mabwiser

library2.7.4pypypi✓ verified 84d ago

MABWiser is a Python library for parallelizable, contextual multi-armed bandits. It supports a wide range of bandit learning policies (e.g., epsilon-greedy, Thompson Sampling, LinUCB) and neighborhood policies for contextual bandits. Version 2.7.4 is the latest release, with active development.

ai-ml

pip install mabwiser

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · MABWISER

mabwiser

ai-mlpythonv2.7.4

Install

18.0s avg

Import

4280ms

Disk

445MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.7.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 18.0s · import 4.280s · 427MB

445MB installed

● package 445MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

MAB

✓ from mabwiser.mab import MAB

✗ from mabwiser import MAB

MAB is not exposed at the package level; it's in the mab submodule.

LearningPolicy

✓ from mabwiser.mab import LearningPolicy

Commonly used to specify learning policies like LearningPolicy.EpsilonGreedy.

NeighborhoodPolicy

✓ from mabwiser.mab import NeighborhoodPolicy

Used for contextual bandits with nearest neighbor policies.

Minimal example: non-contextual epsilon-greedy bandit with partial_fit, and contextual bandit with Cluster neighborhood.

import numpy as np from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy # Non-contextual bandit arms = ['arm1', 'arm2'] mab = MAB(arms, LearningPolicy.EpsilonGreedy(epsilon=0.1)) # Simulate fitting: use dummy rewards for _ in range(100): arm = mab.predict() reward = np.random.binomial(1, 0.7 if arm == 'arm1' else 0.3) mab.partial_fit(arm, reward) # Contextual bandit with nearest neighbor contexts = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]) mab_ctx = MAB(arms, LearningPolicy.EpsilonGreedy(epsilon=0.1), NeighborhoodPolicy.Cluster()) mab_ctx.fit(contexts, np.array(['arm1', 'arm2', 'arm1']), np.array([1, 0, 1])) print(mab_ctx.predict(contexts[-1:]))

Debug

Known issues

breakingIn version 2.4.0, the scaler argument changed from a pre-trained scaler dict to a boolean `scale` flag. Code using `arm_to_scaler` will break.

fix

Replace `arm_to_scaler` argument with `scale=True` and let MABWiser fit scalers internally.

affects: >=2.4.0

breakingnp.Inf removed in 2.7.4. Code referencing `np.Inf` will raise AttributeError.

fix

Replace `np.Inf` with `np.inf`.

affects: >=2.7.4

deprecatedDirect use of `LearningPolicy` strings (e.g., `LearningPolicy('epsilon_greedy')`) is deprecated in favor of the enum-like `LearningPolicy.EpsilonGreedy` objects.

fix

Use `LearningPolicy.EpsilonGreedy(epsilon=0.1)` instead of `LearningPolicy('epsilon_greedy')`.

affects: >=2.7.1

gotchaMAB.predict() for non-contextual policies expects an empty or None context array when using recent versions (>=2.4.0). Providing a non-empty context will raise an error.

fix

Call `mab.predict()` without arguments or pass an empty array like `np.array([[]])` for batch predictions.

affects: >=2.4.0

Errors

Common errors & fixes

AttributeError: module 'mabwiser' has no attribute 'MAB'

Importing MAB from the top-level package instead of the mab submodule.

fix

Use: from mabwiser.mab import MAB

TypeError: 'LearningPolicy' object is not callable

Using LearningPolicy as a function with a string argument instead of using the enum attribute directly.

fix

Use: LearningPolicy.EpsilonGreedy(epsilon=0.1)

ValueError: The truth value of an array with more than one element is ambiguous

Passing a full context matrix to predict() for a non-contextual bandit.

fix

For non-contextual, call predict() with no arguments or an empty array.

Upgrade

Version history

2.7.4latest on PyPI · released Aug 30, 2024

Audit

Dependencies

No dependency data recorded yet.

Agent activity

6 hits · last 30 days

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Resources

githubhttps://github.com/fidelity/mabwiser ↗

packagehttps://pypi.org/project/mabwiser/ ↗

docshttps://fidelity.github.io/mabwiser/ ↗