# tsampling
Thompson Sampling Multi-Armed Bandit for Python
A package that implements the Thompson Sampling approach for a Multi-Armed Bandit.
The purpose of this project is to help people simply create and maintain Thompson Sampling experiments that have Bernoulli and Poisson distributions.
## Usage
### Setting up the experiment:
The following method will instantiate the experiment with the default priors.
```python
from tsampling.bernoulli import BernoulliExperiment
experiment = BernoulliExperiment(arms=2)
```
You can also set your custom priors by using the Priors module:
```python
from tsampling.bernoulli import BernoulliExperiment
from tsampling.priors import BetaPrior
pr = BetaPrior()
pr.add_one(mean=0.5, variance=0.2, effective_size=10, label="option1")
pr.add_one(mean=0.6, variance=0.3, effective_size=30, label="option2")
experiment = BernoulliExperiment(priors=pr)
```
### Performing an action:
You can randomly choos which arm to "pull" in the multi-armed bandit:
```python
experiment.choose_arm()
```
### Updating reward:
You can update the different arms information by adding reward information:
```python
rewards = [{"label":"option1", "reward":1}, {"label":"option2", "reward":0}]
experiment.add_rewards(rewards)
```
### Plotting Posterior Distribution:
You can plot the posterior distribution
```python
experiment.plot_posterior()
```
## Installation
### Pip
```
pip install tsampling
```
## License
Free software: MIT license
## Credits
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
_Cookiecutter: https://github.com/audreyr/cookiecutter
`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
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"description": "# tsampling\nThompson Sampling Multi-Armed Bandit for Python\n\nA package that implements the Thompson Sampling approach for a Multi-Armed Bandit.\nThe purpose of this project is to help people simply create and maintain Thompson Sampling experiments that have Bernoulli and Poisson distributions.\n\n## Usage\n\n### Setting up the experiment:\n\nThe following method will instantiate the experiment with the default priors.\n```python\nfrom tsampling.bernoulli import BernoulliExperiment\n\nexperiment = BernoulliExperiment(arms=2)\n```\n\nYou can also set your custom priors by using the Priors module:\n```python\n\nfrom tsampling.bernoulli import BernoulliExperiment\nfrom tsampling.priors import BetaPrior\n\npr = BetaPrior()\npr.add_one(mean=0.5, variance=0.2, effective_size=10, label=\"option1\")\npr.add_one(mean=0.6, variance=0.3, effective_size=30, label=\"option2\")\nexperiment = BernoulliExperiment(priors=pr)\n```\n\n### Performing an action:\nYou can randomly choos which arm to \"pull\" in the multi-armed bandit:\n```python\nexperiment.choose_arm()\n```\n\n### Updating reward:\nYou can update the different arms information by adding reward information:\n\n```python\nrewards = [{\"label\":\"option1\", \"reward\":1}, {\"label\":\"option2\", \"reward\":0}]\nexperiment.add_rewards(rewards)\n```\n\n### Plotting Posterior Distribution:\nYou can plot the posterior distribution \n\n```python\nexperiment.plot_posterior()\n```\n\n## Installation\n\n### Pip \n```\npip install tsampling\n```\n\n## License\n Free software: MIT license\n\n## Credits\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n_Cookiecutter: https://github.com/audreyr/cookiecutter\n`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n",
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