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Mila Bousquet
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Asked: 2022-05-12T07:38:15+00:00 In:

The easy way to handle error – indexerror: invalid index to scalar variable.

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I’m trying to run a new project. I do a couple of things like this:

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

but in my program, I am getting the warning :

IndexError: invalid index to scalar variable.

Can someone explain to me why the issue of the “ indexerror: invalid index to scalar variable.” happened? Where have I gone wrong? Thank you!

invalid index to scalar variable.
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2 Answers

  1. Best Answer
    Expert

    The cause: You have an iteration, then for each element of that loop which could be a scalar, has no index. If each element is an empty array, single variable, or scalar and is not a list or array you can’t use indices.

    Solution:

    You want to index the value into a non-iterable scalar.

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

    You call [y for y in test] to iterate over the values, so you only get one value in y. Your code will work the same way as if you tried to do the following:

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

    Although I don’t know what you are trying to do with your results array, you should get rid [y[1] for y in y_test]. To append each y to y_test, you will need to increase your list comprehension to something like the following:

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

    You can also use a for loop

    for y in y_test:
    results.append(..., y)
    • 21


  2. YOLO Object Detection

    layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    You don’t have to index i in layer_names[i[0]- 1] . It’s easy to remove it and do layer_names[i-1]

    layer_names = net.getLayerNames() output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]


    It Works For Me

    • 9

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