PyMAB
Website · Interactive Arcade · Documentation
PyMAB is a typed Python library for reliable, reproducible multi-armed bandit experiments. Version 2 separates environment, policy, decision, and observation randomness; treats replicates as the independent unit of analysis; and records enough provenance to reproduce saved results.
Install
pip install pymab
Optional features are available through pymab[plot], pymab[analysis], and
pymab[bayes].
Binary wheels execute all 27 built-in policies and built-in experiment loops in Rust while preserving the Python API. The same core is available to Rust users:
cargo add pymab
Use ExperimentConfig(backend="rust", ...) to require native execution,
backend="python" for the private behavioral reference, or the default
backend="auto" to use Rust whenever every component is native-compatible.
Basic experiment
import numpy as np
from pymab import BanditEnvironment, Experiment, ExperimentConfig
from pymab.policies import EpsilonGreedyPolicy, UCBPolicy
result = Experiment(
environment=BanditEnvironment(means=np.array([0.1, 0.4, 0.8])),
policies={
"epsilon-greedy": EpsilonGreedyPolicy(n_arms=3, epsilon=0.1),
"ucb": UCBPolicy(n_arms=3),
},
config=ExperimentConfig(horizon=100, n_replicates=20, seed=42),
).run()
print(result.average_reward_by_step[-1])
print(result.cumulative_regret[-1])
Policy IDs are explicit and stable. Adding or reordering policies does not change another policy's random stream or the simulated environment path.
Paired comparison
from pymab import compare
from pymab.benchmarking import BenchmarkConfig
from pymab.policies import RandomPolicy, UCBPolicy
from pymab.statistics import BootstrapConfig
benchmark = compare(
{"random": RandomPolicy(n_arms=3), "ucb": UCBPolicy(n_arms=3)},
environment=BanditEnvironment(means=np.array([0.1, 0.3, 0.8])),
config=ExperimentConfig(horizon=100, n_replicates=20, seed=7),
analysis=BenchmarkConfig(
bootstrap=BootstrapConfig(seed=91),
baseline="random",
),
)
print(benchmark.lowest_mean_regret_policy)
print(benchmark.compare_to_baseline())
Intervals are deterministic paired bootstrap intervals over independent replicates. A lower point estimate is not presented as statistical proof of a winner.
Offline evaluation
Fixed-policy estimators require logged-action propensities and report both raw and post-clipping overlap diagnostics:
from pymab import LoggedBanditDataset, estimate_policy_value
from pymab.offline import EstimateMethod, EstimatorConfig
from pymab.statistics import BootstrapConfig
class FixedTarget:
def probabilities(self, context):
return np.array([0.25, 0.75])
logged = LoggedBanditDataset(
actions=np.array([0, 1, 0, 1]),
rewards=np.array([0.0, 1.0, 0.0, 1.0]),
propensities=np.full(4, 0.5),
n_arms=2,
)
estimate = estimate_policy_value(
logged,
FixedTarget(),
config=EstimatorConfig(
method=EstimateMethod.SNIPS,
bootstrap=BootstrapConfig(n_resamples=500, seed=7),
),
)
assert estimate.estimate == 0.75
Adaptive replay requires the logging design explicitly. Use
logging_scheme="uniform" only for uniformly randomized logs; non-uniform
logs also require propensities and use rejection sampling.
For a detailed treatment of single-slot recommendations and defensive verification, including fit checks, offline evaluation and rollout guardrails, see Contextual bandit use cases.
Probability environments
Additive Gaussian drift is intentionally rejected for Bernoulli means. Use log-odds drift instead:
from pymab import BernoulliReward, ProbabilityDrift
environment = BanditEnvironment(
means=np.array([0.2, 0.5, 0.8]),
reward_model=BernoulliReward(),
dynamics=ProbabilityDrift(logit_std=0.05),
)
Development
make sync
make format
make lint
make security
make test
make docs
make docs-linkcheck # external network check; run separately
make benchmark # release-mode same-machine backend comparison
make pages-build
make pages-serve # http://127.0.0.1:8080/pymab/
make docs performs a clean warnings-as-errors HTML build, executes Sphinx
doctests, enforces 100% API docstring coverage, and runs every Python snippet
in this README.
The complete v1-to-v2 migration is documented in docs/source/migration_v2.rst;
the Rust backend migration is documented in docs/source/migration_rust.rst.
1 maintainer
Metadata
1 maintainer
Release files for pymab 2.0.1
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| Tags | CPython 3.11 Linux glibc 2.28+ ARM64 |
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| Tags | CPython 3.11 macOS 11.0+ ARM64 |
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| Tags | CPython 3.11 macOS 10.12+ x86-64 |
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