fossa 0.0.2

Distribution-based anomaly detection for time series.

Distribution-based anomaly detection for time series.

>>> from fossa import LastWindowAnomalyDetector
>>> clf = LastWindowAnomalyDetector(p_threshold=0.005, normalize=True)
>>> clf.fit(historic_data_df)
>>> clf.predict(new_data)
                     direction
date       category
2018-06-01 hockey          1.0
           footbal         0.0
           soccer         -1.0
           tennis          0.0

Contents

• 1 Installation

• 2 Features

• 3 Use

• 4 Contributing

  • 4.1 Installing for development

  • 4.2 Running the tests

  • 4.3 Adding documentation

• 5 Credits

1 Installation

pip install fossa

2 Features

• scikit-learn-like classifier API.

• Pickle-able classifier objects.

• Pure python.

• Supports Python 3.5+.

• Fully tested.

3 Use

TBA

4 Contributing

Current package maintainer (and one of the authors) is Shay Palachy ([e-mail]); You are more than welcome to approach him for help. Contributions are very welcomed.

4.1 Installing for development

Clone:

git clone [e-mail]:shaypal5/fossa.git

Install in development mode, including test dependencies:

cd fossa
pip install -e '.[test]'

4.2 Running the tests

To run the tests use:

cd fossa
pytest

4.3 Adding documentation

The project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow these conventions.

Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.

5 Credits

Created by Shay Palachy ([e-mail]) and Omri Mendels.