Factual package intelligence from PyPI

skga 1.0.0 package intelligence

The python package implementing the HyperBRKGA algorithm optimizes hyperparameters of machine learning algorithms through a hybrid approach based on genetic algorithms.

pip install skga

Current version
1.0.0
Python requirement
Not declared
License
BSD License
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
80.2 KiB
Release history
10 releases with files
Median release cadence
0 days

skga dependencies

PyPI declares 49 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. bayesian-optimization ==1.2.0
  2. bleach ==6.0.0
  3. build ==0.10.0
  4. certifi ==2023.5.7
  5. charset-normalizer ==3.1.0
  6. contourpy ==1.0.7
  7. cycler ==0.11.0
  8. Cython ==0.29.30
  9. docutils ==0.19
  10. fonttools ==4.39.3
  11. idna ==3.4
  12. importlib-metadata ==6.6.0
  13. importlib-resources ==5.12.0
  14. jaraco.classes ==3.2.3
  15. joblib ==1.2.0
  16. keyring ==23.13.1
  17. kiwisolver ==1.4.4
  18. markdown-it-py ==2.2.0

The compact report shows 18 of 49 declarations. The interactive dependency graph loads the complete metadata.

Compatibility and release files

skga publishes 1 wheel and 1 source archive for version 1.0.0. Wheel platform tags: any.

No version-specific Python classifiers are declared.

Metadata completeness

PyPI does not currently declare: Python requirement. PyDeps marks these fields as unknown instead of guessing values.

Release activity and sources

PyPI lists 10 releases with files. The first dated release is ; 10 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .