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Python-package Introduction

This document gives a basic walkthrough of LightGBM Python-package.

List of other helpful links


Install Python-package dependencies, setuptools, wheel, numpy and scipy are required, scikit-learn is required for sklearn interface and recommended:

pip install setuptools wheel numpy scipy scikit-learn -U

Refer to Python-package folder for the installation guide.

To verify your installation, try to import lightgbm in Python:

import lightgbm as lgb

Data Interface

The LightGBM Python module can load data from:

  • libsvm/tsv/csv/txt format file
  • NumPy 2D array(s), pandas DataFrame, SciPy sparse matrix
  • LightGBM binary file

The data is stored in a Dataset object.

To load a libsvm text file or a LightGBM binary file into Dataset:

train_data = lgb.Dataset('train.svm.bin')

To load a numpy array into Dataset:

data = np.random.rand(500, 10)  # 500 entities, each contains 10 features
label = np.random.randint(2, size=500)  # binary target
train_data = lgb.Dataset(data, label=label)

To load a scpiy.sparse.csr_matrix array into Dataset:

csr = scipy.sparse.csr_matrix((dat, (row, col)))
train_data = lgb.Dataset(csr)

Saving Dataset into a LightGBM binary file will make loading faster:

train_data = lgb.Dataset('train.svm.txt')

Create validation data:

test_data = train_data.create_valid('test.svm')


test_data = lgb.Dataset('test.svm', reference=train_data)

In LightGBM, the validation data should be aligned with training data.

Specific feature names and categorical features:

train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3'])

LightGBM can use categorical features as input directly. It doesn't need to convert to one-hot coding, and is much faster than one-hot coding (about 8x speed-up).

Note: You should convert your categorical features to int type before you construct Dataset.

Weights can be set when needed:

w = np.random.rand(500, )
train_data = lgb.Dataset(data, label=label, weight=w)


train_data = lgb.Dataset(data, label=label)
w = np.random.rand(500, )

And you can use Dataset.set_init_score() to set initial score, and Dataset.set_group() to set group/query data for ranking tasks.

Memory efficient usage:

The Dataset object in LightGBM is very memory-efficient, due to it only need to save discrete bins. However, Numpy/Array/Pandas object is memory cost. If you concern about your memory consumption, you can save memory according to the following:

  1. Let free_raw_data=True (default is True) when constructing the Dataset
  2. Explicit set raw_data=None after the Dataset has been constructed
  3. Call gc

Setting Parameters

LightGBM can use either a list of pairs or a dictionary to set Parameters. For instance:

  • Booster parameters:

    param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
    param['metric'] = 'auc'
  • You can also specify multiple eval metrics:

    param['metric'] = ['auc', 'binary_logloss']


Training a model requires a parameter list and data set:

num_round = 10
bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])

After training, the model can be saved:


The trained model can also be dumped to JSON format:

json_model = bst.dump_model()

A saved model can be loaded:

bst = lgb.Booster(model_file='model.txt')  #init model


Training with 5-fold CV:

num_round = 10, train_data, num_round, nfold=5)

Early Stopping

If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. Early stopping requires at least one set in valid_sets. If there is more than one, it will use all of them except the training data:

bst = lgb.train(param, train_data, num_round, valid_sets=valid_sets, early_stopping_rounds=10)
bst.save_model('model.txt', num_iteration=bst.best_iteration)

The model will train until the validation score stops improving. Validation score needs to improve at least every early_stopping_rounds to continue training.

The index of iteration that has the best performance will be saved in the best_iteration field if early stopping logic is enabled by setting early_stopping_rounds. Note that train() will return a model from the best iteration.

This works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC, etc.). Note that if you specify more than one evaluation metric, all of them will be used for early stopping.


A model that has been trained or loaded can perform predictions on datasets:

# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
ypred = bst.predict(data)

If early stopping is enabled during training, you can get predictions from the best iteration with bst.best_iteration:

ypred = bst.predict(data, num_iteration=bst.best_iteration)