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
Metadata
Release files for deep-mab 0.1.1
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| File | Size | Uploaded | Provenance | |
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| deep_mab-0.1.1.tar.gz | 6.2 kB | Without provenance | Details |
Built distribution (wheel)
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|---|---|---|---|---|
| deep_mab-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.0 kB
Release files / deep_mab-0.1.1.tar.gz
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| Size | 8.7 kB |
| Tags | Python 3 |
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