It is recommended to use pip for installation. Please make sure the latest version is installed, as PyOD is updated frequently:
pip install pyod # normal install pip install --upgrade pyod # or update if needed pip install --pre pyod # or include pre-release version for new features
Alternatively, you could clone and run setup.py file:
git clone https://github.com/yzhao062/pyod.git cd pyod pip install .
The maintenance of Python 2.7 will be stopped by January 1, 2020 (see official announcement). To be consistent with the Python change and PyOD’s dependent libraries, e.g., scikit-learn, we will stop supporting Python 2.7 in the near future (dates are still to be decided). We encourage you to use Python 3.5 or newer for the latest functions and bug fixes. More information can be found at Moving to require Python 3.
Python 2.7, 3.5, 3.6, or 3.7
Optional Dependencies (see details below):
keras (optional, required for AutoEncoder)
matplotlib (optional, required for running examples)
pandas (optional, required for running benchmark)
tensorflow (optional, required for AutoEncoder, other backend works)
xgboost (optional, required for XGBOD)
PyOD has multiple neural network based models, e.g., AutoEncoders, which are implemented in Keras. However, PyOD does NOT install keras and/or tensorflow for you. This reduces the risk of interfering with your local copies. If you want to use neural-net based models, please make sure Keras and a backend library, e.g., TensorFlow, are installed. Instructions are provided: neural-net FAQ. Similarly, models depending on xgboost, e.g., XGBOD, would NOT enforce xgboost installation by default.
Running examples needs matplotlib, which may throw errors in conda virtual environment on mac OS. See reasons and solutions mac_matplotlib.
PyOD contains multiple models that also exist in scikit-learn. However, these two libraries’ API is not exactly the same–it is recommended to use only one of them for consistency but not mix the results. Refer sckit-learn and PyOD for more information.