LightGBM使用

可以参考
LightGBM原生/sk接口的常用参数
LightGBM使用

lightGBM调参

---

所有的参数含义，参考：http://lightgbm.apachecn.org/cn/latest/Parameters.html

调参过程：

1. num_leaves

   LightGBM使用的是leaf-wise的算法，因此在调节树的复杂程度时，使用的是num_leaves而不是max_depth。

2. 样本分布非平衡数据集：可以param[‘is_unbalance’]=’true’；

3. Bagging参数：bagging_fraction+bagging_freq（必须同时设置）、feature_fraction。bagging_fraction可以使bagging的更快的运行出结果，feature_fraction设置在每次迭代中使用特征的比例；

4. min_data_in_leaf、min_sum_hessian_in_leaf：调大它的值可以防止过拟合，它的值通常设置的比较大。

sklearn接口形式的LightGBM示例

---

这里主要以sklearn的使用形式来使用lightgbm算法，包含建模，训练，预测，网格参数优化。

import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.datasets import  make_classification
# 加载数据
print('Load data...')

iris = load_iris()
data=iris.data
target = iris.target
X_train,X_test,y_train,y_test =train_test_split(data,target,test_size=0.2)

# df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
# df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
# y_train = df_train[0].values
# y_test = df_test[0].values
# X_train = df_train.drop(0, axis=1).values
# X_test = df_test.drop(0, axis=1).values

print('Start training...')
# 创建模型，训练模型
gbm = lgb.LGBMRegressor(objective='regression',num_leaves=31,learning_rate=0.05,n_estimators=20)
gbm.fit(X_train, y_train,eval_set=[(X_test, y_test)],eval_metric='l1',early_stopping_rounds=5)

print('Start predicting...')
# 测试机预测
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# 模型评估
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)

# feature importances
print('Feature importances:', list(gbm.feature_importances_))

# 网格搜索，参数优化
estimator = lgb.LGBMRegressor(num_leaves=31)

param_grid = {
    'learning_rate': [0.01, 0.1, 1],
    'n_estimators': [20, 40]
}

gbm = GridSearchCV(estimator, param_grid)

gbm.fit(X_train, y_train)

print('Best parameters found by grid search are:', gbm.best_params_)

原生形式使用lightgbm

---

# coding: utf-8
# pylint: disable = invalid-name, C0111
import json
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.datasets import  make_classification

iris = load_iris()
data=iris.data
target = iris.target
X_train,X_test,y_train,y_test =train_test_split(data,target,test_size=0.2)


# 加载你的数据
# print('Load data...')
# df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
# df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
#
# y_train = df_train[0].values
# y_test = df_test[0].values
# X_train = df_train.drop(0, axis=1).values
# X_test = df_test.drop(0, axis=1).values

# 创建成lgb特征的数据集格式
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# 将参数写成字典下形式
params = {
    'task': 'train',
    'boosting_type': 'gbdt',  # 设置提升类型
    'objective': 'regression', # 目标函数
    'metric': {'l2', 'auc'},  # 评估函数
    'num_leaves': 31,   # 叶子节点数
    'learning_rate': 0.05,  # 学习速率
    'feature_fraction': 0.9, # 建树的特征选择比例
    'bagging_fraction': 0.8, # 建树的样本采样比例
    'bagging_freq': 5,  # k 意味着每 k 次迭代执行bagging
    'verbose': 1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息
}

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

print('Save model...')
# 保存模型到文件
gbm.save_model('model.txt')

print('Start predicting...')
# 预测数据集
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
# 评估模型
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)