Metadata-Version: 2.4
Name: sklearn_nominal
Version: 0.0.5
Summary: Extra models for scikit-learn w/ built-in support for nominal attributes
Project-URL: Homepage, https://github.com/facundoq/sklearn_nominal
Project-URL: Bug Reports, https://github.com/facundoq/sklearn_nominal/issues
Project-URL: Source, https://github.com/facundoq/sklearn_nominal
Project-URL: Documentation, https://sklearn-nominal.readthedocs.io/
Project-URL: Package, https://pypi.org/project/sklearn-nominal/
Author-email: Facundo Manuel Quiroga <{first_name}{last_name_initial}@gmail.com>
License-File: LICENSE
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.11
Requires-Dist: data-science-types>=0.2.23
Requires-Dist: graphviz>=0.20.3
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: numpy>=2.2.0
Requires-Dist: pandas>=2.2.3
Requires-Dist: pandas[pyarrow]>=2.2.3
Requires-Dist: pyarrow>=19.0.1
Requires-Dist: scikit-learn>=1.6.1
Provides-Extra: export
Requires-Dist: cairosvg>=2.7.1; extra == 'export'
Requires-Dist: pygraphviz>=1.14; extra == 'export'
Description-Content-Type: text/markdown

# sklearn_nominal
Extra models for scikit-learn, including Tree, PRISM, CN2, OneR and ZeroR Classifiers and Regressors with support for **nominal values**.

## Colab Quickstart

Check our  [classification models notebook](https://colab.research.google.com/github/facundoq/sklearn_nominal/blob/main/examples/Classification%20Models.ipynb) and [regression models notebook](https://colab.research.google.com/github/facundoq/sklearn_nominal/blob/main/examples/Regression%20Models.ipynb) to see samples of `sklearn_nominal` models in action with simple datasets.

## Installation

To use `sklearn_nominal` in your project, you can install it from [pypi](https://pypi.org/project/sklearn-nominal/) (no conda-forge support yet):

Using `pip`:
````
pip install sklearn_nominal
````

Using `uv`:
````
uv add sklearn_nominal
````

## Installation with support for svg/png/pdf export for Tree models

To export tree graphs to those formats, you need `pygraphviz` (and in the future, possibly other dependencies). Regrettably, `pygraphviz` does not include its own binaries for `grpahviz`. Therefore, make sure to install `graphviz` (with headers) and `cairo`. In Ubuntu 24.04, that can be achieved with:

````
sudo apt install libgraphviz-dev
````

Then use the `export` extras version of `sklearn_nominal` installing:

````
pip install  "sklearn_nominal[export]"
````

