XGBoost详细使用指南
xgboost参数
-
选择较高的学习速率(learning rate)。一般情况下，学习速率的值为0.1。但是，对于不同的问题，理想的学习速率有时候会在0.05到0.3之间波动。选择对应于此学习速率的理想决策树数量。XGBoost有一个很有用的函数“cv”，这个函数可以在每一次迭代中使用交叉验证，并返回理想的决策树数量。
-
对于给定的学习速率和决策树数量，进行决策树特定参数调优(max_depth, min_child_weight, gamma, subsample, colsample_bytree)。在确定一棵树的过程中，我们可以选择不同的参数，待会儿我会举例说明。
-
xgboost的正则化参数的调优。(lambda, alpha)。这些参数可以降低模型的复杂度，从而提高模型的表现。
-
降低学习速率，确定理想参数。
1.读取libsvm格式数据并指定参数建模
xgboost的使用方法
- ①使用xgboost自带的数据集格式 + xgboost自带的建模方式
- 把数据读取成xgb.DMatrix格式(libsvm/dataframe.values给定X和Y)
- 准备好一个watch_list(观测和评估的数据集)
- xgb.train(dtrain)
- xgb.predict(dtest)
- ②使用pandas的DataFrame格式 + xgboost的sklearn接口
- estimator = xgb.XGBClassifier()/xgb.XGBRegressor()
- estimator.fit(df_train.values, df_target.values)
#!/usr/bin/python
import numpy as np
#import scipy.sparse
import pickle
import xgboost as xgb
# 基本例子，从libsvm文件中读取数据，做二分类
# 数据是libsvm的格式
#1 3:1 10:1 11:1 21:1 30:1 34:1 36:1 40:1 41:1 53:1 58:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 105:1 117:1 124:1
#0 3:1 10:1 20:1 21:1 23:1 34:1 36:1 39:1 41:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 116:1 120:1
#0 1:1 10:1 19:1 21:1 24:1 34:1 36:1 39:1 42:1 53:1 56:1 65:1 69:1 77:1 86:1 88:1 92:1 95:1 102:1 106:1 116:1 122:1
dtrain = xgb.DMatrix('agaricus.txt.train')
dtest = xgb.DMatrix('agaricus.txt.test')
#超参数设定
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
# 设定watchlist用于查看模型状态
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)
# 使用模型预测
preds = bst.predict(dtest)
# 判断准确率
labels = dtest.get_label()
print ('错误类为%f' % \
(sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
'''
或者
((preds>0.5)*1 != labels).sum()/labels.shape
'''
#[15:49:14] 6513x127 matrix with 143286 entries loaded from ./data/agaricus.txt.train
#[15:49:14] 1611x127 matrix with 35442 entries loaded from ./data/agaricus.txt.test
#[0] eval-error:0.042831 train-error:0.046522
#[1] eval-error:0.021726 train-error:0.022263
#错误类为0.021726
2.配合pandas DataFrame格式数据建模
# 皮马印第安人糖尿病数据集 包含很多字段：怀孕次数 口服葡萄糖耐量试验中血浆葡萄糖浓度 舒张压（mm Hg） 三头肌组织褶厚度（mm）
# 2小时血清胰岛素（μU/ ml） 体重指数（kg/（身高(m)^2） 糖尿病系统功能 年龄（岁）
import pandas as pd
data = pd.read_csv('./data/Pima-Indians-Diabetes.csv')
data.head()
#!/usr/bin/python
import numpy as np
import pandas as pd
import pickle
import xgboost as xgb
from sklearn.model_selection import train_test_split
# 基本例子，从csv文件中读取数据，做二分类
# 用pandas读入数据
data = pd.read_csv('Pima-Indians-Diabetes.csv')
# 做数据切分
train, test = train_test_split(data)
# 转换成Dmatrix格式
feature_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
target_column = 'Outcome'
xgtrain = xgb.DMatrix(train[feature_columns].values, train[target_column].values)
xgtest = xgb.DMatrix(test[feature_columns].values, test[target_column].values)
#参数设定
param = {'max_depth':5, 'eta':0.1, 'silent':1, 'subsample':0.7, 'colsample_bytree':0.7, 'objective':'binary:logistic' }
# 设定watchlist用于查看模型状态
watchlist = [(xgtest,'eval'), (xgtrain,'train')]
num_round = 10
bst = xgb.train(param, xgtrain, num_round, watchlist)
# 使用模型预测
preds = bst.predict(xgtest)
# 判断准确率
labels = xgtest.get_label()
print ('错误类为%f' % \
(sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
#[0] eval-error:0.322917 train-error:0.21875
#[1] eval-error:0.244792 train-error:0.168403
#[2] eval-error:0.255208 train-error:0.182292
#[3] eval-error:0.270833 train-error:0.170139
#[4] eval-error:0.244792 train-error:0.144097
#[5] eval-error:0.25 train-error:0.145833
#[6] eval-error:0.229167 train-error:0.144097
#[7] eval-error:0.25 train-error:0.145833
#[8] eval-error:0.239583 train-error:0.147569
#[9] eval-error:0.234375 train-error:0.140625
#错误类为0.234375
3.使用xgboost的sklearn包
#!/usr/bin/python
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import pickle
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
# 基本例子，从csv文件中读取数据，做二分类
# 用pandas读入数据
data = pd.read_csv('Pima-Indians-Diabetes.csv')
# 做数据切分
train, test = train_test_split(data)
# 取出特征X和目标y的部分
feature_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
target_column = 'Outcome'
train_X = train[feature_columns].values
train_y = train[target_column].values
test_X = test[feature_columns].values
test_y = test[target_column].values
# 初始化模型
xgb_classifier = xgb.XGBClassifier(n_estimators=20,\
max_depth=4, \
learning_rate=0.1, \
subsample=0.7, \
colsample_bytree=0.7)
# 拟合模型
xgb_classifier.fit(train_X, train_y)
# 使用模型预测
preds = xgb_classifier.predict(test_X)
# 判断准确率
print ('错误类为%f' %((preds!=test_y).sum()/float(test_y.shape[0])))
# 模型存储
joblib.dump(xgb_classifier, './model/0003.model')
#错误类为0.276042
#['./model/0003.model']
4.交叉验证
xgb.cv(param, dtrain, num_round, nfold=5,metrics={'error'}, seed = 0)
5.添加预处理的交叉验证
#### 5.添加预处理的交叉验证
#### 5.添加预处理的交叉验证
# 计算正负样本比，调整样本权重
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio = float(np.sum(label == 0)) / np.sum(label==1)
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
# 先做预处理，计算样本权重，再做交叉验证
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed = 0, fpreproc = fpreproc)
6.自定义损失函数与评估准则
print ('running cross validation, with cutomsized loss function')
# 自定义损失函数，需要提供损失函数的一阶导和二阶导
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
# 自定义评估准则，评估预估值和标准答案之间的差距
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
watchlist = [(dtest,'eval'), (dtrain,'train')]
param = {'max_depth':3, 'eta':0.1, 'silent':1}
num_round = 5
# 自定义损失函数训练
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
# 交叉验证
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
#running cross validation, with cutomsized loss function
#[0] eval-rmse:0.306902 train-rmse:0.306163 eval-error:0.518312 train-error:0.517887
#[1] eval-rmse:0.17919 train-rmse:0.177276 eval-error:0.518312 train-error:0.517887
#[2] eval-rmse:0.172566 train-rmse:0.171727 eval-error:0.016139 train-error:0.014433
#[3] eval-rmse:0.269611 train-rmse:0.271113 eval-error:0.016139 train-error:0.014433
#[4] eval-rmse:0.396904 train-rmse:0.398245 eval-error:0.016139 train-error:0.014433
7.只用前n颗树预测
#!/usr/bin/python
import numpy as np
import pandas as pd
import pickle
import xgboost as xgb
from sklearn.model_selection import train_test_split
# 基本例子，从csv文件中读取数据，做二分类
# 用pandas读入数据
data = pd.read_csv('./data/Pima-Indians-Diabetes.csv')
# 做数据切分
train, test = train_test_split(data)
# 转换成Dmatrix格式
feature_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
target_column = 'Outcome'
xgtrain = xgb.DMatrix(train[feature_columns].values, train[target_column].values)
xgtest = xgb.DMatrix(test[feature_columns].values, test[target_column].values)
#参数设定
param = {'max_depth':5, 'eta':0.1, 'silent':1, 'subsample':0.7, 'colsample_bytree':0.7, 'objective':'binary:logistic' }
# 设定watchlist用于查看模型状态
watchlist = [(xgtest,'eval'), (xgtrain,'train')]
num_round = 10
bst = xgb.train(param, xgtrain, num_round, watchlist)
# 只用第1颗树预测
ypred1 = bst.predict(xgtest, ntree_limit=1)
# 用前9颗树预测
ypred2 = bst.predict(xgtest, ntree_limit=9)
label = xgtest.get_label()
print ('用前1颗树预测的错误率为 %f' % (np.sum((ypred1>0.5)!=label) /float(len(label))))
print ('用前9颗树预测的错误率为 %f' % (np.sum((ypred2>0.5)!=label) /float(len(label))))
#[0] eval-error:0.28125 train-error:0.203125
#[1] eval-error:0.182292 train-error:0.1875
#[2] eval-error:0.21875 train-error:0.184028
#[3] eval-error:0.213542 train-error:0.175347
#[4] eval-error:0.223958 train-error:0.164931
#[5] eval-error:0.223958 train-error:0.164931
#[6] eval-error:0.208333 train-error:0.164931
#[7] eval-error:0.192708 train-error:0.15625
#[8] eval-error:0.21875 train-error:0.15625
#[9] eval-error:0.208333 train-error:0.147569
#用前1颗树预测的错误率为 0.281250
#用前9颗树预测的错误率为 0.218750
sklearn与Xgboost配合使用
1.Xgboost建模，sklearn评估
import pickle
import xgboost as xgb
import numpy as np
from sklearn.model_selection import KFold, train_test_split, GridSearchCV
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.datasets import load_iris, load_digits, load_boston
rng = np.random.RandomState(31337)
#二分类：混淆矩阵
print("数字0和1的二分类问题")
digits = load_digits(2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折数据上的交叉验证")
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print("混淆矩阵:")
print(confusion_matrix(actuals, predictions))
#多分类：混淆矩阵
print("\nIris: 多分类")
iris = load_iris()
y = iris['target']
X = iris['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折数据上的交叉验证")
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print("混淆矩阵:")
print(confusion_matrix(actuals, predictions))
#回归问题：MSE
print("\n波士顿房价回归预测问题")
boston = load_boston()
y = boston['target']
X = boston['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折数据上的交叉验证")
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print("MSE:",mean_squared_error(actuals, predictions))
#数字0和1的二分类问题
#在2折数据上的交叉验证
#混淆矩阵:
#[[87 0]
# [ 1 92]]
#混淆矩阵:
#[[91 0]
# [ 3 86]]
#
#Iris: 多分类
#在2折数据上的交叉验证
#混淆矩阵:
#[[19 0 0]
# [ 0 31 3]
# [ 0 1 21]]
#混淆矩阵:
#[[31 0 0]
# [ 0 16 0]
# [ 0 3 25]]
#
#波士顿房价回归预测问题
#在2折数据上的交叉验证
#[15:53:36] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
#MSE: 9.860776812557337
#[15:53:36] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
#MSE: 15.942418468446029
2.网格搜索查找最优超参数
# 第2种训练方法的 调参方法：使用sklearn接口的regressor + GridSearchCV
print("参数最优化：")
y = boston['target']
X = boston['data']
xgb_model = xgb.XGBRegressor()
param_dict = {'max_depth': [2,4,6],
'n_estimators': [50,100,200]}
clf = GridSearchCV(xgb_model, param_dict, verbose=1)
clf.fit(X,y)
print(clf.best_score_)
print(clf.best_params_)
#0.6001029721598573
#{'max_depth': 4, 'n_estimators': 100}
3.early-stopping 早停
# 第1/2种训练方法的 调参方法：early stopping
# 在训练集上学习模型，一颗一颗树添加，在验证集上看效果，当验证集效果不再提升，停止树的添加与生长
X = digits['data']
y = digits['target']
X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=0)
clf = xgb.XGBClassifier()
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_val, y_val)])
'''xgb.train xgb.cv .fit 都有 early_stopping_rounds'''
#[0] validation_0-auc:0.999497
#Will train until validation_0-auc hasn't improved in 10 rounds.
#[1] validation_0-auc:0.999497
#[2] validation_0-auc:0.999497
#[3] validation_0-auc:0.999749
#[4] validation_0-auc:0.999749
#[5] validation_0-auc:0.999749
#[6] validation_0-auc:0.999749
#[7] validation_0-auc:0.999749
#[8] validation_0-auc:0.999749
#[9] validation_0-auc:0.999749
#[10] validation_0-auc:1
#[11] validation_0-auc:1
#[12] validation_0-auc:1
#[13] validation_0-auc:1
#[14] validation_0-auc:1
#[15] validation_0-auc:1
#[16] validation_0-auc:1
#[17] validation_0-auc:1
#[18] validation_0-auc:1
#[19] validation_0-auc:1
#[20] validation_0-auc:1
#Stopping. Best iteration:
#[10] validation_0-auc:1
#XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
# max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
# n_estimators=100, n_jobs=1, nthread=None,
# objective='binary:logistic', random_state=0, reg_alpha=0,
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
# subsample=1, verbosity=1)
4.特征重要度
iris = load_iris()
y = iris['target']
X = iris['data']
xgb_model = xgb.XGBClassifier().fit(X,y)
print('特征排序：')
feature_names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
feature_importances = xgb_model.feature_importances_
indices = np.argsort(feature_importances)[::-1]
for index in indices:
print("特征 %s 重要度为 %f" %(feature_names[index], feature_importances[index]))
%matplotlib inline
import matplotlib.pyplot as plt
plt.figure(figsize=(16,8))
plt.title("feature importances")
plt.bar(range(len(feature_importances)), feature_importances[indices], color='b')
plt.xticks(range(len(feature_importances)), np.array(feature_names)[indices], color='b')
'''或者
feature_names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
df = pd.DataFrame({'feature_names':feature_names,'feature_importances':xgb_model.feature_importances_})
df = df.sort_values('feature_importances',ascending=False)
plt.bar(df.feature_names,df.feature_importances)
'''
#特征排序：
#特征 petal_length 重要度为 0.595834
#特征 petal_width 重要度为 0.358166
#特征 sepal_width 重要度为 0.033481
#特征 sepal_length 重要度为 0.012520
5.并行训练加速
import os
if __name__ == "__main__":
try:
from multiprocessing import set_start_method
except ImportError:
raise ImportError("Unable to import multiprocessing.set_start_method."
" This example only runs on Python 3.4")
set_start_method("forkserver")
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
import xgboost as xgb
rng = np.random.RandomState(31337)
print("Parallel Parameter optimization")
boston = load_boston()
os.environ["OMP_NUM_THREADS"] = "2" # or to whatever you want
y = boston['target']
X = boston['data']
xgb_model = xgb.XGBRegressor()
clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
'n_estimators': [50, 100, 200]}, verbose=1,
n_jobs=2)
clf.fit(X, y)
print(clf.best_score_)
print(clf.best_params_)
陈天奇的库和sklean的区别
#!/usr/bin/python
import numpy as np
import pandas as pd
import pickle
import xgboost as xgb
from sklearn.model_selection import train_test_split
# 基本例子，从csv文件中读取数据，做二分类
# 用pandas读入数据
data = pd.read_csv('Pima-Indians-Diabetes.csv')
# 做数据切分
train, test = train_test_split(data)
# 转换成Dmatrix格式
feature_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
target_column = 'Outcome'
xgtrain = xgb.DMatrix(train[feature_columns].values, train[target_column].values)
xgtest = xgb.DMatrix(test[feature_columns].values, test[target_column].values)
#参数设定
param = {'max_depth':5, 'eta':0.1, 'silent':1, 'subsample':0.7, 'colsample_bytree':0.7, 'objective':'binary:logistic' }
# 设定watchlist用于查看模型状态
watchlist = [(xgtest,'eval'), (xgtrain,'train')]
num_round = 10
bst = xgb.train(param, xgtrain, num_round, watchlist)
xgb.plot_importance(bst)
bst.get_score()
#{'f1': 24, 'f7': 40, 'f3': 13, 'f6': 31, 'f0': 9, 'f4': 21, 'f5': 23, 'f2': 22}
X = digits['data']
y = digits['target']
X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=0)
clf = xgb.XGBClassifier()
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_val, y_val)])
没有watchlist，没有get_score()