boostsrl 0.1

Python wrappers for using BoostSRL jar files.

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<img src="media/box2.png" />
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| License | Build Status | Codecov |
| --- | --- | --- |
| [![][license img]][license] | [](https://travis-ci.org/batflyer/boostsrl-python-package) | [![][codecov img]][codecov link] |

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# boostsrl_java

> **BoostSRL** (Boosting for Statistical Relational Learning) is a gradient-boosting based approach to learning statistical relational models.
> Sriraam Natarajan, Tushar Khot, Kristian Kersting and Jude Shavlik, Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine. SpringerBriefs in Computer Science, ISBN: 978-3-319-13643-1, 2015

*boostsrl_java* is a Python package with wrappers for creating background knowledge and performing learning and inference.

## Getting Started

### Prerequisites

* Java 1.8
* Python (2.6, 2.7, 3.3, 3.4, 3.5, 3.6)
* subprocess32 (if using Python 2.7: `pip install subprocess32`)

### Installation

* The latest stable build can be installed with pip:

```bash
$ pip install boostsrl_java
```

* For versions in development, use the most recent available on GitHub

```bash
# Clone the repository
$ git clone https://github.com/batflyer/boostsrl-python-package.git
# Test the build
$ python tests/test.py
```

### Basic Usage

```python
>>> from boostsrl import boostsrl

'''Step 1: Background Knowledge'''

# Sample data is built in from the 'Toy Cancer' Dataset, retrieve it with sample_data
>>> bk = boostsrl.example_data('background')

# Create the background knowledge or 'Modes,' where 'cancer' is the target we want to predict.
>>> background = boostsrl.modes(bk, ['cancer'], useStdLogicVariables=True, treeDepth=4, nodeSize=2, numOfClauses=8)

'''Step 2: Training a Model'''

# Retrieve the positives, negatives, and facts.
>>> train_pos = boostsrl.example_data('train_pos')
>>> train_neg = boostsrl.example_data('train_neg')
>>> train_facts = boostsrl.example_data('train_facts')

# Train a model using this data:
>>> model = boostsrl.train(background, train_pos, train_neg, train_facts)

# How many seconds did training take?
>>> model.traintime()
0.705

'''Step 3: Test Model on New Data'''

# Retrieve the positives, negatives, and facts.
>>> test_pos = boostsrl.example_data('test_pos')
>>> test_neg = boostsrl.example_data('test_neg')
>>> test_facts = boostsrl.example_data('test_facts')

# Test the data
>>> results = boostsrl.test(model, test_pos, test_neg, test_facts)

'''Step 4: Observe Performance'''

# To see the overall performance of the model on test data:
>>> results.summarize_results()
{'CLL': '-0.223184', 'F1': '1.000000', 'Recall': '1.000000', 'Precision': '1.000000,0.500', 'AUC ROC': '1.000000', 'AUC PR': '1.000000'}

# To see probabilities for individual test examples:
>>> results.inference_results('cancer')
{'!cancer(Watson)': 0.6924179024024251, 'cancer(Xena)': 0.8807961917687174, '!cancer(Voldemort)': 0.6924179024024251, 'cancer(Yoda)': 0.8807961917687174, 'cancer(Zod)': 0.8807961917687174}

```

## Contributing

Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for documentation on submitting issues and pull requests.

## Versioning

We use [SemVer](http://semver.org/) for versioning. See [Releases](https://github.com/batflyer/boostsrl-python-package/releases) for all stable versions that are available.

## Acknowledgements

* Professor Sriraam Natarajan
* Members of STARAI Lab

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### Planned Features:

* saving/loading a model

```python
>>> from boostsrl import boostsrl
>>> boostsrl.save_model('toy_cancer')
```

[license]:license.txt
[license img]:https://img.shields.io/aur/license/yaourt.svg

[codecov img]:https://codecov.io/gh/batflyer/boostsrl-python-package/branch/master/graphs/badge.svg?branch=master
[codecov link]:https://codecov.io/github/batflyer/boostsrl-python-package?branch=master