Xgboost建模

xgboost參數

• 選擇較高的學習速率(learning rate)。通常狀況下，學習速率的值爲0.1。可是，對於不一樣的問題，理想的學習速率有時候會在0.05到0.3之間波動。選擇對應於此學習速率的理想決策樹數量。XGBoost有一個頗有用的函數「cv」，這個函數能夠在每一次迭代中使用交叉驗證，並返回理想的決策樹數量。

• 對於給定的學習速率和決策樹數量，進行決策樹特定參數調優(max_depth, min_child_weight, gamma, subsample, colsample_bytree)。在肯定一棵樹的過程當中，咱們能夠選擇不一樣的參數，待會兒我會舉例說明。

• xgboost的正則化參數的調優。(lambda, alpha)。這些參數能夠下降模型的複雜度，從而提升模型的表現。

• 下降學習速率，肯定理想參數。

1.讀取libsvm格式數據並指定參數建模

xgboost的使用方法

• ①使用xgboost自帶的數據集格式 + xgboost自帶的建模方式
  • 把數據讀取成xgb.DMatrix格式(libsvm/dataframe.values給定X和Y)
  • 準備好一個watch_list(觀測和評估的數據集)
  • 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，從libsvm文件中讀取數據，作二分類
# 數據是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

# 轉換成Dmatrix格式
dtrain = xgb.DMatrix('./data/agaricus.txt.train')
dtest = xgb.DMatrix('./data/agaricus.txt.test')

# 超參數設定
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }

# 設定watchlist用於查看模型狀態
watchlist  = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)

# 使用模型預測
preds = bst.predict(dtest)

# 判斷準確率
labels = dtest.get_label()
print ('錯誤類爲%f' % (sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))

# 模型存儲
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
錯誤類爲0.021726

2.配合pandas DataFrame格式數據建模

# 皮馬印第安人糖尿病數據集 包含不少字段：懷孕次數 口服葡萄糖耐量試驗中血漿葡萄糖濃度 舒張壓（mm Hg） 三頭肌組織褶厚度（mm） 
# 2小時血清胰島素（μU/ ml） 體重指數（kg/（身高(m)^2） 糖尿病系統功能 年齡（歲）
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，從csv文件中讀取數據，作二分類

# 用pandas讀入數據
data = pd.read_csv('./data/Pima-Indians-Diabetes.csv')

# 作數據切分
train, test = train_test_split(data)

# 轉換成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)

# 參數設定
param = {'max_depth':5, 'eta':0.1, 'silent':1, 'subsample':0.7, 'colsample_bytree':0.7, 'objective':'binary:logistic' }

# 設定watchlist用於查看模型狀態
watchlist  = [(xgtest,'eval'), (xgtrain,'train')]
num_round = 10
bst = xgb.train(param, xgtrain, num_round, watchlist)

# 使用模型預測
preds = bst.predict(xgtest)

# 判斷準確率
labels = xgtest.get_label()
print ('錯誤類爲%f' % (sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))

# 模型存儲
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
錯誤類爲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，從csv文件中讀取數據，作二分類

# 用pandas讀入數據
data = pd.read_csv('./data/Pima-Indians-Diabetes.csv')

# 作數據切分
train, test = train_test_split(data)

# 取出特徵X和目標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)

# 擬合模型
xgb_classifier.fit(train_X, train_y)

# 使用模型預測
preds = xgb_classifier.predict(test_X)

# 判斷準確率
print ('錯誤類爲%f' %((preds!=test_y).sum()/float(test_y.shape[0])))

# 模型存儲
joblib.dump(xgb_classifier, './model/0003.model')

錯誤類爲0.276042





['./model/0003.model']

4.交叉驗證

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.添加預處理的交叉驗證

# 計算正負樣本比，調整樣本權重
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)

# 先作預處理，計算樣本權重，再作交叉驗證
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.自定義損失函數與評估準則

print ('running cross validation, with cutomsized loss function')
# 自定義損失函數，須要提供損失函數的一階導和二階導
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

# 自定義評估準則，評估預估值和標準答案之間的差距
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
# 自定義損失函數訓練
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
# 交叉驗證
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.只用前n顆樹預測

#!/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，從csv文件中讀取數據，作二分類

# 用pandas讀入數據
data = pd.read_csv('./data/Pima-Indians-Diabetes.csv')

# 作數據切分
train, test = train_test_split(data)

# 轉換成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)

# 參數設定
param = {'max_depth':5, 'eta':0.1, 'silent':1, 'subsample':0.7, 'colsample_bytree':0.7, 'objective':'binary:logistic' }

# 設定watchlist用於查看模型狀態
watchlist  = [(xgtest,'eval'), (xgtrain,'train')]
num_round = 10
bst = xgb.train(param, xgtrain, num_round, watchlist)

# 只用第1顆樹預測
ypred1 = bst.predict(xgtest, ntree_limit=1)
# 用前9顆樹預測
ypred2 = bst.predict(xgtest, ntree_limit=9)
label = xgtest.get_label()
print ('用前1顆樹預測的錯誤率爲 %f' % (np.sum((ypred1>0.5)!=label) /float(len(label))))
print ('用前9顆樹預測的錯誤率爲 %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顆樹預測的錯誤率爲 0.281250
用前9顆樹預測的錯誤率爲 0.218750

sklearn與Xgboost配合使用

1.Xgboost建模，sklearn評估

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)

# 二分類：混淆矩陣
print("數字0和1的二分類問題")
digits = load_digits(2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折數據上的交叉驗證")
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("混淆矩陣:")
    print(confusion_matrix(actuals, predictions))

# 多分類：混淆矩陣
print("\nIris: 多分類")
iris = load_iris()
y = iris['target']
X = iris['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折數據上的交叉驗證")
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("混淆矩陣:")
    print(confusion_matrix(actuals, predictions))

# 迴歸問題：MSE
print("\n波士頓房價迴歸預測問題")
boston = load_boston()
y = boston['target']
X = boston['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
print("在2折數據上的交叉驗證")
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))

數字0和1的二分類問題
在2折數據上的交叉驗證
混淆矩陣:
[[87  0]
 [ 1 92]]
混淆矩陣:
[[91  0]
 [ 3 86]]

Iris: 多分類
在2折數據上的交叉驗證
混淆矩陣:
[[19  0  0]
 [ 0 31  3]
 [ 0  1 21]]
混淆矩陣:
[[31  0  0]
 [ 0 16  0]
 [ 0  3 25]]

波士頓房價迴歸預測問題
在2折數據上的交叉驗證
[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.網格搜索查找最優超參數

# 第2種訓練方法的 調參方法：使用sklearn接口的regressor + GridSearchCV
print("參數最優化：")
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_)

參數最優化：
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

# 第1/2種訓練方法的 調參方法：early stopping
# 在訓練集上學習模型，一顆一顆樹添加，在驗證集上看效果，當驗證集效果再也不提高，中止樹的添加與生長
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.特徵重要度

iris = load_iris()
y = iris['target']
X = iris['data']
xgb_model = xgb.XGBClassifier().fit(X,y)

print('特徵排序：')
feature_names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
# 獲取特徵重要度
feature_importances = xgb_model.feature_importances_
indices = np.argsort(feature_importances)[::-1]

for index in indices:
    print("特徵 %s 重要度爲 %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')

特徵排序：
特徵 petal_length 重要度爲 0.595834
特徵 petal_width 重要度爲 0.358166
特徵 sepal_width 重要度爲 0.033481
特徵 sepal_length 重要度爲 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.並行訓練加速

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_)