如何修复IndexError:标量变量的索引无效 [英] How to fix IndexError: invalid index to scalar variable

问题描述

此代码生成错误:

IndexError: invalid index to scalar variable.

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

如何解决?

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]

推荐答案

您正尝试索引到标量(不可迭代)值中:

You are trying to index into a scalar (non-iterable) value:

[y[1] for y in y_test]
#  ^ this is the problem

调用[y for y in test]时，您已经在遍历这些值，因此您在y中得到一个值.

When you call [y for y in test] you are iterating over the values already, so you get a single value in y.

您的代码与尝试执行以下操作相同:

Your code is the same as trying to do the following:

y_test = [1, 2, 3]
y = y_test[0] # y = 1
print(y[0]) # this line will fail

我不确定您要尝试进入结果数组中的什么，但是您需要摆脱[y[1] for y in y_test].

I'm not sure what you're trying to get into your results array, but you need to get rid of [y[1] for y in y_test].

如果要将y_test中的每个y追加到结果中，则需要将列表理解进一步扩展为类似以下内容:

If you want to append each y in y_test to results, you'll need to expand your list comprehension out further to something like this:

[results.append(..., y) for y in y_test]

或仅使用for循环:

for y in y_test:
    results.append(..., y)

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