Comments (2)
yzhao062
commented on May 18, 2024
1
Hi Allen,
Sorry for the confusion. XGBOD and LSCP are in pre-lease and in development branch only. So installing from pip would not get that. We are going to submit these two packages along with 2 neural-net models in V0.6.6 by this week. The only way to use these two models now is to check out the development branch...
Stay tuned:)
Best,
Yue
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yzhao062
commented on May 18, 2024
Happy to provide an update. XGBOD and LSCP are included in #44 & pre-lease v.0.6.6b1 (https://pypi.org/project/pyod/0.6.6b1/) and planned to be released in v0.6.6 later this week (likely to be this Sat). If you would like to try the pre-release, then
pip install pyod==0.6.6b1
Otherwise, please wait for 4 more days for an official release... Besides, 2 neural-net based models are also included in this pre-release and the stable version later this week.
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Related Issues (20)
- Update contamination without retraining HOT 4
- ValueError: node array from the pickle has an incompatible dtype HOT 1
- ABOD missing support for kernels
- ABOD weighting schemes / not matching the definition? HOT 1
- ROD models: RuntimeWarning: invalid value encountered in divide
- Which algorithms support ‘Semi-supervised Novelty Detection’？
- Support SHAP for model explanation
- Default parameter inconsistent with docs
- different results depending on time-span HOT 2
- saved model size
- Model prediction in Autoencoder does not support adjusting batch size
- False positive warning when manipulating pandas dataframes
- Grateful
- issues about scikit-learn HOT 3
- TOS selection methods and parameter
- Add wheels to PyPI?
- TypeError: SUOD.__init__() got an unexpected keyword argument 'cost_forecast_loc_fit' HOT 2
- DIF model: duplicate normalization
- How will the effectiveness of the model be evaluated, and does the library provide the appropriate methodology? HOT 1
- Quasi-Monte Carlo Discrepancy always predicts an outlier HOT 1
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