lightGBM_sample.py

#!/usr/bin/env python
# coding=utf-8
"""
python=3.5.2
author=aaron
"""
import lightgbm as lgb
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_iris
# from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split

data = load_iris()
feature = data['data']
target = data['target']
X_train, X_test, y_train, y_test = train_test_split(feature, target, test_size=0.2, random_state=42)

# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# specify your configurations as a dict
params = {
    'task': 'train',
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': {'l2', 'auc'},
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 0
}

print('Start training...')
# train
gbm = lgb.train(params,
                lgb_train,
                num_boost_round=20,
                valid_sets=lgb_eval,
                early_stopping_rounds=5)

print('Save model...')
# save model to file
gbm.save_model('model.txt')

print('Start predicting...')
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
# eval
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)