How to capture what triggers the Warnings of sklearn.GridSearchCV.fit ()?

In a rasa_nlu function they are calling GridSearchCV.fit () with clf.fit () and it generates some Warnings that I would like to capture and modify for know what drives them:

Fitting 2 folds for each of 6 candidates, totalling 12 fits
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s finished

This is how GridSearchCV is built:

cv_splits = self._num_cv_splits(y) #Quando eu imprimi, ele me deu "2", eu esperava algo mais relacionado aos rótulos

GridSearchCV(SVC(C=1,
                probability=True,
                class_weight='balanced'),
            param_grid=tuned_parameters,
            n_jobs=num_threads,
            cv=cv_splits,
            scoring='f1_weighted',
            verbose=1)

Where e are the labels that have been converted into numbers

y: [1 0 2 1 1 1 1 1 1 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 3 3]

labels: ['greet', 'goodbye', 'inform', 'greet', 'greet', 'greet', 'greet', 'greet', 'greet', 'goodbye', 'goodbye', 'goodbye', 'goodbye', 'goodbye', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'laughing', 'laughing']

Ideally, I'd like to know which one triggered the Warnings. I know that this link can help. However, I still could not get the tags.

Update

When I tried to get the source, I still could not find a way to catch the warning:

 fit_result = self.clf.fit (X, y)
 y_pred = self.clf.predict (X)
 print ("set (y) -set (y_pred): \ n", conjunto (y) -set (y_pred))

But this gives me an empty set set()

It is also necessary to use .predict (X) ? Is it different from clf.fit () results?