UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples

I'm getting this weird error:

    classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
    'precision', 'predicted', average, warn_for)`

but then it also prints the f-score the first time I run:

    metrics.f1_score(y_test, y_pred, average='weighted')

The second time I run, it provides the score without error. Why is that?

    >>> y_pred = test.predict(X_test)
    >>> y_test
    array([ 1, 10, 35,  9,  7, 29, 26,  3,  8, 23, 39, 11, 20,  2,  5, 23, 28,
           30, 32, 18,  5, 34,  4, 25, 12, 24, 13, 21, 38, 19, 33, 33, 16, 20,
           18, 27, 39, 20, 37, 17, 31, 29, 36,  7,  6, 24, 37, 22, 30,  0, 22,
           11, 35, 30, 31, 14, 32, 21, 34, 38,  5, 11, 10,  6,  1, 14, 12, 36,
           25,  8, 30,  3, 12,  7,  4, 10, 15, 12, 34, 25, 26, 29, 14, 37, 23,
           12, 19, 19,  3,  2, 31, 30, 11,  2, 24, 19, 27, 22, 13,  6, 18, 20,
            6, 34, 33,  2, 37, 17, 30, 24,  2, 36,  9, 36, 19, 33, 35,  0,  4,
            1])
    >>> y_pred
    array([ 1, 10, 35,  7,  7, 29, 26,  3,  8, 23, 39, 11, 20,  4,  5, 23, 28,
           30, 32, 18,  5, 39,  4, 25,  0, 24, 13, 21, 38, 19, 33, 33, 16, 20,
           18, 27, 39, 20, 37, 17, 31, 29, 36,  7,  6, 24, 37, 22, 30,  0, 22,
           11, 35, 30, 31, 14, 32, 21, 34, 38,  5, 11, 10,  6,  1, 14, 30, 36,
           25,  8, 30,  3, 12,  7,  4, 10, 15, 12,  4, 22, 26, 29, 14, 37, 23,
           12, 19, 19,  3, 25, 31, 30, 11, 25, 24, 19, 27, 22, 13,  6, 18, 20,
            6, 39, 33,  9, 37, 17, 30, 24,  9, 36, 39, 36, 19, 33, 35,  0,  4,
            1])
    >>> metrics.f1_score(y_test, y_pred, average='weighted')
    C:\Users\Michael\Miniconda3\envs\snowflakes\lib\site-packages\sklearn\metrics\classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
      'precision', 'predicted', average, warn_for)
    0.87282051282051276
    >>> metrics.f1_score(y_test, y_pred, average='weighted')
    0.87282051282051276
    >>> metrics.f1_score(y_test, y_pred, average='weighted')
    0.87282051282051276

Also, why is there a trailing 'precision', 'predicted', average, warn_for) error message? There is no open parenthesis so why does it end with a closing parenthesis? I am running sklearn 0.18.1 using Python 3.6.0 in a conda environment on Windows 10.

I also looked at here and I don't know if it's the same bug. This SO post doesn't have solution either.

As mentioned in the comments, some labels in y_true don't appear in y_pred. Specifically in this case, label '2' is never predicted:

    >>> set(y_test) - set(y_pred)
    {2}

This means that there is no F-score to calculate for this label, and thus the F-score for this case is considered to be 0.0. Since you requested an average of the score, you must take into account that a score of 0 was included in the calculation, and this is why scikit-learn is showing you that warning.

This brings me to you not seeing the error a second time. As I mentioned, this is a warning , which is treated differently from an error in python. The default behavior in most environments is to show a specific warning only once. This behavior can be changed:

    import warnings
    warnings.filterwarnings('always')  # "error", "ignore", "always", "default", "module" or "once"

If you set this before importing the other modules, you will see the warning every time you run the code.

There is no way to avoid seeing this warning the first time, aside for setting warnings.filterwarnings('ignore'). What you can do, is decide that you are not interested in the scores of labels that were not predicted, and then explicitly specify the labels you are interested in (which are labels that were predicted at least once):

    >>> metrics.f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
    0.91076923076923078

The warning is not shown in this case.

From: stackoverflow.com/q/43162506