/DeepMAB

MAB and linear/non-linear Contextual MAB algorithms

Primary LanguagePythonMIT LicenseMIT

Deep Contextual MAB

MAB and linear/non-linear Contextual MAB algorithms.

Algorithms

Multi-Arm Bandits

  • Epsilon Greedy
  • UCB
  • Thompson Sampling

Contextual Multi-Arm Bandits

  • Neural Net Epsilon Greedy
  • LinUCB
  • Neural Net UCB

Usage Instructions

  • This project is published on PyPI. To install package, run:

    pip install deep-mab

  • To run the algorithms, import the package and call the respective functions. For example, to run the LinUCB algorithm, run:

    from deep_mab.cmab import LinUCB
model = LinUCB(n_arms=10, alpha=1, fit_intercept=True)
model.fit(X_train, y_train)
model.predict(X_test)

  • For more details, refer to the documentation.

  • To run the examples, clone the repository and run the following commands:

    cd deep-mab
pip install -r requirements.txt
python examples/linucb_example.py

  • To run the tests, run the following commands:

    cd deep-mab
pip install -r requirements.txt
pytest