如何修复索引器错误：标量变量的索引无效

Question

此代码生成错误：

IndexError: invalid index to scalar variable.

在这行：results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))

怎么解决？

import pandas as pd
import numpy as np
from sklearn import ensemble
from sklearn import cross_validation

def ToWeight(y):
    w = np.zeros(y.shape, dtype=float)
    ind = y != 0
    w[ind] = 1./(y[ind]**2)
    return w

def RMSPE(y, yhat):
    w = ToWeight(y)
    rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
    return rmspe

forest = ensemble.RandomForestRegressor(n_estimators=10, min_samples_split=2, n_jobs=-1)

print ("Cross validations")
cv = cross_validation.KFold(len(train), n_folds=5)

results = []
for traincv, testcv in cv:
    y_test = np.expm1(forest.fit(X_train[traincv], y_train[traincv]).predict(X_train[testcv]))
    results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))

testcv是：

[False False False ...,  True  True  True]

Answer 1

基本上，1不是y的有效索引。如果访问者是从他自己的代码提交的，他应该检查他的y是否包含他试图访问的索引（在本例中，索引是1）。