从python中的xgboost.cv获得无法预测的结果

Question

在R xgboost包中,我可以指定predictions=TRUE在交叉验证期间保存折叠后预测,例如:

library(xgboost)
data(mtcars)
xgb_params = list(
  max_depth = 1,
  eta = 0.01
)
x = model.matrix(mpg~0+., mtcars)
train = xgb.DMatrix(x, label=mtcars$mpg)
res = xgb.cv(xgb_params, train, 100, prediction=TRUE, nfold=5)
print(head(res$pred))

我如何在python包中做相同的操作？我在python中找不到prediction参数xgboost.cv.

Answer 1

我不确定这是否是你想要的,但你可以通过使用xgboost的sklearn包装器来实现这一点:(我知道我使用虹膜数据集作为回归问题 - 它不是,但这只是为了说明) .

import xgboost as xgb
from sklearn.cross_validation import cross_val_predict as cvp
from sklearn import datasets
X = datasets.load_iris().data[:, :2]
y = datasets.load_iris().target
xgb_model = xgb.XGBRegressor()
y_pred = cvp(xgb_model, X, y, cv=3, n_jobs = 1)
y_pred


array([  9.07209516e-01,   1.84738374e+00,   1.78878939e+00,
         1.83672094e+00,   9.07209516e-01,   9.07209516e-01,
         1.77482617e+00,   9.07209516e-01,   1.75681138e+00,
         1.83672094e+00,   9.07209516e-01,   1.77482617e+00,
         1.84738374e+00,   1.84738374e+00,   1.12216723e+00,
         9.96944368e-01,   9.07209516e-01,   9.07209516e-01,
         9.96944368e-01,   9.07209516e-01,   9.07209516e-01,
         9.07209516e-01,   1.77482617e+00,   8.35850239e-01,
         1.77482617e+00,   9.87186074e-01,   9.07209516e-01,
         9.07209516e-01,   9.07209516e-01,   1.78878939e+00,
         1.83672094e+00,   9.07209516e-01,   9.07209516e-01,
         8.91427517e-01,   1.83672094e+00,   9.09049034e-01,
         8.91427517e-01,   1.83672094e+00,   1.84738374e+00,
         9.07209516e-01,   9.07209516e-01,   1.01038718e+00,
         1.78878939e+00,   9.07209516e-01,   9.07209516e-01,
         1.84738374e+00,   9.07209516e-01,   1.78878939e+00,
         9.07209516e-01,   8.35850239e-01,   1.99947178e+00,
         1.99947178e+00,   1.99947178e+00,   1.94922602e+00,
         1.99975276e+00,   1.91500926e+00,   1.99947178e+00,
         1.97454870e+00,   1.99947178e+00,   1.56287444e+00,
         1.96453893e+00,   1.99947178e+00,   1.99715066e+00,
         1.99947178e+00,   2.84575284e-01,   1.99947178e+00,
         2.84575284e-01,   2.00303388e+00,   1.99715066e+00,
         2.04597521e+00,   1.99947178e+00,   1.99975276e+00,
         2.00527954e+00,   1.99975276e+00,   1.99947178e+00,
         1.99947178e+00,   1.99975276e+00,   1.99947178e+00,
         1.99947178e+00,   1.91500926e+00,   1.95735490e+00,
         1.95735490e+00,   2.00303388e+00,   1.99975276e+00,
         5.92201948e-04,   1.99947178e+00,   1.99947178e+00,
         1.99715066e+00,   2.84575284e-01,   1.95735490e+00,
         1.89267385e+00,   1.99947178e+00,   2.00303388e+00,
         1.96453893e+00,   1.98232651e+00,   2.39597082e-01,
         2.39597082e-01,   1.99947178e+00,   1.97454870e+00,
         1.91500926e+00,   9.99531507e-01,   1.00023842e+00,
         1.00023842e+00,   1.00023842e+00,   1.00023842e+00,
         1.00023842e+00,   9.22234297e-01,   1.00023842e+00,
         1.00100708e+00,   1.16144836e-01,   1.00077248e+00,
         1.00023842e+00,   1.00023842e+00,   1.00100708e+00,
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         1.13711983e-01,   1.00023842e+00,   1.00135887e+00,
         1.00077248e+00,   1.00023842e+00,   1.00023842e+00,
         1.00023842e+00,   9.99531507e-01,   1.00077248e+00,
         1.00023842e+00,   1.00023842e+00,   1.00023842e+00,
         1.00023842e+00,   1.00023842e+00,   1.13711983e-01,
         1.00023842e+00,   1.00023842e+00,   1.00023842e+00,
         1.00023842e+00,   9.78098869e-01,   1.00023842e+00,
         1.00023842e+00,   1.00023842e+00,   1.00023842e+00,
         1.00023842e+00,   1.00023842e+00,   1.00077248e+00,
         9.99531507e-01,   1.00023842e+00,   1.00100708e+00,
         1.00023842e+00,   9.78098869e-01,   1.00023842e+00], dtype=float32)