python - Scikit learn Error Message 'Precision and F-score are ill-defined and being set to 0.0 in labels' -

im working on binary classification model, classifier naive bayes. have balanced dataset following error message when predict:

undefinedmetricwarning: precision , f-score ill-defined , being set 0.0 in labels no predicted samples.   'precision', 'predicted', average, warn_for) 

i'm using gridsearch cv k-fold 10. test set , predictions contain both classes, don't understand message. i'm working on same dataset, train/test split, cv , random seed 6 other models , work perfect. data ingested externally dataframe, randomize , seed fixed. naive bayes classification model class file @ beginning of before code snippet.

x_train, x_test, y_train, y_test, len_train, len_test = \      train_test_split(data['x'], data['y'], data['len'], test_size=0.4) pipeline = pipeline([     ('classifier', multinomialnb())  ])  cv=stratifiedkfold(len_train, n_folds=10)  len_train = len_train.reshape(-1,1) len_test = len_test.reshape(-1,1)  params = [   {'classifier__alpha': [0, 0.0001, 0.001, 0.01]}  ]  grid = gridsearchcv(     pipeline,     param_grid=params,     refit=true,       n_jobs=-1,      scoring='accuracy',     cv=cv,  )  nb_fit = grid.fit(len_train, y_train)  preds = nb_fit.predict(len_test)  print(confusion_matrix(y_test, preds, labels=['1','0'])) print(classification_report(y_test, preds)) 

i 'forced' python alter shape of series, maybe culprit?