Complete guide on how to Use LightGBM in Python

Arnab Mondal 30 Apr, 2024
7 min read

Introduction

A Gradient Boosting Decision Tree (GBDT), such as LightGBM in Python, is a highly favored machine learning algorithm renowned for its effectiveness. Alongside implementations like XGBoost, it offers various optimization techniques. However, despite its popularity, the efficiency and scalability of the model can falter when handling datasets with numerous features. This limitation stems from the fact that each feature needs to scan through all data instances to estimate potential split points, a process that proves time-consuming and tedious.

Light GBM

Source: https://s3.ap-south-1.amazonaws.com/techleerimages/0504748b-5e9b-49ee-9824-3ab4ac76760e.jpg

To solve this problem, The LGBM or Light Gradient Boosting Model is used. It uses two types of techniques which are gradient Based on side sampling or GOSS and Exclusive Feature bundling or EFB. So GOSS will actually exclude the significant portion of the data part which have small gradients and only use the remaining data to estimate the overall information gain. The data instances which have large gradients actually play a greater role for computation on information gain. GOSS can get accurate results with a significant information gain despite using a smaller dataset than other models.

With the EFB, It puts the mutually exclusive features along with nothing but it will rarely take any non-zero value at the same time to reduce the number of features. This impacts the overall result for an effective feature elimination without compromising the accuracy of the split point.

By combining the two changes, it will fasten up the training time of any algorithm by 20 times. So Light GBM in Python can be thought of as gradient boosting trees with the combination for EFB and GOSS. You can access their official documentation here.

The main features of the LightGBM in Python model are as follows :

  • Higher accuracy and a faster training speed.
  • Low memory utilization
  • Comparatively better accuracy than other boosting algorithms and handles overfitting much better while working with smaller datasets.
  • Parallel Learning support.
  • Compatible with both small and large datasets

With the above-mentioned features and advantages of LGBM, it has become the default algorithm for machine learning competitions when someone is working with a tabular kind of data regarding both regression and classification problems.

This article was published as a part of the Data Science Blogathon

What is LightGBM(Light gradient Boosting Machine)?

LightGBM is a powerful and efficient open-source gradient boosting framework for machine learning. It’s specifically designed to handle large datasets and perform well in terms of speed and memory usage. LightGBM uses a technique called gradient boosting, which combines multiple weak learners (usually decision trees) to create a strong predictive model.

Demystifying the Maths behind LightGBM in Python

We use a concept known as verdict trees so that we can cram a function like for example, from the input space X, towards the gradient space G. A training set with the instances like x1,x2 and up to xn is assumed where each element is a vector with s dimensions in the space X. In each of the restatements of a gradient boosting, all the negative gradients of a loss function with respect towards the output model are denoted as g1, g2, and up to gn. The decision tree actually divides each and every node at the most revealing feature, it also gives rise to the largest evidence gain. In this type of model, the data improvement can be measured by the variance after segregating. It can be represented by the following formula :

“Y=Base_tree(X)-lr*Tree1(X)-lr*Tree2(X)-lr*Tree3(X)”

Explanation, Let O be a training dataset on a fixed node of a decision tree and then the variance gain of dividing measure j at a point d for a node is defined as :

lightgbm

Source: https://ejmcm.com/article_9403_15c24bd9c676c28d90c3fc5fad8b42ea.pdf

Gradient One-Sided Sampling or GOSS utilizes every instance with a larger gradient and does the task of random sampling on the various instances with the small gradients. The training dataset is given by the notation of O for each particular node of the Decision tree. The variance gain of j or the dividing measure at the point d for the node is given by :

maths behind light gbm

Source: https://ejmcm.com/article_9403_15c24bd9c676c28d90c3fc5fad8b42ea.pdf

This is achieved by the method of GOSS in LightGBM models.

Coding an LGBM in Python

The LightGBM model can be installed by using the Python pip function and the command is “pip install lightbgm” LGBM also has a custom API support in it and using it we can implement both Classifier and regression algorithms where both the models operate in a similar fashion. The Dataset used here is of the Titanic Passengers which will be used in the below code and can be found in my drive at this location.

( Dataset Link: https://drive.google.com/file/d/1uuFe0f2gjEE77-PL9LhMrEd6n-1L5vfW/view?usp=sharing )

Code :

Importing all the libraries
Python Code:

Output :

Coding an LGBM in Python head

 

Here we can see that there are 8 columns out of which the passenger ID will be dropped and the embarked will be finally chosen as a target variable for the following classification challenge.

Loading the variables:

# To define the input and output feature
x = data.drop(['Embarked','PassengerId'],axis=1)
y = data.Embarked
# train and test split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42)

Loading and fitting the model:

The initial process of initializing a model is very similar to a normal model initializing and the main difference is that we will get much more parameter settings adjustments while we are initializing the model. We will define the max_depth, learning rate and random state in the following code. In the fit model, we have passed eval_matrix and eval_set to evaluate the model during training itself.

Code :

model = lgb.LGBMClassifier(learning_rate=0.09,max_depth=-5,random_state=42)
model.fit(x_train,y_train,eval_set=[(x_test,y_test),(x_train,y_train)],
          verbose=20,eval_metric='logloss')

Output:

fit the model

Since our model has very low instances, we need to first check for overfitting with the following code and then we will proceed for the next few steps :

Code :

print('Training accuracy {:.4f}'.format(model.score(x_train,y_train)))
print('Testing accuracy {:.4f}'.format(model.score(x_test,y_test)))

Output :

Training accuracy 0.9647
Testing accuracy 0.8163

As we can clearly see that there is absolutely no significant difference between both the accuracies and hence the model has made an estimation that is quite accurate.

LGBM also comes with additional plotting functions like plotting the various feature importance, metric evaluation and the tree plot.

Code :

lgb.plot_importance(model)

Output :

feature importance

If you do not mention the eval_set during the fitment, then you will actually get an error while plotting the metric evaluation

Code :

lgb.plot_metric(model)

Output

And as you can clearly see here, the validation curve will tend to increase after it has crossed the 100th evaluation. This can be totally fixed by tuning and setting the hyperparameters of the model. We can also plot the tree using a function.

Code:

lgb.plot_tree(model,figsize=(30,40))

Output:

LightGBM in Python 2

Now we will plot a few metrics by using the sklearn library

Code :

metrics.plot_confusion_matrix(model,x_test,y_test,cmap='Blues_r')

Output :

Code :

print(metrics.classification_report(y_test,model.predict(x_test)))

Output :

LightGBM in Python

Now as we can clearly see from the confusion matrix combined with the classification report, the model is struggling to predict class 1 because of the few instances that we have but if we compare the same result with the other various ensemble algorithm, then LGBM performs the best. We can also perform the same process for the regressor model but there we need to change the estimator to the LGBMRegressor()

End Notes:

From this article, we can see and understand how to use an LGBM model and how to make LightGBM in Python how it can tackle the problem by using a GODD and EFB and then we implemented it for a real-life classification problem and the overall process is also very similar to the other ML algorithms. The in-built plotting functionality also makes the library much more attractive and reduces the overall effort for the evaluation side.

Frequently Asked Questions

Q1. What is LightGBM used for?

A. LightGBM (Light Gradient Boosting Machine) is used for efficient and accurate machine learning tasks. It excels in handling large datasets and offers fast training and prediction times. Primarily used for classification and regression, it’s favored in competitions and real-world applications. Its key features include gradient boosting, handling categorical features, customizable objectives, and distributed computing, making it a powerful tool for various predictive modeling tasks.

Q2. Is LightGBM better than XGBoost?

A. Both LightGBM and XGBoost are powerful gradient boosting frameworks, each with its strengths. LightGBM is known for its speed and memory efficiency, making it suitable for large datasets, while XGBoost offers extensive features and tuning options. The choice depends on your specific needs: if speed and memory efficiency are critical, LightGBM may be better; if you need more tuning flexibility and interpretability, XGBoost might be preferred.

Q3. What is the function of LightGBM?

LightGBM efficiently produces accurate predictions through gradient boosting, which combines weak learners (like decision trees) iteratively to create a robust model for classification and regression tasks.

Q4.What is the principle of LightGBM?

LightGBM’s principle is to optimize speed and performance. It achieves this through a leaf-wise growth strategy for trees, minimizing loss function effectively and employing histogram-based algorithms for feature discretization, which reduces memory usage and enhances training efficiency.

Stay Safe and get vaccinated everyone.

Arnab Mondal

Data Engineer | Python Developer

https://www.linkedin.com/in/arnab1408/

Collab Notebook Link :

https://colab.research.google.com/drive/1KJAGA3NRyy7wMPEWPnBo1ooMOJtvKcrG?usp=sharing

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Arnab Mondal 30 Apr, 2024

Just a guy who loves to code and learn new languages and concepts

Frequently Asked Questions

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Responses From Readers

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Ifechuku
Ifechuku 21 Aug, 2021

This is awesome. Clear analysis

Jonathan
Jonathan 03 Nov, 2022

Hi, Thanks for this article! I noticed a mispelling in the pip function just underneath heading 'Coding an LGBM in Python' to install lightGBM. It should read: pip install lightgmb instead of : pip install lightbgm Best.