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

Question

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.

Answer 1

As mentioned in the comments, some labels in y_test 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 will be gone.

Answer 2

the same problem also happened to me when i training my classification model. the reason caused this problem is as what the warning message said "in labels with no predicated samples", it will caused the zero-division when compute f1-score. I found another solution when i read sklearn.metrics.f1_score doc, there is a note as follows:

When true positive + false positive == 0, precision is undefined; When true positive + false negative == 0, recall is undefined. In such cases, by default the metric will be set to 0, as will f-score, and UndefinedMetricWarning will be raised. This behavior can be modified with zero_division

the zero_division default value is "warn", you could set it to 0 or 1 to avoid UndefinedMetricWarning. it works for me ;) oh wait, there is another problem when i using zero_division, my sklearn report that no such keyword argument by using scikit-learn 0.21.3. Just update your sklearn to the latest version by running pip install scikit-learn -U

Answer 3

I ended up here with the same error but after reading @Shovalt's answer, I realized I was quite low in my test/train split. I had a large data set to start with but had split it down and one group was quite small. By making the sample size bigger, this warning went away and I got my f1 score. From this

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

to this

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Answer 4

The accepted answer explains already well why the warning occurs. If you simply want to control the warnings, one could use precision_recall_fscore_support. It offers a (semi-official) argument warn_for that could be used to mute the warnings.

(_, _, f1, _) = metrics.precision_recall_fscore_support(y_test, y_pred,
                                                        average='weighted', 
                                                        warn_for=tuple())

As mentioned already in some comments, use this with care.

Answer 5

As I have noticed this error occurs under two circumstances,

1. If you have used train_test_split() to split your data, you have to make sure that you reset the index of the data (specially when taken using a pandas series object): y_train, y_test indices should be resetted. The problem is when you try to use one of the scores from sklearn.metrics such as; precision_score, this will try to match the shuffled indices of the y_test that you got from train_test_split().

so, either use np.array(y_test) for y_true in scores or y_test.reset_index(drop=True)

2. Then again you can still have this error if your predicted 'True Positives' is 0, which is used for precision, recall and f1_scores. You can visualize this using a confusion_matrix. If the classification is multilabel and you set param: average='weighted'/micro/macro you will get an answer as long as the diagonal line in the matrix is not 0

Hope this helps.

Answer 6

As the error message states, the method used to get the F score is from the "Classification" part of sklearn - thus the talking about "labels".

Do you have a regression problem? Sklearn provides a "F score" method for regression under the "feature selection" group: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html

In case you do have a classification problem, @Shovalt's answer seems correct to me.