ngboost 0.5.1

Library for probabilistic predictions via gradient boosting.

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn, and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck.

Installation

via pip

pip install --upgrade ngboost

via conda-forge

conda install -c conda-forge ngboost

Usage

Probabilistic regression example on the Boston housing dataset:

from ngboost import NGBRegressor

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

#Load Boston housing dataset
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
Y = raw_df.values[1::2, 2]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

ngb = NGBRegressor().fit(X_train, Y_train)
Y_preds = ngb.predict(X_test)
Y_dists = ngb.pred_dist(X_test)

# test Mean Squared Error
test_MSE = mean_squared_error(Y_preds, Y_test)
print('Test MSE', test_MSE)

# test Negative Log Likelihood
test_NLL = -Y_dists.logpdf(Y_test).mean()
print('Test NLL', test_NLL)

Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our user guide, which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost.

License

Apache License 2.0.

Reference

Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. arXiv