ValueError: The base estimator doesn't support sample weight
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ValueError                                Traceback (most recent call last)
~/Documents/scratch/issue_sample_weights.py in <module>
      39 # Simulate that we wrap the predictor in a Pipeline
      40 model = BaggingClassifier(make_pipeline(DecisionTreeClassifier()), random_state=42)
----> 41 model.fit(X_train, y_train, sample_weight=sample_weights_train)
      42 y_pred = model.predict(X_test)
      43 Counter(y_pred)

~/Documents/packages/scikit-learn/sklearn/ensemble/_bagging.py in fit(self, X, y, sample_weight)
    258             Fitted estimator.
    259         """
--> 260         return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
    261 
    262     def _parallel_args(self):

~/Documents/packages/scikit-learn/sklearn/ensemble/_bagging.py in _fit(self, X, y, max_samples, max_depth, sample_weight)
    392         self._seeds = seeds
    393 
--> 394         all_results = Parallel(
    395             n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args()
    396         )(

~/Documents/packages/joblib/joblib/parallel.py in __call__(self, iterable)
   1039             # remaining jobs.
   1040             self._iterating = False
-> 1041             if self.dispatch_one_batch(iterator):
   1042                 self._iterating = self._original_iterator is not None
   1043 

~/Documents/packages/joblib/joblib/parallel.py in dispatch_one_batch(self, iterator)
    857                 return False
    858             else:
--> 859                 self._dispatch(tasks)
    860                 return True
    861 

~/Documents/packages/joblib/joblib/parallel.py in _dispatch(self, batch)
    775         with self._lock:
    776             job_idx = len(self._jobs)
--> 777             job = self._backend.apply_async(batch, callback=cb)
    778             # A job can complete so quickly than its callback is
    779             # called before we get here, causing self._jobs to

~/Documents/packages/joblib/joblib/_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~/Documents/packages/joblib/joblib/_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~/Documents/packages/joblib/joblib/parallel.py in __call__(self)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/Documents/packages/joblib/joblib/parallel.py in <listcomp>(.0)
    260         # change the default number of processes to -1
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)
    263                     for func, args, kwargs in self.items]
    264 

~/Documents/packages/scikit-learn/sklearn/utils/fixes.py in __call__(self, *args, **kwargs)
    207     def __call__(self, *args, **kwargs):
    208         with config_context(**self.config):
--> 209             return self.function(*args, **kwargs)
    210 
    211 

~/Documents/packages/scikit-learn/sklearn/ensemble/_bagging.py in _parallel_build_estimators(n_estimators, ensemble, X, y, sample_weight, seeds, total_n_estimators, verbose)
     80     support_sample_weight = has_fit_parameter(ensemble.base_estimator_, "sample_weight")
     81     if not support_sample_weight and sample_weight is not None:
---> 82         raise ValueError("The base estimator doesn't support sample weight")
     83 
     84     # Build estimators

ValueError: The base estimator doesn't support sample weight