deprecation error in sklearn about empty array without any empty array in my code

Question

I am just playing around encoding and decoding but I get this error from sklearn:

Warning (from warnings module): File "C:\Python36\lib\site-packages\sklearn\preprocessing\label.py", line 151 if diff: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use array.size > 0 to check that an array is not empty.

Here is the full code, you can run it yourself in python 3+

My question is why is it saying I use an empty array as I clearly don't in my code, thanks for taking your time to answer my question.

### label encoding ###

import numpy as np
from sklearn import preprocessing

# Sample input labels
input_labels = ["red", "black", "red", "green",\
                "black", "yellow", "white"]

# Create label encoder abd fit the label
encoder = preprocessing.LabelEncoder()
encoder.fit(input_labels)

# Print the mapping
print("\nLabel mapping:")
for i, item in enumerate(encoder.classes_):
    print(item, "-->", i)

# Encode a set of labels using encoder
test_labels = ["green", "red", "black"]
encoded_values = encoder.transform(test_labels)
print("\nLabels =", test_labels)
print("Encoded values =", list(encoded_values))

# Decode a set of values using the encoder
encoded_values = [3, 0, 4, 1]
decoded_list = encoder.inverse_transform(encoded_values)
print("\nEncoded values =", encoded_values)
print("Decoded labels=", list(decoded_list))

Answer 1

TLDR: You can ignore the warning. It is caused by sklearn doing something internally that is not quite ideal.

---

The warning is actually caused by numpy, which deprecated truth testing on empty arrays:

The long and short is that truth-testing on empty arrays is dangerous, misleading, and not in any way useful, and should be deprecated.

This means one is not supposed to do something like if array: to check if array is empty. However, sklearn does this in the 0.19.1 release:

    diff = np.setdiff1d(y, np.arange(len(self.classes_)))
    if diff:
        raise ValueError("y contains new labels: %s" % str(diff))

Because your version of numpy is recent enough it complains and issues a warning.

The problem has been fixed in sklearn's current master branch, so I'd expect the fix to be included in the next release.

Answer 2

After updated numpy, the warning was cleared away:

conda update numpy

You can also update it by pip or something else.