Metadata-Version: 2.5
Name: amm-optimizer
Version: 0.2.0
Summary: AMM: budget-aware routing of classical derivative-free solvers by online landscape probes (import amm, or atm)
Project-URL: Homepage, https://github.com/BjBodner/amm-optimizer
Project-URL: Source, https://github.com/BjBodner/amm-optimizer
Project-URL: Issues, https://github.com/BjBodner/amm-optimizer/issues
Project-URL: Changelog, https://github.com/BjBodner/amm-optimizer/blob/main/CHANGELOG.md
Author: Benjy Bodner
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License-File: LICENSE
Keywords: BBOB,CMA-ES,COCO,algorithm selection,black-box optimization,derivative-free
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.10
Requires-Dist: numpy>=1.23
Requires-Dist: scipy>=1.16
Provides-Extra: bench
Requires-Dist: coco-experiment; extra == 'bench'
Requires-Dist: cocopp; extra == 'bench'
Provides-Extra: dev
Requires-Dist: hypothesis; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: pytest>=7; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Description-Content-Type: text/markdown

# AMM: budget-aware routing of classical solvers by online probes

AMM is a derivative-free optimizer for continuous black-box problems with a **known evaluation
budget** and a **known box**. It does not run one algorithm: it spends a small, counted part of the
budget on *online probes* (curvature, plateau, separability, kink, basin and fractal readings of the
objective near the incumbent) and uses their outcomes to route the rest of the budget among
quasi-Newton (SLSQP, L-BFGS-B), trust-region (COBYQA), coordinate (STEP), multistart (MLSL) and
population (CMA-ES family, the ATM sampler) engines. A probe that does not fire leaves the run on
a general path, so a misreading costs evaluations, not the run. Every rule reads only the
dimension, the budget, the box and evaluated function values.

The method, its development protocol and its evaluation on COCO/bbob are described in the paper
(citation below). The package was developed under the code name ATM; the implementation therefore
lives in `atm`, and `amm` is an alias for the same objects.

## Install

```bash
pip install amm-optimizer            # NumPy and SciPy only
pip install -e ".[dev]"              # from a checkout; add ",bench" for the COCO/bbob tooling
```

## Quick start (the configuration frozen for the paper)

```python
import numpy as np
import amm                                  # `import atm` is equivalent

def f(x):
    return float(np.sum((x - 1.0) ** 2))

result = amm.minimize(
    f,
    x0=np.zeros(10),
    budget=100 * 10,                        # maximum number of function evaluations
    seed=0,                                 # runs are deterministic given the seed
    bounds=(-5, 5),                         # required: the box (scalars or one value per coordinate)
    config=amm.presets.get("c46"),          # the frozen configuration; see docs/FREEZE_2026-10-22.md
)
print(result.x, result.fun, result.nfev)
```

`bounds` is required by this preset (it raises `ValueError` without it). The box is used, not
enforced as a constraint: samples are folded into it before the function is called. The objective
must be deterministic. Tested on noiseless, continuous, box-constrained problems with budgets of
10 to 1000 x the dimension.

**Reproducing the paper.** The code is that of git tag `freeze-2026-10-22`; the configuration hash of
`amm.presets.get("c46").config_hash()` is `2153948ebed51150`. Pre-registrations and result notes of
every batch are in `docs/research/batches/`, and `docs/FREEZE_2026-10-22.md` records what was frozen
and why.

**Citation.** See `CITATION.cff` (the paper's reference will be added when it is public).

## Legacy ATM presets

The sections below document the original ATM sampler and the older presets
(`ATMConfig()`, `composite()`, `improved()`); they are kept because every recorded result and test
depends on them. This repository is a clean, tested re-implementation of the original ATM that
reproduces its results exactly (see [docs/DEVIATIONS.md](docs/DEVIATIONS.md)).

```python
import numpy as np
import atm

def sphere(x):
    return np.sum((x - 2) ** 2)

result = atm.minimize(sphere, x0=np.zeros(10), budget=2000, seed=0)
print(result.x, result.fun, result.nfev)
```

`budget` is the maximum number of function evaluations, and ATM uses all of them (the
best point found is kept even if the budget ends mid-iteration). Pass `seed=` (or an
`rng=np.random.RandomState`) for reproducible runs. A starting point where the function is
NaN or infinite raises `ValueError`; NaN or infinite values elsewhere count as failed
evaluations and never win.

The defaults reproduce the original ATM exactly (see `docs/DEVIATIONS.md`);
`ATMConfig(strict_budget=False)` also restores the original's looser budget accounting.

## Better results: the improved preset

If you know the search box, `ATMConfig.improved()` gets far more out of the same budget:

```python
result = atm.minimize(
    sphere,
    x0=np.zeros(10),
    budget=2000,
    seed=0,
    bounds=(-5, 5),                      # scalars or one value per coordinate
    config=atm.ATMConfig.improved(),
)
print(result.fun)
```

The box is used, not enforced as a constraint: samples are folded back into it before the
function is called, so the function only ever sees points inside. Without `bounds=` the
preset raises `ValueError`. The objective must be deterministic.

**This preset is a hybrid, not just ATM.** It takes ATM's sampler with five improvements
(success-rate control of the search width, a uniform first pool over the box, bounds folded
with a 25 % corner clip, a pool sized from the budget, no re-evaluation of the incumbent) and
adds local-search stages: a simplex search in up to 4 dimensions and a (1+1)-ES above, for the
first part of small budgets; a learned covariance up to 10 dimensions; and, up to 10
dimensions, a simplex search over the last half of the budget. Each option is documented in
`docs/DEVIATIONS.md` and `docs/EXPERIMENTS.md`.

Presets, from simplest to largest:

| Preset | What it is | Evidence |
|---|---|---|
| `ATMConfig()` | the original ATM, bit for bit | `tests/golden`, 400-case parity sweep |
| `ATMConfig.composite()` | ATM's sampler plus the five improvements; stays pure ATM | +.101, +.097, +.099 on three untouched holdouts |
| `ATMConfig.improved()` | the composite plus the local stages above (recommended) | +.196 over the original, +.096 over the composite on a fourth holdout |
| `ATMConfig.improved_wave6()` | an earlier version, kept for reproducibility; loses on multimodal functions at medium budgets | Tier 4, `docs/HOLDOUT_TIER4.md` |

`tests/golden/presets.json` pins the exact cost traces of all of them.

**What to expect from `improved()`**, measured on COCO bbob (2021 suite) instances 96-100 that
were not used to design it (`docs/HOLDOUT_TIER5.md`, 18,000 runs per arm, raw COCO evaluation):

* ECDF value (fraction of targets reached) improves by **+.196** [95 % CI +.180, +.212] over
  the defaults, averaged over budgets of 30, 100 and 300 x dimension, and by **+.096**
  [+.085, +.108] over `composite()`.
* The gain shrinks as the dimension grows: over the defaults +.412 at D = 2, +.197 at D = 5,
  +.127 at D = 10, +.082 at D = 20, +.055 at D = 40; over `composite()` +.215 at D = 2 down to
  +.017 at D = 40, where the two are almost the same algorithm.
* It does **not** beat everything: a plain (1+1)-ES with the 1/5 rule is still ahead at 40
  dimensions (-.031) and level at 20, and CMA-ES is far ahead of every ATM variant at large
  budgets (on the Tier 4 instances: .597 against .504 for the closest hybrid at 1000 x D).
* Only noiseless, continuous, box-constrained problems with budgets of 10 to 1000 x D were
  tested. The three-algorithm design means its gain should not be described as an improvement
  of ATM's sampler alone; use `composite()` if you want that.

## Development

```bash
pytest -m "not slow"      # fast suite, includes golden-master and parity tests
pytest -m slow            # 400 random cases against the frozen original in reference/
python tools/record_golden.py   # re-record golden masters (only for deliberate changes)
python benchmarks/perf.py       # repeatable speed benchmark, original vs this package
python benchmarks/block_profile.py   # where the time goes, per algorithm block
```

| Path | What it is |
|------|------------|
| `src/atm/` | the package |
| `reference/` | byte-identical copy of the original module, used only by tests |
| `tests/golden/` | recorded trajectories from the original |
| `docs/AUDIT.md` | audit of the original, with severity and evidence |
| `docs/REFACTOR_PLAN.md` | phases, principles, definition of done |
| `docs/DEVIATIONS.md` | every difference from the original |
| `docs/PERF.md` | speed measurements, method and repeatability |
| `docs/BENCHMARKS.md` | COCO protocol, harness usage, parity and profiling results |
| `docs/EXPERIMENTS.md` | prioritized, lightweight experiments to improve ATM on bbob (protocol, cards, log) |
| `docs/research/` | literature report, code-review report and their notes |
| `docs/WORKPLAN.md` | scan of the codebase and the plan for readability, tests, extensibility, profiling |
| `benchmarks/` | speed harness (`perf.py`), per-block timing (`block_profile.py`), COCO/bbob harness (`run_bbob.py`, `compare_bbob.py`, `postprocess_bbob.py`) and stored results |

## CI runs on your laptop

There is deliberately no GitHub Actions CI for now (it would use Actions minutes). Run the same checks locally with the project's virtual environment:

```bash
.venv\Scripts\python tools/ci.py          # fast gate, about 2 minutes: lint, format, types, quick tests
.venv\Scripts\python tools/ci.py --full   # about 3 minutes (longer on a busy machine): all tests incl. the 400-case parity sweep, 100 % coverage gate
```

Both stop at the first failing step and exit non-zero (`--keep-going` runs everything). To run the fast gate automatically before every `git push`, opt in with `git config core.hooksPath .githooks` (undo with `git config --unset core.hooksPath`; skip once with `git push --no-verify`). It is not enabled by default.

A GitHub Actions workflow is parked, switched to manual start, in `ci/github-actions-ci.yml`; moving it to `.github/workflows/` needs a token with the `workflow` scope.

Licensed under Apache-2.0.
