XGBoost算法案例与调参实例

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我是靠谱客的博主懦弱宝马，最近开发中收集的这篇文章主要介绍XGBoost算法案例与调参实例，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

XGBoost

优化的分布式梯度增强库，旨在实现高效，灵活和便携。
在Gradient Boosting框架下实现机器学习算法。
提供了并行树提升（也称为GBDT，GBM），可以快速准确地解决许多数据科学问题。相同的代码在主要的分布式环境（Hadoop，SGE，MPI）上运行，并且可以解决超过数十亿个样例的问题。
利用了核外计算并且能够使数据科学家在一个主机上处理数亿的样本数据。最终，将这些技术进行结合来做一个端到端的系统以最少的集群系统来扩展到更大的数据集上。
以CART决策树为子模型，通过Gradient Tree Boosting实现多棵CART树的集成学习，得到最终模型。
在生成新树的过程中，最基本的操作是节点分裂。节点分裂中最重要的环节是找到最优特征及最优切分点, 然后将叶子节点按照最优特征和最优切分点进行分裂。

XGBoost最优特征和最优切分点划分

精确贪心算法首先找到所有的候选特征及所有的候选切分点, 然后选择最大的特征及对应切分点作为最优特征和最优切分点。
基于直方图的近似算法对某一特征寻找最优切分点时，首先对该特征的所有切分点按分位数 (如百分位) 分桶, 得到一个候选切分点集。特征的每一个切分点都可以分到对应的分桶; 然后，对每个桶计算特征统计得到直方图, 最后，选择所有候选特征及候选切分点中对应桶的特征统计收益最大的作为最优特征及最优切分点。

XGBoost分类案例

from sklearn.datasets import load_iris
import xgboost as xgb
from xgboost import plot_importance
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 准确率
# 加载样本数据集
iris = load_iris()
X,y = iris.data,iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565) # 数据集分割
# 算法参数
params = {
'booster': 'gbtree',
'objective': 'multi:softmax',
'num_class': 3,
'gamma': 0.1,
'max_depth': 6,
'lambda': 2,
'subsample': 0.7,
'colsample_bytree': 0.75,
'min_child_weight': 3,
'silent': 0,
'eta': 0.1,
'seed': 1,
'nthread': 4,
}
plst = params.items()
dtrain = xgb.DMatrix(X_train, y_train) # 生成数据集格式
num_rounds = 500
model = xgb.train(plst, dtrain, num_rounds) # xgboost模型训练
# 对测试集进行预测
dtest = xgb.DMatrix(X_test)
y_pred = model.predict(dtest)
# 计算准确率
accuracy = accuracy_score(y_test,y_pred)
print("accuarcy: %.2f%%" % (accuracy*100.0))
# 显示重要特征
plot_importance(model)
plt.show()

Xgboost回归案例

import xgboost as xgb
from xgboost import plot_importance
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error
# 加载数据集
boston = load_boston()
X,y = boston.data,boston.target
# XGBoost训练过程
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
params = {
'booster': 'gbtree',
'objective': 'reg:squarederror',
'gamma': 0.1,
'max_depth': 5,
'lambda': 3,
'subsample': 0.7,
'colsample_bytree': 0.7,
'min_child_weight': 3,
'silent': 1,
'eta': 0.1,
'seed': 1000,
'nthread': 4,
}
dtrain = xgb.DMatrix(X_train, y_train)
num_rounds = 300
plst = params.items()
model = xgb.train(plst, dtrain, num_rounds)
# 对测试集进行预测
dtest = xgb.DMatrix(X_test)
ans = model.predict(dtest)
# 显示重要特征
plot_importance(model)
plt.show()

XGBoost结合sklearn网格搜索调参

import xgboost as xgb
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score
iris = load_iris()
X,y = iris.data,iris.target
col = iris.target_names
train_x, valid_x, train_y, valid_y = train_test_split(X, y, test_size=0.3, random_state=1)
# 分训练集和验证集
parameters = {
'max_depth': [5, 10, 15, 20, 25],
'learning_rate': [0.01, 0.02, 0.05, 0.1, 0.15],
'n_estimators': [500, 1000, 2000, 3000, 5000],
'min_child_weight': [0, 2, 5, 10, 20],
'max_delta_step': [0, 0.2, 0.6, 1, 2],
'subsample': [0.6, 0.7, 0.8, 0.85, 0.95],
'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],
'reg_alpha': [0, 0.25, 0.5, 0.75, 1],
'reg_lambda': [0.2, 0.4, 0.6, 0.8, 1],
'scale_pos_weight': [0.2, 0.4, 0.6, 0.8, 1]
}
xlf = xgb.XGBClassifier(max_depth=10,
learning_rate=0.01,
n_estimators=2000,
silent=True,
objective='multi:softmax',
num_class=3 ,
nthread=-1,
gamma=0,
min_child_weight=1,
max_delta_step=0,
subsample=0.85,
colsample_bytree=0.7,
colsample_bylevel=1,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
seed=0,
missing=None)
gs = GridSearchCV(xlf, param_grid=parameters, scoring='accuracy', cv=3)
gs.fit(train_x, train_y)
print("Best score: %0.3f" % gs.best_score_)
print("Best parameters set: %s" % gs.best_params_ )

LightGBM与网格搜索结合调参

import lightgbm as lgb
from sklearn import metrics
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
canceData=load_breast_cancer()
X=canceData.data
y=canceData.target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0,test_size=0.2)
### 数据转换
print('数据转换')
lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,free_raw_data=False)
### 设置初始参数--不含交叉验证参数
print('设置参数')
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'nthread':4,
'learning_rate':0.1
}
### 交叉验证(调参)
print('交叉验证')
max_auc = float('0')
best_params = {}
# 准确率
print("调参1：提高准确率")
for num_leaves in range(5,100,5):
for max_depth in range(3,8,1):
params['num_leaves'] = num_leaves
params['max_depth'] = max_depth
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc = mean_auc
best_params['num_leaves'] = num_leaves
best_params['max_depth'] = max_depth
if 'num_leaves' and 'max_depth' in best_params.keys():
params['num_leaves'] = best_params['num_leaves']
params['max_depth'] = best_params['max_depth']
# 过拟合
print("调参2：降低过拟合")
for max_bin in range(5,256,10):
for min_data_in_leaf in range(1,102,10):
params['max_bin'] = max_bin
params['min_data_in_leaf'] = min_data_in_leaf
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc = mean_auc
best_params['max_bin']= max_bin
best_params['min_data_in_leaf'] = min_data_in_leaf
if 'max_bin' and 'min_data_in_leaf' in best_params.keys():
params['min_data_in_leaf'] = best_params['min_data_in_leaf']
params['max_bin'] = best_params['max_bin']
print("调参3：降低过拟合")
for feature_fraction in [0.6,0.7,0.8,0.9,1.0]:
for bagging_fraction in [0.6,0.7,0.8,0.9,1.0]:
for bagging_freq in range(0,50,5):
params['feature_fraction'] = feature_fraction
params['bagging_fraction'] = bagging_fraction
params['bagging_freq'] = bagging_freq
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['feature_fraction'] = feature_fraction
best_params['bagging_fraction'] = bagging_fraction
best_params['bagging_freq'] = bagging_freq
if 'feature_fraction' and 'bagging_fraction' and 'bagging_freq' in best_params.keys():
params['feature_fraction'] = best_params['feature_fraction']
params['bagging_fraction'] = best_params['bagging_fraction']
params['bagging_freq'] = best_params['bagging_freq']
print("调参4：降低过拟合")
for lambda_l1 in [1e-5,1e-3,1e-1,0.0,0.1,0.3,0.5,0.7,0.9,1.0]:
for lambda_l2 in [1e-5,1e-3,1e-1,0.0,0.1,0.4,0.6,0.7,0.9,1.0]:
params['lambda_l1'] = lambda_l1
params['lambda_l2'] = lambda_l2
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['lambda_l1'] = lambda_l1
best_params['lambda_l2'] = lambda_l2
if 'lambda_l1' and 'lambda_l2' in best_params.keys():
params['lambda_l1'] = best_params['lambda_l1']
params['lambda_l2'] = best_params['lambda_l2']
print("调参5：降低过拟合2")
for min_split_gain in [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]:
params['min_split_gain'] = min_split_gain
cv_results = lgb.cv(
params,
lgb_train,
seed=1,
nfold=5,
metrics=['auc'],
early_stopping_rounds=10,
verbose_eval=True
)
mean_auc = pd.Series(cv_results['auc-mean']).max()
boost_rounds = pd.Series(cv_results['auc-mean']).idxmax()
if mean_auc >= max_auc:
max_auc=mean_auc
best_params['min_split_gain'] = min_split_gain
if 'min_split_gain' in best_params.keys():
params['min_split_gain'] = best_params['min_split_gain']
print(best_params)