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

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I'm getting this weird error:

Code:
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:

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

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

Code:
>>> 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.
<p>I'm getting this weird error:</p>

<pre><code>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)`
</code></pre>

<p>but then it also prints the f-score the first time I run: </p>

<pre><code>metrics.f1_score(y_test, y_pred, average='weighted')
</code></pre>

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

<pre><code>>>> 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
</code></pre>

<p>Also, why is there a trailing <code>'precision', 'predicted', average, warn_for)</code> 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.</p>

<p>I also looked at <a href="https://stackoverflow.com/questions/31677218/scikit-f-score-metric-error">here</a> and I don't know if it's the same bug. This <a href="https://stackoverflow.com/questions...ion-and-f-score-are-ill-defined-and-being-set">SO post</a> doesn't have solution either.</p>
 

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