Getting Started

Installation

Install the latest release from PyPI:

pip install skordinal

Binary wheels are published for Linux x86-64. On every other platform pip builds the three C extensions (REDSVM, SVOR, ORBoost) from source, which requires a C/C++ compiler.

Progress bars during a benchmark run need one optional extra:

pip install "skordinal[progress]"

For a development install, in editable mode and with every optional dependency:

git clone https://github.com/ayrna/skordinal.git
cd skordinal
pip install -e ".[dev,docs]"

Quickstart

The example below loads a bundled ordinal dataset, trains a POM classifier, and evaluates it with two ordinal metrics:

from sklearn.model_selection import train_test_split

from skordinal.datasets import load_era
from skordinal.classifiers import POM
from skordinal.metrics import mean_absolute_error, weighted_kappa

X, y = load_era(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0, stratify=y
)

clf = POM()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print(f"MAE:   {mean_absolute_error(y_test, y_pred):.3f}")
print(f"Kappa: {weighted_kappa(y_test, y_pred):.3f}")

Every classifier implements the standard scikit-learn fit / predict interface, so it composes with Pipeline, cross_val_score() and GridSearchCV without adaptation. The API Reference documents every class and function.