Factual package intelligence from PyPI

wavetrainer 0.3.22 package intelligence

A library for automatically finding the optimal model within feature and hyperparameter space.

pip install wavetrainer

Current version
0.3.22
Python requirement
Not declared
License
MIT
Distribution type
No wheel classification available
Release files
1 (0 wheels, 1 source)
Download size
44.5 KiB
Release history
143 releases with files
Median release cadence
1 days

wavetrainer dependencies

PyPI declares 25 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. pandas >=2.2.3
  2. optuna >=4.2.1
  3. scikit-learn >=1.5.2
  4. feature-engine >=1.8.3
  5. tqdm >=4.67.1
  6. numpy >=1.26.4
  7. scipy >=1.15.2
  8. catboost >=1.2.7
  9. venn-abers ==1.4.6
  10. pytz >=2025.1
  11. torch >=2.6.0
  12. tabpfn >=2.0.6
  13. pytest-is-running >=1.5.1
  14. xgboost >=3.0.0
  15. jax >=0.6.1
  16. tabpfn_extensions >=0.0.4
  17. hyperopt >=0.2.7
  18. pycaleva >=0.8.2

The compact report shows 18 of 25 declarations. The interactive dependency graph loads the complete metadata.

Compatibility and release files

wavetrainer publishes 0 wheels and 1 source archive for version 0.3.22. Wheel platform tags: No wheel platform tags.

No version-specific Python classifiers are declared.

Security snapshot for wavetrainer 0.3.22

An OSV query completed on and found 0 known vulnerabilities affecting this version. 0 advisories are classified as critical. 0 advisories appear in the CISA Known Exploited Vulnerabilities catalog.

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 143 releases with files. The first dated release is ; 68 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .