error_0

ValueError                                Traceback (most recent call last)
    <ipython-input-226-02cf30bd9f21> in <module>()
         38 gsearch_gbc = GridSearchCV(estimator = GradientBoostingClassifier(n_estimators=10),
         39                         param_grid = param_test, scoring="neg_log_loss", n_jobs=1, iid=False, cv=cv_indices)
    ---> 40 gsearch_gbc.fit(df_attr, Se_targets)

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups)
        943             train/test set.
        944         """
    --> 945         return self._fit(X, y, groups, ParameterGrid(self.param_grid))
        946
        947

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _fit(self, X, y, groups, parameter_iterable)
        562                                   return_times=True, return_parameters=True,
        563                                   error_score=self.error_score)
    --> 564           for parameters in parameter_iterable
        565           for train, test in cv_iter)
        566

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
        756             # was dispatched. In particular this covers the edge
        757             # case of Parallel used with an exhausted iterator.
    --> 758             while self.dispatch_one_batch(iterator):
        759                 self._iterating = True
        760             else:

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
        606                 return False
        607             else:
    --> 608                 self._dispatch(tasks)
        609                 return True
        610

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
        569         dispatch_timestamp = time.time()
        570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
    --> 571         job = self._backend.apply_async(batch, callback=cb)
        572         self._jobs.append(job)
        573

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
        107     def apply_async(self, func, callback=None):
        108         """Schedule a func to be run"""
    --> 109         result = ImmediateResult(func)
        110         if callback:
        111             callback(result)

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
        324         # Don't delay the application, to avoid keeping the input
        325         # arguments in memory
    --> 326         self.results = batch()
        327
        328     def get(self):

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
        129
        130     def __call__(self):
    --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        132
        133     def __len__(self):

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
        129
        130     def __call__(self):
    --> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        132
        133     def __len__(self):

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
        258     else:
        259         fit_time = time.time() - start_time
    --> 260         test_score = _score(estimator, X_test, y_test, scorer)
        261         score_time = time.time() - start_time - fit_time
        262         if return_train_score:

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _score(estimator, X_test, y_test, scorer)
        286         score = scorer(estimator, X_test)
        287     else:
    --> 288         score = scorer(estimator, X_test, y_test)
        289     if hasattr(score, 'item'):
        290         try:

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/metrics/scorer.py in __call__(self, clf, X, y, sample_weight)
        132                                                  **self._kwargs)
        133         else:
    --> 134             return self._sign * self._score_func(y, y_pred, **self._kwargs)
        135
        136     def _factory_args(self):

    /Users/jespinoz/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py in log_loss(y_true, y_pred, eps, normalize, sample_weight, labels)
       1620             raise ValueError('y_true contains only one label ({0}). Please '
       1621                              'provide the true labels explicitly through the '
    -> 1622                              'labels argument.'.format(lb.classes_[0]))
       1623         else:
       1624             raise ValueError('The labels array needs to contain at least two '

    ValueError: y_true contains only one label (1). Please provide the true labels explicitly through the labels argument.