python模型lgbm调优工具_机器学习应用之LGBM详解

内置方式建模要把数据读取成Dataset格式

lgb.train去训练

# coding: utf-8

import json

import lightgbm as lgb

import pandas as pd

from sklearn.metrics import mean_squared_error

# 加载数据集合

print('加载数据...')

df_train = pd.read_csv('../data/regression.train.txt', header=None, sep='\t')

df_test = pd.read_csv('../data/regression.test.txt', header=None, sep='\t')

# 设定训练集和测试集

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

# 构建lgb中的Dataset格式

lgb_train = lgb.Dataset(X_train, y_train)

lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# 敲定好一组参数

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('开始训练...')

# 训练

gbm = lgb.train(params,

lgb_train,

num_boost_round=20,

valid_sets=lgb_eval,

early_stopping_rounds=5)

# 保存模型

print('保存模型...')

# 保存模型到文件中

gbm.save_model('../../tmp/model.txt')

print('开始预测...')

# 预测

y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

# 评估

print('预估结果的rmse为:')

print(mean_squared_error(y_test, y_pred) ** 0.5)添加样本权重训练

# coding: utf-8

import json

import lightgbm as lgb

import pandas as pd

import numpy as np

from sklearn.metrics import mean_squared_error

import warnings

warnings.filterwarnings("ignore")

# 加载数据集

print('加载数据...')

df_train = pd.read_csv('../data/binary.train', header=None, sep='\t')

df_test = pd.read_csv('../data/binary.test', header=None, sep='\t')

W_train = pd.read_csv('../data/binary.train.weight', header=None)[0]

W_test = pd.read_csv('../data/binary.test.weight', header=None)[0]

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

num_train, num_feature = X_train.shape

# 加载数据的同时加载权重

lgb_train = lgb.Dataset(X_train, y_train,

weight=W_train, free_raw_data=False)

lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,

weight=W_test, free_raw_data=False)

# 设定参数

params = {

'boosting_type': 'gbdt',

'objective': 'binary',

'metric': 'binary_logloss',

'num_leaves': 31,

'learning_rate': 0.05,

'feature_fraction': 0.9,

'bagging_fraction': 0.8,

'bagging_freq': 5,

'verbose': 0

}

# 产出特征名称

feature_name = ['feature_' + str(col) for col in range(num_feature)]

print('开始训练...')

gbm = lgb.train(params,

lgb_train,

num_boost_round=10,

valid_sets=lgb_train, # 评估训练集

feature_name=feature_name,

categorical_feature=[21])模型的载入与预测

# 查看特征名称

print('完成10轮训练...')

print('第7个特征为:')

print(repr(lgb_train.feature_name[6]))

# 存储模型

gbm.save_model('../../tmp/lgb_model.txt')

# 特征名称

print('特征名称:')

print(gbm.feature_name())

# 特征重要度

print('特征重要度:')

print(list(gbm.feature_importance()))

# 加载模型

print('加载模型用于预测')

bst = lgb.Booster(model_file='../../tmp/lgb_model.txt')

# 预测

y_pred = bst.predict(X_test)

# 在测试集评估效果

print('在测试集上的rmse为:')

print(mean_squared_error(y_test, y_pred) ** 0.5)接着之前的模型继续训练

# 继续训练

# 从../../tmp/model.txt中加载模型初始化

gbm = lgb.train(params,

lgb_train,

num_boost_round=10,

init_model='../../tmp/lgb_model.txt',

valid_sets=lgb_eval)

print('以旧模型为初始化，完成第 10-20 轮训练...')

# 在训练的过程中调整超参数

# 比如这里调整的是学习率

gbm = lgb.train(params,

lgb_train,

num_boost_round=10,

init_model=gbm,

learning_rates=lambda iter: 0.05 * (0.99 ** iter),

valid_sets=lgb_eval)

print('逐步调整学习率完成第 20-30 轮训练...')

# 调整其他超参数

gbm = lgb.train(params,

lgb_train,

num_boost_round=10,

init_model=gbm,

valid_sets=lgb_eval,

callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)])

print('逐步调整bagging比率完成第 30-40 轮训练...')自定义损失函数

# 类似在xgboost中的形式

# 自定义损失函数需要

def loglikelood(preds, train_data):

labels = train_data.get_label()

preds = 1. / (1. + np.exp(-preds))

grad = preds - labels

hess = preds * (1. - preds)

return grad, hess

# 自定义评估函数

def binary_error(preds, train_data):

labels = train_data.get_label()

return 'error', np.mean(labels != (preds > 0.5)), False

gbm = lgb.train(params,

lgb_train,

num_boost_round=10,

init_model=gbm,

fobj=loglikelood,

feval=binary_error,

valid_sets=lgb_eval)

print('用自定义的损失函数与评估标准完成第40-50轮...')sklearn与LightGBM配合使用

LightGBM建模，sklearn评估

# coding: utf-8

import lightgbm as lgb

import pandas as pd

from sklearn.metrics import mean_squared_error

from sklearn.model_selection import GridSearchCV

# 加载数据

print('加载数据...')

df_train = pd.read_csv('../data/regression.train.txt', header=None, sep='\t')

df_test = pd.read_csv('../data/regression.test.txt', header=None, sep='\t')

# 取出特征和标签

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('开始训练...')

# 直接初始化LGBMRegressor

# 这个LightGBM的Regressor和sklearn中其他Regressor基本是一致的

gbm = lgb.LGBMRegressor(objective='regression',

num_leaves=31,

learning_rate=0.05,

n_estimators=20)

# 使用fit函数拟合

gbm.fit(X_train, y_train,

eval_set=[(X_test, y_test)],

eval_metric='l1',

early_stopping_rounds=5)

# 预测

print('开始预测...')

y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)

# 评估预测结果

print('预测结果的rmse是:')

print(mean_squared_error(y_test, y_pred) ** 0.5)网格搜索查找最优超参数

# 配合scikit-learn的网格搜索交叉验证选择最优超参数

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('用网格搜索找到的最优超参数为:')

print(gbm.best_params_)

绘图解释

%matplotlib inline

# coding: utf-8

import lightgbm as lgb

import pandas as pd

try:

import matplotlib.pyplot as plt

except ImportError:

raise ImportError('You need to install matplotlib for plotting.')

# 加载数据集

print('加载数据...')

df_train = pd.read_csv('../data/regression.train.txt', header=None, sep='\t')

df_test = pd.read_csv('../data/regression.test.txt', header=None, sep='\t')

# 取出特征和标签

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

# 构建lgb中的Dataset数据格式

lgb_train = lgb.Dataset(X_train, y_train)

lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)

# 设定参数

params = {

'num_leaves': 5,

'metric': ('l1', 'l2'),

'verbose': 0

}

evals_result = {} # to record eval results for plotting

print('开始训练...')

# 训练

gbm = lgb.train(params,

lgb_train,

num_boost_round=100,

valid_sets=[lgb_train, lgb_test],

feature_name=['f' + str(i + 1) for i in range(28)],

categorical_feature=[21],

evals_result=evals_result,

verbose_eval=10)

print('在训练过程中绘图...')

ax = lgb.plot_metric(evals_result, metric='l1')

plt.show()

print('画出特征重要度...')

ax = lgb.plot_importance(gbm, max_num_features=10)

plt.show()

print('画出第84颗树...')

ax = lgb.plot_tree(gbm, tree_index=83, figsize=(20, 8), show_info=['split_gain'])

plt.show()

#print('用graphviz画出第84颗树...')

#graph = lgb.create_tree_digraph(gbm, tree_index=83, name='Tree84')

#graph.render(view=True)