) + problem._autoopt_eval_workers = int(get_flex(setting, "EvalWorkers", 1)) + problem._autoopt_eval_lock = threading.RLock() + if bool(get_flex(setting, "EvalCache", False)): + if not isinstance(getattr(problem, "_autoopt_eval_cache", None), dict): + problem._autoopt_eval_cache = {} + elif hasattr(problem, "_autoopt_eval_cache"): + delattr(problem, "_autoopt_eval_cache") + + +def _checkpoint_payload( + *, + solutions: SolutionSet, + aux_cache: list[list[Any]], + archives: list[Any], + history: list[Optional[Solution]], + fit_history: list[float], + evaluations: int, + elapsed_seconds: float, + generation: int, + rng: np.random.Generator, + population_size: int, + evaluation_budget: int, + complete: bool, +) -> dict[str, Any]: + return { + "schema": "autooptlib.checkpoint", + "schema_version": 1, + "population_size": population_size, + "evaluation_budget": evaluation_budget, + "solutions": list(solutions), + "aux_cache": aux_cache, + "archives": archives, + "history": history, + "fit_history": fit_history, + "evaluations": evaluations, + "elapsed_seconds": elapsed_seconds, + "generation": generation, + "rng_state": rng.bit_generator.state, + "complete": complete, + } + + +def _write_checkpoint(path: Path, payload: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + with temporary.open("wb") as handle: + pickle.dump(payload, handle, protocol=pickle.HIGHEST_PROTOCOL) + temporary.replace(path) + + +def _load_checkpoint( + path: Path, *, population_size: int, evaluation_budget: int +) -> dict[str, Any]: + warnings.warn( + "Loading an AutoOptLib checkpoint uses pickle and can execute arbitrary " + "code. Resume only checkpoints produced by a trusted run.", + UserWarning, + stacklevel=2, + ) + with path.open("rb") as handle: + payload = pickle.load(handle) + if ( + not isinstance(payload, dict) + or payload.get("schema") != "autooptlib.checkpoint" + ): + raise ValueError(f"Invalid AutoOptLib checkpoint: {path}") + if payload.get("schema_version") != 1: + raise ValueError("Unsupported AutoOptLib checkpoint schema version.") + if payload.get("population_size") != population_size: + raise ValueError("Checkpoint population size does not match ProbN.") + if payload.get("evaluation_budget") != evaluation_budget: + raise ValueError("Checkpoint evaluation budget does not match ProbFE.") + return payload + + +def _sequence_checkpoint_payload( + *, + solutions: Sequence[Solution], + cumulative: float, + remaining_budget: int, + population_size: int, + evaluation_budget: int, + rng: np.random.Generator, + complete: bool, +) -> dict[str, Any]: + return { + "schema": "autooptlib.sequence-checkpoint", + "schema_version": 1, + "population_size": population_size, + "evaluation_budget": evaluation_budget, + "solutions": list(solutions), + "cumulative": cumulative, + "remaining_budget": remaining_budget, + "rng_state": rng.bit_generator.state, + "complete": complete, + } + + +def _load_sequence_checkpoint( + path: Path, *, population_size: int, evaluation_budget: int +) -> dict[str, Any]: + warnings.warn( + "Loading an AutoOptLib checkpoint uses pickle and can execute arbitrary " + "code. Resume only checkpoints produced by a trusted run.", + UserWarning, + stacklevel=2, + ) + with path.open("rb") as handle: + payload = pickle.load(handle) + if ( + not isinstance(payload, dict) + or payload.get("schema") != "autooptlib.sequence-checkpoint" + ): + raise ValueError(f"Invalid AutoOptLib sequential checkpoint: {path}") + if payload.get("schema_version") != 1: + raise ValueError("Unsupported AutoOptLib sequential checkpoint schema version.") + if payload.get("population_size") != population_size: + raise ValueError("Sequential checkpoint population size does not match ProbN.") + if payload.get("evaluation_budget") != evaluation_budget: + raise ValueError( + "Sequential checkpoint evaluation budget does not match ProbFE." + ) + return payload def make_solutions(decs: np.ndarray, problem: Any, data: Any) -> SolutionSet: - items: List[Solution] = [] + repaired: List[np.ndarray] = [] for i in range(decs.shape[0]): d = np.array(decs[i], dtype=float) d = repair_sol(d, problem) - obj, con, _ = _call_evaluator(problem, data, d) + repair_fn = getattr(problem, "repair", None) + if callable(repair_fn): + try: + d = np.asarray(repair_fn(data, d)) + except TypeError: + d = np.asarray(repair_fn(d)) + else: + name = getattr(problem, "name", None) + # Legacy MATLAB-style problem callables expose repair/evaluate + # through ``name``. Modern Python definitions expose ``evaluate`` + # separately and their construct callable must not be invoked as a + # repair hook. + if callable(name) and not callable(getattr(problem, "evaluate", None)): + try: + repaired_output = name(data, d, "repair") + except TypeError: + repaired_output = d + if isinstance(repaired_output, tuple): + repaired_output = repaired_output[0] + d = np.asarray(repaired_output) + repaired.append(d) + + cache = getattr(problem, "_autoopt_eval_cache", None) + unique_repaired = repaired + original_keys: List[tuple[str, tuple[int, ...], bytes]] | None = None + if isinstance(cache, dict): + original_keys = [_evaluation_cache_key(decision) for decision in repaired] + unique_by_key: dict[tuple[str, tuple[int, ...], bytes], np.ndarray] = {} + for key, decision in zip(original_keys, repaired): + unique_by_key.setdefault(key, decision) + unique_repaired = list(unique_by_key.values()) + + workers = int(getattr(problem, "_autoopt_eval_workers", 1)) + if workers == 1 or len(unique_repaired) <= 1: + unique_results = [_call_evaluator(problem, data, d) for d in unique_repaired] + else: + with ThreadPoolExecutor( + max_workers=min(workers, len(unique_repaired)) + ) as executor: + unique_results = list( + executor.map( + lambda d: _call_evaluator(problem, data, d), unique_repaired + ) + ) + + if original_keys is None: + results = unique_results + else: + unique_results_by_key = { + _evaluation_cache_key(decision): result + for decision, result in zip(unique_repaired, unique_results) + } + seen: set[tuple[str, tuple[int, ...], bytes]] = set() + results = [] + for key, decision in zip(original_keys, repaired): + if key in seen: + # The first in-flight duplicate has completed and populated the + # cache, so this records a normal cache-hit event without a + # second objective call. + results.append(_call_evaluator(problem, data, decision)) + else: + seen.add(key) + results.append(unique_results_by_key[key]) + + items: List[Solution] = [] + for d, (obj, con, acc) in zip(repaired, results): feasible = con <= 0.0 fit = obj if feasible else (con + 1e8) - items.append(Solution(dec=d, obj=obj, con=con, fit=fit)) + items.append(Solution(dec=d, obj=obj, con=con, fit=fit, acc=acc)) return SolutionSet(items) -def _init_population(rng: np.random.Generator, problem: Any, data: Any, setting: Any) -> SolutionSet: +def _init_population( + rng: np.random.Generator, problem: Any, data: Any, setting: Any +) -> SolutionSet: ptype = get_problem_type(problem) or "continuous" - pop_n = int(get_flex(problem, "N", get_flex(setting, "prob_n", 10))) + pop_n = int(get_flex(problem, "N", get_flex(setting, "ProbN", 10))) if ptype == "continuous": bound = np.asarray(get_flex(problem, "bound"), dtype=float) lower = bound[0] @@ -160,7 +623,10 @@ def _init_population(rng: np.random.Generator, problem: Any, data: Any, setting: lower = bound[0] upper = bound[1] decs = np.vstack( - [rng.integers(lower[j], upper[j] + 1, size=pop_n) for j in range(lower.shape[-1])] + [ + rng.integers(lower[j], upper[j] + 1, size=pop_n) + for j in range(lower.shape[-1]) + ] ).T elif ptype == "permutation": bound = np.asarray(get_flex(problem, "bound"), dtype=int) @@ -169,7 +635,9 @@ def _init_population(rng: np.random.Generator, problem: Any, data: Any, setting: else: d = int(get_flex(problem, "dimension", 0) or get_flex(problem, "D", 0)) if d == 0: - raise ValueError("Permutation problem requires explicit dimension or bound") + raise ValueError( + "Permutation problem requires explicit dimension or bound" + ) domain = np.arange(1, d + 1) decs = np.vstack([rng.permutation(domain) for _ in range(pop_n)]) else: @@ -180,85 +648,297 @@ def _init_population(rng: np.random.Generator, problem: Any, data: Any, setting: def _split_indices(indices: Sequence[int], alg_p: int, total: int) -> List[List[int]]: array = np.asarray(indices, dtype=int).flatten() if array.size == 0: - indices = list(range(total)) + normalized_indices = list(range(total)) else: - indices = array.tolist() + normalized_indices = [int(value) for value in array.tolist()] if alg_p <= 1: - return [indices] + return [normalized_indices] splits: List[List[int]] = [] - chunk = max(1, len(indices) // alg_p) + chunk = max(1, len(normalized_indices) // alg_p) start = 0 for i in range(alg_p): if i == alg_p - 1: - splits.append(indices[start:]) + splits.append(normalized_indices[start:]) else: - splits.append(indices[start:start + chunk]) + splits.append(normalized_indices[start : start + chunk]) start += chunk if not splits[-1]: splits[-1] = list(range(total)) return splits +def _update_archives( + solutions: SolutionSet, + archive_names: Sequence[str], + archives: List[Any], + problem: Any, +) -> List[Any]: + for index, name in enumerate(archive_names): + archive_fn = get_component(name) + if name in {"archive_best", "archive_statistic"}: + archives[index], _ = archive_fn(solutions, archives[index], "execute") + else: + archives[index], _ = archive_fn( + solutions, archives[index], problem, "execute" + ) + return archives + + +def _execute_single_path( + path: Any, + params: Any, + solutions: SolutionSet, + aux_state: Any, + problem: Any, + data: Any, + setting: Any, + generation: int, + remaining_evaluations: int, + archive_names: Sequence[str], + archives: List[Any], +) -> Tuple[SolutionSet, Any, int, int, List[Any], List[Solution]]: + """Execute a serial pathway with an update after every search call. + + This mirrors the reference MATLAB execution loop: each inner search + chooses from the current population, creates offspring, updates the + population immediately, and lets the next search step see that update. + """ + if not isinstance(aux_state, dict): + aux_state = {} + aux_state.setdefault("rng", ensure_rng(setting)) + current = solutions + evaluations = 0 + iterations = 0 + update_fn = get_component(path.update) + update_param = getattr(params, "update", None) + iteration_bests: List[Solution] = [] + + for step_index, step in enumerate(path.search): + improve = None + inner_generation = 1 + limit = int(step.termination[1]) if step.termination.size > 1 else 1 + threshold = float(step.termination[0]) if step.termination.size else -np.inf + primary_param = params.search[step_index].primary if params.search else None + secondary_param = params.search[step_index].secondary if params.search else None + while ( + (improve is None or improve[0] >= threshold) + and inner_generation <= limit + and evaluations < remaining_evaluations + ): + component_generation = generation + iterations + choose_fn = get_component(path.choose) + choose_param = getattr(params, "choose", None) + selected, choose_aux = choose_fn( + current, + problem, + choose_param, + aux_state, + component_generation, + inner_generation, + data, + "execute", + ) + if isinstance(choose_aux, dict): + aux_state = choose_aux + aux_state.setdefault("rng", ensure_rng(setting)) + selected_indices = np.asarray(selected, dtype=int).reshape(-1) + parent = SolutionSet([current[int(index)] for index in selected_indices]) + + primary_fn = get_component(step.primary) + new_dec, aux_state = primary_fn( + parent, + problem, + primary_param, + aux_state, + component_generation, + inner_generation, + data, + "execute", + ) + if step.secondary: + new_dec = repair_sol(np.asarray(new_dec), problem) + secondary_fn = get_component(step.secondary) + new_dec, aux_state = secondary_fn( + new_dec, + problem, + secondary_param, + aux_state, + component_generation, + inner_generation, + data, + "execute", + ) + + decisions = np.asarray(new_dec) + if decisions.ndim == 1: + decisions = decisions.reshape(1, -1) + remaining = remaining_evaluations - evaluations + if decisions.shape[0] > remaining: + decisions = decisions[:remaining] + new = make_solutions(decisions, problem, data) + if step.primary == "search_cma": + aux_state = get_component("para_cma")( + new, problem, aux_state, "solution" + ) + elif step.primary == "search_pso": + aux_state = get_component("para_pso")(new, problem, aux_state) + + if len(new) < len(current): + updated = sorted( + list(current) + list(new), key=lambda solution: solution.fit + )[: len(current)] + else: + updated, update_aux = update_fn( + list(current) + list(new), + problem, + update_param, + aux_state, + component_generation, + inner_generation, + data, + "execute", + ) + if isinstance(update_aux, dict): + aux_state = update_aux + aux_state.setdefault("rng", ensure_rng(setting)) + current = SolutionSet(updated) + evaluations += len(new) + iterations += 1 + improve = improve_rate(current, improve, inner_generation, "solution") + archives = _update_archives(current, archive_names, archives, problem) + if len(current): + iteration_bests.append(min(list(current), key=lambda sol: sol.fit)) + inner_generation += 1 + + return current, aux_state, evaluations, iterations, archives, iteration_bests + + def _execute_path( path: Any, params: Any, subset: SolutionSet, - aux_states: List[Any], + aux_state: Any, problem: Any, data: Any, setting: Any, generation: int, -) -> Tuple[List[Any], List[Any], int]: - if not aux_states or len(aux_states) != len(path.search): - aux_states = [None] * len(path.search) + remaining_evaluations: int, +) -> Tuple[List[Any], Any, int]: + if not isinstance(aux_state, dict): + aux_state = {} + aux_state.setdefault("rng", ensure_rng(setting)) current_parent = subset evals = 0 for step_idx, step in enumerate(path.search): + if evals >= remaining_evaluations: + break improve = None inner_g = 1 limit = int(step.termination[1]) if step.termination.size > 1 else 1 threshold = float(step.termination[0]) if step.termination.size > 0 else -np.inf primary_param = params.search[step_idx].primary if params.search else None secondary_param = params.search[step_idx].secondary if params.search else None - aux_state = aux_states[step_idx] or {} while (improve is None or improve[0] >= threshold) and inner_g <= limit: + remaining = remaining_evaluations - evals + if remaining <= 0: + break primary_fn = get_component(step.primary) new_dec, aux_state = primary_fn( - current_parent, problem, primary_param, aux_state, generation, inner_g, data, "execute" + current_parent, + problem, + primary_param, + aux_state, + generation, + inner_g, + data, + "execute", ) if step.secondary: new_dec = repair_sol(np.asarray(new_dec), problem) secondary_fn = get_component(step.secondary) new_dec, aux_state = secondary_fn( - new_dec, problem, secondary_param, aux_state, generation, inner_g, data, "execute" + new_dec, + problem, + secondary_param, + aux_state, + generation, + inner_g, + data, + "execute", ) - new = make_solutions(np.asarray(new_dec), problem, data) + new_dec = np.asarray(new_dec) + if new_dec.ndim == 1: + new_dec = new_dec.reshape(1, -1) + if new_dec.shape[0] > remaining: + new_dec = new_dec[:remaining] + new = make_solutions(new_dec, problem, data) if step.primary == "search_cma": - aux_state = get_component("para_cma")(new, problem, aux_state, "solution") + aux_state = get_component("para_cma")( + new, problem, aux_state, "solution" + ) elif step.primary == "search_pso": aux_state = get_component("para_pso")(new, problem, aux_state) current_parent = new evals += len(new) improve = improve_rate(new, improve, inner_g, "solution") - aux_states[step_idx] = aux_state inner_g += 1 - return list(current_parent), aux_states, evals + return list(current_parent), aux_state, evals -def run_design(pathways: List[Any], params: List[Any], problem: Any, data: Any, setting: Any) -> dict: +def run_design( + pathways: List[Any], params: List[Any], problem: Any, data: Any, setting: Any +) -> dict: rng = ensure_rng(setting) - solutions = _init_population(rng, problem, data, setting) - aux_cache: List[List[Any]] = [[None] * len(path.search) for path in pathways] - archive_names = list(get_flex(setting, "archive", [])) - archives: List[Any] = [] - for name in archive_names: - if name == "archive_statistic": - archives.append(np.empty((0, 2))) - else: - archives.append([]) + _configure_evaluation_runtime(problem, setting) + try: + problem.rng = rng + except (AttributeError, TypeError): + pass + population_size = int(get_flex(problem, "N", get_flex(setting, "ProbN", 10))) + evaluation_budget = int( + get_flex( + setting, + "ProbFE", + population_size * int(get_flex(problem, "Gmax", 10)), + ) + ) + if evaluation_budget < population_size: + raise ValueError( + f"ProbFE ({evaluation_budget}) must be at least the initial population size " + f"({population_size})." + ) + + checkpoint_value = get_flex(setting, "_checkpoint_path", None) + checkpoint_path = Path(checkpoint_value) if checkpoint_value else None + resume = bool(get_flex(setting, "Resume", False)) + checkpoint_every = int(get_flex(setting, "CheckpointEvery", 1)) + + embedded_archives = getattr(pathways[0], "archive", []) if pathways else [] + archive_names = list(embedded_archives or get_flex(setting, "archive", [])) + resume_state = None + if resume and checkpoint_path is not None and checkpoint_path.exists(): + resume_state = _load_checkpoint( + checkpoint_path, + population_size=population_size, + evaluation_budget=evaluation_budget, + ) + solutions = SolutionSet(resume_state["solutions"]) + aux_cache = resume_state["aux_cache"] + archives = resume_state["archives"] + rng.bit_generator.state = resume_state["rng_state"] + else: + solutions = _init_population(rng, problem, data, setting) + # MATLAB keeps one Aux structure per pathway and shares it among the + # choose, search, parameter-update and population-update components. + aux_cache = [{} for _ in pathways] + archives = [] + for name in archive_names: + if name == "archive_statistic": + archives.append(np.empty((0, 2))) + else: + archives.append([]) metric = get_flex(setting, "metric", "quality") - prob_n = float(get_flex(setting, "prob_n", 1.0)) tmax = get_flex(setting, "tmax", np.inf) if tmax is None: tmax = np.inf @@ -273,7 +953,8 @@ def run_design(pathways: List[Any], params: List[Any], problem: Any, data: Any, Tmax = np.inf threshold = -np.inf elif metric == "runtimeFE": - Tmax = int(np.ceil(tmax / max(prob_n, 1))) + requested_limit = evaluation_budget if not np.isfinite(tmax) else int(tmax) + Tmax = min(evaluation_budget, requested_limit) threshold = thres elif metric == "runtimeSec": Tmax = float(tmax) @@ -284,72 +965,218 @@ def run_design(pathways: List[Any], params: List[Any], problem: Any, data: Any, history: List[Optional[Solution]] = [] fit_history: List[float] = [] - evaluations = 0 + # Initial-population objective calls are part of the public ProbFE budget. + evaluations = len(solutions) elapsed_seconds = 0.0 generation = 1 - while True: + if resume_state is not None: + history = resume_state["history"] + fit_history = resume_state["fit_history"] + evaluations = int(resume_state["evaluations"]) + elapsed_seconds = float(resume_state["elapsed_seconds"]) + generation = int(resume_state["generation"]) + if resume_state.get("complete", False): + candidates = [item for item in history if item is not None] + best = min(candidates, key=lambda item: item.fit) if candidates else None + return { + "solutions": solutions, + "history": history, + "fit_history": fit_history, + "evaluations": evaluations, + "elapsed": elapsed_seconds, + "archives": archives, + "best_solution": best, + } + + if not history: if len(solutions): - best_solution = min(list(solutions), key=lambda s: s.fit) - history.append(best_solution) - fit_history.append(float(best_solution.fit)) + initial_best = min(list(solutions), key=lambda s: s.fit) + history.append(initial_best) + fit_history.append(float(initial_best.fit)) else: history.append(None) fit_history.append(np.inf) + while True: if generation > gmax: break + if evaluations >= evaluation_budget: + break if metric == "quality" and fit_history[-1] <= threshold: break - if metric == "runtimeFE" and evaluations >= Tmax: + if metric == "runtimeFE" and ( + evaluations >= Tmax or fit_history[-1] <= threshold + ): break if metric == "runtimeSec" and elapsed_seconds >= Tmax: break iteration_start = time.perf_counter() - choose_fn = get_component(pathways[0].choose) - choose_param = getattr(params[0], "choose", None) - selected, _ = choose_fn(solutions, problem, choose_param, None, generation, 1, data, "execute") - selected = np.asarray(selected, dtype=int).reshape(-1) - index_groups = _split_indices(selected, len(pathways), len(solutions)) - - new_items: List[Any] = [] - for p_idx, path in enumerate(pathways): - subset_idx = index_groups[p_idx] if p_idx < len(index_groups) else index_groups[-1] - subset = SolutionSet([solutions[i] for i in subset_idx]) if subset_idx else solutions - produced, aux_state, evals = _execute_path( - path, params[p_idx], subset, aux_cache[p_idx], problem, data, setting, generation + iterations_used = 1 + if len(pathways) == 1: + remaining = evaluation_budget - evaluations + if metric == "runtimeFE": + remaining = min(remaining, int(Tmax) - evaluations) + ( + solutions, + aux_cache[0], + evals, + iterations_used, + archives, + iteration_bests, + ) = _execute_single_path( + pathways[0], + params[0], + solutions, + aux_cache[0], + problem, + data, + setting, + generation, + remaining, + archive_names, + archives, ) - aux_cache[p_idx] = aux_state - new_items.extend(produced) evaluations += evals + for current_best in iteration_bests: + previous = history[-1] + best_so_far = ( + current_best + if previous is None or current_best.fit < previous.fit + else previous + ) + history.append(best_so_far) + fit_history.append(float(best_so_far.fit)) + if evals == 0: + break + else: + choose_fn = get_component(pathways[0].choose) + choose_param = getattr(params[0], "choose", None) + selected, _ = choose_fn( + solutions, + problem, + choose_param, + {"rng": rng}, + generation, + 1, + data, + "execute", + ) + selected = np.asarray(selected, dtype=int).reshape(-1) + index_groups = _split_indices(selected, len(pathways), len(solutions)) + + new_items: List[Any] = [] + for p_idx, path in enumerate(pathways): + remaining = evaluation_budget - evaluations + if metric == "runtimeFE": + remaining = min(remaining, int(Tmax) - evaluations) + if remaining <= 0: + break + subset_idx = ( + index_groups[p_idx] + if p_idx < len(index_groups) + else index_groups[-1] + ) + subset = ( + SolutionSet([solutions[i] for i in subset_idx]) + if subset_idx + else solutions + ) + produced, aux_state, evals = _execute_path( + path, + params[p_idx], + subset, + aux_cache[p_idx], + problem, + data, + setting, + generation, + remaining, + ) + aux_cache[p_idx] = aux_state + new_items.extend(produced) + evaluations += evals - update_fn = get_component(pathways[0].update) - update_param = getattr(params[0], "update", None) - updated, _ = update_fn(list(solutions) + new_items, problem, update_param, None, generation, 1, data, "execute") - solutions = SolutionSet(updated) - - for j, name in enumerate(archive_names): - archive_fn = get_component(name) - if name == "archive_best": - archives[j], _ = archive_fn(solutions, archives[j], "execute") - elif name == "archive_statistic": - archives[j], _ = archive_fn(solutions, archives[j], "execute") + if not new_items: + break + + if len(new_items) < len(solutions): + updated = sorted( + list(solutions) + new_items, key=lambda solution: solution.fit + )[: len(solutions)] else: - archives[j], _ = archive_fn(solutions, archives[j], problem, "execute") + update_fn = get_component(pathways[0].update) + update_param = getattr(params[0], "update", None) + updated, _ = update_fn( + list(solutions) + new_items, + problem, + update_param, + {"rng": rng}, + generation, + 1, + data, + "execute", + ) + solutions = SolutionSet(updated) + archives = _update_archives(solutions, archive_names, archives, problem) + current_best = min(list(solutions), key=lambda sol: sol.fit) + previous = history[-1] + best_so_far = ( + current_best + if previous is None or current_best.fit < previous.fit + else previous + ) + history.append(best_so_far) + fit_history.append(float(best_so_far.fit)) if metric == "runtimeFE": pass elif metric == "runtimeSec": elapsed_seconds += time.perf_counter() - iteration_start - generation += 1 + generation += max(1, iterations_used) + if checkpoint_path is not None and generation % checkpoint_every == 0: + _write_checkpoint( + checkpoint_path, + _checkpoint_payload( + solutions=solutions, + aux_cache=aux_cache, + archives=archives, + history=history, + fit_history=fit_history, + evaluations=evaluations, + elapsed_seconds=elapsed_seconds, + generation=generation, + rng=rng, + population_size=population_size, + evaluation_budget=evaluation_budget, + complete=False, + ), + ) - if history and history[-1] is None: - final_solution = None - else: - final_solution = history[-1] + candidates = [item for item in history if item is not None] + final_solution = min(candidates, key=lambda item: item.fit) if candidates else None + + if checkpoint_path is not None: + _write_checkpoint( + checkpoint_path, + _checkpoint_payload( + solutions=solutions, + aux_cache=aux_cache, + archives=archives, + history=history, + fit_history=fit_history, + evaluations=evaluations, + elapsed_seconds=elapsed_seconds, + generation=generation, + rng=rng, + population_size=population_size, + evaluation_budget=evaluation_budget, + complete=True, + ), + ) return { "solutions": solutions, @@ -366,8 +1193,11 @@ def run_design(pathways: List[Any], params: List[Any], problem: Any, data: Any, # Utility helpers # --------------------------------------------------------------------------- + def _placeholder_solution() -> Solution: - return Solution(dec=np.zeros((1, 0)), obj=float("inf"), con=float("inf"), fit=float("inf")) + return Solution( + dec=np.zeros((1, 0)), obj=float("inf"), con=float("inf"), fit=float("inf") + ) def _ensure_solution_instance(obj: Any) -> Solution: @@ -389,6 +1219,7 @@ def _ensure_solution_instance(obj: Any) -> Solution: # Solve-mode helpers (InputAlg / RunAlg translation) # --------------------------------------------------------------------------- + def _normalize_setting(setting: Any) -> Any: if isinstance(setting, dict): return type("Setting", (), setting)() @@ -399,15 +1230,19 @@ def _normalize_setting(setting: Any) -> Any: def _to_array(value: Any) -> Optional[np.ndarray]: - if value is None or value == []: + if value is None or (isinstance(value, (list, tuple)) and len(value) == 0): return None arr = np.asarray(value, dtype=float) return arr.reshape(-1) if arr.ndim > 0 else arr -def _make_search_step(primary: str, secondary: Optional[str], termination: Sequence[float]) -> SearchStep: +def _make_search_step( + primary: str, secondary: Optional[str], termination: Sequence[float] +) -> SearchStep: term = np.asarray(termination, dtype=float).reshape(-1) - return SearchStep(primary=primary, secondary=secondary if secondary else None, termination=term) + return SearchStep( + primary=primary, secondary=secondary if secondary else None, termination=term + ) def _make_search_param(primary: Any, secondary: Any) -> SearchParam: @@ -416,12 +1251,18 @@ def _make_search_param(primary: Any, secondary: Any) -> SearchParam: def _build_default_algorithm(name: str, setting: Any) -> "Design": name = name.strip().lower() - tmpl = { + tmpl: dict[str, list[Any]] = { "continuous genetic algorithm": [ ( "choose_tournament", [ - ("cross_sim_binary", "search_mu_polynomial", (-np.inf, 1), [20.0], [0.2, 20.0]), + ( + "cross_sim_binary", + "search_mu_polynomial", + (-np.inf, 1), + [20.0], + [0.2, 20.0], + ), ], "update_round_robin", [], @@ -511,7 +1352,13 @@ def _build_default_algorithm(name: str, setting: Any) -> "Design": ( "choose_tournament", [ - ("cross_point_uniform", "search_reset_rand", (-np.inf, 1), [0.2], [0.2]), + ( + "cross_point_uniform", + "search_reset_rand", + (-np.inf, 1), + [0.2], + [0.2], + ), ], "update_round_robin", [], @@ -606,7 +1453,9 @@ def _build_default_algorithm(name: str, setting: Any) -> "Design": } if name not in tmpl: - raise NotImplementedError(f"Preset algorithm {name!r} is not available in the Python translation.") + raise NotImplementedError( + f"Preset algorithm {name!r} is not available in the Python translation." + ) pathways_cfg = tmpl[name] pathways: List[Pathway] = [] @@ -625,7 +1474,9 @@ def _build_default_algorithm(name: str, setting: Any) -> "Design": search_steps.append(_make_search_step(primary, secondary, termination)) search_params.append(_make_search_param(primary_param, secondary_param)) - pathways.append(Pathway(choose=choose, search=search_steps, update=update, archive=archive)) + pathways.append( + Pathway(choose=choose, search=search_steps, update=update, archive=archive) + ) params.append( PathwayParam( choose=None, @@ -648,13 +1499,25 @@ def input_algorithm(setting: Any) -> Tuple["Design", Any]: path = Path(alg_file) if not path.exists(): raise FileNotFoundError(f"Algorithm file {alg_file} not found.") - with path.open("rb") as handle: - data = pickle.load(handle) - if isinstance(data, dict) and "algs" in data: - alg = data["algs"][0] + if path.suffix.lower() == ".json": + from ...serialization import load_algorithm + + alg = load_algorithm(path) else: - alg = data[0] if isinstance(data, (list, tuple)) else data + warnings.warn( + "Loading pickle algorithm files can execute arbitrary code. Only load trusted " + "files; use AutoOptLib JSON algorithm files for portable exchange.", + UserWarning, + stacklevel=2, + ) + with path.open("rb") as handle: + data = pickle.load(handle) + if isinstance(data, dict) and "algs" in data: + alg = data["algs"][0] + else: + alg = data[0] if isinstance(data, (list, tuple)) else data from ..design import Design + if not isinstance(alg, Design): raise TypeError("Loaded algorithm is not a Design instance.") setting.AlgP = len(getattr(alg, "operator_pheno", []) or []) @@ -675,12 +1538,43 @@ def _extract_mode(problem: Any) -> str: return "static" -def run_algorithm(alg: 'Design', problems: Sequence[Any], data: Sequence[Any], app: Any, setting: Any): +def _setting_with_budget(setting: Any, budget: int) -> Any: + """Return a shallow setting copy with synchronized ProbFE aliases.""" + cloned = copy(setting) + if isinstance(cloned, dict): + cloned["ProbFE"] = int(budget) + cloned["prob_fe"] = int(budget) + else: + setattr(cloned, "ProbFE", int(budget)) + setattr(cloned, "prob_fe", int(budget)) + return cloned + + +def _advance_sequential_problem( + current_problem: Any, current_data: Any, best: Solution +) -> tuple[Any, Any] | None: + next_fn = getattr(current_problem, "advance_sequence", None) + if callable(next_fn): + return next_fn(best, current_data) + name = getattr(current_problem, "name", None) + if callable(name): + next_problem, next_data, _ = name( + current_problem, current_data, best, "sequence" + ) + return next_problem, next_data + return None + + +def run_algorithm( + alg: "Design", problems: Sequence[Any], data: Sequence[Any], app: Any, setting: Any +): + if not alg.operator_pheno or not alg.parameter_pheno: + raise ValueError("The algorithm must be decoded before solve execution.") pathways = alg.operator_pheno[0] params = alg.parameter_pheno[0] alg_runs = int(get_flex(setting, "AlgRuns", 1)) best_solutions: List[List[Solution]] = [] - all_solutions: List[List[List[Solution]]] = [] + all_solutions: List[List[Solution]] = [] for idx, problem in enumerate(problems): data_obj = data[idx] if idx < len(data) else None @@ -690,9 +1584,24 @@ def run_algorithm(alg: 'Design', problems: Sequence[Any], data: Sequence[Any], a fitness_sequence: List[float] = [] for run in range(alg_runs): + run_setting = setting + checkpoint_dir = get_flex(setting, "CheckpointDir", None) + if checkpoint_dir is not None: + run_setting = copy(setting) + if mode == "static": + run_setting._checkpoint_path = str( + Path(checkpoint_dir) / f"instance_{idx + 1}_run_{run + 1}.pkl" + ) + elif mode == "sequential": + run_setting._sequence_checkpoint_path = str( + Path(checkpoint_dir) + / f"instance_{idx + 1}_run_{run + 1}_sequence.pkl" + ) if mode == "static": - result = run_design(pathways, params, problem, data_obj, setting) - history = [sol for sol in result["history"] if isinstance(sol, Solution)] + result = run_design(pathways, params, problem, data_obj, run_setting) + history = [ + sol for sol in result["history"] if isinstance(sol, Solution) + ] best = result["best_solution"] if best is None and history: best = history[-1] @@ -703,29 +1612,121 @@ def run_algorithm(alg: 'Design', problems: Sequence[Any], data: Sequence[Any], a current_data = data_obj sequence_solutions: List[Solution] = [] cumulative = 0.0 + total_budget = int(get_flex(setting, "ProbFE", 0)) + remaining_budget = total_budget + population_size = int( + get_flex(current_problem, "N", get_flex(setting, "ProbN", 1)) + ) + sequence_path_value = get_flex( + run_setting, "_sequence_checkpoint_path", None + ) + sequence_path = ( + Path(sequence_path_value) if sequence_path_value else None + ) + sequence_state = None + sequence_rng = ensure_rng(run_setting) + if ( + bool(get_flex(run_setting, "Resume", False)) + and sequence_path is not None + and sequence_path.exists() + ): + sequence_state = _load_sequence_checkpoint( + sequence_path, + population_size=population_size, + evaluation_budget=total_budget, + ) + sequence_solutions = list(sequence_state["solutions"]) + cumulative = float(sequence_state["cumulative"]) + remaining_budget = int(sequence_state["remaining_budget"]) + sequence_rng.bit_generator.state = sequence_state["rng_state"] + + if sequence_state is not None and sequence_state.get("complete", False): + run_histories.append(sequence_solutions) + run_best.append( + sequence_solutions[-1] + if sequence_solutions + else _placeholder_solution() + ) + fitness_sequence.append(cumulative) + continue + + for completed_solution in sequence_solutions: + advanced = _advance_sequential_problem( + current_problem, current_data, completed_solution + ) + if advanced is None: + break + current_problem, current_data = advanced + while getattr(current_data, "continue", False): - result = run_design(pathways, params, current_problem, current_data, setting) + # Each sequential instance receives the full configured + # budget, matching the original sequence protocol. + stage_setting = run_setting + if checkpoint_dir is not None: + stage_number = len(sequence_solutions) + 1 + stage_path = ( + Path(checkpoint_dir) + / f"instance_{idx + 1}_run_{run + 1}_stage_{stage_number}.pkl" + ) + if isinstance(stage_setting, dict): + stage_setting["_checkpoint_path"] = str(stage_path) + else: + stage_setting._checkpoint_path = str(stage_path) + result = run_design( + pathways, params, current_problem, current_data, stage_setting + ) best = result["best_solution"] if best is None and result["history"]: - history = [sol for sol in result["history"] if isinstance(sol, Solution)] + history = [ + sol + for sol in result["history"] + if isinstance(sol, Solution) + ] best = history[-1] if history else None if best is None: break sequence_solutions.append(best) cumulative += float(best.fit) - next_fn = getattr(current_problem, "advance_sequence", None) - if callable(next_fn): - current_problem, current_data = next_fn(best, current_data) - else: - name = getattr(current_problem, "name", None) - if callable(name): - current_problem, current_data, _ = name(current_problem, current_data, best, "sequence") - else: - break + if sequence_path is not None: + _write_checkpoint( + sequence_path, + _sequence_checkpoint_payload( + solutions=sequence_solutions, + cumulative=cumulative, + remaining_budget=remaining_budget, + population_size=population_size, + evaluation_budget=total_budget, + rng=sequence_rng, + complete=False, + ), + ) + advanced = _advance_sequential_problem( + current_problem, current_data, best + ) + if advanced is None: + break + current_problem, current_data = advanced if current_problem is problem and current_data is data_obj: break + if sequence_path is not None: + _write_checkpoint( + sequence_path, + _sequence_checkpoint_payload( + solutions=sequence_solutions, + cumulative=cumulative, + remaining_budget=remaining_budget, + population_size=population_size, + evaluation_budget=total_budget, + rng=sequence_rng, + complete=True, + ), + ) run_histories.append(sequence_solutions) - run_best.append(sequence_solutions[-1] if sequence_solutions else _placeholder_solution()) + run_best.append( + sequence_solutions[-1] + if sequence_solutions + else _placeholder_solution() + ) fitness_sequence.append(cumulative) else: raise NotImplementedError(f"Unsupported problem mode: {mode}") @@ -754,6 +1755,3 @@ def run_algorithm(alg: 'Design', problems: Sequence[Any], data: Sequence[Any], a "input_algorithm", "run_algorithm", ] - - - diff --git a/src/autooptlib/utils/space.py b/src/autooptlib/utils/space.py index 5a9f8b2..014cde8 100644 --- a/src/autooptlib/utils/space.py +++ b/src/autooptlib/utils/space.py @@ -5,7 +5,7 @@ import numpy as np -from ..components import get_component +from ..components import compatible_custom_components, get_component from .design._helpers import get_problem_type @@ -15,7 +15,11 @@ def _to_namespace(setting: Any) -> SimpleNamespace: if isinstance(setting, dict): return SimpleNamespace(**setting) # Fallback: copy attributes - data = {k: getattr(setting, k) for k in dir(setting) if not k.startswith("__") and not callable(getattr(setting, k))} + data = { + k: getattr(setting, k) + for k in dir(setting) + if not k.startswith("__") and not callable(getattr(setting, k)) + } return SimpleNamespace(**data) @@ -43,7 +47,9 @@ def _to_behavior_matrix(behavior: Any) -> list[list[Any]] | None: return matrix -def _compute_local_bounds(behavior: list[list[Any]] | None, bounds: np.ndarray | None, ls_range: float) -> np.ndarray | None: +def _compute_local_bounds( + behavior: list[list[Any]] | None, bounds: np.ndarray | None, ls_range: float +) -> np.ndarray | None: """Compute the parameter subspace used for local search, mirroring MATLAB Space.m.""" if behavior is None or bounds is None or bounds.size == 0: return None @@ -54,7 +60,9 @@ def _compute_local_bounds(behavior: list[list[Any]] | None, bounds: np.ndarray | if first_col in (None, "", 0): return None - has_global = len(behavior) > 1 and behavior[1] and behavior[1][0] not in (None, "", 0) + has_global = ( + len(behavior) > 1 and behavior[1] and behavior[1][0] not in (None, "", 0) + ) if not has_global: return bounds.copy() @@ -72,7 +80,9 @@ def _compute_local_bounds(behavior: list[list[Any]] | None, bounds: np.ndarray | local_bounds[idx, 0] = lower local_bounds[idx, 1] = lower + span[idx] * ls_range elif trend == "large": - local_bounds[idx, 0] = upper - span[idx] * ls_range + # Keep the reference search region used by the published MATLAB + # experiments (Space.m), including its upper-bound scaling rule. + local_bounds[idx, 0] = upper - upper * ls_range local_bounds[idx, 1] = upper else: local_bounds[idx, 0] = lower @@ -89,26 +99,44 @@ def space(problem: Any, setting: Any) -> SimpleNamespace: ptype = get_problem_type(problem) if ptype == "continuous": - choose = ["choose_traverse", "choose_tournament", "choose_roulette_wheel", "choose_nich"] + choose = [ + "choose_traverse", + "choose_tournament", + "choose_roulette_wheel", + "choose_nich", + ] search = [ "search_de_current", "search_de_current_best", "search_de_random", - "search_cma", - "search_pso", + "cross_arithmetic", + "cross_sim_binary", "cross_point_one", "cross_point_two", - "cross_point_uniform", "cross_point_n", + "cross_point_uniform", "search_mu_gaussian", "search_mu_cauchy", "search_mu_polynomial", "search_mu_uniform", + "search_eda", + "search_cma", "reinit_continuous", ] - update = ["update_greedy", "update_round_robin", "update_pairwise", "update_always", "update_simulated_annealing"] + update = [ + "update_greedy", + "update_round_robin", + "update_pairwise", + "update_always", + "update_simulated_annealing", + ] elif ptype == "discrete": - choose = ["choose_traverse", "choose_tournament", "choose_roulette_wheel", "choose_nich"] + choose = [ + "choose_traverse", + "choose_tournament", + "choose_roulette_wheel", + "choose_nich", + ] search = [ "cross_point_one", "cross_point_two", @@ -116,12 +144,22 @@ def space(problem: Any, setting: Any) -> SimpleNamespace: "cross_point_n", "search_reset_one", "search_reset_rand", - "search_reset_creep", "reinit_discrete", ] - update = ["update_greedy", "update_round_robin", "update_pairwise", "update_always", "update_simulated_annealing"] + update = [ + "update_greedy", + "update_round_robin", + "update_pairwise", + "update_always", + "update_simulated_annealing", + ] elif ptype == "permutation": - choose = ["choose_traverse", "choose_tournament", "choose_roulette_wheel", "choose_nich"] + choose = [ + "choose_traverse", + "choose_tournament", + "choose_roulette_wheel", + "choose_nich", + ] search = [ "cross_order_two", "cross_order_n", @@ -131,9 +169,33 @@ def space(problem: Any, setting: Any) -> SimpleNamespace: "search_insert", "reinit_permutation", ] - update = ["update_greedy", "update_round_robin", "update_pairwise", "update_always", "update_simulated_annealing"] + update = [ + "update_greedy", + "update_round_robin", + "update_pairwise", + "update_always", + "update_simulated_annealing", + ] else: - raise NotImplementedError("space() currently supports continuous, discrete, and permutation problems") + raise NotImplementedError( + "space() currently supports continuous, discrete, and permutation problems" + ) + + choose.extend( + name + for name in compatible_custom_components("choose", ptype) + if name not in choose + ) + search.extend( + name + for name in compatible_custom_components("search", ptype) + if name not in search + ) + update.extend( + name + for name in compatible_custom_components("update", ptype) + if name not in update + ) all_op = choose + search + update op_space = np.array( @@ -194,8 +256,10 @@ def space(problem: Any, setting: Any) -> SimpleNamespace: set_obj.ParaLocalSpace = para_local_space # Provide defaults for downstream code - set_obj.TunePara = getattr(set_obj, "TunePara", False) - set_obj.alg_p = getattr(set_obj, "alg_p", 1) - set_obj.alg_q = getattr(set_obj, "alg_q", 3) - set_obj.alg_n = getattr(set_obj, "alg_n", 1) + set_obj.TunePara = getattr( + set_obj, "TunePara", getattr(set_obj, "tune_para", False) + ) + set_obj.alg_p = getattr(set_obj, "alg_p", getattr(set_obj, "AlgP", 1)) + set_obj.alg_q = getattr(set_obj, "alg_q", getattr(set_obj, "AlgQ", 3)) + set_obj.alg_n = getattr(set_obj, "alg_n", getattr(set_obj, "AlgN", 1)) return set_obj diff --git a/src/solve_f1.py b/src/solve_f1.py deleted file mode 100644 index 687da8b..0000000 --- a/src/solve_f1.py +++ /dev/null @@ -1,28 +0,0 @@ -from types import SimpleNamespace -from autooptlib.utils.general.process import process -from autooptlib.problems.cec2013 import cec2013_f1 - -setting = SimpleNamespace( - Mode="design", - AlgN=6, # 同时维护的候选算法数，适当增大保持多样性 - AlgFE=1000, # 设计阶段的总评估次数（原来 50 太低） - AlgRuns=3, # 每个候选算法重复运行 2 次，结果更稳定 - ProbN=40, # 问题侧的种群规模 - ProbFE=5000, # 每次 run_design 的函数评估额度 - Metric="quality", - Evaluate="racing", # racing 会先用少量实例筛选，省掉大量全量评估 - Compare="statistic", - RacingK=1, # 每次先用 1 个实例（可视需要调大） - IncRate=0.03, - archive=["archive_best"], -) - -final_algs, alg_trace = process( - cec2013_f1, - [30, 30, 30], # 训练实例（可以保留 3 个 30 维） - [30], # 测试实例 - setting=setting, -) - -print(f"设计完成的算法数: {len(final_algs)}") -print("测试集性能:\n", final_algs[0].performance) diff --git a/src/temp_test.py b/src/temp_test.py deleted file mode 100644 index 22bd90b..0000000 --- a/src/temp_test.py +++ /dev/null @@ -1,274 +0,0 @@ -from __future__ import annotations - -import itertools -from dataclasses import dataclass -from typing import Dict, Iterable, List, Sequence, Tuple - -import numpy as np - -from autooptlib.components import __all__ as COMPONENT_NAMES, get_component - - -@dataclass -class SimpleSolution: - dec: np.ndarray - obj: float - con: float - fit: float - - -class SimpleSolutionSet(Sequence[SimpleSolution]): - def __init__(self, items: Iterable[SimpleSolution]): - self._items: List[SimpleSolution] = list(items) - - def __len__(self) -> int: - return len(self._items) - - def __getitem__(self, idx): - return self._items[idx] - - def decs(self) -> np.ndarray: - if not self._items: - return np.zeros((0, 0)) - return np.vstack([sol.dec for sol in self._items]) - - def objs(self) -> np.ndarray: - return np.asarray([sol.obj for sol in self._items]).reshape(-1, 1) - - def cons(self) -> np.ndarray: - return np.asarray([sol.con for sol in self._items]).reshape(-1, 1) - - def fits(self) -> np.ndarray: - return np.asarray([sol.fit for sol in self._items]).reshape(-1, 1) - - -class AuxState(dict): - def __getattr__(self, item): - try: - return self[item] - except KeyError as exc: - raise AttributeError(item) from exc - - def __setattr__(self, key, value) -> None: - self[key] = value - - -@dataclass -class DummyProblem: - kind: str - dim: int = 4 - pop_size: int = 6 - - def __post_init__(self) -> None: - if self.kind == "continuous": - lower = np.full(self.dim, -5.0) - upper = np.full(self.dim, 5.0) - elif self.kind == "discrete": - lower = np.zeros(self.dim, dtype=int) - upper = np.full(self.dim, 9, dtype=int) - elif self.kind == "permutation": - lower = np.ones(self.dim, dtype=int) - upper = np.full(self.dim, self.dim, dtype=int) - else: - raise ValueError(f"Unsupported problem kind: {self.kind}") - self.bound = np.vstack((lower, upper)) - self.N = self.pop_size - self.Gmax = 10 - self.type = [self.kind, "static", "certain"] - - def evaluate(self, data, dec): # noqa: ANN001 - arr = np.asarray(dec, dtype=float) - obj = float(np.sum(arr ** 2)) - return obj, 0.0, None - - -def make_population(problem: DummyProblem, rng: np.random.Generator) -> SolutionSet: - if problem.kind == "continuous": - lower, upper = problem.bound - decs = lower + (upper - lower) * rng.random((problem.N, problem.dim)) - elif problem.kind == "discrete": - lower, upper = problem.bound.astype(int) - decs = np.vstack( - [rng.integers(lower[i], upper[i] + 1, size=problem.N) for i in range(problem.dim)] - ).T - elif problem.kind == "permutation": - domain = np.arange(1, problem.dim + 1) - decs = np.vstack([rng.permutation(domain) for _ in range(problem.N)]) - else: - raise ValueError(f"Unknown problem type: {problem.kind}") - - items: List[SimpleSolution] = [] - for row in decs: - dec = np.asarray(row, dtype=float if problem.kind == "continuous" else int) - obj = float(np.sum(np.asarray(row, dtype=float) ** 2)) - items.append(SimpleSolution(dec=dec, obj=obj, con=0.0, fit=obj)) - return SimpleSolutionSet(items) - - -def solution_set_from_decs(decs: np.ndarray, problem: DummyProblem) -> SolutionSet: - rng = np.random.default_rng(9876) - items: List[SimpleSolution] = [] - for row in np.asarray(decs): - dec = np.asarray(row, dtype=float if problem.kind == "continuous" else int) - obj = float(np.sum(np.asarray(row, dtype=float) ** 2)) - items.append(SimpleSolution(dec=dec, obj=obj, con=0.0, fit=obj + rng.random() * 1e-6)) - return SimpleSolutionSet(items) - - -def unpack(result): - if isinstance(result, tuple) and len(result) >= 1: - return result[0] - return result - - -def sample_parameter(spec): - if spec is None: - return None - arr = np.asarray(spec, dtype=float) - if arr.ndim == 0: - return float(arr) - if arr.ndim == 1: - if arr.size == 0: - return None - if arr.size == 2: - return float(arr.mean()) - return arr - if arr.ndim == 2 and arr.shape[1] == 2: - return arr.mean(axis=1) - return arr - - -def make_aux(seed_counter: Iterable[int]) -> AuxState: - seed = next(seed_counter) - return AuxState(rng=np.random.default_rng(10_000 + seed)) - - -def validate_components() -> None: - seed_counter = itertools.count() - rng = np.random.default_rng(12345) - - problem_cont = DummyProblem("continuous") - problem_disc = DummyProblem("discrete") - problem_perm = DummyProblem("permutation") - - pop_cont = make_population(problem_cont, rng) - pop_disc = make_population(problem_disc, rng) - pop_perm = make_population(problem_perm, rng) - - doubled_pop = list(pop_cont) + list(pop_cont) - - discrete_components = { - "cross_point_one", - "cross_point_two", - "cross_point_n", - "cross_point_uniform", - "reinit_discrete", - "search_reset_one", - "search_reset_rand", - "search_reset_creep", - } - permutation_components = { - "cross_order_two", - "cross_order_n", - "reinit_permutation", - "search_swap", - "search_swap_multi", - "search_scramble", - "search_insert", - } - - errors: List[Tuple[str, Exception]] = [] - - def get_context(name: str) -> Tuple[SimpleSolutionSet, DummyProblem]: - if name in permutation_components: - return pop_perm, problem_perm - if name in discrete_components: - return pop_disc, problem_disc - return pop_cont, problem_cont - - # Ensure search_cma and search_pso states are captured for para components - extra_states: Dict[str, Tuple[np.ndarray, dict]] = {} - - for name in sorted(COMPONENT_NAMES): - func = get_component(name) - try: - if name.startswith("para_"): - continue # handled later - - population, problem = get_context(name) - - param_spec = func(problem, "parameter") if name not in {"archive_tabu"} else func("parameter") - param_spec = unpack(param_spec) - param_value = sample_parameter(param_spec) - - behavior = func("behavior") - unpack(behavior) # ensure callable exists - - if name.startswith("archive_"): - if name == "archive_best": - func(population, [], "execute") - elif name == "archive_diversity": - func(population, [], problem, "execute") - elif name == "archive_statistic": - func(population, [], "execute") - elif name == "archive_tabu": - func(population, "execute") - elif name.startswith("choose_"): - aux = make_aux(seed_counter) - func(population, problem, param_value, aux, 0, "execute") - elif name.startswith("cross_"): - aux = make_aux(seed_counter) - func(population, problem, param_value, aux, "execute") - elif name.startswith("search_"): - aux = make_aux(seed_counter) - offspring, aux_out = func(population, problem, param_value, aux, "execute") - if name in {"search_cma", "search_pso"}: - extra_states[name] = (np.asarray(offspring), aux_out if isinstance(aux_out, dict) else {}) - elif name.startswith("reinit_"): - aux = make_aux(seed_counter) - func(population, problem, param_value, aux, "execute") - elif name.startswith("update_"): - aux = make_aux(seed_counter) - g_val = 1 - func(doubled_pop, problem_cont, param_value, aux, g_val, "execute") - else: - raise ValueError(f"Unhandled component category for {name}") - except Exception as exc: # pylint: disable=broad-except - errors.append((name, exc)) - - # Validate para components separately - try: - search_cma_func = get_component("search_cma") - if "search_cma" not in extra_states: - aux = make_aux(seed_counter) - offspring, aux_out = search_cma_func(pop_cont, problem_cont, None, aux, "execute") - extra_states["search_cma"] = (np.asarray(offspring), aux_out if isinstance(aux_out, dict) else {}) - decs, aux_cma = extra_states["search_cma"] - sol_set = solution_set_from_decs(decs, problem_cont) - get_component("para_cma")(sol_set, problem_cont, aux_cma, "solution") - except Exception as exc: # pylint: disable=broad-except - errors.append(("para_cma", exc)) - - try: - search_pso_func = get_component("search_pso") - if "search_pso" not in extra_states: - aux = make_aux(seed_counter) - offspring, aux_out = search_pso_func(pop_cont, problem_cont, None, aux, "execute") - extra_states["search_pso"] = (np.asarray(offspring), aux_out if isinstance(aux_out, dict) else {}) - decs, aux_pso = extra_states["search_pso"] - sol_set = solution_set_from_decs(decs, problem_cont) - get_component("para_pso")(sol_set, problem_cont, aux_pso) - except Exception as exc: # pylint: disable=broad-except - errors.append(("para_pso", exc)) - - if errors: - print("Component validation FAILED:") - for name, err in errors: - print(f" - {name}: {err}") - raise SystemExit(1) - - print(f"All {len(COMPONENT_NAMES)} components executed successfully.") - - -if __name__ == "__main__": - validate_components() diff --git a/src/test_run.py b/src/test_run.py deleted file mode 100644 index 2b67056..0000000 --- a/src/test_run.py +++ /dev/null @@ -1,79 +0,0 @@ -from __future__ import annotations - -from types import SimpleNamespace - -import numpy as np - -from autooptlib.utils.design import Design -from autooptlib.utils.design._helpers import Pathway, PathwayParam, SearchStep, SearchParam -from autooptlib.utils.general.process import process -from autooptlib.problems.cec2013 import cec2013_f1 - - -def demo_design_operator() -> None: - class DummyProblem: - def __init__(self) -> None: - self.bound = np.array([[-5.0, -5.0], [5.0, 5.0]]) - self.N = 6 - self.Gmax = 5 - self.type = ["continuous", "static", "certain"] - - def evaluate(self, data, dec): - obj = float(np.sum(dec**2)) - return obj, 0.0, None - - class DummySetting: - def __init__(self) -> None: - self.metric = "quality" - self.alg_runs = 1 - self.archive = ["archive_best"] - self.prob_n = 1 - self.tmax = np.inf - self.thres = -np.inf - - problem = DummyProblem() - setting = DummySetting() - - step = SearchStep( - primary="search_de_random", - secondary="search_mu_uniform", - termination=np.array([0.0, 3]), - ) - params = SearchParam(primary=np.array([0.5, 0.8]), secondary=np.array([0.1])) - pathway = Pathway( - choose="choose_traverse", - search=[step], - update="update_greedy", - archive=["archive_best"], - ) - path_params = PathwayParam(choose=None, search=[params], update=None) - - design = Design() - design.operator_pheno = [[pathway]] - design.parameter_pheno = [[path_params]] - design.performance = np.zeros((1, setting.alg_runs)) - design.performance_approx = np.zeros((1, setting.alg_runs)) - - design.evaluate(problem, None, setting, [0]) - print("Design demo performance:", design.performance) - - -def demo_cec2013_solve() -> None: - setting = SimpleNamespace( - Mode="solve", - AlgName="Continuous Random Search", - AlgRuns=1, - ProbN=30, - ProbFE=5000, - Metric="quality", - Tmax=None, - Thres=None, - ) - best_solutions, _ = process(cec2013_f1, [50], setting=setting) - best_fit = best_solutions[0][0].fit - print("CEC2013 f1 best fitness (one run):", best_fit) - - -if __name__ == "__main__": - demo_design_operator() - demo_cec2013_solve() diff --git a/tests/conftest.py b/tests/conftest.py index 6c63c0d..99f1af1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,3 +1,4 @@ import sys from pathlib import Path -sys.path.insert(0, str(Path(__file__).resolve().parents[1] / 'src')) + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) diff --git a/tests/system/test_workflow.py b/tests/system/test_workflow.py index aa296f4..cc24907 100644 --- a/tests/system/test_workflow.py +++ b/tests/system/test_workflow.py @@ -3,12 +3,15 @@ from __future__ import annotations import os +from contextlib import contextmanager from pathlib import Path +from types import SimpleNamespace import numpy as np import pytest -from autooptlib import autoopt +from autooptlib import autoopt, save_algorithm +from autooptlib.utils.solve import input_algorithm @pytest.fixture() @@ -22,6 +25,17 @@ def chdir_tmp(tmp_path): os.chdir(prev) +@contextmanager +def working_directory(path: Path): + previous = Path.cwd() + path.mkdir(parents=True, exist_ok=True) + os.chdir(path) + try: + yield + finally: + os.chdir(previous) + + def test_autoopt_design_creates_outputs(chdir_tmp): final_algs, alg_trace = autoopt( Mode="design", @@ -52,6 +66,7 @@ def test_autoopt_design_creates_outputs(chdir_tmp): "Algs_best_algs_iter.csv", "Algs_perf_final_algs.csv", "ConvergenceCurve.csv", + "experiment.json", ] for name in expected: assert Path(name).exists(), f"Missing expected design output {name}" @@ -80,6 +95,186 @@ def test_autoopt_solve_generates_solution_logs(chdir_tmp): "Solutions.pkl", "Solutions.csv", "Fitness_all_runs.csv", + "experiment.json", ] for name in expected: assert Path(name).exists(), f"Missing expected solve output {name}" + + +def test_seed_reproduces_end_to_end_solve(tmp_path): + kwargs = dict( + Mode="solve", + Problem="cec2013_f1", + InstanceSolve=[10], + AlgName="Continuous Random Search", + AlgRuns=2, + ProbN=12, + ProbFE=360, + Metric="quality", + Seed=8675309, + ) + + results = [] + for name in ("first", "second"): + with working_directory(tmp_path / name): + best, histories = autoopt(**kwargs) + results.append( + ( + np.array([solution.fit for solution in best[0]]), + np.array([solution.dec for solution in best[0]]), + np.array([solution.fit for solution in histories[0]]), + ) + ) + + for left, right in zip(results[0], results[1]): + np.testing.assert_array_equal(left, right) + + +@pytest.mark.parametrize( + ("evaluation_mode", "extra"), + [ + ("intensification", {}), + ("approximate", {"Surro": 1}), + ], +) +def test_less_common_design_modes_complete_with_exact_final_test( + tmp_path, evaluation_mode, extra +): + output_dir = tmp_path / evaluation_mode + algorithms, trace = autoopt( + Mode="design", + Problem="cec2013_f1", + InstanceTrain=[2], + InstanceTest=[2], + AlgN=2, + AlgFE=4, + AlgRuns=1, + ProbN=5, + ProbFE=10, + Evaluate=evaluation_mode, + Compare="average", + Seed=44, + OutputDir=output_dir, + **extra, + ) + assert len(algorithms) == 2 + assert trace + assert np.all(np.isfinite(algorithms[0].performance)) + assert (output_dir / "Algorithm_1.json").is_file() + + +def test_exported_json_algorithm_can_be_solved_without_pickle(tmp_path): + preset, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + algorithm_file = save_algorithm(preset, tmp_path / "portable.json") + best, history = autoopt( + Mode="solve", + Problem="cec2013_f1", + InstanceSolve=[2], + AlgFile=algorithm_file, + AlgRuns=1, + ProbN=5, + ProbFE=15, + Seed=7, + OutputDir=tmp_path / "solve", + ) + assert np.isfinite(best[0][0].fit) + assert history[0] + + +@pytest.mark.parametrize("problem", ["cec2013_f6", "cec2013_f13", "cec2013_f21"]) +def test_published_cec_algorithm_profiles_execute(tmp_path, problem): + algorithm_file = ( + Path(__file__).resolve().parents[2] + / "examples" + / "reproducibility" + / "algorithms" + / f"{problem}_alg_star.json" + ) + best, history = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[10], + AlgFile=algorithm_file, + AlgRuns=1, + ProbN=10, + ProbFE=100, + Seed=19, + OutputDir=tmp_path / problem, + ) + assert np.isfinite(best[0][0].fit) + assert history[0] + + +def test_sequential_problem_allocates_budget_per_stage(tmp_path): + calls = {"count": 0} + + def sequential_problem(records, instances, mode): + if mode != "construct": + raise ValueError(mode) + data_entries = [] + for record, dimension in zip(records, instances): + dimension = int(dimension) + record.type = ["continuous", "sequential", "certain"] + record.bound = np.vstack( + (np.full(dimension, -1.0), np.full(dimension, 1.0)) + ) + + def evaluate(data, decision): + calls["count"] += 1 + return float(np.sum(np.asarray(decision) ** 2) + data.stage), 0.0, None + + def advance(best, data, problem=record): + next_stage = data.stage + 1 + return problem, SimpleNamespace( + stage=next_stage, continue_=next_stage < 3 + ) + + record.evaluate = evaluate + record.advance_sequence = advance + data_entries.append(SimpleNamespace(stage=0, continue_=True)) + # The runtime uses the attribute name "continue"; assign it without + # using a reserved word in constructor syntax. + for data in data_entries: + setattr(data, "continue", data.continue_) + original_advances = [record.advance_sequence for record in records] + for record, original in zip(records, original_advances): + + def wrapped(best, data, original=original): + problem, next_data = original(best, data) + setattr(next_data, "continue", next_data.continue_) + return problem, next_data + + record.advance_sequence = wrapped + return list(records), data_entries, None + + common = dict( + Mode="solve", + Problem=sequential_problem, + InstanceSolve=[3], + AlgName="Continuous Random Search", + AlgRuns=1, + ProbN=5, + ProbFE=30, + Metric="runtimeFE", + Tmax=30, + Thres=1e99, + Seed=2, + CheckpointDir=tmp_path / "checkpoints", + ) + best, histories = autoopt(**common, OutputDir=tmp_path / "sequential") + assert len(histories[0]) == 3 + assert len(best[0]) == 1 + assert calls["count"] == 15 + + calls_after_first = calls["count"] + with pytest.warns(UserWarning, match="trusted run"): + resumed_best, resumed_histories = autoopt( + **common, + Resume=True, + OutputDir=tmp_path / "sequential-resumed", + ) + assert calls["count"] == calls_after_first + assert resumed_best[0][0].fit == best[0][0].fit + assert [solution.fit for solution in resumed_histories[0]] == [ + solution.fit for solution in histories[0] + ] diff --git a/tests/unit/test_applications.py b/tests/unit/test_applications.py new file mode 100644 index 0000000..9622851 --- /dev/null +++ b/tests/unit/test_applications.py @@ -0,0 +1,186 @@ +"""Tests for the public application reference models.""" + +from __future__ import annotations + +from dataclasses import replace +from pathlib import Path +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib import ( + autoopt, + generate_ris_instance, + generate_stacking_instance, + load_algorithm, + load_ris_matlab, + make_material_stacking_problem, + make_ris_beamforming_problem, +) +from autooptlib.applications import ( + StackingWeights, + reciprocal_sum_rate, + stacking_constraints, + stacking_objective, +) + + +def test_ris_generator_and_objective_are_deterministic(): + left = generate_ris_instance(6, seed=14, n_antennas=2, n_users=2) + right = generate_ris_instance(6, seed=14, n_antennas=2, n_users=2) + np.testing.assert_array_equal(left.bs_to_ris, right.bs_to_ris) + phases = np.arange(6) % (2**left.phase_bits) + assert reciprocal_sum_rate(phases, left) == pytest.approx( + reciprocal_sum_rate(phases, right) + ) + assert np.isfinite(reciprocal_sum_rate(phases, left)) + + +def test_ris_validation_and_factory_errors(): + with pytest.raises(ValueError, match="positive"): + generate_ris_instance(0, seed=1) + valid = generate_ris_instance(4, seed=2, n_antennas=2, n_users=2) + with pytest.raises(ValueError, match="Incompatible"): + replace(valid, direct_channels=np.ones((2, 3))).validate() + with pytest.raises(ValueError, match="phase_bits"): + replace(valid, phase_bits=0).validate() + with pytest.raises(ValueError, match="user_weights"): + replace(valid, user_weights=np.ones(3)).validate() + with pytest.raises(ValueError, match="Expected"): + reciprocal_sum_rate(np.zeros(3), valid) + with pytest.raises(ValueError, match="must be in"): + reciprocal_sum_rate(np.full(4, 99), valid) + with pytest.raises(ValueError, match="At least one"): + make_ris_beamforming_problem({}) + + problem = make_ris_beamforming_problem({"known": valid}) + with pytest.raises(KeyError, match="Unknown RIS"): + problem([SimpleNamespace()], ["missing"], "construct") + + +def test_ris_problem_runs_through_solve(tmp_path): + instance = generate_ris_instance(5, seed=3, n_antennas=2, n_users=2) + problem = make_ris_beamforming_problem({"small": instance}) + best, histories = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=["small"], + AlgName="Discrete Random Search", + ProbN=4, + ProbFE=8, + Seed=4, + OutputDir=tmp_path, + ) + assert np.isfinite(best[0][0].fit) + assert histories[0] + + +def test_stacking_model_objective_constraints_and_validation(): + instance = generate_stacking_instance( + 6, seed=7, n_levels=3, n_positions=4, rack_width=20 + ) + decision = np.concatenate((np.arange(6) % 3, np.arange(6) % 4, np.arange(6) % 2)) + assert np.isfinite(stacking_objective(decision, instance)) + assert stacking_constraints(decision, instance).shape == (6,) + with pytest.raises(ValueError, match="Expected"): + stacking_objective(decision[:-1], instance) + invalid = decision.copy() + invalid[0] = 99 + with pytest.raises(ValueError, match="outside"): + stacking_constraints(invalid, instance) + with pytest.raises(ValueError, match="positive"): + generate_stacking_instance(0, seed=1) + with pytest.raises(ValueError, match="non-negative"): + StackingWeights(heavy_low=-1).validate() + with pytest.raises(ValueError, match="At least one"): + make_material_stacking_problem({}) + + +def test_stacking_problem_runs_through_solve(tmp_path): + instance = generate_stacking_instance( + 5, + seed=8, + n_levels=3, + n_positions=4, + rack_width=20, + max_level_weight=100, + ) + problem = make_material_stacking_problem({"small": instance}) + records, data, _ = problem([SimpleNamespace(N=4, Gmax=2)], ["small"], "construct") + assert records[0].bound.shape == (2, 15) + assert data[0] is instance + + best, histories = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=["small"], + AlgName="Discrete Random Search", + ProbN=4, + ProbFE=8, + Seed=9, + OutputDir=tmp_path, + ) + assert np.isfinite(best[0][0].fit) + assert histories[0] + + +@pytest.mark.parametrize( + ("filename", "choose", "primary", "secondary", "parameter"), + [ + ( + "material_stacking_alg_star.json", + "choose_roulette_wheel", + "cross_point_one", + "search_reset_rand", + 0.1342, + ), + ( + "ris_beamforming_alg_star.json", + "choose_nich", + "cross_point_uniform", + "search_reset_one", + 0.1229, + ), + ], +) +def test_paper_application_algorithm_json_is_valid( + filename, choose, primary, secondary, parameter +): + path = ( + Path(__file__).resolve().parents[2] + / "examples" + / "applications" + / "algorithms" + / filename + ) + algorithm = load_algorithm(path) + pathway = algorithm.operator_pheno[0][0] + params = algorithm.parameter_pheno[0][0].search[0] + assert pathway.choose == choose + assert pathway.search[0].primary == primary + assert pathway.search[0].secondary == secondary + values = [params.primary, params.secondary] + assert any( + value is not None and value[0] == pytest.approx(parameter) for value in values + ) + + +def test_optional_matlab_ris_loader(tmp_path): + scipy_io = pytest.importorskip("scipy.io") + record = { + "G": np.ones((3, 2), dtype=complex), + "Hd": np.ones((2, 2), dtype=complex), + "Hr": np.ones((2, 3), dtype=complex), + "b": 2, + "PT": 1.5, + "omega": np.array([1.0, 2.0]), + } + source = tmp_path / "Beanforming.mat" + scipy_io.savemat(source, {"Data": np.asarray([record], dtype=object)}) + instances = load_ris_matlab(source) + assert list(instances) == [1] + assert instances[1].n_elements == 3 + np.testing.assert_array_equal(instances[1].user_weights, [1.0, 2.0]) + with pytest.raises(FileNotFoundError): + load_ris_matlab(tmp_path / "missing.mat") diff --git a/tests/unit/test_cli.py b/tests/unit/test_cli.py index c3b0ee0..5e77696 100644 --- a/tests/unit/test_cli.py +++ b/tests/unit/test_cli.py @@ -56,4 +56,3 @@ def test_autoopt_solve_smoke(tmp_workdir): assert len(best_solutions) == 1 assert Path("Solutions.pkl").exists() assert Path("Fitness_all_runs.csv").exists() - diff --git a/tests/unit/test_component_contracts.py b/tests/unit/test_component_contracts.py new file mode 100644 index 0000000..3739fa4 --- /dev/null +++ b/tests/unit/test_component_contracts.py @@ -0,0 +1,147 @@ +"""Contracts for component helpers, archives, and metadata modes.""" + +from __future__ import annotations + +from dataclasses import dataclass +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib.components import _utils +from autooptlib.components.archive_best import _extract_fits, archive_best +from autooptlib.components.archive_diversity import archive_diversity +from autooptlib.components.archive_statistic import archive_statistic +from autooptlib.components.archive_tabu import archive_tabu +from autooptlib.utils.space import ( + _compute_local_bounds, + _to_behavior_matrix, + _to_namespace, + space, +) + + +@dataclass +class Item: + dec: np.ndarray + obj: float = 0.0 + con: object = None + fit: object = None + + +@pytest.mark.parametrize( + "component", + [archive_best, archive_diversity, archive_statistic, archive_tabu], +) +def test_archive_metadata_and_unknown_modes(component): + assert component("parameter") == (None, None) + assert component("behavior") == (["", ""], None) + with pytest.raises(ValueError, match="Unsupported mode"): + component("unknown") + + +def test_tabu_archive_returns_latest_collection(): + solutions = [Item(np.array([0.0]))] + assert archive_tabu(solutions, "execute") == (solutions, None) + + +def test_best_archive_fitness_fallbacks_and_errors(): + class WithFits: + fits = np.array([2.0, 1.0]) + + np.testing.assert_array_equal(_extract_fits(WithFits()), [2.0, 1.0]) + items = [ + Item(np.array([0]), obj=3.0, con=None), + Item(np.array([1]), obj=1.0, con=[0.0]), + Item(np.array([2]), obj=0.0, con=[2.0]), + ] + np.testing.assert_array_equal(_extract_fits(items), [3.0, 1.0, 100000002.0]) + archive, _ = archive_best(items, [items[0]], "execute") + assert archive[-1] is items[1] + with pytest.raises(ValueError, match="expose fits"): + _extract_fits(1) + with pytest.raises(ValueError, match="lacks fit"): + _extract_fits([SimpleNamespace()]) + + +def test_diversity_archive_empty_and_discrete_distance(): + assert archive_diversity([], [], SimpleNamespace(), "execute") == ([], None) + solutions = [ + Item(np.array([1, 2])), + Item(np.array([1, 3])), + Item(np.array([3, 2])), + ] + problem = SimpleNamespace(type=["permutation"], N=2, rng=np.random.default_rng(4)) + archive, _ = archive_diversity(solutions, [], problem, "execute") + assert len(archive) == 2 + + +def test_statistic_archive_accepts_property_and_appends(): + solution = SimpleNamespace(fits=np.array([1.0, 3.0])) + first, _ = archive_statistic(solution, None, "execute") + second, _ = archive_statistic(solution, first, "execute") + assert first.shape == (1, 2) + assert second.shape == (2, 2) + + +def test_component_utility_contracts(): + assert _utils.flex_get(None, "x", 2) == 2 + assert _utils.flex_get({"some_value": 3}, "some_value") == 3 + assert _utils.flex_get(SimpleNamespace(value=lambda: 4), "value") == 4 + generator = np.random.default_rng(1) + assert _utils.ensure_rng(generator) is generator + assert _utils.ensure_rng({"rng": generator}) is generator + assert _utils.ensure_rng(SimpleNamespace(rng=generator)) is generator + assert isinstance(_utils.ensure_rng(None), np.random.Generator) + np.testing.assert_array_equal(_utils.to_numpy([1, 2]), [1, 2]) + + assert _utils.extract_fits([SimpleNamespace(fitness=1.0)])[0] == 1.0 + assert _utils.extract_fits([SimpleNamespace(fits=2.0)])[0] == 2.0 + with pytest.raises(ValueError, match="must expose"): + _utils.extract_fits([SimpleNamespace()]) + with pytest.raises(ValueError, match="scalar-like"): + _utils.extract_fits([SimpleNamespace(fit=[1, 2])]) + with pytest.raises(ValueError, match="fitness information"): + _utils.extract_fits(object()) + assert _utils.solution_as_list([1]) == [1] + assert _utils.solution_as_list((1, 2)) == [1, 2] + with pytest.raises(TypeError, match="indexable"): + _utils.solution_as_list(object()) + + distances = _utils.pairwise_distances(np.array([[0.0], [2.0]])) + np.testing.assert_array_equal(distances, [[0.0, 2.0], [2.0, 0.0]]) + assert sorted(_utils.randperm(3, generator).tolist()) == [0, 1, 2] + np.testing.assert_array_equal(_utils.reshape_pairs([1, 2]), [[1, 2]]) + with pytest.raises(ValueError, match="even length"): + _utils.reshape_pairs([1]) + assert _utils.as_int("2") == 2 + assert _utils.as_int("x", 3) == 3 + assert _utils.ensure_column([1, 2]).shape == (2, 1) + assert _utils.ensure_column([[1, 2]]).shape == (1, 2) + + +def test_space_conversion_and_local_bounds_helpers(): + assert _to_namespace({"x": 1}).x == 1 + + class Setting: + x = 2 + + assert _to_namespace(Setting()).x == 2 + assert _to_behavior_matrix(None) is None + assert _to_behavior_matrix(1) is None + assert _to_behavior_matrix((("LS", "small"), ("GS",))) == [ + ["LS", "small"], + ["GS"], + ] + converted = _to_behavior_matrix(np.array([["LS", "large"], ["GS", ""]])) + assert converted[0] == ["LS", "large"] + assert _to_behavior_matrix([None, "GS"]) == [[], ["GS"]] + assert _compute_local_bounds(None, None, 0.5) is None + assert _compute_local_bounds([], np.array([[0, 1]]), 0.5) is None + assert _compute_local_bounds([[None]], np.array([[0, 1]]), 0.5) is None + bounds = np.array([[0.0, 10.0], [0.0, 10.0], [0.0, 10.0]]) + local = _compute_local_bounds([["LS", "small", "large"], ["GS"]], bounds, 0.25) + np.testing.assert_array_equal(local, [[0, 2.5], [7.5, 10], [0, 10]]) + np.testing.assert_array_equal(_compute_local_bounds([["LS"]], bounds, 0.5), bounds) + with pytest.raises(NotImplementedError, match="continuous, discrete"): + space(SimpleNamespace(type=["graph"]), SimpleNamespace()) diff --git a/tests/unit/test_components.py b/tests/unit/test_components.py index a9b2bb9..11990b5 100644 --- a/tests/unit/test_components.py +++ b/tests/unit/test_components.py @@ -4,12 +4,19 @@ from dataclasses import dataclass from types import SimpleNamespace +from typing import Any import numpy as np from autooptlib.components.archive_best import archive_best from autooptlib.components.archive_diversity import archive_diversity from autooptlib.components.archive_statistic import archive_statistic +from autooptlib.components.choose_brainstorm import choose_brainstorm +from autooptlib.components.choose_ica import choose_ica +from autooptlib.components.choose_nich import choose_nich +from autooptlib.components.choose_roulette_wheel import choose_roulette_wheel +from autooptlib.components.choose_tournament import choose_tournament +from autooptlib.components.choose_traverse import choose_traverse from autooptlib.components.cross_arithmetic import cross_arithmetic from autooptlib.components.cross_order_n import cross_order_n from autooptlib.components.cross_order_two import cross_order_two @@ -18,12 +25,6 @@ from autooptlib.components.cross_point_two import cross_point_two from autooptlib.components.cross_point_uniform import cross_point_uniform from autooptlib.components.cross_sim_binary import cross_sim_binary -from autooptlib.components.choose_brainstorm import choose_brainstorm -from autooptlib.components.choose_ica import choose_ica -from autooptlib.components.choose_nich import choose_nich -from autooptlib.components.choose_roulette_wheel import choose_roulette_wheel -from autooptlib.components.choose_tournament import choose_tournament -from autooptlib.components.choose_traverse import choose_traverse from autooptlib.components.para_cma import para_cma from autooptlib.components.para_cmaes import para_cmaes from autooptlib.components.para_pso import para_pso @@ -35,14 +36,13 @@ from autooptlib.components.search_de_random import search_de_random from autooptlib.components.search_eda import search_eda from autooptlib.components.search_ica import search_ica -from autooptlib.components.search_mu_gaussian import search_mu_gaussian +from autooptlib.components.search_insert import search_insert from autooptlib.components.search_mu_polynomial import search_mu_polynomial from autooptlib.components.search_mu_uniform import search_mu_uniform from autooptlib.components.search_pso import search_pso from autooptlib.components.search_reset_creep import search_reset_creep from autooptlib.components.search_reset_rand import search_reset_rand from autooptlib.components.search_scramble import search_scramble -from autooptlib.components.search_insert import search_insert from autooptlib.components.search_swap import search_swap from autooptlib.components.search_swap_multi import search_swap_multi from autooptlib.components.update_greedy import update_greedy @@ -88,7 +88,9 @@ def fits(self): def test_choose_traverse_indexes_all(): problem = SimpleNamespace(N=5) - idx, meta = choose_traverse(DummyPopulation(np.zeros((5, 1)), np.arange(5)), problem, None, None, "execute") + idx, meta = choose_traverse( + DummyPopulation(np.zeros((5, 1)), np.arange(5)), problem, None, None, "execute" + ) assert np.all(idx == np.arange(5)) assert meta is None @@ -96,14 +98,22 @@ def test_choose_traverse_indexes_all(): def test_choose_tournament_returns_indices(): problem = SimpleNamespace(N=3) rng_aux = {"rng": np.random.default_rng(0)} - idx, _ = choose_tournament(DummyPopulation(np.zeros((4, 1)), [4, 1, 3, 2]), problem, None, rng_aux, "execute") + idx, _ = choose_tournament( + DummyPopulation(np.zeros((4, 1)), [4, 1, 3, 2]), + problem, + None, + rng_aux, + "execute", + ) assert idx.shape == (problem.N,) def test_choose_roulette_wheel_biases_low_fitness(): problem = SimpleNamespace(N=2) rng_aux = {"rng": np.random.default_rng(1)} - idx, _ = choose_roulette_wheel(DummyPopulation(np.zeros((3, 1)), [3, 1, 5]), problem, None, rng_aux, "execute") + idx, _ = choose_roulette_wheel( + DummyPopulation(np.zeros((3, 1)), [3, 1, 5]), problem, None, rng_aux, "execute" + ) assert idx.shape == (problem.N,) assert np.all(idx < 3) @@ -116,10 +126,14 @@ def test_choose_ica_records_imperialist_count(): def test_choose_brainstorm_runs_kmeans(): - solution = DummyPopulation(np.array([[0.0, 0.0], [5.0, 5.0], [4.0, 4.5], [-1.0, -1.2]]), [1, 2, 3, 4]) + solution = DummyPopulation( + np.array([[0.0, 0.0], [5.0, 5.0], [4.0, 4.5], [-1.0, -1.2]]), [1, 2, 3, 4] + ) problem = SimpleNamespace(type=["continuous", "static"]) rng_aux = {"rng": np.random.default_rng(2)} - idx, _ = choose_brainstorm(solution, problem, np.array([2, 0.5]), rng_aux, "execute") + idx, _ = choose_brainstorm( + solution, problem, np.array([2, 0.5]), rng_aux, "execute" + ) assert idx.shape[0] == len(solution.fits()) @@ -169,13 +183,17 @@ def test_cross_order_variants_return_permutations(): def test_search_de_current_shape_matches(): parent = np.array([[0.0, 0.0], [1.0, 1.0], [-1.0, -1.0]]) - offspring, _ = search_de_current(parent, None, np.array([[0.5, 0.8], [0.5, 0.8]]), None, "execute") + offspring, _ = search_de_current( + parent, None, np.array([[0.5, 0.8], [0.5, 0.8]]), None, "execute" + ) assert offspring.shape == parent.shape def test_search_de_random_returns_population(): parent = np.array([[0.0, 0.0], [1.0, -1.0], [0.5, 0.5]]) - offspring, _ = search_de_random(parent, None, np.array([[0.6, 0.7]]), None, "execute") + offspring, _ = search_de_random( + parent, None, np.array([[0.6, 0.7]]), None, "execute" + ) assert offspring.shape == parent.shape @@ -189,7 +207,9 @@ def test_search_mu_uniform_within_bounds(): def test_search_reset_rand_respects_integer_bounds(): parent = np.array([[0, 0], [1, 1]]) problem = SimpleNamespace(bound=np.array([[0, 0], [5, 5]])) - offspring, _ = search_reset_rand(parent, problem, np.array([0.5]), {"rng": np.random.default_rng(0)}, "execute") + offspring, _ = search_reset_rand( + parent, problem, np.array([0.5]), {"rng": np.random.default_rng(0)}, "execute" + ) assert offspring.shape == parent.shape assert np.issubdtype(offspring.dtype, np.integer) @@ -204,7 +224,9 @@ def test_search_ica_respects_auxiliary_imperialist_count(): parent = np.array([[0.0, 0.0], [5.0, 5.0], [10.0, 10.0]]) problem = SimpleNamespace(bound=np.array([[0.0, 0.0], [10.0, 10.0]])) aux = {"imperialist_count": 2} - offspring, aux_out = search_ica(parent, problem, np.array([0.7, 0.1]), aux, "execute") + offspring, aux_out = search_ica( + parent, problem, np.array([0.7, 0.1]), aux, "execute" + ) assert offspring.shape == parent.shape assert aux_out["imperialist_count"] == 2 @@ -212,7 +234,9 @@ def test_search_ica_respects_auxiliary_imperialist_count(): def test_search_mu_polynomial_respects_bounds(): parent = np.array([[0.1, 0.9], [0.4, 0.6]]) problem = SimpleNamespace(bound=np.array([[0.0, 0.0], [1.0, 1.0]])) - offspring, _ = search_mu_polynomial(parent, problem, np.array([0.3, 20.0]), None, "execute") + offspring, _ = search_mu_polynomial( + parent, problem, np.array([0.3, 20.0]), None, "execute" + ) assert offspring.shape == parent.shape assert np.all(offspring >= 0.0) and np.all(offspring <= 1.0) @@ -220,7 +244,9 @@ def test_search_mu_polynomial_respects_bounds(): def test_search_reset_creep_returns_integers(): parent = np.array([[1, 2, 3], [3, 2, 1]]) problem = SimpleNamespace(bound=np.array([[0, 0, 0], [5, 5, 5]])) - offspring, _ = search_reset_creep(parent, problem, np.array([0.5]), {"rng": np.random.default_rng(9)}, "execute") + offspring, _ = search_reset_creep( + parent, problem, np.array([0.5]), {"rng": np.random.default_rng(9)}, "execute" + ) assert offspring.shape == parent.shape assert np.issubdtype(offspring.dtype, np.integer) @@ -228,7 +254,9 @@ def test_search_reset_creep_returns_integers(): def test_search_swap_variants_return_permutations(): parent = np.array([[1, 2, 3, 4], [4, 3, 2, 1]]) for fn in (search_swap, search_swap_multi, search_scramble, search_insert): - offspring, _ = fn(parent, None, None, {"rng": np.random.default_rng(10)}, "execute") + offspring, _ = fn( + parent, None, None, {"rng": np.random.default_rng(10)}, "execute" + ) assert offspring.shape == parent.shape for row in offspring: assert sorted(row.tolist()) == [1, 2, 3, 4] @@ -273,7 +301,9 @@ def __init__(self, dec): def test_reinit_continuous_returns_within_bounds(): solution = [DummySolution([0.0, 0.0], 0.0)] problem = SimpleNamespace(bound=np.array([[0.0, 0.0], [1.0, 1.0]])) - offspring, _ = reinit_continuous(solution, problem, None, {"rng": np.random.default_rng(4)}, "execute") + offspring, _ = reinit_continuous( + solution, problem, None, {"rng": np.random.default_rng(4)}, "execute" + ) assert offspring.shape == (len(solution), 2) assert np.all(offspring >= 0.0) and np.all(offspring <= 1.0) @@ -281,7 +311,9 @@ def test_reinit_continuous_returns_within_bounds(): def test_reinit_discrete_generates_integers(): solution = np.array([[0, 0], [1, 1]], dtype=int) problem = SimpleNamespace(bound=np.array([[0, 0], [3, 3]])) - offspring, _ = reinit_discrete(solution, problem, None, {'rng': np.random.default_rng(5)}, 'execute') + offspring, _ = reinit_discrete( + solution, problem, None, {"rng": np.random.default_rng(5)}, "execute" + ) assert offspring.shape == solution.shape assert np.issubdtype(offspring.dtype, np.integer) @@ -289,7 +321,9 @@ def test_reinit_discrete_generates_integers(): def test_reinit_permutation_generates_permutations(): solution = np.array([[1, 2, 3], [3, 2, 1]], dtype=int) problem = SimpleNamespace(bound=np.array([[1, 2, 3], [1, 2, 3]])) - offspring, _ = reinit_permutation(solution, problem, None, {'rng': np.random.default_rng(6)}, 'execute') + offspring, _ = reinit_permutation( + solution, problem, None, {"rng": np.random.default_rng(6)}, "execute" + ) assert offspring.shape == solution.shape for row in offspring: assert sorted(row.tolist()) == [1, 2, 3] @@ -319,7 +353,9 @@ def test_update_pairwise_compares_old_new_pairs(): def test_update_round_robin_keeps_high_scores(): sols = [DummySolution([0], fit) for fit in [1, 2, 3, 4]] problem = SimpleNamespace(N=2) - updated, _ = update_round_robin(sols, problem, None, {"rng": np.random.default_rng(7)}, "execute") + updated, _ = update_round_robin( + sols, problem, None, {"rng": np.random.default_rng(7)}, "execute" + ) assert len(updated) == 2 @@ -328,7 +364,9 @@ def test_update_simulated_annealing_uses_acceptance(): new = [DummySolution([0], fit) for fit in [4, 8, 6.5]] combined = old + new problem = SimpleNamespace(N=3) - updated, _ = update_simulated_annealing(combined, problem, np.array([0.5]), {}, 1, 'execute') + updated, _ = update_simulated_annealing( + combined, problem, np.array([0.5]), {}, 1, "execute" + ) assert len(updated) == problem.N @@ -340,7 +378,9 @@ def test_archive_best_appends_minimum(): def test_archive_diversity_returns_spread(): sols = DummyPopulation(np.array([[0, 0], [1, 1], [2, 2]]), [1, 2, 3]) - archive, _ = archive_diversity(sols, [], SimpleNamespace(type=["continuous"]), "execute") + archive, _ = archive_diversity( + sols, [], SimpleNamespace(type=["continuous"]), "execute" + ) assert len(archive) > 0 @@ -408,16 +448,7 @@ def __init__(self, dec, fit): sol = SolutionSet([Particle([0.0, 0.0], 3.0), Particle([1.0, 1.0], 1.0)]) problem = SimpleNamespace() - aux = {'Pbest': SolutionSet([Particle([2.0, 2.0], 2.0), Particle([2.5, 2.5], 1.5)])} + aux = {"Pbest": SolutionSet([Particle([2.0, 2.0], 2.0), Particle([2.5, 2.5], 1.5)])} new_aux = para_pso(sol, problem, aux) - assert 'Pbest' in new_aux and len(new_aux['Pbest']) == len(sol) - assert 'Gbest' in new_aux - - - - - - - - - + assert "Pbest" in new_aux and len(new_aux["Pbest"]) == len(sol) + assert "Gbest" in new_aux diff --git a/tests/unit/test_evaluation_modes.py b/tests/unit/test_evaluation_modes.py new file mode 100644 index 0000000..c953299 --- /dev/null +++ b/tests/unit/test_evaluation_modes.py @@ -0,0 +1,183 @@ +"""Focused tests for exact, approximate, and sequential design evaluation.""" + +from __future__ import annotations + +import importlib +import sys +from types import ModuleType, SimpleNamespace + +import numpy as np +import pytest + +from autooptlib.utils.design import Design +from autooptlib.utils.solve import Solution + +evaluation_module = importlib.import_module("autooptlib.utils.design._evaluate") + + +def _design(): + design = Design() + design.construct([[object()]], [[object()]]) + return design + + +def _problem(behavior="static"): + return SimpleNamespace( + type=["continuous", behavior, "certain"], + N=2, + bound=np.array([[-1.0], [1.0]]), + ) + + +def _setting(metric="quality", evaluate="exact"): + return SimpleNamespace( + Metric=metric, + Evaluate=evaluate, + AlgRuns=1, + ProbN=2, + ProbFE=6, + Tmax=[2, 4, 6] if metric == "auc" else None, + Thres=[5, 3, 1] if metric == "auc" else None, + ) + + +def test_matrix_resize_preserves_existing_values(): + matrix = np.array([[3.0]]) + assert evaluation_module._ensure_matrix(matrix, 1, 1) is matrix + resized = evaluation_module._ensure_matrix(matrix, 2, 2) + assert resized.shape == (2, 2) + assert resized[0, 0] == 3.0 + assert evaluation_module._ensure_matrix(resized, 1, 1) is resized + + +def test_design_performance_aggregates_one_value_per_algorithm(): + design = Design() + design.performance = np.array([[1.0, 3.0], [5.0, 7.0]]) + design.performance_approx = np.array([[2.0, 4.0], [6.0, 8.0]]) + np.testing.assert_array_equal(design.ave_perform_all(), [4.0]) + np.testing.assert_array_equal(design.ave_perform_approx_all(), [5.0]) + np.testing.assert_array_equal(design.ave_perform_per([1]), [6.0]) + np.testing.assert_array_equal(design.ave_perform_approx_per([0]), [3.0]) + + +def test_sequential_update_supports_method_module_and_fallback(monkeypatch): + marker = object() + direct = SimpleNamespace(advance_sequence=lambda best, data: (marker, data + 1)) + assert evaluation_module._update_sequential(direct, 1, object()) == (marker, 2) + + module = ModuleType("autooptlib_test_sequence") + module.advance = lambda problem, data, best, mode: (marker, mode) + monkeypatch.setitem(sys.modules, module.__name__, module) + plugin = SimpleNamespace(name="autooptlib_test_sequence.advance") + assert evaluation_module._update_sequential(plugin, None, None) == ( + marker, + "sequence", + ) + broken = SimpleNamespace(name="missing.module") + assert evaluation_module._update_sequential(broken, 1, None) == (broken, 1) + plain = SimpleNamespace(name=None) + assert evaluation_module._update_sequential(plain, 2, None) == (plain, 2) + + +@pytest.mark.parametrize( + ("metric", "expected"), + [ + ("quality", 2.0), + ("auc", 1.5), + ("runtimeFE", 6.0), + ("runtimeSec", 0.25), + ("custom", 2.0), + ], +) +def test_static_evaluation_metrics(monkeypatch, metric, expected): + monkeypatch.setattr( + evaluation_module, + "run_design", + lambda *args: { + "fit_history": [5.0, 3.0, 2.0], + "evaluations": 6, + "elapsed": 0.25, + }, + ) + design = _design() + design.evaluate([_problem()], [None], _setting(metric), [0]) + assert design.performance[0, 0] == pytest.approx(expected) + + +def test_static_approximate_and_empty_auc(monkeypatch): + monkeypatch.setattr( + evaluation_module, + "run_design", + lambda *args: {"fit_history": [], "evaluations": 0, "elapsed": 0.0}, + ) + design = _design() + design.evaluate(_problem(), None, _setting("auc", "approximate"), [1]) + assert design.performance_approx.shape == (2, 1) + assert np.isinf(design.performance_approx[1, 0]) + + +def _sequential_problem(next_stage=True): + problem = _problem("sequential") + + def advance(best, data): + next_data = SimpleNamespace(stage=data.stage + 1) + setattr(next_data, "continue", next_stage and next_data.stage < 1) + return problem, next_data + + problem.advance_sequence = advance + return problem + + +@pytest.mark.parametrize( + ("metric", "expected"), + [ + ("quality", 2.0), + ("auc", 1.5), + ("runtimeFE", 2.0), + ("runtimeSec", 0.5), + ("custom", 2.0), + ], +) +def test_sequential_evaluation_metrics(monkeypatch, metric, expected): + best = Solution(np.array([0.0]), 2.0, 0.0, 2.0) + monkeypatch.setattr( + evaluation_module, + "run_design", + lambda *args: { + "fit_history": [5.0, 3.0, 2.0], + "evaluations": 2, + "elapsed": 0.5, + "best_solution": best, + }, + ) + data = SimpleNamespace(stage=0) + setattr(data, "continue", True) + design = _design() + design.evaluate([_sequential_problem()], [data], _setting(metric), [0]) + assert design.performance[0, 0] == pytest.approx(expected) + + +def test_sequential_empty_history_and_missing_best_stop(monkeypatch): + monkeypatch.setattr( + evaluation_module, + "run_design", + lambda *args: { + "fit_history": [], + "evaluations": 2, + "elapsed": 0.0, + "best_solution": None, + }, + ) + data = SimpleNamespace() + setattr(data, "continue", True) + design = _design() + design.evaluate([_sequential_problem()], [data], _setting("auc"), [0]) + assert np.isinf(design.performance[0, 0]) + + +def test_evaluation_no_phenotype_and_unknown_behavior(): + empty = Design() + assert empty.evaluate([], [], _setting(), []) is empty + design = _design() + with pytest.raises(NotImplementedError, match="Unsupported problem type behavior"): + design.evaluate([_problem("dynamic")], [None], _setting(), [0]) diff --git a/tests/unit/test_helper_contracts.py b/tests/unit/test_helper_contracts.py new file mode 100644 index 0000000..4f33784 --- /dev/null +++ b/tests/unit/test_helper_contracts.py @@ -0,0 +1,117 @@ +"""Direct contracts for shared design helpers.""" + +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib.utils.design._helpers import ( + as_list, + ceil_divide, + copy_if_array, + create_zero_matrix, + deep_copy_operator_matrix, + ensure_aux_list, + ensure_para_slot, + ensure_rng, + get_behavior, + get_flex, + get_problem_type, + has_global_behavior, + has_local_behavior, + inclusive_range, + problem_list, + reinit_parameters, + sample_without, + set_behavior, + to_numpy, +) + + +def test_flexible_lookup_handles_dicts_acronyms_defaults_and_required(): + assert get_flex({"value": 3}, "value") == 3 + assert get_flex(SimpleNamespace(ProbFE=9), "prob_fe") == 9 + assert get_flex(None, "x", 4) == 4 + with pytest.raises(AttributeError, match="None"): + get_flex(None, "x", required=True) + with pytest.raises(AttributeError, match="not found"): + get_flex(SimpleNamespace(), "x", required=True) + + class Slotted: + __slots__ = () + + assert get_flex(Slotted(), "x", 5) == 5 + + +def test_rng_and_array_helpers_cover_supported_inputs(): + generator = np.random.default_rng(1) + assert ensure_rng(SimpleNamespace(rng=generator)) is generator + assert isinstance(ensure_rng(SimpleNamespace(rng=2)), np.random.Generator) + assert ensure_rng(SimpleNamespace(random_state=generator)) is generator + assert isinstance(ensure_rng(SimpleNamespace(random_state=2)), np.random.Generator) + assert isinstance(ensure_rng(SimpleNamespace()), np.random.Generator) + assert to_numpy(None) is None + assert to_numpy([1, 2], dtype=float).dtype == float + + +def test_parameter_and_collection_helpers_cover_edge_cases(): + with pytest.raises(ValueError, match="cannot be None"): + ensure_para_slot(None, 1) + params = [] + assert ensure_para_slot(params, 2) == [None, None] + params = [None] + assert ensure_para_slot(params, 1) == [None, None] + params = [(1, 2)] + assert ensure_para_slot(params, 1) == [1, 2] + + assert problem_list(None) == [] + assert problem_list((1, 2)) == [1, 2] + assert problem_list("x") == ["x"] + assert get_problem_type(None) is None + assert get_problem_type(SimpleNamespace(type="discrete")) == "discrete" + assert get_problem_type(SimpleNamespace()) is None + assert as_list(None) == [] + original = [1] + assert as_list(original) is original + assert as_list((1, 2)) == [1, 2] + assert as_list(1) == [1] + + +def test_behavior_sampling_and_matrix_helpers(): + assert has_global_behavior([[None], ["GS"]]) + assert not has_global_behavior(None) + assert has_local_behavior([["LS"]]) + assert not has_local_behavior([]) + rng = np.random.default_rng(2) + assert sample_without([1, 2], 1, rng) == 2 + with pytest.raises(ValueError, match="No alternative"): + sample_without([1], 1, rng) + assert inclusive_range(3, 2) == [] + assert inclusive_range(2, 3) == [2, 3] + + matrix = np.array([[1, 2]]) + copied = deep_copy_operator_matrix(matrix) + assert np.array_equal(copied, matrix) and copied is not matrix + assert create_zero_matrix(2).shape == (2, 2) + entry = [] + set_behavior(entry, "GS") + assert get_behavior(entry) == "GS" + assert get_behavior([]) is None + + +def test_numeric_and_copy_helpers(): + rng = np.random.default_rng(3) + values = reinit_parameters(np.array([[0.0, 1.0], [2.0, 3.0]]), rng) + assert np.all(values >= [0, 2]) and np.all(values <= [1, 3]) + assert ceil_divide(5, 0) == 0 + assert ceil_divide(5, 2) == 3 + array = np.array([1]) + assert copy_if_array(array) is not array + assert copy_if_array((1, 2)) == [1, 2] + marker = object() + assert copy_if_array(marker) is marker + assert ensure_aux_list(None, 2) == [None, None] + assert ensure_aux_list([1], 2) == [1, None] + assert ensure_aux_list([1, 2], 1) == [1, 2] diff --git a/tests/unit/test_input_validation.py b/tests/unit/test_input_validation.py new file mode 100644 index 0000000..4f31d4b --- /dev/null +++ b/tests/unit/test_input_validation.py @@ -0,0 +1,214 @@ +"""Tests for public option parsing and validation failures.""" + +from __future__ import annotations + +from types import SimpleNamespace + +import pytest + +from autooptlib.utils.general.input import ( + _check_setting, + _ensure_namespace, + _find_argument, + _to_list, + _to_sequence, + input_handler, + normalize_options, +) + + +def _design_setting(**overrides): + values = dict( + Mode="design", + AlgP=1, + AlgQ=3, + ProbN=5, + ProbFE=20, + InnerFE=1, + AlgN=2, + AlgFE=4, + AlgRuns=2, + Metric="quality", + Compare="average", + Evaluate="exact", + ) + values.update(overrides) + return SimpleNamespace(**values) + + +def _solve_setting(**overrides): + values = dict( + Mode="solve", + ProbN=5, + ProbFE=20, + AlgRuns=1, + AlgName="Continuous Random Search", + Metric="quality", + ) + values.update(overrides) + return SimpleNamespace(**values) + + +def test_option_helpers_cover_aliases_and_container_forms(): + with pytest.raises(TypeError, match="supplied more than once"): + normalize_options({"ProbFE": 10, "prob_fe": 20}) + with pytest.raises(TypeError, match="Unknown AutoOpt option"): + normalize_options({1: 2}) + assert _to_sequence("x") == ("x",) + assert _to_sequence([1, 2]) == [1, 2] + assert _to_list((1, 2)) == [1, 2] + assert _to_list(1) == [1] + assert _ensure_namespace({"x": 1}).x == 1 + + class Setting: + x = 2 + + assert _ensure_namespace(Setting()).x == 2 + assert _find_argument([], "x") == (False, None) + assert _find_argument(["y", 1], "x") == (False, None) + with pytest.raises(ValueError, match="Missing value"): + _find_argument(["x"], "x") + + +def test_input_handler_data_and_defaults(): + with pytest.raises(ValueError, match="targeted problem"): + input_handler([], SimpleNamespace(Mode="solve"), "data") + with pytest.raises(ValueError, match="instance indexes"): + input_handler(["Problem", "p"], SimpleNamespace(Mode="design"), "data") + with pytest.raises(ValueError, match="instance indexes"): + input_handler(["Problem", "p"], SimpleNamespace(Mode="solve"), "data") + with pytest.raises(ValueError, match="mode"): + input_handler(["Problem", "p"], SimpleNamespace(Mode="other"), "data") + + assert input_handler( + ["Problem", "p", "InstanceSolve", (1, 2)], + SimpleNamespace(Mode="solve"), + "data", + ) == ("p", [1, 2]) + setting = input_handler(["ProbN", 7], SimpleNamespace(Mode="solve"), "parameter") + assert setting.ProbN == 7 + assert setting.ProbFE == 50000 + assert setting.ProbN == 7 + assert setting.AlgRuns == 5 + + design_defaults = input_handler( + ["InstanceTrain", [1, 2, 3, 4, 5]], + SimpleNamespace(Mode="design"), + "parameter", + ) + assert design_defaults.AlgQ == 4 + assert design_defaults.InnerFE == 500 + assert design_defaults.AlgN == 10 + assert design_defaults.AlgFE == 5000 + assert design_defaults.AlgRuns == 5 + assert design_defaults.RacingK == 1 + assert design_defaults.Surro == 1500 + with pytest.raises(ValueError, match="Unsupported mode"): + input_handler([], setting, "other") + + +@pytest.mark.parametrize( + ("overrides", "message"), + [ + ({"ProbN": 0}, "positive integer"), + ({"AlgP": 4, "AlgQ": 3}, "pathways"), + ({"AlgN": 5, "AlgFE": 4}, "algorithms"), + ({"AlgRuns": 21}, "runs should not exceed"), + ({"ProbN": 21}, "ProbFE must be at least"), + ({"Evaluate": "unknown"}, "Evaluate must be"), + ({"Compare": "unknown"}, "Compare must be"), + ({"Metric": "unknown"}, "Metric must be"), + ({"Evaluate": "racing"}, "should be used with"), + ( + {"Evaluate": "racing", "Compare": "statistic", "RacingK": None}, + "Setting.K", + ), + ({"Evaluate": "approximate"}, "Setting.Surro"), + ({"Evaluate": "approximate", "Surro": 5}, "Surro must be"), + ( + {"Evaluate": "approximate", "Surro": 2, "Compare": "statistic"}, + "not necessary", + ), + ({"Compare": "statistic", "AlgRuns": 1}, "run the design multiple"), + ], +) +def test_design_validation_rejects_invalid_combinations(overrides, message): + with pytest.raises(ValueError, match=message): + _check_setting(_design_setting(**overrides)) + + +def test_design_validation_normalizes_values_and_warns_on_weak_settings(): + setting = _design_setting(Evaluate="EXACT", Compare="AVERAGE") + _check_setting(setting) + assert setting.Evaluate == "exact" + assert setting.Compare == "average" + with pytest.warns(UserWarning, match="large population"): + _check_setting(_design_setting(ProbN=4, AlgP=2, AlgQ=1)) + with pytest.warns(UserWarning, match="recommended"): + _check_setting(_design_setting(AlgQ=5)) + + +@pytest.mark.parametrize( + ("overrides", "message"), + [ + ({"ProbFE": 0}, "positive integer"), + ({"ProbN": 21}, "ProbFE must be at least"), + ({"AlgName": None}, "specify an algorithm"), + ({"Metric": "unknown"}, "Metric must be"), + ({"Metric": "runtimeFE", "Thres": None}, "Setting.Thres"), + ({"Metric": "runtimeSec", "Tmax": None, "Thres": 0}, "Setting.Tmax"), + ({"Metric": "runtimeSec", "Tmax": 1, "Thres": None}, "Setting.Thres"), + ({"Metric": "auc", "Tmax": [1], "Thres": [0]}, "multiple time points"), + ( + {"Metric": "auc", "Tmax": [1, 2], "Thres": [0]}, + "number of thresholds", + ), + ], +) +def test_solve_validation_rejects_invalid_combinations(overrides, message): + with pytest.raises(ValueError, match=message): + _check_setting(_solve_setting(**overrides)) + + +def test_solve_runtimefe_defaults_tmax_and_accepts_valid_auc(): + runtime = _solve_setting(Metric="runtimeFE", Tmax=None, Thres=0) + _check_setting(runtime) + assert runtime.Tmax == runtime.ProbFE + _check_setting(_solve_setting(Metric="auc", Tmax=[5, 10], Thres=[2, 1])) + + +def test_check_handler_and_invalid_mode(): + setting = _solve_setting() + assert input_handler([], setting, "check") is setting + with pytest.raises(ValueError, match="mode"): + _check_setting(SimpleNamespace(Mode="other")) + + +@pytest.mark.parametrize( + ("overrides", "message"), + [ + ({"EvalRetries": -1}, "non-negative"), + ({"EvalTimeoutSec": 0}, "positive finite"), + ({"EvalFailure": "ignore"}, "raise.*penalize"), + ({"EvalPenalty": float("inf")}, "finite numeric"), + ({"EvalCache": 1}, "boolean"), + ({"EvalLog": 4}, "filesystem path"), + ({"EvalWorkers": 0}, "positive integer"), + ( + {"EvalWorkers": 2, "EvalTimeoutSec": 1}, + "cannot currently be combined", + ), + ({"CheckpointDir": 4}, "filesystem path"), + ({"CheckpointEvery": 0}, "positive integer"), + ({"Resume": 1}, "boolean"), + ], +) +def test_evaluation_policy_validation(overrides, message): + with pytest.raises(ValueError, match=message): + _check_setting(_solve_setting(**overrides)) + + +def test_checkpointing_is_accepted_in_design_mode(): + setting = _design_setting(CheckpointDir="checkpoints") + _check_setting(setting) + assert setting.CheckpointDir == "checkpoints" diff --git a/tests/unit/test_matlab_parity.py b/tests/unit/test_matlab_parity.py new file mode 100644 index 0000000..c87f76a --- /dev/null +++ b/tests/unit/test_matlab_parity.py @@ -0,0 +1,181 @@ +"""Golden contracts for intentional MATLAB/Python semantic parity.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib import ObjectiveEvaluationError +from autooptlib.utils.design._disturb import disturb +from autooptlib.utils.design._evaluate import _auc_score +from autooptlib.utils.design._helpers import ( + Pathway, + PathwayParam, + SearchParam, + SearchStep, +) +from autooptlib.utils.select import select +from autooptlib.utils.solve import make_solutions, run_design + + +def _encoded_algorithm(): + return SimpleNamespace( + operator=[ + np.array([[1, 3], [3, 5]]), + np.array([[1, 4], [4, 5]]), + ], + parameter=[[None, None] for _ in range(6)], + ) + + +def _disturb_setting(): + return SimpleNamespace( + op_space=np.array([[1, 2], [3, 4], [5, 6]]), + para_space=[None] * 6, + para_local_space=[None] * 6, + alg_p=2, + alg_q=1, + alg_n=2, + tune_para=False, + rng=np.random.default_rng(8), + ) + + +def test_choose_and_update_disturbance_are_shared_across_pathways(): + choose_ops, _, _ = disturb( + [_encoded_algorithm()], _disturb_setting(), 2, [{"seed": 1}] + ) + assert [path[0, 0] for path in choose_ops[0]] == [2, 2] + + # The encoded positions are choose, two searches, update. + update_ops, _, _ = disturb( + [_encoded_algorithm()], _disturb_setting(), 2, [{"seed": 4}] + ) + assert [path[-1, -1] for path in update_ops[0]] == [6, 6] + + +class _Performance: + def __init__(self, values): + self.values = np.asarray(values, dtype=float) + self.performance = np.ones((1, len(self.values))) + + def get_performance(self, setting, seeds): + return self.values + + +def test_racing_keeps_all_significant_survivors(monkeypatch): + select_module = importlib.import_module("autooptlib.utils.select") + monkeypatch.setattr( + select_module, "_statistic_wins", lambda matrix, alpha: np.array([2, 1, 0]) + ) + algorithms = [_Performance([1, 1]), _Performance([2, 2]), _Performance([3, 3])] + setting = SimpleNamespace( + Compare="statistic", + Evaluate="racing", + AlgN=1, + AlgRuns=2, + Seed=4, + ) + assert select(algorithms, None, None, setting, [0]) == algorithms[:2] + + +def test_auc_uses_fe_checkpoints_and_reciprocal_success_fraction(): + assert _auc_score([5.0, 3.0, 2.0], [2, 4, 6], [5, 3, 1], 2) == pytest.approx(1.5) + assert np.isinf(_auc_score([], [2, 4], [2, 1], 2)) + + +def test_problem_repair_uncertainty_and_accessory_data_are_retained(): + repaired_problem = SimpleNamespace( + type=["continuous", "static", "certain"], + bound=np.array([[-10.0], [10.0]]), + repair=lambda data, decision: np.zeros_like(decision), + evaluate=lambda data, decision: (4.0, 0.0, {"source": "simulator"}), + ) + repaired = make_solutions(np.array([[7.0]]), repaired_problem, None)[0] + np.testing.assert_array_equal(repaired.dec, [0.0]) + assert repaired.acc == {"source": "simulator"} + + samples = iter([1.0, 3.0, 5.0]) + uncertain = SimpleNamespace( + type=["continuous", "static", "uncertain"], + bound=np.array([[-1.0], [1.0]]), + sampleN=3, + setting="uncertain_average", + evaluate=lambda data, decision: (next(samples), 0.0, None), + ) + assert make_solutions(np.array([[0.0]]), uncertain, None)[0].obj == 3.0 + + worst_samples = iter([1.0, 3.0, 5.0]) + uncertain.setting = "uncertain_worst" + uncertain.evaluate = lambda data, decision: (next(worst_samples), 0.0, None) + assert make_solutions(np.array([[0.0]]), uncertain, None)[0].obj == 5.0 + + uncertain.sampleN = 0 + with pytest.raises(ObjectiveEvaluationError, match="positive sampleN"): + make_solutions(np.array([[0.0]]), uncertain, None) + uncertain.sampleN = 1 + uncertain.setting = "" + uncertain.evaluate = lambda data, decision: (1.0, 0.0, None) + with pytest.raises(ObjectiveEvaluationError, match="uncertain_average"): + make_solutions(np.array([[0.0]]), uncertain, None) + + +def test_execution_tracks_historical_best_and_uses_embedded_archive(monkeypatch): + solve_module = importlib.import_module("autooptlib.utils.solve") + seen = [] + + def choose_all(population, problem, parameter, aux, *args): + aux["choose"] = True + return np.arange(len(population)), aux + + def make_worse(parent, problem, parameter, aux, *args): + assert aux["choose"] + aux["search"] = True + return parent.decs() + 1.0, aux + + def replace_with_new(combined, problem, parameter, aux, *args): + assert aux["search"] + seen.append(aux) + return combined[-problem.N :], None + + components = { + "choose_parity": choose_all, + "search_parity": make_worse, + "update_parity": replace_with_new, + } + original = solve_module.get_component + + def lookup(name): + return components[name] if name in components else original(name) + + monkeypatch.setattr(solve_module, "get_component", lookup) + + path = Pathway( + "choose_parity", + [SearchStep("search_parity", np.array([-np.inf, 1.0]))], + "update_parity", + ["archive_best"], + ) + params = PathwayParam(None, [SearchParam(None, None)], None) + problem = SimpleNamespace( + type=["continuous", "static", "certain"], + bound=np.array([[0.0], [10.0]]), + N=2, + Gmax=1, + evaluate=lambda data, decision: (float(decision[0]), 0.0, None), + ) + result = run_design( + [path], + [params], + problem, + None, + SimpleNamespace(ProbN=2, ProbFE=4, Seed=2), + ) + assert seen and seen[0]["choose"] and seen[0]["search"] + assert result["fit_history"] == sorted(result["fit_history"], reverse=True) + assert result["best_solution"].fit == min(result["fit_history"]) + assert len(result["archives"]) == 1 + assert result["archives"][0] diff --git a/tests/unit/test_problem_contract.py b/tests/unit/test_problem_contract.py new file mode 100644 index 0000000..e8639e8 --- /dev/null +++ b/tests/unit/test_problem_contract.py @@ -0,0 +1,121 @@ +"""Behavioral tests for the public custom-problem contract.""" + +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib import ProblemDefinition, make_problem +from autooptlib.problems.base import validate_constructed_problems + + +def test_problem_factory_supports_data_constraints_and_direct_modes(): + definition = make_problem( + lambda decision, data: float(np.sum(decision) + data["offset"]), + bounds=lambda instance: [[-instance, -instance], [instance, instance]], + constraint=lambda decision, data: [decision[0] - data["limit"]], + data_factory=lambda instance: {"offset": instance, "limit": 0.5}, + name="bounded_sum", + ) + assert isinstance(definition, ProblemDefinition) + records = [SimpleNamespace(N=2, Gmax=3)] + problems, data, auxiliary = definition(records, [2], "construct") + validate_constructed_problems(problems, data) + assert auxiliary is None + assert definition.__name__ == "bounded_sum" + assert problems[0].dimension == 2 + + repaired, _, _ = definition(None, [1.0, -1.0], "repair") + np.testing.assert_array_equal(repaired, [1.0, -1.0]) + value, violation, _ = definition(data[0], [1.0, 2.0], "evaluate") + assert value == 5.0 + assert violation == [0.5] + values, violations, _ = definition(data[0], [[0.0, 0.0], [1.0, 1.0]], "evaluate") + np.testing.assert_array_equal(values, [2.0, 4.0]) + np.testing.assert_array_equal(violations, [[-0.5], [0.5]]) + + +@pytest.mark.parametrize( + ("kwargs", "error", "message"), + [ + ({"objective": 1, "bounds": (-1, 1)}, TypeError, "objective"), + ( + {"objective": lambda d, x: 0, "bounds": (-1, 1), "problem_type": "graph"}, + ValueError, + "problem_type", + ), + ( + {"objective": lambda d, x: 0, "bounds": (-1, 1), "constraint": 1}, + TypeError, + "constraint", + ), + ( + {"objective": lambda d, x: 0, "bounds": (-1, 1), "data_factory": 1}, + TypeError, + "data_factory", + ), + ], +) +def test_problem_factory_rejects_invalid_arguments(kwargs, error, message): + with pytest.raises(error, match=message): + make_problem(**kwargs) + + +def test_problem_factory_rejects_missing_or_unexpandable_bounds(): + continuous = make_problem(lambda decision, data: 0.0) + with pytest.raises(ValueError, match="require explicit bounds"): + continuous([SimpleNamespace()], [2], "construct") + + scalar_bounds = make_problem(lambda decision, data: 0.0, bounds=(-1, 1)) + with pytest.raises(ValueError, match="integer instance dimension"): + scalar_bounds([SimpleNamespace()], [object()], "construct") + + +def test_problem_factory_validates_construct_count_and_mode(): + definition = make_problem(lambda decision, data: 0.0, bounds=(-1, 1)) + with pytest.raises(ValueError, match="number of problem records"): + definition([], [2], "construct") + with pytest.raises(ValueError, match="Unsupported problem mode"): + definition([], [], "unknown") + + +def test_permutation_problem_infers_domain_from_instance(): + definition = make_problem( + lambda decision, data: float(np.sum(decision)), problem_type="permutation" + ) + problems, data, _ = definition([SimpleNamespace(N=2, Gmax=1)], [4], "construct") + np.testing.assert_array_equal(problems[0].bound[0], [1, 2, 3, 4]) + assert data == [4] + + +def _valid_problem(**overrides): + values = dict( + type=["continuous", "static", "certain"], + bound=np.array([[-1.0], [1.0]]), + evaluate=lambda data, decision: (0.0, 0.0, None), + N=2, + Gmax=3, + ) + values.update(overrides) + return SimpleNamespace(**values) + + +@pytest.mark.parametrize( + ("problems", "data", "error", "message"), + [ + ([], [], ValueError, "no problem"), + ([_valid_problem()], [], ValueError, "data entries"), + ([_valid_problem(type="continuous")], [None], TypeError, "non-empty sequence"), + ([_valid_problem(type=["graph"])], [None], ValueError, "unsupported type"), + ([_valid_problem(bound=[-1, 1])], [None], ValueError, "shape"), + ([_valid_problem(bound=[[-np.inf], [1]])], [None], ValueError, "finite"), + ([_valid_problem(evaluate=None)], [None], TypeError, "must expose evaluate"), + ([_valid_problem(N=0)], [None], ValueError, "attribute N"), + ([_valid_problem(Gmax=0)], [None], ValueError, "attribute Gmax"), + ], +) +def test_constructed_problem_validation_errors(problems, data, error, message): + with pytest.raises(error, match=message): + validate_constructed_problems(problems, data) diff --git a/tests/unit/test_public_api.py b/tests/unit/test_public_api.py new file mode 100644 index 0000000..a16d347 --- /dev/null +++ b/tests/unit/test_public_api.py @@ -0,0 +1,163 @@ +"""Tests for the public package API and bundled resources.""" + +from __future__ import annotations + +from importlib import resources +from types import SimpleNamespace + +import numpy as np +import pytest + +import autooptlib +import autooptlib.problems as problems +from autooptlib import get_component, register_component + + +def test_public_version_matches_release(): + assert autooptlib.__version__ == "1.2.0" + + +def test_cec_archive_is_a_package_resource(): + archive = resources.files("autooptlib.problems.data").joinpath("cec2013.npz") + assert archive.is_file() + + +@pytest.mark.parametrize("name", problems.__all__) +def test_all_cec2013_problems_construct_and_evaluate(name): + problem_fn = getattr(problems, name) + problem_list, data, _ = problem_fn([SimpleNamespace()], [10], "construct") + values, constraints, _ = problem_fn(data[0], np.zeros((2, 10)), "evaluate") + assert problem_list[0].bound.shape == (2, 10) + assert np.asarray(values).shape == (2,) + assert np.all(np.isfinite(values)) + assert np.all(np.asarray(constraints) == 0) + + +def test_cec2013_reference_optima_match_published_biases(): + with np.load( + resources.files("autooptlib.problems.data").joinpath("cec2013.npz") + ) as archive: + composition_optimum = archive["shift_data"][0, :10] + + expected = list(range(-1400, -99, 100)) + list(range(100, 1401, 100)) + for index, bias in enumerate(expected, start=1): + problem_fn = getattr(problems, f"cec2013_f{index}") + _, data, _ = problem_fn([SimpleNamespace()], [10], "construct") + optimum = data[0].o if index <= 20 else composition_optimum + values, _, _ = problem_fn( + data[0], np.asarray(optimum).reshape(1, -1), "evaluate" + ) + assert float(np.asarray(values)[0]) == pytest.approx(bias, abs=2e-4) + + +@pytest.mark.parametrize( + ("point", "expected"), + [ + ( + 0.0, + [ + 17398.270025643684, + 2396412610.9019618, + 7.2542451564562992e20, + 75132346.84986454, + 40434.08125354802, + 961.2132235027589, + 62885586.662445866, + -678.0156101069512, + -579.7523754268578, + 2958.0111652935962, + -68.85490363852517, + 24.409324082253647, + 158.00167500061042, + 4523.575143387677, + 3075.1654636826634, + 217.50478678005405, + 509.5833597461297, + 645.0303148911825, + 113720.48150316134, + 605.0, + 1689.8570200417998, + 5442.9812724881785, + 4297.650206927683, + 1579.9075365188896, + 1415.6995850587002, + 9036.72162529505, + 2330.500864913557, + 3009.2459654501627, + ], + ), + ( + 1.0, + [ + 17297.327650567775, + 2369973380.5645466, + 6.674683402465664e20, + 64674277.3745981, + 39204.023022324676, + 862.838458684628, + 67319103.6960722, + -678.1139448621506, + -580.5125515533873, + 2929.4272910000964, + -53.09494281653241, + 25.88382972532719, + 158.27365776223303, + 4235.953246789083, + 3042.7039280101953, + 209.32994533227844, + 590.765721405681, + 625.822891727017, + 123842.75727931673, + 605.0, + 1692.318552845361, + 5261.297868191961, + 4287.962546015057, + 1581.1037754768658, + 1420.204178402104, + 9132.718679394688, + 2322.6991067044996, + 3030.563592994674, + ], + ), + ], +) +def test_cec2013_matches_official_c_reference_at_fixed_points(point, expected): + """Values generated with the CEC 2013 authors' 27-Jan-2013 C reference.""" + actual = [] + decisions = np.full((1, 10), point) + for index in range(1, 29): + problem_fn = getattr(problems, f"cec2013_f{index}") + _, data, _ = problem_fn([SimpleNamespace()], [10], "construct") + values, _, _ = problem_fn(data[0], decisions, "evaluate") + actual.append(float(np.asarray(values)[0])) + np.testing.assert_allclose(actual, expected, rtol=1e-9, atol=1e-7) + + +def test_custom_component_registration_without_internal_edits(): + def search_test_extension(*args): + mode = args[-1] + if mode == "execute": + parent = args[0] + decisions = ( + parent.decs() if callable(getattr(parent, "decs", None)) else parent + ) + return decisions, args[3] if len(args) > 3 else None + if mode in {"parameter", "behavior"}: + return None, None + raise ValueError(mode) + + register_component( + "search_test_extension", + search_test_extension, + category="search", + problem_types=["continuous"], + ) + assert get_component("search_test_extension") is search_test_extension + with pytest.raises(ValueError, match="already registered"): + register_component("search_test_extension", search_test_extension) + + from autooptlib.utils.space import space + + problem = SimpleNamespace(type=["continuous", "static", "certain"]) + design_space = space([problem], SimpleNamespace()) + assert "search_test_extension" in design_space.AllOp diff --git a/tests/unit/test_reliability.py b/tests/unit/test_reliability.py new file mode 100644 index 0000000..4cabd7e --- /dev/null +++ b/tests/unit/test_reliability.py @@ -0,0 +1,764 @@ +"""Reliability tests for public configuration, budgets, and serialization.""" + +from __future__ import annotations + +import importlib +import json +import multiprocessing +import threading +import time +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib import ( + ObjectiveEvaluationError, + autoopt, + load_algorithm, + make_problem, + save_algorithm, +) +from autooptlib.serialization import algorithm_to_dict +from autooptlib.utils.solve import ( + _configure_evaluation_runtime, + input_algorithm, + make_solutions, + run_design, +) + + +def test_unknown_public_option_is_rejected(): + with pytest.raises(TypeError, match="Unknown AutoOpt option.*ProbFee"): + autoopt(Mode="solve", ProbFee=10) + + +def test_public_options_accept_snake_case_and_output_directory(tmp_path): + problem = make_problem( + lambda decision, dimension: float(np.sum(decision**2)), + bounds=(-5.0, 5.0), + ) + algorithms, _ = autoopt( + mode="design", + problem=problem, + instance_train=[3], + instance_test=[3], + alg_n=2, + alg_fe=4, + prob_n=5, + prob_fe=10, + archive=["archive_best"], + tune_para=True, + seed=8, + output_dir=tmp_path, + ) + assert algorithms[0].operator_pheno[0][0].archive == ["archive_best"] + assert (tmp_path / "Algorithm_1.json").is_file() + + +def test_problem_factory_and_design_budgets_count_every_objective_call(tmp_path): + calls = {"count": 0} + + def objective(decision, dimension): + calls["count"] += 1 + return float(np.sum(decision**2)) + + problem = make_problem(objective, bounds=(-5.0, 5.0), name="counted_sphere") + algorithms, _ = autoopt( + Mode="design", + Problem=problem, + InstanceTrain=[3], + InstanceTest=[4], + AlgN=2, + AlgFE=4, + ProbN=5, + ProbFE=10, + AlgRuns=1, + Seed=9, + OutputDir=tmp_path, + ) + # Initialization evaluates AlgN incumbents; AlgFE then counts four new + # proposals. Two selected algorithms are finally run on held-out data. + assert calls["count"] == (2 + 4 + 2) * 10 + assert len(algorithms) == 2 + + +@pytest.mark.parametrize( + ("algorithm_name", "problem_type", "bounds"), + [ + ("Discrete Iterative Local Search", "discrete", (0, 5)), + ("Permutation Variable Neighborhood Search", "permutation", None), + ], +) +def test_multistep_algorithms_never_exceed_probfe(algorithm_name, problem_type, bounds): + calls = {"count": 0} + + def objective(decision, instance): + calls["count"] += 1 + return float(np.sum(np.asarray(decision, dtype=float) ** 2)) + + definition = make_problem(objective, bounds=bounds, problem_type=problem_type) + records = [SimpleNamespace(N=10, Gmax=10)] + problems, data, _ = definition(records, [5], "construct") + setting = SimpleNamespace( + AlgName=algorithm_name, + AlgRuns=1, + ProbN=10, + ProbFE=100, + Metric="quality", + rng=np.random.default_rng(1), + ) + algorithm, setting = input_algorithm(setting) + result = run_design( + algorithm.operator_pheno[0], + algorithm.parameter_pheno[0], + problems[0], + data[0], + setting, + ) + assert result["evaluations"] == 100 + assert calls["count"] == 100 + + +@pytest.mark.parametrize( + ("algorithm_name", "problem_type"), + [ + ("Continuous Genetic Algorithm", "continuous"), + ("Evolutionary Programming", "continuous"), + ("Fast Evolutionary Programming", "continuous"), + ("CMA-ES", "continuous"), + ("Estimation of Distribution", "continuous"), + ("Particle Swarm Optimization", "continuous"), + ("Differential Evolution", "continuous"), + ("Continuous Random Search", "continuous"), + ("ICA", "continuous"), + ("Discrete Genetic Algorithm", "discrete"), + ("Discrete Iterative Local Search", "discrete"), + ("Discrete Simulated Annealing", "discrete"), + ("Discrete Random Search", "discrete"), + ("Permutation Genetic Algorithm", "permutation"), + ("Permutation Iterative Local Search", "permutation"), + ("Permutation Simulated Annealing", "permutation"), + ("Permutation Variable Neighborhood Search", "permutation"), + ("Permutation Random Search", "permutation"), + ], +) +def test_every_preset_handles_a_partial_final_population(algorithm_name, problem_type): + calls = {"count": 0} + + def objective(decision, instance): + calls["count"] += 1 + return float(np.sum(np.asarray(decision, dtype=float) ** 2)) + + if problem_type == "permutation": + bounds = None + elif problem_type == "discrete": + bounds = (0, 5) + else: + bounds = (-5.0, 5.0) + definition = make_problem(objective, bounds=bounds, problem_type=problem_type) + problems, data, _ = definition([SimpleNamespace(N=7, Gmax=10)], [6], "construct") + setting = SimpleNamespace( + AlgName=algorithm_name, + ProbN=7, + ProbFE=23, + Metric="quality", + rng=np.random.default_rng(3), + ) + algorithm, setting = input_algorithm(setting) + result = run_design( + algorithm.operator_pheno[0], + algorithm.parameter_pheno[0], + problems[0], + data[0], + setting, + ) + assert result["evaluations"] == 23 + assert calls["count"] == 23 + + +def test_json_algorithm_round_trip_and_schema_validation(tmp_path): + setting = SimpleNamespace(AlgName="Continuous Random Search") + algorithm, _ = input_algorithm(setting) + target = save_algorithm( + algorithm, tmp_path / "algorithm.json", metadata={"purpose": "test"} + ) + document = json.loads(target.read_text(encoding="utf-8")) + assert document["schema"] == "autooptlib.algorithm" + assert document["schema_version"] == 1 + assert document["metadata"] == {"purpose": "test"} + + restored = load_algorithm(target) + assert restored.operator_pheno[0][0].search[0].primary == "reinit_continuous" + assert restored.parameter_pheno[0][0].search[0].primary is None + + document["schema_version"] = 999 + target.write_text(json.dumps(document), encoding="utf-8") + with pytest.raises(ValueError, match="Unsupported algorithm schema version"): + load_algorithm(target) + + +def test_invalid_constructed_problem_fails_before_optimization(tmp_path): + invalid = make_problem(lambda decision, data: 0.0, bounds=[[1.0], [-1.0]]) + with pytest.raises(ValueError, match="lower bounds greater"): + autoopt( + Mode="solve", + Problem=invalid, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=5, + ProbFE=10, + OutputDir=tmp_path, + ) + + +@pytest.mark.parametrize( + ("value", "message"), + [ + (np.nan, "non-finite"), + ([1.0, 2.0], "one scalar objective"), + ("not-a-number", "numeric scalar"), + ((), "one scalar objective"), + ], +) +def test_invalid_objective_results_fail_with_context(value, message): + definition = make_problem(lambda decision, instance: value, bounds=(-1.0, 1.0)) + records = [SimpleNamespace(N=2, Gmax=1)] + problems, data, _ = definition(records, [2], "construct") + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + with pytest.raises(ObjectiveEvaluationError, match=message): + run_design( + algorithm.operator_pheno[0], + algorithm.parameter_pheno[0], + problems[0], + data[0], + SimpleNamespace(ProbN=2, ProbFE=2), + ) + + +def test_objective_exception_is_wrapped_with_decision_context(): + def broken_objective(decision, instance): + raise TimeoutError("simulator timed out") + + definition = make_problem(broken_objective, bounds=(-1.0, 1.0)) + problems, data, _ = definition([SimpleNamespace(N=2, Gmax=1)], [2], "construct") + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + with pytest.raises( + ObjectiveEvaluationError, + match="Objective evaluation failed.*simulator timed out", + ): + run_design( + algorithm.operator_pheno[0], + algorithm.parameter_pheno[0], + problems[0], + data[0], + SimpleNamespace(ProbN=2, ProbFE=2), + ) + + +def test_failed_objective_is_retried_then_succeeds(tmp_path): + calls = {"count": 0} + + def flaky(decision, instance): + calls["count"] += 1 + if calls["count"] <= 2: + raise ConnectionError("temporary simulator failure") + return float(np.sum(decision**2)) + + problem = make_problem(flaky, bounds=(-1.0, 1.0)) + best, _ = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=2, + ProbFE=2, + AlgRuns=1, + EvalRetries=2, + OutputDir=tmp_path, + ) + assert calls["count"] == 4 + assert np.isfinite(best[0][0].fit) + + +def test_penalty_log_and_cache_are_opt_in(tmp_path): + calls = {"count": 0} + + def broken(decision, instance): + calls["count"] += 1 + raise RuntimeError("unavailable") + + log_path = tmp_path / "events.jsonl" + problem = make_problem(broken, bounds=(0.0, 0.0)) + best, _ = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=2, + ProbFE=2, + AlgRuns=1, + EvalRetries=1, + EvalFailure="penalize", + EvalPenalty=1234.0, + EvalLog=log_path, + OutputDir=tmp_path / "penalty", + ) + assert best[0][0].fit == 1234.0 + assert calls["count"] == 4 + events = [json.loads(line) for line in log_path.read_text().splitlines()] + assert [event["status"] for event in events] == ["failure"] * 4 + assert all(len(event["decision_sha256"]) == 64 for event in events) + + cached_calls = {"count": 0} + + def constant(decision, instance): + cached_calls["count"] += 1 + return 1.0 + + cached = make_problem(constant, bounds=(0.0, 0.0)) + autoopt( + Mode="solve", + Problem=cached, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=3, + ProbFE=6, + EvalCache=True, + OutputDir=tmp_path / "cache", + ) + assert cached_calls["count"] == 1 + + +def test_candidate_evaluations_can_run_in_parallel(tmp_path): + rendezvous = threading.Barrier(2) + thread_ids = set() + lock = threading.Lock() + + def synchronized_objective(decision, instance): + with lock: + thread_ids.add(threading.get_ident()) + rendezvous.wait(timeout=2) + return float(np.sum(decision**2)) + + problem = make_problem(synchronized_objective, bounds=(-1.0, 1.0)) + best, _ = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=2, + ProbFE=2, + EvalWorkers=2, + OutputDir=tmp_path, + ) + assert len(thread_ids) == 2 + assert np.isfinite(best[0][0].fit) + + +def test_parallel_evaluation_preserves_order_cache_logs_and_retries(tmp_path): + calls = {} + call_lock = threading.Lock() + + def objective(data, decision): + value = int(np.asarray(decision).reshape(-1)[0]) + with call_lock: + calls[value] = calls.get(value, 0) + 1 + attempt = calls[value] + time.sleep(0.005 * (5 - value)) + if value == 2 and attempt == 1: + raise ConnectionError("transient") + if value == 4: + raise RuntimeError("permanent") + return float(value) + + problem = SimpleNamespace( + type=["continuous", "static", "certain"], + bound=np.array([[0.0], [4.0]]), + evaluate=objective, + name="parallel_policy_test", + ) + log_path = tmp_path / "parallel.jsonl" + _configure_evaluation_runtime( + problem, + SimpleNamespace( + EvalWorkers=4, + EvalRetries=1, + EvalFailure="penalize", + EvalPenalty=99.0, + EvalCache=True, + EvalLog=log_path, + ), + ) + solutions = make_solutions( + np.array([[3.0], [1.0], [2.0], [1.0], [4.0]]), problem, None + ) + + assert [solution.obj for solution in solutions] == [3.0, 1.0, 2.0, 1.0, 99.0] + assert calls == {1: 1, 2: 2, 3: 1, 4: 2} + events = [json.loads(line) for line in log_path.read_text().splitlines()] + assert len(events) == 7 + assert sum(event["status"] == "cache_hit" for event in events) == 1 + assert sum(event["status"] == "failure" for event in events) == 3 + assert sum(event["status"] == "success" for event in events) == 3 + + +def test_parallel_seeded_runs_are_deterministic(tmp_path): + problem = make_problem( + lambda decision, dimension: float(np.sum(decision**2)), + bounds=(-5.0, 5.0), + ) + common = dict( + Mode="solve", + Problem=problem, + InstanceSolve=[4], + AlgName="Continuous Random Search", + ProbN=8, + ProbFE=32, + Seed=2026, + EvalWorkers=4, + ) + first_best, first_history = autoopt(**common, OutputDir=tmp_path / "parallel-first") + second_best, second_history = autoopt( + **common, OutputDir=tmp_path / "parallel-second" + ) + np.testing.assert_array_equal(first_best[0][0].dec, second_best[0][0].dec) + assert [solution.fit for solution in first_history[0]] == [ + solution.fit for solution in second_history[0] + ] + + +@pytest.mark.skipif( + "fork" not in multiprocessing.get_all_start_methods(), + reason="closure-based timeout test requires the fork start method", +) +def test_objective_timeout_can_be_penalized(tmp_path): + def slow(decision, instance): + time.sleep(0.2) + return 1.0 + + problem = make_problem(slow, bounds=(-1.0, 1.0)) + best, _ = autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[1], + AlgName="Continuous Random Search", + ProbN=1, + ProbFE=1, + EvalTimeoutSec=0.02, + EvalFailure="penalize", + EvalPenalty=999.0, + OutputDir=tmp_path, + ) + assert best[0][0].fit == 999.0 + + +def test_experiment_manifest_records_environment_and_options(tmp_path): + problem = make_problem(lambda decision, instance: 0.0, bounds=(0.0, 0.0)) + autoopt( + Mode="solve", + Problem=problem, + InstanceSolve=[2], + AlgName="Continuous Random Search", + ProbN=1, + ProbFE=1, + Seed=42, + OutputDir=tmp_path, + ) + manifest = json.loads((tmp_path / "experiment.json").read_text()) + assert manifest["schema"] == "autooptlib.experiment" + assert manifest["software"]["autooptlib"] == "1.2.0" + assert manifest["options"]["Seed"] == 42 + assert manifest["options"]["Problem"].endswith(":make_problem..definition") + + +def test_completed_solve_can_resume_from_atomic_checkpoint_without_evaluation(tmp_path): + calls = {"count": 0} + + def objective(decision, instance): + calls["count"] += 1 + return float(np.sum(decision**2)) + + problem = make_problem(objective, bounds=(-1.0, 1.0)) + common = dict( + Mode="solve", + Problem=problem, + InstanceSolve=[2], + AlgName="Continuous Random Search", + ProbN=3, + ProbFE=6, + Seed=10, + CheckpointDir=tmp_path / "checkpoints", + ) + first, _ = autoopt(**common, OutputDir=tmp_path / "first") + calls_after_first = calls["count"] + checkpoint = tmp_path / "checkpoints" / "instance_1_run_1.pkl" + assert checkpoint.is_file() + assert not checkpoint.with_suffix(".pkl.tmp").exists() + + with pytest.warns(UserWarning, match="trusted run"): + second, _ = autoopt(**common, Resume=True, OutputDir=tmp_path / "second") + assert calls["count"] == calls_after_first + assert second[0][0].fit == first[0][0].fit + + +def test_completed_design_can_resume_without_objective_evaluation(tmp_path): + calls = {"count": 0} + + def objective(decision, instance): + calls["count"] += 1 + return float(np.sum(decision**2)) + + problem = make_problem(objective, bounds=(-1.0, 1.0)) + common = dict( + Mode="design", + Problem=problem, + InstanceTrain=[2], + InstanceTest=[2], + ProbN=2, + ProbFE=4, + AlgN=1, + AlgFE=2, + AlgRuns=1, + Seed=12, + CheckpointDir=tmp_path / "checkpoints", + ) + first, _ = autoopt(**common, OutputDir=tmp_path / "first") + calls_after_first = calls["count"] + checkpoint = tmp_path / "checkpoints" / "design.pkl" + assert checkpoint.is_file() + assert not checkpoint.with_suffix(".pkl.tmp").exists() + + with pytest.warns(UserWarning, match="trusted run"): + second, _ = autoopt(**common, Resume=True, OutputDir=tmp_path / "second") + assert calls["count"] == calls_after_first + assert second[0].ave_perform_all().tolist() == first[0].ave_perform_all().tolist() + + +def test_interrupted_static_solve_resumes_identically(monkeypatch, tmp_path): + solve_module = importlib.import_module("autooptlib.utils.solve") + interrupted_calls = {"count": 0} + + def interrupted_objective(decision, instance): + interrupted_calls["count"] += 1 + return float(np.sum(decision**2)) + + problem = make_problem(interrupted_objective, bounds=(-1.0, 1.0)) + checkpoint_dir = tmp_path / "static-checkpoints" + common = dict( + Mode="solve", + Problem=problem, + InstanceSolve=[3], + AlgName="Continuous Random Search", + ProbN=3, + ProbFE=9, + AlgRuns=1, + Seed=41, + CheckpointDir=checkpoint_dir, + CheckpointEvery=1, + ) + original_writer = solve_module._write_checkpoint + crashed = {"value": False} + + def crash_after_checkpoint(path, payload): + original_writer(path, payload) + if ( + not crashed["value"] + and payload.get("schema") == "autooptlib.checkpoint" + and not payload.get("complete") + and payload.get("evaluations", 0) > 3 + ): + crashed["value"] = True + raise RuntimeError("injected static interruption") + + monkeypatch.setattr(solve_module, "_write_checkpoint", crash_after_checkpoint) + with pytest.raises(RuntimeError, match="injected static interruption"): + autoopt(**common, OutputDir=tmp_path / "static-interrupted") + assert crashed["value"] + assert (checkpoint_dir / "instance_1_run_1.pkl").is_file() + + monkeypatch.setattr(solve_module, "_write_checkpoint", original_writer) + with pytest.warns(UserWarning, match="trusted run"): + resumed_best, resumed_history = autoopt( + **common, + Resume=True, + OutputDir=tmp_path / "static-resumed", + ) + + baseline_calls = {"count": 0} + + def baseline_objective(decision, instance): + baseline_calls["count"] += 1 + return float(np.sum(decision**2)) + + baseline_problem = make_problem(baseline_objective, bounds=(-1.0, 1.0)) + baseline_best, baseline_history = autoopt( + **{**common, "Problem": baseline_problem, "CheckpointDir": None}, + OutputDir=tmp_path / "static-baseline", + ) + assert interrupted_calls["count"] == baseline_calls["count"] == 9 + np.testing.assert_array_equal(resumed_best[0][0].dec, baseline_best[0][0].dec) + assert [solution.fit for solution in resumed_history[0]] == [ + solution.fit for solution in baseline_history[0] + ] + + +def test_interrupted_design_resumes_identically(monkeypatch, tmp_path): + process_module = importlib.import_module("autooptlib.utils.general.process") + interrupted_calls = {"count": 0} + + def interrupted_objective(decision, instance): + interrupted_calls["count"] += 1 + return float(np.sum(decision**2)) + + problem = make_problem(interrupted_objective, bounds=(-1.0, 1.0)) + checkpoint_dir = tmp_path / "design-checkpoints" + common = dict( + Mode="design", + Problem=problem, + InstanceTrain=[3], + InstanceTest=[3], + ProbN=3, + ProbFE=6, + AlgN=1, + AlgFE=3, + AlgRuns=1, + Seed=52, + CheckpointDir=checkpoint_dir, + CheckpointEvery=1, + ) + original_writer = process_module._write_design_checkpoint + crashed = {"value": False} + + def crash_after_checkpoint(path, payload): + original_writer(path, payload) + if ( + not crashed["value"] + and not payload.get("complete") + and payload.get("evaluated_count", 0) > 1 + ): + crashed["value"] = True + raise RuntimeError("injected design interruption") + + monkeypatch.setattr( + process_module, "_write_design_checkpoint", crash_after_checkpoint + ) + with pytest.raises(RuntimeError, match="injected design interruption"): + autoopt(**common, OutputDir=tmp_path / "design-interrupted") + assert crashed["value"] + assert (checkpoint_dir / "design.pkl").is_file() + + monkeypatch.setattr(process_module, "_write_design_checkpoint", original_writer) + with pytest.warns(UserWarning, match="trusted run"): + resumed, resumed_trace = autoopt( + **common, + Resume=True, + OutputDir=tmp_path / "design-resumed", + ) + + baseline_calls = {"count": 0} + + def baseline_objective(decision, instance): + baseline_calls["count"] += 1 + return float(np.sum(decision**2)) + + baseline_problem = make_problem(baseline_objective, bounds=(-1.0, 1.0)) + baseline, baseline_trace = autoopt( + **{**common, "Problem": baseline_problem, "CheckpointDir": None}, + OutputDir=tmp_path / "design-baseline", + ) + assert interrupted_calls["count"] == baseline_calls["count"] + assert algorithm_to_dict(resumed[0]) == algorithm_to_dict(baseline[0]) + assert [algorithm_to_dict(algorithm) for algorithm in resumed_trace] == [ + algorithm_to_dict(algorithm) for algorithm in baseline_trace + ] + + +def test_interrupted_sequential_solve_resumes_identically(monkeypatch, tmp_path): + solve_module = importlib.import_module("autooptlib.utils.solve") + + def sequential_definition(counter): + def definition(records, instances, mode): + if mode != "construct": + raise ValueError(mode) + data_entries = [] + for record, dimension in zip(records, instances): + dimension = int(dimension) + record.type = ["continuous", "sequential", "certain"] + record.bound = np.vstack( + (np.full(dimension, -1.0), np.full(dimension, 1.0)) + ) + + def evaluate(data, decision): + counter["count"] += 1 + return float(np.sum(np.asarray(decision) ** 2) + data.stage) + + def advance(best, data, problem=record): + next_stage = data.stage + 1 + next_data = SimpleNamespace(stage=next_stage) + setattr(next_data, "continue", next_stage < 3) + return problem, next_data + + record.evaluate = evaluate + record.advance_sequence = advance + first_data = SimpleNamespace(stage=0) + setattr(first_data, "continue", True) + data_entries.append(first_data) + return list(records), data_entries, None + + return definition + + interrupted_calls = {"count": 0} + checkpoint_dir = tmp_path / "sequential-checkpoints" + common = dict( + Mode="solve", + Problem=sequential_definition(interrupted_calls), + InstanceSolve=[3], + AlgName="Continuous Random Search", + ProbN=3, + ProbFE=18, + AlgRuns=1, + Metric="runtimeFE", + Tmax=18, + Thres=1e99, + Seed=63, + CheckpointDir=checkpoint_dir, + ) + original_writer = solve_module._write_checkpoint + crashed = {"value": False} + + def crash_after_checkpoint(path, payload): + original_writer(path, payload) + if ( + not crashed["value"] + and payload.get("schema") == "autooptlib.sequence-checkpoint" + and not payload.get("complete") + and len(payload.get("solutions", [])) == 1 + ): + crashed["value"] = True + raise RuntimeError("injected sequential interruption") + + monkeypatch.setattr(solve_module, "_write_checkpoint", crash_after_checkpoint) + with pytest.raises(RuntimeError, match="injected sequential interruption"): + autoopt(**common, OutputDir=tmp_path / "sequential-interrupted") + assert crashed["value"] + + monkeypatch.setattr(solve_module, "_write_checkpoint", original_writer) + with pytest.warns(UserWarning, match="trusted run"): + resumed_best, resumed_history = autoopt( + **common, + Resume=True, + OutputDir=tmp_path / "sequential-resumed", + ) + + baseline_calls = {"count": 0} + baseline_problem = sequential_definition(baseline_calls) + baseline_best, baseline_history = autoopt( + **{**common, "Problem": baseline_problem, "CheckpointDir": None}, + OutputDir=tmp_path / "sequential-baseline", + ) + assert interrupted_calls["count"] == baseline_calls["count"] == 9 + np.testing.assert_array_equal(resumed_best[0][0].dec, baseline_best[0][0].dec) + assert [solution.fit for solution in resumed_history[0]] == [ + solution.fit for solution in baseline_history[0] + ] diff --git a/tests/unit/test_selection_and_improvement.py b/tests/unit/test_selection_and_improvement.py new file mode 100644 index 0000000..ef3d672 --- /dev/null +++ b/tests/unit/test_selection_and_improvement.py @@ -0,0 +1,157 @@ +"""Behavioral tests for improvement tracking and algorithm selection.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib.utils.design import Design +from autooptlib.utils.general.improve_rate import improve_rate +from autooptlib.utils.select import ( + _ensure_design_list, + _require_performance, + _statistic_wins, + select, +) + +select_module = importlib.import_module("autooptlib.utils.select") + + +def test_improvement_rate_supports_solution_interfaces_and_constraints(): + callable_fits = SimpleNamespace(fits=lambda: np.array([3.0, 1.0])) + assert improve_rate(callable_fits, None, 1, "solution")[-1] == 1.0 + property_fits = SimpleNamespace(fits=np.array([4.0, 2.0])) + assert improve_rate(property_fits, None, 1, "solution")[-1] == 2.0 + + feasible = SimpleNamespace(objs=lambda: [3.0], cons=lambda: [-1.0]) + infeasible = SimpleNamespace(obj=1.0, cons=[2.0]) + result = improve_rate([feasible, infeasible], None, 1, "solution") + assert result[-1] == 3.0 + single = improve_rate(SimpleNamespace(obj=4.0), None, 1, "solution") + assert single[-1] == 4.0 + + +def test_improvement_rate_window_algorithm_and_errors(): + algorithm = SimpleNamespace(avePerformAll=lambda: [3.0, 1.0]) + result = improve_rate(algorithm, [1.0, 4.0], 3, "algorithm") + assert len(result) == 4 + assert np.isfinite(result[0]) + empty = improve_rate(SimpleNamespace(), None, 1, "algorithm") + assert np.isinf(empty[-1]) + zeros = improve_rate( + SimpleNamespace(avePerformAll=lambda: [0.0]), + [1.0, 0.0, 0.0, 0.0], + 3, + "algorithm", + ) + assert zeros[0] == 0.0 + with pytest.raises(ValueError, match="typ must be"): + improve_rate([], None, 1, "other") + + +class FakeDesign: + def __init__(self, values, performance=None): + self.values = np.asarray(values, dtype=float) + self.performance = performance + self.calls = [] + + def get_performance(self, setting, seeds): + return self.values + + def evaluate(self, problem, data, setting, seeds): + self.calls.append(list(seeds)) + + +def _setting(compare="average", evaluate="exact", alg_n=1): + return SimpleNamespace( + Compare=compare, Evaluate=evaluate, AlgN=alg_n, AlgRuns=2, alpha=0.05 + ) + + +def test_racing_requires_missing_or_zero_performance(): + none = FakeDesign([1, 1], None) + scalar = FakeDesign([1, 1], np.array(0.0)) + short = FakeDesign([1, 1], np.zeros((1, 2))) + ready = FakeDesign([1, 1], np.ones((2, 2))) + _require_performance( + [none, scalar, short, ready], None, None, _setting(evaluate="racing"), [1] + ) + assert none.calls == [] + assert scalar.calls == [[1]] + assert short.calls == [[1]] + assert ready.calls == [] + _require_performance([ready], None, None, _setting(), [0]) + assert ready.calls == [] + + +def test_statistic_wins_fallback_and_significant_ranks(monkeypatch): + monkeypatch.setattr( + select_module, + "friedman_nemenyi", + lambda matrix: (_ for _ in ()).throw(ValueError("too few")), + ) + np.testing.assert_array_equal( + _statistic_wins(np.array([[1, 3], [2, 4]]), 0.05), [1, 0] + ) + + monkeypatch.setattr( + select_module, + "friedman_nemenyi", + lambda matrix: ( + np.array([1.0, 2.0]), + np.array([[np.nan, 0.01], [0.01, np.nan]]), + ), + ) + np.testing.assert_array_equal(_statistic_wins(np.ones((2, 2)), 0.05), [1, 0]) + + +def test_selection_modes_and_failures(monkeypatch): + better = FakeDesign([1.0, 1.0], np.ones((1, 2))) + worse = FakeDesign([3.0, 3.0], np.ones((1, 2))) + assert _ensure_design_list(iter([better, worse])) == [better, worse] + assert select([], None, None, _setting(), [0]) == [] + assert select([worse, better], None, None, _setting(), [0]) == [better] + assert select( + [worse, better], None, None, _setting(evaluate="approximate"), [0] + ) == [better] + assert ( + select([better, worse], None, None, _setting(evaluate="intensification"), [0]) + == [] + ) + with pytest.raises(NotImplementedError, match="evaluate mode"): + select([better], None, None, _setting(evaluate="other"), [0]) + + monkeypatch.setattr( + select_module, "_statistic_wins", lambda matrix, alpha: np.array([0, 1]) + ) + monkeypatch.setattr( + select_module, + "_new_algorithm_wins", + lambda matrix, old, alpha: np.array([0, 1]), + ) + assert select([better, worse], None, None, _setting("statistic", "exact"), [0]) == [ + worse + ] + assert select( + [better, worse], None, None, _setting("statistic", "intensification"), [0] + ) == [worse] + with pytest.raises(NotImplementedError, match="evaluate mode"): + select([better], None, None, _setting("statistic", "other"), [0]) + with pytest.raises(NotImplementedError, match="compare mode"): + select([better], None, None, _setting("other"), [0]) + + +def test_design_empty_averages_and_approximate_performance(): + design = Design() + assert design.ave_perform_all().size == 0 + assert design.ave_perform_approx_all().size == 0 + assert design.ave_perform_per([0]).size == 0 + assert design.ave_perform_approx_per([0]).size == 0 + design.performance_approx = np.array([[1.0, 3.0]]) + setting = SimpleNamespace(Evaluate="approximate", AlgRuns=2) + np.testing.assert_array_equal(design.get_performance(setting, [0]), [[1.0], [3.0]]) + np.testing.assert_array_equal(design.ave_perform_approx_all(), [2.0]) + np.testing.assert_array_equal(design.ave_perform_approx_per([0]), [2.0]) diff --git a/tests/unit/test_serialization_contracts.py b/tests/unit/test_serialization_contracts.py new file mode 100644 index 0000000..22f14ed --- /dev/null +++ b/tests/unit/test_serialization_contracts.py @@ -0,0 +1,122 @@ +"""Validation and compatibility tests for the portable algorithm schema.""" + +from __future__ import annotations + +import json +from copy import deepcopy +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib.serialization import ( + _decode_value, + _encode_value, + algorithm_from_dict, + algorithm_to_dict, + load_algorithm, +) +from autooptlib.utils.design import Design +from autooptlib.utils.solve import input_algorithm + + +def _document(): + algorithm, _ = input_algorithm( + SimpleNamespace(AlgName="Continuous Genetic Algorithm") + ) + return algorithm_to_dict( + algorithm, + metadata={"special": [np.nan, np.inf, -np.inf], "count": np.int64(2)}, + ) + + +def test_special_json_values_round_trip_and_reject_unknown_types(): + encoded = _encode_value([np.nan, np.inf, -np.inf, np.int64(2)]) + decoded = _decode_value(encoded) + assert np.isnan(decoded[0]) + assert decoded[1:] == [np.inf, -np.inf, 2] + assert _decode_value({"nested": {"$float": "inf"}}) == {"nested": np.inf} + with pytest.raises(ValueError, match="Unknown encoded float"): + _decode_value({"$float": "other"}) + with pytest.raises(TypeError, match="not JSON serializable"): + _encode_value(object()) + + +def test_export_requires_decoded_matching_pathways(): + with pytest.raises(ValueError, match="decoded"): + algorithm_to_dict(Design()) + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + algorithm.parameter_pheno[0].append(algorithm.parameter_pheno[0][0]) + with pytest.raises(ValueError, match="pathway counts"): + algorithm_to_dict(algorithm) + + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + algorithm.parameter_pheno[0][0].search.clear() + with pytest.raises(ValueError, match="Search-step"): + algorithm_to_dict(algorithm) + + +@pytest.mark.parametrize( + ("mutate", "error", "message"), + [ + (lambda d: d.update(schema="wrong"), ValueError, "Expected schema"), + (lambda d: d.update(pathways=[]), ValueError, "at least one pathway"), + (lambda d: d.update(pathways=[1]), TypeError, "must be an object"), + ( + lambda d: d["pathways"][0].update(choose=None), + TypeError, + "string choose", + ), + ( + lambda d: d["pathways"][0].update(search=[]), + ValueError, + "at least one search", + ), + ( + lambda d: d["pathways"][0].update(search=[1]), + TypeError, + "must be an object", + ), + ( + lambda d: d["pathways"][0]["search"][0].update(primary=None), + TypeError, + "string primary", + ), + ( + lambda d: d["pathways"][0]["search"][0].update(secondary=1), + TypeError, + "Secondary component", + ), + ( + lambda d: d["pathways"][0]["search"][0].update(termination=None), + ValueError, + "termination vector", + ), + ( + lambda d: d["pathways"][0].update(archive="archive_best"), + TypeError, + "archive must be", + ), + ], +) +def test_schema_validation_rejects_malformed_documents(mutate, error, message): + document = deepcopy(_document()) + mutate(document) + with pytest.raises(error, match=message): + algorithm_from_dict(document) + + +def test_schema_load_rejects_non_object_root(tmp_path): + path = tmp_path / "list.json" + path.write_text(json.dumps([]), encoding="utf-8") + with pytest.raises(TypeError, match="root must be an object"): + load_algorithm(path) + + +def test_schema_reconstructs_secondary_components_and_parameters(): + restored = algorithm_from_dict(_document()) + step = restored.operator_pheno[0][0].search[0] + params = restored.parameter_pheno[0][0].search[0] + assert step.secondary == "search_mu_polynomial" + assert params.primary is not None + assert params.secondary is not None diff --git a/tests/unit/test_solve_contracts.py b/tests/unit/test_solve_contracts.py new file mode 100644 index 0000000..9c72c85 --- /dev/null +++ b/tests/unit/test_solve_contracts.py @@ -0,0 +1,317 @@ +"""Focused contracts for solution repair, evaluation, and solve helpers.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import numpy as np +import pytest + +from autooptlib import ObjectiveEvaluationError +from autooptlib.utils.design._helpers import ( + Pathway, + PathwayParam, + SearchParam, + SearchStep, +) +from autooptlib.utils.solve import ( + Solution, + SolutionSet, + _call_evaluator, + _ensure_solution_instance, + _extract_mode, + _init_population, + _normalize_setting, + _setting_with_budget, + _split_indices, + _to_array, + input_algorithm, + make_solutions, + repair_sol, + run_algorithm, + run_design, +) + +solve_module = importlib.import_module("autooptlib.utils.solve") + + +def _problem(problem_type="continuous", bound=None, **overrides): + if bound is None: + bound = np.array([[-1.0, -1.0], [1.0, 1.0]]) + values = dict( + type=[problem_type, "static", "certain"], + bound=np.asarray(bound), + N=2, + Gmax=2, + evaluate=lambda data, decision: (float(np.sum(decision**2)), 0.0, None), + ) + values.update(overrides) + return SimpleNamespace(**values) + + +def test_solution_set_aggregations_and_empty_decisions(): + empty = SolutionSet([]) + assert empty.decs().shape == (0, 0) + solutions = SolutionSet( + [ + Solution(np.array([1.0]), 2.0, 0.0, 2.0), + Solution(np.array([2.0]), 4.0, 1.0, 100000001.0), + ] + ) + np.testing.assert_array_equal(solutions.decs(), [[1.0], [2.0]]) + np.testing.assert_array_equal(solutions.objs(), [[2.0], [4.0]]) + np.testing.assert_array_equal(solutions.cons(), [[0.0], [1.0]]) + np.testing.assert_array_equal(solutions.fits(), [[2.0], [100000001.0]]) + + +def test_repair_supports_all_spaces_and_distinct_discrete_values(): + raw = np.array([2.0]) + assert repair_sol(raw, SimpleNamespace(type=["continuous"])) is raw + np.testing.assert_array_equal( + repair_sol(np.array([-2.0, 2.0]), _problem()), [-1.0, 1.0] + ) + discrete = _problem( + "discrete", + [[0, 0, 0], [2, 2, 2]], + setting="dec_diff", + rng=np.random.default_rng(2), + ) + repaired = repair_sol(np.array([[1.2, 1.2, 2.8]]), discrete) + assert len(set(repaired[0])) == 3 + np.testing.assert_array_equal( + repair_sol(np.array([[0.2, 3.0]]), _problem("discrete", [[0, 0], [2, 2]])), + [[0, 2]], + ) + permutation = _problem("permutation", [[1, 2, 3], [1, 2, 3]]) + np.testing.assert_array_equal( + repair_sol(np.array([1, 1, 3]), permutation), [1, 2, 3] + ) + repaired_rows = repair_sol(np.array([[1, 1, 2], [3, 3, 1]]), permutation) + assert all(sorted(row.tolist()) == [1, 2, 3] for row in repaired_rows) + unknown = SimpleNamespace(type=["graph"], bound=[[0], [1]]) + np.testing.assert_array_equal(repair_sol(np.array([2]), unknown), [2]) + + +def test_evaluator_normalizes_result_shapes_and_callable_name(): + decision = np.array([1.0]) + assert _call_evaluator( + SimpleNamespace(evaluate=lambda d, x: (2.0,)), None, decision + )[:2] == (2.0, 0.0) + assert _call_evaluator( + SimpleNamespace(evaluate=lambda d, x: (2.0, [-1, 3])), None, decision + )[:2] == (2.0, 3.0) + assert _call_evaluator(SimpleNamespace(evaluate=lambda d, x: 2.0), None, decision)[ + :2 + ] == (2.0, 0.0) + assert _call_evaluator(SimpleNamespace(name=lambda d, x: 3.0), None, decision)[ + :2 + ] == (3.0, 0.0) + with pytest.raises(ObjectiveEvaluationError, match="must provide"): + _call_evaluator(SimpleNamespace(), None, decision) + with pytest.raises(ObjectiveEvaluationError, match="Constraint did not return"): + _call_evaluator( + SimpleNamespace(evaluate=lambda d, x: (1.0, "bad")), None, decision + ) + with pytest.raises(ObjectiveEvaluationError, match="non-finite"): + _call_evaluator( + SimpleNamespace(evaluate=lambda d, x: (1.0, [np.nan])), None, decision + ) + + +def test_make_solutions_applies_constraint_penalty(): + problem = _problem(evaluate=lambda data, decision: (1.0, [2.0], None)) + solution = make_solutions(np.array([[0.0, 0.0]]), problem, None)[0] + assert solution.fit == 100000002.0 + + +def test_population_initialization_errors_and_index_splitting(): + rng = np.random.default_rng(3) + malformed = _problem("permutation", np.empty((1, 0)), dimension=0, D=0) + with pytest.raises(ValueError, match="requires explicit dimension"): + _init_population(rng, malformed, None, SimpleNamespace(ProbN=2)) + with pytest.raises(NotImplementedError, match="Unsupported problem type"): + _init_population( + rng, + _problem("graph"), + None, + SimpleNamespace(ProbN=2), + ) + assert _split_indices([], 1, 3) == [[0, 1, 2]] + assert _split_indices([0, 1], 3, 2) == [[0], [1], [0, 1]] + + +def test_run_design_rejects_budget_smaller_than_initial_population(): + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + with pytest.raises(ValueError, match="initial population size"): + run_design( + algorithm.operator_pheno[0], + algorithm.parameter_pheno[0], + _problem(N=3), + None, + SimpleNamespace(ProbN=3, ProbFE=2), + ) + + +def test_serial_path_updates_population_after_each_search_step(monkeypatch): + updates = [] + + def choose_all(population, *args): + return np.arange(len(population)), None + + def add_one(parent, *args): + return parent.decs() + 1.0, args[2] + + def add_ten(parent, *args): + return parent.decs() + 10.0, args[2] + + def update_to_new(combined, problem, *args): + updates.append(1) + return combined[-problem.N :], None + + components = { + "choose_test": choose_all, + "search_add_one": add_one, + "search_add_ten": add_ten, + "update_test": update_to_new, + } + original_get_component = solve_module.get_component + + def lookup_component(name): + if name in components: + return components[name] + return original_get_component(name) + + monkeypatch.setattr(solve_module, "get_component", lookup_component) + path = Pathway( + "choose_test", + [ + SearchStep("search_add_one", np.array([-np.inf, 1.0])), + SearchStep("search_add_ten", np.array([-np.inf, 1.0])), + ], + "update_test", + [], + ) + parameters = PathwayParam( + None, + [SearchParam(None, None), SearchParam(None, None)], + None, + ) + result = run_design( + [path], + [parameters], + _problem(bound=[[-100.0, -100.0], [100.0, 100.0]], Gmax=2), + None, + SimpleNamespace(ProbN=2, ProbFE=6, Seed=3), + ) + assert len(updates) == 2 + assert result["evaluations"] == 6 + + +def test_parallel_path_helper_repairs_secondary_and_truncates(monkeypatch): + def primary(parent, *args): + return parent.decs() + 2.0, args[2] + + def secondary(parent, *args): + return np.asarray(parent) + 1.0, args[2] + + components = {"primary_test": primary, "secondary_test": secondary} + original_get_component = solve_module.get_component + + def lookup_component(name): + if name in components: + return components[name] + return original_get_component(name) + + monkeypatch.setattr(solve_module, "get_component", lookup_component) + path = Pathway( + "choose_traverse", + [ + SearchStep( + "primary_test", + np.array([-np.inf, 1.0]), + "secondary_test", + ) + ], + "update_greedy", + [], + ) + parameters = PathwayParam(None, [SearchParam(None, None)], None) + parent = SolutionSet( + [ + Solution(np.array([0.0, 0.0]), 0.0, 0.0, 0.0), + Solution(np.array([1.0, 1.0]), 2.0, 0.0, 2.0), + ] + ) + produced, aux, evaluations = solve_module._execute_path( + path, + parameters, + parent, + [], + _problem(bound=[[-10.0, -10.0], [10.0, 10.0]]), + None, + SimpleNamespace(Seed=4), + 1, + 1, + ) + assert evaluations == 1 + assert len(produced) == 1 + assert len(aux) == 1 + + +def test_solve_helper_normalization_and_algorithm_errors(tmp_path): + normalized = _normalize_setting({"AlgName": "Continuous Random Search"}) + assert normalized.AlgName == "Continuous Random Search" + setting = SimpleNamespace(ProbFE=3) + _normalize_setting(setting) + assert setting.probfe == 3 + assert _to_array(None) is None + assert _to_array([]) is None + assert np.asarray(_to_array(2.0)).shape == () + np.testing.assert_array_equal(_to_array([[1, 2]]), [1, 2]) + + solution = Solution(np.array([1]), 1, 0, 1) + assert _ensure_solution_instance(solution) is solution + assert np.isinf(_ensure_solution_instance(None).fit) + converted = _ensure_solution_instance(SimpleNamespace(dec=[1], fit=2)) + assert converted.fit == 2 + assert np.isinf(_ensure_solution_instance(object()).fit) + assert _extract_mode(SimpleNamespace(type="continuous")) == "static" + assert _setting_with_budget({}, 7) == {"ProbFE": 7, "prob_fe": 7} + + with pytest.raises(FileNotFoundError): + input_algorithm(SimpleNamespace(AlgFile=tmp_path / "missing.json")) + with pytest.raises(ValueError, match="specify AlgFile"): + input_algorithm(SimpleNamespace(AlgName="")) + with pytest.raises(NotImplementedError, match="not available"): + input_algorithm(SimpleNamespace(AlgName="Unknown Optimizer")) + + +def test_run_algorithm_static_fallback_progress_and_unknown_mode(monkeypatch): + algorithm, _ = input_algorithm(SimpleNamespace(AlgName="Continuous Random Search")) + solution = Solution(np.array([0.0]), 1.0, 0.0, 1.0) + monkeypatch.setattr( + solve_module, + "run_design", + lambda *args: {"history": [solution], "best_solution": None}, + ) + app = SimpleNamespace(TextArea=SimpleNamespace(Value="")) + best, histories = run_algorithm( + algorithm, + [_problem()], + [], + app, + SimpleNamespace(AlgRuns=1), + ) + assert best[0][0] is solution + assert histories[0] == [solution] + assert app.TextArea.Value == "Solving... 100.0%" + + with pytest.raises(NotImplementedError, match="Unsupported problem mode"): + run_algorithm( + algorithm, + [_problem(type=["continuous", "dynamic"])], + [None], + None, + SimpleNamespace(AlgRuns=1), + ) diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index 9e44f5c..20e80ec 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -61,8 +61,15 @@ def test_design_initialize_and_evaluate_small_problem(): def test_space_supports_discrete_and_permutation(): - discrete = SimpleNamespace(type=["discrete", "static"], bound=np.array([[0, 0], [3, 3]]), N=3, Gmax=5) - permutation = SimpleNamespace(type=["permutation", "static"], bound=np.array([[1, 2, 3], [1, 2, 3]]), N=3, Gmax=5) + discrete = SimpleNamespace( + type=["discrete", "static"], bound=np.array([[0, 0], [3, 3]]), N=3, Gmax=5 + ) + permutation = SimpleNamespace( + type=["permutation", "static"], + bound=np.array([[1, 2, 3], [1, 2, 3]]), + N=3, + Gmax=5, + ) base_kwargs = dict( Mode="design", archive=["archive_best"], @@ -79,8 +86,22 @@ def test_space_supports_discrete_and_permutation(): ) discrete_setting = space([discrete], SimpleNamespace(**base_kwargs)) permutation_setting = space([permutation], SimpleNamespace(**base_kwargs)) - assert 'reinit_discrete' in discrete_setting.AllOp - assert 'reinit_permutation' in permutation_setting.AllOp + assert "reinit_discrete" in discrete_setting.AllOp + assert "reinit_permutation" in permutation_setting.AllOp + + +def test_continuous_space_contains_paper_search_components(): + result = space([DummyProblem()], SimpleNamespace()) + for component in ("cross_arithmetic", "cross_sim_binary", "search_eda"): + assert component in result.AllOp + + +def test_space_preserves_matlab_style_algorithm_settings(): + setting = SimpleNamespace(AlgP=2, AlgQ=4, AlgN=3) + result = space([DummyProblem()], setting) + assert result.alg_p == 2 + assert result.alg_q == 4 + assert result.alg_n == 3 def test_design_get_new_preserves_dimensions(): @@ -105,3 +126,40 @@ def test_design_get_new_preserves_dimensions(): assert new_design.performance.shape == design.performance.shape assert aux is None or isinstance(aux, dict) + +def test_parameter_tuning_uses_stateful_cma_for_one_algorithm(): + problem = DummyProblem() + setting = SimpleNamespace( + Mode="design", + archive=["archive_best"], + AlgP=1, + AlgQ=2, + IncRate=0.05, + ProbN=4, + ProbFE=40, + AlgN=1, + AlgFE=20, + AlgRuns=1, + Evaluate="exact", + Compare="average", + TunePara=True, + rng=np.random.default_rng(7), + ) + setting = space([problem], setting) + design = Design([problem], setting) + new_design, aux = design.get_new([problem], setting, inner_g=1, aux=None) + + assert new_design.performance.shape == design.performance.shape + assert isinstance(aux, dict) + assert aux["seed"] > sum(path.shape[0] + 1 for path in design.operator) + active = [ + int(value) + for path in design.operator + for value in np.concatenate(([path[0, 0]], path[1:, 0], [path[-1, -1]])) + ] + has_tunable_parameter = any( + 0 < index <= len(design.parameter) + and design.parameter[index - 1][0] is not None + for index in active + ) + assert ("cma_Disturb" in aux) == has_tunable_parameter From 2d441ab6f7c2dc07d3cd73cf97ac8cac3b9db849 Mon Sep 17 00:00:00 2001 From: Qi Zhao Date: Sun, 19 Jul 2026 20:36:38 +0800 Subject: [PATCH 2/4] Fix cross-platform CI checks --- .github/workflows/tests.yml | 2 ++ src/autooptlib/applications/stacking.py | 8 ++++---- tests/unit/test_reliability.py | 3 ++- 3 files changed, 8 insertions(+), 5 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1cc528e..6569a43 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -2,6 +2,8 @@ name: tests on: push: + branches: + - main pull_request: permissions: diff --git a/src/autooptlib/applications/stacking.py b/src/autooptlib/applications/stacking.py index b5c0de3..6fe71bd 100644 --- a/src/autooptlib/applications/stacking.py +++ b/src/autooptlib/applications/stacking.py @@ -124,7 +124,7 @@ def stacking_objective( levels, positions, orientations = _decode(decision, instance) heights = np.where(orientations == 0, instance.heights, instance.widths) - level_heights = np.zeros(instance.n_levels, dtype=float) + level_heights: np.ndarray = np.zeros(instance.n_levels, dtype=float) for level in range(instance.n_levels): members = levels == level if np.any(members): @@ -162,8 +162,8 @@ def stacking_constraints( instance.validate() levels, _, orientations = _decode(decision, instance) widths = np.where(orientations == 0, instance.widths, instance.heights) - width_excess = np.zeros(instance.n_levels, dtype=float) - weight_excess = np.zeros(instance.n_levels, dtype=float) + width_excess: np.ndarray = np.zeros(instance.n_levels, dtype=float) + weight_excess: np.ndarray = np.zeros(instance.n_levels, dtype=float) for level in range(instance.n_levels): members = levels == level width_excess[level] = float(np.sum(widths[members]) - instance.rack_width) @@ -221,7 +221,7 @@ def get_data(instance_id: Hashable) -> MaterialStackingInstance: def bounds(instance_id: Hashable) -> np.ndarray: instance = get_data(instance_id) - lower = np.zeros(instance.dimension, dtype=int) + lower: np.ndarray = np.zeros(instance.dimension, dtype=int) upper = np.concatenate( ( np.full(instance.n_items, instance.n_levels - 1, dtype=int), diff --git a/tests/unit/test_reliability.py b/tests/unit/test_reliability.py index 4cabd7e..ec832ad 100644 --- a/tests/unit/test_reliability.py +++ b/tests/unit/test_reliability.py @@ -271,9 +271,9 @@ def flaky(decision, instance): Problem=problem, InstanceSolve=[1], AlgName="Continuous Random Search", + AlgRuns=1, ProbN=2, ProbFE=2, - AlgRuns=1, EvalRetries=2, OutputDir=tmp_path, ) @@ -347,6 +347,7 @@ def synchronized_objective(decision, instance): Problem=problem, InstanceSolve=[1], AlgName="Continuous Random Search", + AlgRuns=1, ProbN=2, ProbFE=2, EvalWorkers=2, From 36b815eff71663c089691c2c9799dfc16713cca7 Mon Sep 17 00:00:00 2001 From: Qi Zhao Date: Sun, 19 Jul 2026 20:39:09 +0800 Subject: [PATCH 3/4] Update GitHub Actions runtimes --- .github/workflows/release.yml | 4 ++-- .github/workflows/tests.yml | 28 ++++++++++++++-------------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 905ead2..aa183bf 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -13,8 +13,8 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" - run: python -m pip install --upgrade pip build twine diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6569a43..01a2f44 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -32,8 +32,8 @@ jobs: - os: windows-latest python: "3.11" steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: ${{ matrix.python }} cache: pip @@ -44,8 +44,8 @@ jobs: coverage: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip @@ -60,8 +60,8 @@ jobs: package: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip @@ -101,8 +101,8 @@ jobs: docs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip @@ -113,8 +113,8 @@ jobs: typecheck: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip @@ -125,8 +125,8 @@ jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip @@ -138,8 +138,8 @@ jobs: performance: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/checkout@v7 + - uses: actions/setup-python@v6 with: python-version: "3.11" cache: pip From 99794a04334c8668bf1ac430f2038047e85a662f Mon Sep 17 00:00:00 2001 From: Qi Zhao Date: Sun, 19 Jul 2026 20:44:24 +0800 Subject: [PATCH 4/4] Finalize 1.2.0 release metadata --- CHANGELOG.md | 2 +- CITATION.cff | 2 +- README.md | 6 ++---- docs/getting-started.md | 5 ++--- 4 files changed, 6 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c40ca81..26ac7fb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,7 @@ All notable changes to AutoOptLib are documented here. -## 1.2.0 - 2026-07-18 +## 1.2.0 - 2026-07-19 ### Added diff --git a/CITATION.cff b/CITATION.cff index 21623c8..3450b26 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -3,7 +3,7 @@ message: "If you use AutoOptLib, please cite this software and the accompanying title: "AutoOptLib" type: software version: 1.2.0 -date-released: 2026-07-18 +date-released: 2026-07-19 license: Apache-2.0 repository-code: "https://github.com/auto4opt/AutoOptLib" url: "https://github.com/auto4opt/AutoOptLib" diff --git a/README.md b/README.md index ac4b007..85ca078 100644 --- a/README.md +++ b/README.md @@ -35,12 +35,10 @@ historical releases. AutoOptLib requires Python 3.9 or newer. -Until 1.2.0 is published on PyPI, install it from a clone: +Install the latest release from PyPI: ```bash -git clone https://github.com/auto4opt/AutoOptLib.git -cd AutoOptLib -python -m pip install . +python -m pip install autooptlib ``` For development: diff --git a/docs/getting-started.md b/docs/getting-started.md index c08f360..7c1a88c 100644 --- a/docs/getting-started.md +++ b/docs/getting-started.md @@ -2,11 +2,10 @@ ## Installation -AutoOptLib requires Python 3.9 or later. Until version 1.2.0 is available on -PyPI, install it from a checkout: +AutoOptLib requires Python 3.9 or later. Install the latest release from PyPI: ```bash -python -m pip install . +python -m pip install autooptlib ``` For development and documentation builds: