pybayesbandit 0.0.4

# pybayesbandit

Bayesian bandits in Python3.

## Quickstart

```text
$ pip install pybayesbandit
```

## Usage

```text
$ pybayesbandit --help
usage: pybayesbandit [-h] [-p PARAMS [PARAMS ...]] [-d MAXDEPTH] [-t TRIALS]
[-C C] [-e EPISODES] [-hr HORIZON] [--plot] [-v]
{random,ucb,thompson,vi,uct,rollout,aotree} {bernoulli}
{total,simple}

Bayesian bandits in Python3.

positional arguments:
{random,ucb,thompson,vi,uct,rollout,aotree}
learner type
{bernoulli} bandit type
{total,simple} game setting

optional arguments:
-h, --help show this help message and exit
-p PARAMS [PARAMS ...], --params PARAMS [PARAMS ...]
bandit parameters
-d MAXDEPTH, --maxdepth MAXDEPTH
maximum number of timesteps in the tree lookahead
(default=10)
-t TRIALS, --trials TRIALS
number of trials in Monte-Carlo sampling (default=30)
-C C UCT exploration constant (default=2.0)
-e EPISODES, --episodes EPISODES
number of simulation episodes (default=200)
-hr HORIZON, --horizon HORIZON
number of timesteps in each episode (default=100)
--plot plot cumulative regret
-v, --verbose verbose mode
```

## Examples

```text
$ pybayesbandit ucb bernoulli total -p 0.5 0.8 0.3 -e 100 -hr 50 -v

Running pybayesbandit ...
>> learner = ucb
>> bandit = bernoulli([0.5, 0.8, 0.3])
>> episodes = 100
>> horizon = 50
Done in 0.257 sec.

Results:
>> Reward = 32.3900 ± 3.4173
>> Regret = 7.6530 ± 1.7081
```

```text
$ pybayesbandit thompson bernoulli total -p 0.5 0.8 0.3 -e 100 -hr 50 -v

Running pybayesbandit ...
>> learner = thompson
>> bandit = bernoulli([0.5, 0.8, 0.3])
>> episodes = 100
>> horizon = 50
Done in 0.297 sec.

Results:
>> Reward = 35.2200 ± 3.8822
>> Regret = 4.4560 ± 2.6086
```

```text
$ pybayesbandit uct bernoulli total -p 0.5 0.8 0.3 -e 100 -hr 50 --trials 15 --maxdepth 5 -v

Running pybayesbandit ...
>> learner = uct(trials=15, maxdepth=5, C=2.0)
>> bandit = bernoulli([0.5, 0.8, 0.3])
>> episodes = 100
>> horizon = 50
Done in 7.066 sec.

Results:
>> Reward = 36.2800 ± 5.4828
>> Regret = 3.4360 ± 4.5856
```

## License

Copyright (c) 2018 Thiago Pereira Bueno All Rights Reserved.

pybayesbandit is free software: you can redistribute it and/or modify it
under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.

pybayesbandit is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser
General Public License for more details.

You should have received a copy of the GNU Lesser General Public License
along with pybayesbandit. If not, see http://www.gnu.org/licenses/.