OptGBM
OptGBM (= Optuna + LightGBM) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.
Examples
import optgbm as lgb
from sklearn.datasets import load_boston
reg = lgb.LGBMRegressor(random_state=0)
X, y = load_boston(return_X_y=True)
reg.fit(X, y)
y_pred = reg.predict(X, y)
By default, the following hyperparameters will be searched.
bagging_fractionbagging_freqfeature_fractrionlambda_l1lambda_l2max_depthmin_data_in_leafnum_leaves
Installation
pip install optgbm
Testing
tox
1 maintainer
Metadata
1 maintainer
Release files for OptGBM 0.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| OptGBM-0.10.0.tar.gz | 17.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| OptGBM-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.3 kB
Release files / OptGBM-0.10.0.tar.gz
| Download URL | OptGBM-0.10.0.tar.gz |
|---|---|
| Size | 17.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/3.4.1 importlib_metadata/4.2.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5
|
Release files / OptGBM-0.10.0-py3-none-any.whl
| Download URL | OptGBM-0.10.0-py3-none-any.whl |
|---|---|
| Size | 13.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/3.4.1 importlib_metadata/4.2.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5
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