metawalker

Learned black-box optimizers for big, complex search spaces, packaged as a drop-in replacement for scikit-learn's GridSearchCV / RandomizedSearchCV and as a plain minimize() function.

The optimizers were trained once, with evolution strategies, on cheap synthetic problems. After that they run on numpy alone, and each policy is a few KB.

pip install "metawalker[sklearn]"        # from the repo: pip install -e ".[sklearn]"

Try it with no setup: Colab demo. Code and experiments: GitHub.

Use it

from metawalker import minimize, Float, Int, Categorical

space = {"lr": Float(1e-5, 1e-1, log=True), "layers": Int(1, 8), "act": ["relu", "tanh"], "dropout": (0.0, 0.5)}
result = minimize(train_and_score, space, budget=300, method="auto", maximize=True, recheck=3)
result.best_params, result.best_value, result.recheck_values

As a scikit-learn search estimator (it uses the same internal hook as GridSearchCV, so cv, scoring, refit, n_jobs, cv_results_, best_params_ and best_estimator_ all behave the same):

from metawalker.sklearn import MetaWalkerCV

search = MetaWalkerCV(model, {"C": Float(1e-3, 1e3, log=True), "kernel": ["rbf", "linear"]},
                     n_iter=100, method="rlcma", cv=5).fit(X, y)

A GridSearchCV param_grid (a dict of value lists) works unchanged as param_space.

Choose your own panel: test methods on your own losses

You know your problem best, so the package helps you measure which method fits it. Give compare() your own loss functions, and optionally some simulated stand-ins shaped like your problem. It runs every method several times on each, with the same budget, and reports average rank, head-to-head win rates and time per run:

from metawalker import compare, make_problem, minimize

res = compare({"my_loss": (my_fn, my_space),
               "stand-in": make_problem("plateau", n=80, noise=0.01)},  # sphere, ellipsoid, rastrigin,
              methods=["rlcma", "walker_hp", "cmaes", "gp"],          # rosenbrock, ackley, levy, plateau
              budget=200, seeds=5)
print(res.summary())          # average rank, win rates (* = clear: 95% bootstrap interval excludes 0.5), time
print(res.success_table())    # how often each method reaches a target, and how many evals / seconds it took
print(res.budget_table())     # average rank at 1/8, 1/4, 1/2 and all of the budget, from the same runs
res.plot()                    # share of runs at the target vs evaluations and vs seconds (needs matplotlib)

panel = res.suggest_panel(3)  # best panel of up to 3 methods; often one method with the whole budget
print(panel.summary())
minimize(my_fn, my_space, budget=600, method=panel)             # it is a list, so pass it straight in

The simulated problems come from the same generator as our benchmark: rotated and shifted, with a known optimum, so compare() scores them by true regret rather than by lucky noisy readings.

Success means a run ends at or below a target score and stays there. The default target, per problem, is the score the best quarter of all runs reached. Pass target= a number, or {problem: number}, to set your own. Seconds include the optimizer's own time, so a slow optimizer is penalized as it would be in practice.

suggest_panel(k) scores a panel of j methods on each run's first budget // j evaluations, because that is what a panel gives each method. It tries every size from 1 to k and keeps the one that reaches the target on the most runs. Splitting the budget often hurts when the signal is rare, so the answer is sometimes a single method. Rankings at smaller budgets, and the panel scores, come from prefixes of the full runs. A learned or CMA-based method plans for its full budget, so these are approximations; rerun compare() at the budget you will use to confirm.

Bring your own optimizer

register() adds any optimizer under a method name. After that, the name works everywhere a built-in one does: minimize(), panels, compare(), MetaWalkerCV and AskTell. Decoding into your space, failed trials, caching, warmup and recheck are handled for you.

import itertools, numpy as np
import metawalker as mw

# a policy you trained on your own kind of problem (the object, or the file it was saved to)
mw.register("my_walker", "my_optimizer.npz")

# or any function: call f(x) on points in the unit cube [0, 1]^n until f.evals reaches budget (lower is better)
def grid(f, n, budget, rng):
    for x in itertools.product(np.linspace(0, 1, 5), repeat=n):
        if f.evals >= budget:
            break
        f(np.array(x))
mw.register("grid", grid)

mw.compare({"mine": (my_fn, my_space)}, methods=["my_walker", "grid", "rlcma"], seeds=5)
mw.minimize(my_fn, my_space, budget=300, method=["my_walker", "cmaes"])     # in a panel
MetaWalkerCV(model, space, n_iter=50, method="grid").fit(X, y)              # in a scikit-learn search

Tune an RL agent, or train an optimizer for your own kind of problem

Tuning an RL agent on your own environment is an ordinary search. Wrap one training run as a function that returns the score you care about:

import metawalker as mw

def train_agent(p, timesteps=200_000, seed=0):  # one training run on your env; returns mean evaluation return
    agent = make_agent(my_env, lr=p["lr"], gamma=p["gamma"], ent_coef=p["ent_coef"], seed=seed)
    agent.learn(timesteps)
    return evaluate(agent, my_env, episodes=20)

space = {"lr": mw.Float(1e-5, 1e-2, log=True), "gamma": mw.Float(0.9, 0.9999),
         "ent_coef": mw.Float(1e-4, 0.1, log=True)}
res = mw.minimize(train_agent, space, budget=100, maximize=True, recheck=3)  # recheck: RL returns are noisy
res = mw.minimize(train_agent, space, budget=150, method="panel", maximize=True, n_jobs=3)  # 3 methods at once

If you run searches like this often (new environments, new agent variants), you can also train the optimizer itself for your kind of problem. That is how the shipped policies were made: trained on cheap synthetic functions, then used unchanged on the expensive problem. train() takes factories that return a fresh (function, space) on every call, fine-tunes one of the shipped policies with evolution strategies, and returns a policy you can save, register and compare:

import random

def like_my_problem():  # a fresh cheap instance each call, shaped like yours: 12 knobs, mostly flat, noisy
    return mw.make_problem("plateau", n=12, noise=0.05, seed=random.randrange(10**9))

policy = mw.train([like_my_problem], init="walker_hp", budget=100, generations=50, n_jobs=8)
policy.save("my_tuner.npz")
mw.register("my_tuner", "my_tuner.npz")
res = mw.minimize(train_agent, space, budget=100, method="my_tuner", maximize=True, recheck=3)

• init picks the policy to fine-tune: "walker_hp", "walker", "rlcma" or "rlbo", or "scratch" for a walker from random weights. Fine-tuning is far faster and more stable than starting over.
• The factories can return anything minimize() accepts: a make_problem() stand-in, your own analytic proxy, or a tiny version of the real thing (a small env, a few hundred steps). Pass maximize=True if they return a reward. A function with a .regret(params) method (as make_problem() problems have) is scored by true regret, which is much less noisy than the best observed value.
• Cost: each generation runs 2 * pairs * instances_per_gen searches (320 by default) of budget evaluations each, so a stand-in has to evaluate in milliseconds. Real RL runs are almost always too slow for this. Train on a stand-in, then search the real environment with the result. rlbo costs about 20 times a walker per evaluation, so keep its training budgets small.
• Train with about the budget you will search with. Check the result on instances it never trained on, with compare() against the shipped policies, before you trust it.
• callback(gen, policy) runs after every generation (save checkpoints there), and policy.history holds the mean training score per generation. It is noisy, because every generation draws new instances.
• With n_jobs > 1, the functions the factories return are sent to worker processes, so they must be picklable: a make_problem() problem, an instance of a top-level class, or a top-level function, not a lambda.

With compare(n_jobs > 1), define your function at the top level of a module so it can be sent to the worker processes, the same rule as for the objective.

What else it does for you

import metawalker as mw

# 1. don't know your landscape? probe it (~8% of the budget), then let it pick from the benchmark's rules
res = mw.minimize_auto(my_fn, my_space, budget=500)
print(res.recommendation.reasons)   # e.g. "92% of probes returned the same value: a plateau objective -> walker_hp"

# 2. a best result you can trust: re-test the top candidates on fresh evaluations, pick by mean, report a CI
cert = mw.certify(my_fn, res, top_k=5, repeats=5)
print(cert.summary())               # cert.shrinkage = how much the raw best score was inflated by noise

# 3. which parameters mattered, which direction helps, and the best range (free, from the search history)
print(mw.importance(res).summary())

# 4. train an optimizer for YOUR kind of problem on cheap stand-ins (see "Tune an RL agent" above)
policy = mw.train([like_my_problem], init="rlcma", generations=50, n_jobs=8)
policy.save("my_optimizer.npz"); policy.minimize(my_fn, my_space, budget=300)

# 5. evaluate on your own workers / cluster, or plug into an existing Optuna study
with mw.AskTell(my_space, budget=200, method="rlcma") as at:
    while not at.done:
        tid, params = at.ask(); at.tell(tid, run_somewhere(params))
from metawalker.asktell import MetaWalkerSampler   # optuna.create_study(sampler=MetaWalkerSampler(dists, budget=200))

# 6. compare methods on your own losses, in parallel
mw.compare({"mine": (my_fn, my_space)}, methods=["rlcma", "gp", "cmaes"], seeds=5, n_jobs=8)

Notes:

• compare(n_jobs>1) and train(n_jobs>1) need picklable functions, plus an if __name__ == "__main__": guard on Windows.
• certify() and importance() read the search direction from the Result.
• Importance computed from an optimizer's history is biased toward the regions that optimizer sampled. Random-search histories give the cleanest estimate.

Which method

Any method also takes warmup=f. It spends that fraction of the budget on Latin-hypercube points first, and those points count toward the budget and appear in the history. The walkers then start from the best 4 points and the CMA-based methods from the best one, while the other methods simply run on what is left. Use it when the signal is rare: without it the walkers shrink and restart instead of exploring, so plain random search finds a tiny live region more often than they do. The distilled rules come from the policy study (results/analysis/policy_report.md). The numbers above are medians over 3 seeds at budget 1000, from sphere, Rosenbrock, Rastrigin, Ackley, Levy and plateau at 10 and 50 parameters. compare() accepts all of these methods.

Benchmark scope. Rotated and shifted synthetic functions (sphere, ellipsoid, Rastrigin, Rosenbrock, plus held-out Ackley and Levy) with and without noise, a honeypot-like plateau family, and a real RL honeypot search. Budgets were 300–10,000 evaluations. Each learned method was trained 3 times from scratch, with results reported per training run. A comparison against Optuna GP, Optuna multivariate TPE, SMAC3 and Nevergrad NGOpt is in progress.

Behavior to know

• Invalid configurations: if your function raises or returns nan/inf, that counts as a failed trial (result.n_failed). The search keeps going.
• Noisy objectives: the best observed value is biased upward (the winner's curse). Use recheck=k to re-evaluate the winner k more times.
• Categorical parameters are one coordinate split into equal bins. That's fine for a few choices, but unordered categoricals with many choices are a known weak spot.
• 1-parameter searches with the CMA-based methods add an ignored dummy coordinate, because pycma needs 2+.
• The panel budget is split equally, with no racing between methods. minimize(..., n_jobs=3) runs the methods at the same time, in threads, with the same result. That helps when your function spends its time outside Python (torch, jax, a subprocess, a remote job), and it must be safe to call from several threads.

Tests

pytest from the repo root. tests/test_regression.py checks that every optimizer reproduces the pre-refactor benchmark code bit for bit (tests/golden.json).