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boostsrl
boostsrl is a Python wrapper for BoostSRL to help automate learning and inference tasks.
Getting Started
Prerequisites
- Java 1.8
- Python (2.7, 3.3, 3.4, 3.5, 3.6)
- subprocess32 (if using Python 2.7:
pip install subprocess32) - graphviz-0.8
Installation
-
The latest stable build can be installed with pip:
$ pip install git+git://github.com/batflyer/boostsrl-python-package.git
Basic Usage
>>> from boostsrl import boostsrl
'''Step 1: Background Knowledge'''
# Sample data is built in from the 'Toy Cancer' Dataset, retrieve it with example_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 for documentation on submitting issues and pull requests.
Versioning
We use SemVer for versioning. See Releases for all stable versions that are available.
Acknowledgements
- Professor Sriraam Natarajan
- Members of StARLinG Lab