We are working on a way to upload the data files which is too large for GitHub. For now, you could use the GoogleDrive version for it.
Development Status: As of 09/24/2022, ELECT is under active development and in its alpha stage. Please follow, star, and fork to get the latest update!
Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)? ELECT is a novel unsupervised outlier model selection method.
To run the demo in the two testbeds, first install the required libraries by executing "pip install -r requirements.txt".
Required Dependencies:
- Python 3.6+
- joblib>=0.14.1
- liac-arff
- lightgbm
- numpy>=1.13
- scipy>=0.19.1
- scikit_learn>=0.19.1
- pandas
- psutil
- pyod>=0.9
To run the demo in the wild testbed, execute:
.. code-block:: bash "python elect_wild.py".
Similarly, to run demo in the controlled testbed, execute:
.. code-block:: bash "python elect_controlled.py".
More file description:
- initialization_converage.py includes the implementation of coverage driven initialization.
- utility.py includes a set of helper functions.
- intermediate_files folder includes some useful intermediate_files for fast replication.
- testbeds and datasets folder includes the raw file of all datasets.