Custom Scorer (WMAE) fails TPOT optimization process

Passing my own scorer that calculates weighted mean absolute error for a regression problem, results in error

## Context of the issue

I followed the instructions from the TPOT documentation page to create my own scorer for a regression problem (I am trying to use tpot for Walmart kaggle competition https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting). The scorer I created calculates the weighted mean absolute error. I am pasting my code below:

```python
from sklearn.metrics import mean_absolute_error

def mape_score(y_test, y_pred, weights):
mean_absolute_error(y_test, y_pred, sample_weight= weights)

from sklearn.metrics import make_scorer
my_custom_scorer = make_scorer(mape_score, greater_is_better=False)

tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2, random_state=42, n_jobs=-1, scoring=my_custom_scorer)

with joblib.parallel_backend("dask"):
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
```
weights & y_test & y_pred is type pandas.core.series.Series

I am also using local dask cluster to distribute my workload. Please let me know if it is a user error or something that may need to be looked into.

## Current result
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in fit(self, features, target, sample_weight, groups)
699 warnings.simplefilter('ignore')
--> 700 self._pop, _ = eaMuPlusLambda(
701 population=self._pop,

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/gp_deap.py in eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, pbar, stats, halloffame, verbose, per_generation_function)
235 if per_generation_function is not None:
--> 236 per_generation_function(gen)
237 # Vary the population

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in _check_periodic_pipeline(self, gen)
1002 """
-> 1003 self._update_top_pipeline()
1004 if self.periodic_checkpoint_folder is not None:

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in _update_top_pipeline(self)
792 if not self._optimized_pipeline:
--> 793 raise RuntimeError('There was an error in the TPOT optimization '
794 'process. This could be because the data was '

RuntimeError: There was an error in the TPOT optimization process. This could be because the data was not formatted properly, or because data for a regression problem was provided to the TPOTClassifier object. Please make sure you passed the data to TPOT correctly.

During handling of the above exception, another exception occurred:

RuntimeError Traceback (most recent call last)
in
1 with joblib.parallel_backend("dask"):
----> 2 tpot.fit(X_train, y_train)
3 print(tpot.score(X_test, y_test))
4 tpot.export('tpot_Nelson_pipeline.py')

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in fit(self, features, target, sample_weight, groups)
740 # raise the exception if it's our last attempt
741 if attempt == (attempts - 1):
--> 742 raise e
743 return self
744

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in fit(self, features, target, sample_weight, groups)
731 self._pbar.close()
732
--> 733 self._update_top_pipeline()
734 self._summary_of_best_pipeline(features, target)
735 # Delete the temporary cache before exiting

/opt/anaconda3/envs/Nelson_Dask/lib/python3.8/site-packages/tpot/base.py in _update_top_pipeline(self)
791
792 if not self._optimized_pipeline:
--> 793 raise RuntimeError('There was an error in the TPOT optimization '
794 'process. This could be because the data was '
795 'not formatted properly, or because data for '

RuntimeError: There was an error in the TPOT optimization process. This could be because the data was not formatted properly, or because data for a regression problem was provided to the TPOTClassifier object. Please make sure you passed the data to TPOT correctly.