Градиент бустинг фреймворк LightGBM для ml

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

  • Faster training speed and higher efficiency.
  • Lower memory usage.
  • Better accuracy.
  • Support of parallel, distributed, and GPU learning.
  • Capable of handling large-scale data.

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Python quik start


pip install lightgbm

import lightgbm as lgb

The LightGBM Python module can load data from:

  • LibSVM (zero-based)/TSV/CSV/ XT format file
  • NumPy 2D array(s), pandas DataFrame, H2O DataTable’s Frame, SciPy sparse matrix
  • LightGBM binary file
  • LightGBM Sequence object(s)

The data is stored in a Dataset object.

To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset:

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

To load a numpy array into Dataset:

import numpy as np

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 scipy.sparse.csr_matrix array into Dataset:

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

Load from Sequence objects. We can implement Sequence interface to read binary files. The following example shows reading HDF5 file with h5py:

import h5py

class HDFSequence(lgb.Sequence):
    def __init__(self, hdf_dataset, batch_size): = hdf_dataset
        self.batch_size = batch_size

    def __getitem__(self, idx):

    def __len__(self):
        return len(

f = h5py.File('train.hdf5', 'r')
train_data = lgb.Dataset(HDFSequence(f['X'], 8192), label=f['Y'][:])

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

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

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

validation_data = train_data.create_valid('validation.svm')
# or
validation_data = lgb.Dataset('validation.svm', reference=train_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 encoding, and is much faster than one-hot encoding (about 8x speed-up). But 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)
# or
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.

Setting parameters:

# Booster parameters
param = {'num_leaves': 31, '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=[validation_data])

# After training, the model can be saved
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:, 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=5)
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


# 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

Как устроен LightGBM

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Интерактивное описание параметров


params = {
   "monotone_constraints": [-1, 0, 1]

Parameters Tuning


Несколько подходов, достыпных в lightgbm:

Полностю статья

Python API


GPU tutorial


Advanced Topics


  • Missing Value Handle
  • Categorical Feature Support
  • LambdaRank
  • Cost Efficient Gradient Boosting