scikit-learn AdaBoostRegressor：IndexError：索引1超出了大小为1的轴0的范围

Question

这是我要运行的Python代码。

from numpy import *
import pylab as pl
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
from sklearn import datasets
from sklearn.ensemble import AdaBoostRegressor

boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target)
offset = int(0.7*len(X))
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]

regressor = AdaBoostRegressor(n_estimators=5)
regressor.fit(X_train, y_train)

x = [11.95, 0.00, 18.100, 0, 0.6590, 5.6090, 90.00, 1.385, 24, 680.0, 20.20, 332.09, 12.13]
y = regressor.predict(x)
print "Prediction for " + str(x) + " = " + str(y)

这是我收到的错误信息。

Traceback (most recent call last):
  File "bug.py", line 18, in <module>
    y = regressor.predict(x)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py", line 1075, in predict
    return self._get_median_predict(X, len(self.estimators_))
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py", line 1050, in _get_median_predict
    median_estimators = sorted_idx[np.arange(X.shape[0]), median_idx]
IndexError: index 1 is out of bounds for axis 0 with size 1

我可以将回归函数更改为KNeighborsRegressor或DecisionTreeRegressor，它们都能给出良好的预测结果。

我不确定该如何解决这个问题。感谢您的所有帮助。

- stackerflow

Answer 1

回归器的预测方法需要一个特征向量数组。请更改为：

x = [11.95, 0.00, 18.100, 0, 0.6590, 5.6090, 90.00, 1.385, 24, 680.0, 20.20, 332.09, 12.13]

to:

x = [[11.95, 0.00, 18.100, 0, 0.6590, 5.6090, 90.00, 1.385, 24, 680.0, 20.20, 332.09, 12.13]]

那么代码将返回这个特征向量的预测结果。

- JAB

Answer 2

在我的情况下，类没有被定义，我想使用metrics.plot_roc_curve(model,X_test,y_test)绘制roc_curve。因此，我遇到了同样的错误。我通过定义类来解决了这个问题。

所以，如果model是你的分类器，例如：

model = XGBClassifier(...)

你可以设置

model.classes_ = np.array([0,1])

如果你在进行二元分类。

- horseshoe