ogboost 0.5.1

Ordinal Gradient Boosting

Ordinal Gradient Boosting (OGBoost)

OGBoost is a scikit-learn-compatible, Python package designed for gradient boosting tailored to ordinal regression problems. It does so by alternating between 1) fitting a base learner - which is, by default, a DecisionTreeRegressor - to predict a latent score that specifies the mean of a probability density function (PDF), and 2) fitting a set of thresholds that generate discrete outcomes from the PDF. In other words, OGBoost implements coordinate-descent optimization that combines functional gradient descent (the boosting stage) with ordinary gradient descent (for adjusting thresholds).

The main class in the package, GradientBoostingOrdinal, supports custom link functions, sample weighting, early stopping criteria, and staged predictions. In addition to methods for predicting class labels and probabilities, similar to nominal classifiers, the decision_function method of the class predicts the latent score, which can be used to achieve superior performance in discreiminative/ranking tasks.

Features

• Fully compatible with scikit-learn pipelines.
• Customizable link functions: Probit, Logit, and Complementary Log-Log.
• Sample weighting for robust handling of imbalanced datasets.
• Subsampling for stochastic gradient boosting.
• Early stopping based on validation-set loss.
• Utility for downlaoding and using the wine-quality dataset from the UCI ML repository.

Installation

pip install ogboost

Quick Start

Load the Wine Quality Dataset

The package includes a utility to load the wine quality dataset from the UCI repository.

from ogboost import load_wine_quality

# Load data
red_wine, white_wine = load_wine_quality()
X = red_wine.drop(columns="quality")
y = red_wine["quality"]

Train a Gradient Boosting Ordinal Model

from ogboost import GradientBoostingOrdinal

# Initialize and fit the model
model = GradientBoostingOrdinal(n_estimators=100, link_function='logit', verbose=1)
model.fit(X, y)

# Predict class labels and probabilities
predicted_labels = model.predict(X)
predicted_probabilities = model.predict_proba(X)

# Evaluate using the concordance index
from ogboost import concordance_index
c_index = concordance_index(y, model.decision_function(X))
print(f"Concordance Index: {c_index:.3f}")

License

This package is licensed under the MIT License.