xgboost parameters
Choosing a higher rate of learning (learning rate). In general, the learning rate is 0.1. However, different problems, sometimes the ideal learning rate fluctuated between 0.05 and 0.3. Decision tree corresponding to this number over the selected learning rate. There XGBoost function "cv" a useful, this function can be used cross-validation at each iteration, and returns the number of tree ideal.
For a given amount and rate decision tree learning, a decision tree for a particular parameter tuning (max_depth, min_child_weight, gamma, subsample, colsample_bytree). In determining a tree in the process, we can choose different parameters, later I will illustrate.
xgboost Regularization tuning parameters. (Lambda, alpha). These parameters can reduce the complexity of the model, thereby improving the performance of the model.
Reduce the learning rate, to determine the ideal parameters.
1. Read libsvm format data and specify parameters modeling
xgboost to use
- ① using xgboost own data set format comes modeling + xgboost
- Reading the data into the format xgb.DMatrix (libsvm / dataframe.values given X and Y)
- Ready for a watch_list (observation and assessment of the data set)
- xgb.train(dtrain)
- xgb.predict(dtest)
- ② use of pandas DataFrame format + xgboost of sklearn Interface
- 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
# 转换成Dmatrix格式
dtrain = xgb.DMatrix('./data/agaricus.txt.train')
dtest = xgb.DMatrix('./data/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))))
# 模型存储
bst.save_model('./model/0001.model')[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0.0217262. pandas format with data modeling DataFrame
# 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()| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 |
#!/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)
# 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))))
# 模型存储
bst.save_model('./model/0002.model')[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0.2343753. Use of sklearn packet xgboost
#!/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('./data/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. Cross-validation
xgb.cv(param, dtrain, num_round, nfold=5,metrics={'error'}, seed = 0)| train-error-mean | train-error-std | test-error-mean | test-error-std | |
|---|---|---|---|---|
| 0 | 0.006832 | 0.001012 | 0.006756 | 0.001407 |
| 1 | 0.002994 | 0.002806 | 0.002303 | 0.002524 |
| 2 | 0.001382 | 0.000352 | 0.001382 | 0.001228 |
| 3 | 0.001190 | 0.000658 | 0.001382 | 0.001228 |
| 4 | 0.001382 | 0.000282 | 0.001075 | 0.000921 |
| 5 | 0.000921 | 0.000506 | 0.001228 | 0.001041 |
| 6 | 0.000921 | 0.000506 | 0.001228 | 0.001041 |
| 7 | 0.000921 | 0.000506 | 0.001228 | 0.001041 |
| 8 | 0.000921 | 0.000506 | 0.001228 | 0.001041 |
| 9 | 0.000921 | 0.000506 | 0.001228 | 0.001041 |
5. Add pre-treated cross-validation
# 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)| train-auc-mean | train-auc-std | test-auc-mean | test-auc-std | |
|---|---|---|---|---|
| 0 | 0.999772 | 0.000126 | 0.999731 | 0.000191 |
| 1 | 0.999942 | 0.000044 | 0.999909 | 0.000085 |
| 2 | 0.999964 | 0.000035 | 0.999926 | 0.000084 |
| 3 | 0.999979 | 0.000036 | 0.999950 | 0.000089 |
| 4 | 0.999976 | 0.000043 | 0.999946 | 0.000098 |
| 5 | 0.999994 | 0.000010 | 0.999988 | 0.000020 |
| 6 | 0.999993 | 0.000012 | 0.999988 | 0.000020 |
| 7 | 0.999993 | 0.000012 | 0.999988 | 0.000020 |
| 8 | 0.999993 | 0.000012 | 0.999988 | 0.000020 |
| 9 | 0.999993 | 0.000012 | 0.999988 | 0.000020 |
6. Since the loss function defined evaluation criteria and
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| train-error-mean | train-error-std | train-rmse-mean | train-rmse-std | test-error-mean | test-error-std | test-rmse-mean | test-rmse-std | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.517887 | 0.001085 | 0.308880 | 0.005170 | 0.517886 | 0.004343 | 0.309038 | 0.005207 |
| 1 | 0.517887 | 0.001085 | 0.176504 | 0.002046 | 0.517886 | 0.004343 | 0.177802 | 0.003767 |
| 2 | 0.014433 | 0.000223 | 0.172680 | 0.003719 | 0.014433 | 0.000892 | 0.174890 | 0.009391 |
| 3 | 0.014433 | 0.000223 | 0.275761 | 0.001776 | 0.014433 | 0.000892 | 0.276689 | 0.005918 |
| 4 | 0.014433 | 0.000223 | 0.399889 | 0.003369 | 0.014433 | 0.000892 | 0.400118 | 0.006243 |
7. Only the first n pieces of prediction tree
#!/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 0.218750sklearn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: 15.9424184684460292.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_)参数最优化：
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:37] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[15:53:38] WARNING: d:\build\xgboost\xgboost-0.90.git\src\objective\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
0.6001029721598573
{'max_depth': 4, 'n_estimators': 100}
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 0.7s finished3.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)])[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']
# 获取特征重要度
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')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
([<matplotlib.axis.XTick at 0x1ed5a5bc7b8>,
<matplotlib.axis.XTick at 0x1ed5a3e6278>,
<matplotlib.axis.XTick at 0x1ed5a65c780>,
<matplotlib.axis.XTick at 0x1ed5a669748>],
<a list of 4 Text xticklabel objects>)
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_)