# LightGBM Callbacks
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A collection of [LightGBM](https://github.com/microsoft/LightGBM) [callbacks](https://lightgbm.readthedocs.io/en/latest/Python-API.html#callbacks).
Provides implementations of `ProgressBarCallback` ([#5867](https://github.com/microsoft/LightGBM/pull/5867)) and `DartEarlyStoppingCallback` ([#4805](https://github.com/microsoft/LightGBM/issues/4805)), as well as an `LGBMDartEarlyStoppingEstimator` that automatically passes these callbacks. ([#3313](https://github.com/microsoft/LightGBM/issues/3313), [#5808](https://github.com/microsoft/LightGBM/pull/5808))
## Installation
Install this via pip (or your favourite package manager):
```shell
pip install lightgbm-callbacks
```
## Usage
### SciKit-Learn API, simple
```python
from lightgbm import LGBMRegressor
from lightgbm_callbacks import LGBMDartEarlyStoppingEstimator
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
LGBMDartEarlyStoppingEstimator(
LGBMRegressor(boosting_type="dart"), # or "gbdt", ...
stopping_rounds=10, # or n_iter_no_change=10
test_size=0.2, # or validation_fraction=0.2
shuffle=False,
tqdm_cls="rich", # "auto", "autonotebook", ...
).fit(X_train, y_train)
```
### Scikit-Learn API, manually passing callbacks
```python
from lightgbm import LGBMRegressor
from lightgbm_callbacks import ProgressBarCallback, DartEarlyStoppingCallback
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train)
early_stopping_callback = DartEarlyStoppingCallback(stopping_rounds=10)
LGBMRegressor(
).fit(
X_train,
y_train,
eval_set=[(X_train, y_train), (X_val, y_val)],
callbacks=[
early_stopping_callback,
ProgressBarCallback(early_stopping_callback=early_stopping_callback),
],
)
```
### Details on `DartEarlyStoppingCallback`
Below is a description of the `DartEarlyStoppingCallback` `method` parameter and `lgb.plot_metric` for each `lgb.LGBMRegressor(boosting_type="dart", n_estimators=1000)` trained with entire `sklearn_datasets.load_diabetes()` dataset.
| Method | Description | iteration | Image | Actual iteration |
| ---------- | -------------------------------------------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------------- | ---------------- |
| (Baseline) | If Early stopping is not used. | `n_estimators` |  | 1000 |
| `"none"` | Do nothing and return the original estimator. | `min(best_iteration + early_stopping_rounds, n_estimators)` |  | 50 |
| `"save"` | Save the best model by deepcopying the estimator and return the best model (using `pickle`). | `min(best_iteration + 1, n_estimators)` |  | 21 |
| `"refit"` | Refit the estimator with the best iteration and return the refitted estimator. | `min(best_iteration, n_estimators)` |  | 20 |
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
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"description": "# LightGBM Callbacks\n\n<p align=\"center\">\n <a href=\"https://github.com/34j/lightgbm-callbacks/actions/workflows/ci.yml?query=branch%3Amain\">\n <img src=\"https://img.shields.io/github/actions/workflow/status/34j/lightgbm-callbacks/ci.yml?branch=main&label=CI&logo=github&style=flat-square\" alt=\"CI Status\" >\n </a>\n <a href=\"https://lightgbm-callbacks.readthedocs.io\">\n <img src=\"https://img.shields.io/readthedocs/lightgbm-callbacks.svg?logo=read-the-docs&logoColor=fff&style=flat-square\" alt=\"Documentation Status\">\n </a>\n <a href=\"https://codecov.io/gh/34j/lightgbm-callbacks\">\n <img src=\"https://img.shields.io/codecov/c/github/34j/lightgbm-callbacks.svg?logo=codecov&logoColor=fff&style=flat-square\" alt=\"Test coverage percentage\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"https://python-poetry.org/\">\n <img src=\"https://img.shields.io/badge/packaging-poetry-299bd7?style=flat-square&logo=data:image/png;base64,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\" alt=\"Poetry\">\n </a>\n <a href=\"https://github.com/ambv/black\">\n <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square\" alt=\"black\">\n </a>\n <a href=\"https://github.com/pre-commit/pre-commit\">\n <img src=\"https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=flat-square\" alt=\"pre-commit\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"https://pypi.org/project/lightgbm-callbacks/\">\n <img src=\"https://img.shields.io/pypi/v/lightgbm-callbacks.svg?logo=python&logoColor=fff&style=flat-square\" alt=\"PyPI Version\">\n </a>\n <img src=\"https://img.shields.io/pypi/pyversions/lightgbm-callbacks.svg?style=flat-square&logo=python&logoColor=fff\" alt=\"Supported Python versions\">\n <img src=\"https://img.shields.io/pypi/l/lightgbm-callbacks.svg?style=flat-square\" alt=\"License\">\n</p>\n\nA collection of [LightGBM](https://github.com/microsoft/LightGBM) [callbacks](https://lightgbm.readthedocs.io/en/latest/Python-API.html#callbacks).\nProvides implementations of `ProgressBarCallback` ([#5867](https://github.com/microsoft/LightGBM/pull/5867)) and `DartEarlyStoppingCallback` ([#4805](https://github.com/microsoft/LightGBM/issues/4805)), as well as an `LGBMDartEarlyStoppingEstimator` that automatically passes these callbacks. ([#3313](https://github.com/microsoft/LightGBM/issues/3313), [#5808](https://github.com/microsoft/LightGBM/pull/5808))\n\n## Installation\n\nInstall this via pip (or your favourite package manager):\n\n```shell\npip install lightgbm-callbacks\n```\n\n## Usage\n\n### SciKit-Learn API, simple\n\n```python\nfrom lightgbm import LGBMRegressor\nfrom lightgbm_callbacks import LGBMDartEarlyStoppingEstimator\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\nX, y = load_diabetes(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nLGBMDartEarlyStoppingEstimator(\n LGBMRegressor(boosting_type=\"dart\"), # or \"gbdt\", ...\n stopping_rounds=10, # or n_iter_no_change=10\n test_size=0.2, # or validation_fraction=0.2\n shuffle=False,\n tqdm_cls=\"rich\", # \"auto\", \"autonotebook\", ...\n).fit(X_train, y_train)\n```\n\n### Scikit-Learn API, manually passing callbacks\n\n```python\nfrom lightgbm import LGBMRegressor\nfrom lightgbm_callbacks import ProgressBarCallback, DartEarlyStoppingCallback\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\nX, y = load_diabetes(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train)\nearly_stopping_callback = DartEarlyStoppingCallback(stopping_rounds=10)\nLGBMRegressor(\n).fit(\n X_train,\n y_train,\n eval_set=[(X_train, y_train), (X_val, y_val)],\n callbacks=[\n early_stopping_callback,\n ProgressBarCallback(early_stopping_callback=early_stopping_callback),\n ],\n)\n```\n\n### Details on `DartEarlyStoppingCallback`\n\nBelow is a description of the `DartEarlyStoppingCallback` `method` parameter and `lgb.plot_metric` for each `lgb.LGBMRegressor(boosting_type=\"dart\", n_estimators=1000)` trained with entire `sklearn_datasets.load_diabetes()` dataset.\n\n| Method | Description | iteration | Image | Actual iteration |\n| ---------- | -------------------------------------------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------------- | ---------------- |\n| (Baseline) | If Early stopping is not used. | `n_estimators` |  | 1000 |\n| `\"none\"` | Do nothing and return the original estimator. | `min(best_iteration + early_stopping_rounds, n_estimators)` |  | 50 |\n| `\"save\"` | Save the best model by deepcopying the estimator and return the best model (using `pickle`). | `min(best_iteration + 1, n_estimators)` |  | 21 |\n| `\"refit\"` | Refit the estimator with the best iteration and return the refitted estimator. | `min(best_iteration, n_estimators)` |  | 20 |\n\n## Contributors \u2728\n\nThanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n<!-- prettier-ignore-start -->\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n <tbody>\n <tr>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/34j\"><img src=\"https://avatars.githubusercontent.com/u/55338215?v=4?s=80\" width=\"80px;\" alt=\"34j\"/><br /><sub><b>34j</b></sub></a><br /><a href=\"https://github.com/34j/lightgbm-callbacks/commits?author=34j\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#ideas-34j\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/34j/lightgbm-callbacks/commits?author=34j\" title=\"Documentation\">\ud83d\udcd6</a></td>\n </tr>\n </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n<!-- prettier-ignore-end -->\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. 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