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Showing content from https://github.com/ArdalanM/pyLightGBM below:

ArdalanM/pyLightGBM: Python binding for Microsoft LightGBM

Features:

• Regression, Classification (binary, multi class)
• Feature importance (clf.feature_importance())
• Early stopping (clf.best_round)
• Works with scikit-learn: GridSearchCV, cross_val_score, etc...
• Silent mode (verbose=False)

Install lastest verion of Microsoft LightGBM then install the wrapper:

 pip install git+https://github.com/ArdalanM/pyLightGBM.git

• Regression:

import numpy as np
from sklearn import datasets, metrics, model_selection
from pylightgbm.models import GBMRegressor

# full path to lightgbm executable (on Windows include .exe)
exec = "~/Documents/apps/LightGBM/lightgbm"

X, y = datasets.load_diabetes(return_X_y=True)
clf = GBMRegressor(exec_path=exec,
                   num_iterations=100, early_stopping_round=10,
                   num_leaves=10, min_data_in_leaf=10)

x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

clf.fit(x_train, y_train, test_data=[(x_test, y_test)])
print("Mean Square Error: ", metrics.mean_squared_error(y_test, clf.predict(x_test)))

• Binary Classification:

import numpy as np
from sklearn import datasets, metrics, model_selection
from pylightgbm.models import GBMClassifier

# full path to lightgbm executable (on Windows include .exe)
exec = "~/Documents/apps/LightGBM/lightgbm"

X, Y = datasets.make_classification(n_samples=200, n_features=10)
x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.2)

clf = GBMClassifier(exec_path=exec, min_data_in_leaf=1)
clf.fit(x_train, y_train, test_data=[(x_test, y_test)])
y_pred = clf.predict(x_test)
print("Accuracy: ", metrics.accuracy_score(y_test, y_pred))

• Grid Search:

import numpy as np
from sklearn import datasets, metrics, model_selection
from pylightgbm.models import GBMClassifier

# full path to lightgbm executable (on Windows include .exe)
exec = "~/Documents/apps/LightGBM/lightgbm"

X, Y = datasets.make_classification(n_samples=1000, n_features=10)

gbm = GBMClassifier(exec_path=exec,
                    metric='binary_error', early_stopping_round=10, bagging_freq=10)

param_grid = {'learning_rate': [0.1, 0.04], 'bagging_fraction': [0.5, 0.9]}

scorer = metrics.make_scorer(metrics.accuracy_score, greater_is_better=True)
clf = model_selection.GridSearchCV(gbm, param_grid, scoring=scorer, cv=2)

clf.fit(X, Y)

print("Best score: ", clf.best_score_)
print("Best params: ", clf.best_params_)

• Using pyLightGBM for Kaggle competition (Allstate Claims Severity)
• [Bayesian global optimization with pyLightGBM using data from Kaggle competition (Allstate Claims Severity)](https://github.com/ArdalanM/pyLightGBM/blob/master/notebooks/bayesian optimization_example_kaggle_allstate.ipynb)

• application="regression"
• num_iterations=10
• learning_rate=0.1
• num_leaves=127
• tree_learner="serial"
• num_threads=1
• min_data_in_leaf=100
• metric='l2'
• is_training_metric=False
• feature_fraction=1.
• feature_fraction_seed=2
• bagging_fraction=1.
• bagging_freq=0
• bagging_seed=3
• metric_freq=1
• early_stopping_round=0
• max_bin=255
• is_unbalance=False
• num_class=1
• boosting_type='gbdt'
• min_sum_hessian_in_leaf=10
• drop_rate=0.01
• drop_seed=4
• max_depth=-1
• lambda_l1=0.
• lambda_l2=0.
• min_gain_to_split=0.
• verbose=True
• model=None

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