gbm_autosplit

LightGBM / XGBoost scikit learn interfaces which performs "early stopping" with single data set during fit.

Motivation

"Early stopping" is great practice to tune the number of estimators for gradient boosting models. However it is not easy to use it in tuning module in scikit-learn such as RandomizedSearchCV / GridSearchCV because to use early stopping module requires two data sets but scikit learn does not have such interface.

Algorithm

To solve this situation, this interface performs following steps with in fit.

User instanciates Classifier / Regressor with additional hyper parameters max_n_estimators, ratio_training, and eval_metric.
User calls fit with x and y as usual
1. Randomly split sample (x, y) into training and validation as ratio of sample size of training = ratio_training,
2. Call fit of original GBM, using early stopping with split training and validation for the metric eval_metric with n_estimators = max_n_estimators
3. Get best_n_estimators as the number of trees of stopped model of step 2-2.
4. Call fit of original GBM with entire (x, y) and n_estimators = best_n_estimators of step 2-3.

Install

pip install gbm_autosplit

Requires Python 3.10+. Tip-of-tree XGBoost 3.3+ requires Python 3.12+.

Usage

import gbm_autosplit

estimator = gbm_autosplit.LGBMClassifier()
estimator.fit(x, y)

License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3

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