Xgboost建模

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)

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#!/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')

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[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#

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# 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

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#!/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')

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[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#

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#!/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')

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错误类为0.276042





['./model/0003.model']

4.交叉验证#

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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.添加预处理的交叉验证#

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# 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.自定义损失函数与评估准则#

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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)

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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.只用前n颗树预测#

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#!/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))))

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[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#

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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))

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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#

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# 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_)

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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 finished

3.early-stopping 早停#

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# 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)])

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[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#

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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')

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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





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  <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#

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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_)