[python] How to fix IndexError: invalid index to scalar variable

This code generates error:

IndexError: invalid index to scalar variable.

at the line: results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))

How to fix it?

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 is:

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

In the for, you have an iteration, then for each element of that loop which probably is a scalar, has no index. When each element is an empty array, single variable, or scalar and not a list or array you cannot use indices.

Basically, 1 is not a valid index of y. If the visitor is comming from his own code he should check if his y contains the index which he tries to access (in this case the index is 1).