https://github.com/yzhao062/hpod

AutoML 2024: HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection
https://github.com/yzhao062/hpod

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AutoML 2024: HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection

• Host: GitHub
• URL: https://github.com/yzhao062/hpod
• Owner: yzhao062
• Created: 2024-07-12T17:23:43.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-12T17:25:47.000Z (5 months ago)
• Last Synced: 2024-12-09T09:15:49.553Z (13 days ago)
• Language: Python
• Size: 18.7 MB
• Stars: 10
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

**Supplementary Material**: HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection

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To run the demo for the three OD algorithms, first install the required libraries by executing the following command:

"pip install -r requirements.txt". It is working with Python 3.7+. Our experiment is in Python 3.9.

To run the demo for RAE, execute:

"python demo_hpod_rae.py".

Similarly, to run demo for LOF, execute:

"python demo_hpod_lof.py".

Also, to run demo for iForest, execute:

"python demo_hpod_iforest.py".

More file description:

- init_meta.py includes the implementation of meta-initialization

- utility.py includes a set of helper functions.

- models folder includes pre-trained models for fast replication.

- datasets folder includes the raw file of all datasets.

If you face any execution issue, please feel free to open an issue (anonmously if you are a reviewer.)