What is early stopping in LightGBM and how to use it

by vinitha@openweaver.com Updated: Sep 19, 2023

Solution Kit

LightGBM stands for "Light Gradient Boosting Machine". It is an open-source machine learning framework developed by Microsoft. The design is for gradient-boosting tasks.

Various machine learning algorithms and data science applications use it. It handles classification, regression tasks, and ranking problems. People like to use it because it's fast. They use it for small or big machine-learning tasks.

You can access the LightGBM framework by installing it or using cloud-based services. Here are the general steps to get started with LightGBM:

Install LightGBM
Import LightGBM in Your Code
Prepare Your Data
Create a LightGBM Dataset
Define and Train a LightGBM Model
Make Predictions
Evaluate Model Performance
Tune Hyperparameters
Deploy Your Model

To prevent overfitting, gradient-boosting models use a technique called early stopping. It determines the optimal number of boosting rounds (iteration). It involves monitoring the performance of the model on separate validation sets. If the performance on the validation dataset doesn't improve, the training process stops.

Here are the best practices for creating testing sets for early stopping in LightGBM :

Split Your Data into Training and Validation Sets
Create LightGBM Datasets
Specify Early Stopping Criteria
Train the Model
Check the Final Model
Adjust Hyperparameters

LightGBM is popular in machine learning competitions due to its speed and effectiveness. It is available in several programming languages, including Python. Many machine learning libraries and frameworks offer integrations with LightGBM. It is accessible and easy to use for practitioners and researchers in ML.

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using LightGBM library

Provide Additional Custom Metric to LightGBM for Early Stopping

PythonLines of Code : 56License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

import lightgbm as lgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

dtrain = lgb.Dataset(
    data=X_train,
    label=y_train
)

dvalid = lgb.Dataset(
    data=X_test,
    label=y_test,
    reference=dtrain
)

def _constant_metric(dy_true, dy_pred):
    """An eval metric that always returns the same value"""
    metric_name = 'constant_metric'
    value = 0.708
    is_higher_better = False
    return metric_name, value, is_higher_better

evals_result = {}

model = lgb.train(
    params={
        "objective": "binary",
        "metric": "None",
        "num_iterations": 100,
        "first_metric_only": True,
        "verbose": 0,
        "num_leaves": 8
    },
    train_set=dtrain,
    valid_sets=[dvalid],
    feval=_constant_metric,
    early_stopping_rounds=5,
    evals_result=evals_result,
)

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[1] valid_0's constant_metric: 0.708
Training until validation scores don't improve for 5 rounds
[2] valid_0's constant_metric: 0.708
[3] valid_0's constant_metric: 0.708
[4] valid_0's constant_metric: 0.708
[5] valid_0's constant_metric: 0.708
[6] valid_0's constant_metric: 0.708
Early stopping, best iteration is:
[1] valid_0's constant_metric: 0.708
Evaluated only: constant_metric

Instructions

Follow the steps carefully to get the output easily.

Download and Install the PyCharm Community Edition on your computer.
Open the terminal and install the required libraries with the following commands.
Install LightGBM- pip install LIghtGBM
Pls install these versions: lightgbm==3.2.1 and scikit-learn==0.24.1
Create a new Python file on your IDE.
Copy the snippet using the 'copy' button and paste it into your python file.
Run the current file to generate the output.

I hope you found this useful.

I found this code snippet by searching for ' Provide Additional Custom Metric to LightGBM for Early Stopping ' in Kandi. You can try any such use case!

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

PyCharm Community Edition 2023.1
The solution is created in Python 3.11.1 Version
lightGBM 3.2.1 Version

Using this solution, we can able to use early stopping in LightGBM with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use early stopping in LightGBM.

Dependency library

LightGBMby microsoft

C++

15042

Version:v3.3.5

License: Permissive (MIT)

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

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LightGBMby microsoft

C++ 15042 Version:v3.3.5 License: Permissive (MIT)

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You can search for any dependent library on kandi like ' LightGBM '.

FAQ:

1. What is LightGBM, and how does it differ from the Gradient Boosting Decision Tree (GBDT)?

LightGBM is a gradient-boosting framework designed for high-performance machine-learning tasks. It differs from traditional Gradient Boosting Decision Trees (GBDT) in several ways:

Tree Growth Strategy
Histogram-Based Learning
Gradient Computation

2. How can I access the LightGBM framework?

You can access the LightGBM framework by installing it or using cloud-based services. Here are the general steps to get started with LightGBM:

Install LightGBM
Import LightGBM in Your Code
Prepare Your Data
Create a LightGBM Dataset
Define and Train a LightGBM Model
Make Predictions
Evaluate Model Performance
Tune Hyperparameters
Deploy Your Model

3. Where can I find the official LightGBM GitHub Repository?

You can find the official LightGBM GitHub repository on GitHub. Microsoft hosts this repository. It is the primary source for LightGBM's official codebase, documentation, and updates. You can access the source code and other resources on this GitHub repository. You can find info on how to install, use, and contribute to the project.

4. How much faster is a LightGBM machine learning model compared to other models?

The speed of a LightGBM ML model depends on various factors, including other models.

dataset size,
the complexity of the model,
specific algorithms
using frameworks.
We train and evaluate the model on hardware.

5. How should we create testing sets for early stopping in LightGBM?

When making testing sets for early stopping in LightGBM, consider these techniques.

Data Splitting
Time Series Data
Stratified Sampling
Random Seed
Validation Set
Cross-Validation

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