{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"MABWiser implements multi-armed bandit algorithms for decision-making under uncertainty, supporting context-free, parametric, and non-parametric contextual models with built-in parallelization for training and testing.","skillfed_tags":["reinforcement-learning","decision-optimization","research-library"],"use_cases":["Build a personalized recommendation system that learns which content to show each user by treating items as arms and user feedback as rewards.","Implement A/B testing or multi-variant testing where you need to dynamically allocate traffic to better-performing variants while still exploring.","Solve combinatorial optimization problems (e.g., vehicle routing, scheduling) by using bandits to select destroy-repair operations in a metaheuristic.","Prototype and compare different exploration-exploitation strategies on a decision problem before deploying to production.","Tune hyperparameters of bandit policies using the built-in simulation utility to find optimal settings for your reward structure."],"what_it_does":"MABWiser is a research library for prototyping multi-armed bandit algorithms\u2014a class of decision-making strategies that balance exploring new options against exploiting known good ones. It provides multiple learning policies (Epsilon Greedy, LinUCB, Thompson Sampling, UCB1, and others) and neighborhood policies (Clusters, K-Nearest, LSH Nearest, Radius, TreeBandit) to handle both simple arm-selection problems and contextual variants where decisions depend on feature data. The library follows scikit-learn conventions, includes a simulation utility for policy comparison and hyperparameter tuning, and supports parallelization across training and prediction.\n\nThe package is built on standard scientific Python dependencies (numpy, pandas, scikit-learn, scipy, joblib) and is maintained by Fidelity's AI Center of Excellence. It is used in downstream projects like Mab2Rec for personalized recommendations and ALNS for combinatorial optimization. The library is dormant\u2014last release was 2024-08-30 with no active development\u2014but remains stable for research and prototyping use.","worth_installing":"Yes, if you are prototyping or researching multi-armed bandit algorithms in Python. The library is stable, well-documented, has no known vulnerabilities, and low install friction. However, dormant maintenance means no active bug fixes or feature development\u2014suitable for research and learning, less ideal if you need ongoing support or expect breaking changes in your dependencies."},"id":"mabwiser","links":{"html":"https://skillfed.io/packages/mabwiser","md":"https://skillfed.io/packages/mabwiser.md","pypi":"https://pypi.org/project/mabwiser/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-08-30","license_spdx":null,"license_treatment":"permissive","name":"mabwiser","python_support":"unspecified","summary":"MABWiser: Parallelizable Contextual Multi-Armed Bandits Library"},"popularity":{"monthly_downloads":99270,"position":13031,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.7.4"}