---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/dev/scikit-learn/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py in <module>()
     84 # Select the optimal percentage of features with grid search
     85 clf = GridSearchCV(clf, {'anova__percentile': [5, 10, 20]}, cv=cv)
---> 86 clf.fit(X, y)  # set the best parameters
     87 coef_ = clf.best_estimator_.steps[-1][1].coef_
     88 coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_.reshape(1, -1))

~/dev/scikit-learn/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
    720                 return results_container[0]
    721 
--> 722             self._run_search(evaluate_candidates)
    723 
    724         results = results_container[0]

~/dev/scikit-learn/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
   1174     def _run_search(self, evaluate_candidates):
   1175         """Search all candidates in param_grid"""
-> 1176         evaluate_candidates(ParameterGrid(self.param_grid))
   1177 
   1178 

~/dev/scikit-learn/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)
    709                                for parameters, (train, test)
    710                                in product(candidate_params,
--> 711                                           cv.split(X, y, groups)))
    712 
    713                 all_candidate_params.extend(candidate_params)

~/dev/joblib/joblib/parallel.py in __call__(self, iterable)
    979             # remaining jobs.
    980             self._iterating = False
--> 981             if self.dispatch_one_batch(iterator):
    982                 self._iterating = self._original_iterator is not None
    983 

~/dev/joblib/joblib/parallel.py in dispatch_one_batch(self, iterator)
    821                 return False
    822             else:
--> 823                 self._dispatch(tasks)
    824                 return True
    825 

~/dev/joblib/joblib/parallel.py in _dispatch(self, batch)
    778         with self._lock:
    779             job_idx = len(self._jobs)
--> 780             job = self._backend.apply_async(batch, callback=cb)
    781             # A job can complete so quickly than its callback is
    782             # called before we get here, causing self._jobs to

~/dev/joblib/joblib/_parallel_backends.py in apply_async(self, func, callback)
    181     def apply_async(self, func, callback=None):
    182         """Schedule a func to be run"""
--> 183         result = ImmediateResult(func)
    184         if callback:
    185             callback(result)

~/dev/joblib/joblib/_parallel_backends.py in __init__(self, batch)
    541         # Don't delay the application, to avoid keeping the input
    542         # arguments in memory
--> 543         self.results = batch()
    544 
    545     def get(self):

~/dev/joblib/joblib/parallel.py in __call__(self)
    259         with parallel_backend(self._backend):
    260             return [func(*args, **kwargs)
--> 261                     for func, args, kwargs in self.items]
    262 
    263     def __len__(self):

~/dev/joblib/joblib/parallel.py in <listcomp>(.0)
    259         with parallel_backend(self._backend):
    260             return [func(*args, **kwargs)
--> 261                     for func, args, kwargs in self.items]
    262 
    263     def __len__(self):

~/dev/scikit-learn/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, return_estimator, error_score)             
    522             estimator.fit(X_train, **fit_params)
    523         else:
--> 524             estimator.fit(X_train, y_train, **fit_params)
    525 
    526     except Exception as e:

~/dev/scikit-learn/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    263             This estimator
    264         """
--> 265         Xt, fit_params = self._fit(X, y, **fit_params)
    266         if self._final_estimator is not None:
    267             self._final_estimator.fit(Xt, y, **fit_params)

~/dev/scikit-learn/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
    228                 Xt, fitted_transformer = fit_transform_one_cached(
    229                     cloned_transformer, Xt, y, None,
--> 230                     **fit_params_steps[name])
    231                 # Replace the transformer of the step with the fitted                                                        
    232                 # transformer. This is necessary when loading the transformer

~/dev/joblib/joblib/memory.py in __call__(self, *args, **kwargs)
    320 
    321     def __call__(self, *args, **kwargs):
--> 322         return self.func(*args, **kwargs)
    323 
    324     def call_and_shelve(self, *args, **kwargs):

~/dev/scikit-learn/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, **fit_params)
    612 def _fit_transform_one(transformer, X, y, weight, **fit_params):
    613     if hasattr(transformer, 'fit_transform'):
--> 614         res = transformer.fit_transform(X, y, **fit_params)
    615     else:
    616         res = transformer.fit(X, y, **fit_params).transform(X)

~/dev/scikit-learn/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    460         else:
    461             # fit method of arity 2 (supervised transformation)
--> 462             return self.fit(X, y, **fit_params).transform(X)
    463 
    464 

~/dev/scikit-learn/sklearn/feature_selection/univariate_selection.py in fit(self, X, y)
    347 
    348         self._check_params(X, y)
--> 349         score_func_ret = self.score_func(X, y)
    350         if isinstance(score_func_ret, (list, tuple)):
    351             self.scores_, self.pvalues_ = score_func_ret

~/dev/joblib/joblib/memory.py in __call__(self, *args, **kwargs)
    509 
    510     def __call__(self, *args, **kwargs):
--> 511         return self._cached_call(args, kwargs)[0]
    512 
    513     def __reduce__(self):

~/dev/joblib/joblib/memory.py in _cached_call(self, args, kwargs)
    440         # Compare the function code with the previous to see if the
    441         # function code has changed
--> 442         func_id, args_id = self._get_output_identifiers(*args, **kwargs)
    443         metadata = None
    444         msg = None

~/dev/joblib/joblib/memory.py in _get_output_identifiers(self, *args, **kwargs)
    530         """Return the func identifier and input parameter hash of a result."""
    531         func_id = _build_func_identifier(self.func)
--> 532         argument_hash = self._get_argument_hash(*args, **kwargs)
    533         return func_id, argument_hash
    534 

~/dev/joblib/joblib/memory.py in _get_argument_hash(self, *args, **kwargs)
    524 
    525     def _get_argument_hash(self, *args, **kwargs):
--> 526         return hashing.hash(filter_args(self.func, self.ignore, args, kwargs),
    527                             coerce_mmap=(self.mmap_mode is not None))
    528 

~/dev/joblib/joblib/func_inspect.py in filter_args(func, ignore_lst, args, kwargs)
    306 
    307     # Now remove the arguments to be ignored
--> 308     for item in ignore_lst:
    309         if item in arg_dict:
    310             arg_dict.pop(item)

TypeError: 'bool' object is not iterable