Cool, but not frozen, packages to augment your Python data analytics experience.
pybear is a Python computing library that augments data analytics functionality found in the popular numpy, scikit-learn, dask, and dask_ml libraries.
See documentation for more information.
Website: https://github.com/PylarBear/pybear
BSD 3-Clause License. See License File.
pybear requires:
- Python (>=3.9, <3.14)
- dask (>=X.X.X)
- dask_ml (>=X.X.X)
- distributed (>=X.X.X)
- joblib (>=X.X.X)
- numpy (>=1.26.0)
- pandas (>=X.X.X)
- scikit-learn (>=X.X.X)
pybear 0.1 is tested via GitHub Actions to run on Linux, Windows, and MacOS, with Python versions 3.9, 3.10, 3.11, and 3.12. pybear is not tested on earlier versions, but some features may work.
The only way to install pybear is from PyPI using pip:
pip install pybear
Conda distributions are not available at this time.
Description: Perform multiple uninterrupted passes of grid search with sci-kit learn GridSearchCV utilizing progressively narrower search grids. Access via pybear.model_selection.AutoGridSearchCV.
Description: Perform multiple uninterrupted passes of grid search with dask_ml GridSearchCV and dask objects utilizing progressively narrower search grids. Access via pybear.model_selection.AutoGridSearchCVDask.
Description: Create your own auto-gridsearch class. A function that wraps any scikit-learn or dask_ml GridSearchCV module to create an identical GridSearch class that performs multiple passes of grid search using progressively narrower search grids. Access via pybear.model_selection.autogridsearch_wrapper.
Description: Perform conventional grid search with concurrent threshold search. Finds the global optima for the passed parameters and thresholds. Is fully compliant with the scikit-learn GridSearchCV API. Access via pybear.model_selection.GSTCV.
Description: Perform conventional grid search with concurrent threshold search using dask objects in parallel and distributed environments. Finds the global optima for the passed parameters and thresholds. Is fully compliant with the dask_ml GridSearchCV API. Access via pybear.model_selection.GSTCVDask.
Description: Perform multiple uninterrupted passes of grid search with pybear GSTCV utilizing progressively narrower search grids. Access via pybear.model_selection.AutoGSTCV.
Description: Perform multiple uninterrupted passes of grid search with pybear GSTCVDask utilizing progressively narrower search grids. Access via pybear.model_selection.AutoGSTCVDask.
Description: Perform minimum frequency thresholding on numerical or categorical data simultaneously across an entire array of data. Violates the scikit-learn API in that datasets are modified along the example axis (examples may be deleted.) Otherwise is fully compliant with the sci-kit learn transformer API, with fit, transform, and partial_fit methods. Access via pybear.preprocessing.MinCountTransformer.
Description: Identify and selectively remove duplicate columns in numerical or categorical data. Fully compliant with the scikit-learn transformer API, with fit, transform, and partial_fit methods. Perfect for removing duplicate columns in one-hot encoded data in a scikit-learn pipeline. Can also fit and transform data batch-wise, such as with dask-ml Incremental and ParallelPostFit wrappers. Access via pybear.preprocessing.ColumnDeduplicateTransformer.
Description: A scikit-style transformer that identifies and manages the constant columns in a dataset. IM can remove all, selectively keep one, or append a column of constants. Handles numerical data, non-numerical data, and nan-like values. Does batch-wise fitting via a partial_fit method. Is suitable for sklearn pipelines and can be wrapped with dask_ml Incremental and ParallelPostFit wrappers. Access via pybear.preprocessing.InterceptManager.
See the changelog for a history of notable changes to pybear.
- Official source code repo: https://github.com/PylarBear/pybear
- Download releases: https://pypi.org/project/pybear/
- Issue tracker: https://github.com/PylarBear/pybear/issues
You can clone the latest source code with the command:
git clone https://github.com/PylarBear/pybear.git
pybear is not ready for contributions at this time!
After installation, you can launch the test suite from outside the pybear root directory (you will need to have pytest installed in your environment):
pytest pybear
The project originated in the early 2020's as a collection of miscellaneous private modules to enhance the scikit-learn and dask_ml ecosystem. In 2024, the modules were formalized and bundled together for their first release as pybear.
- HTML documentation: https://pybear.readthedocs.io/
- GitHub Discussions: https://github.com/PylarBear/pybear/discussions
- Website: https://github.com/PylarBear/pybear