DataException: MissingColumnsInData, Expected column(s) 0 not found in fitted data #209

# link to an AutoMLRun
from azureml.train.automl.run import AutoMLRun
automl_run = AutoMLRun(experiment=experiment, run_id='AutoML_ae0c7f63-a1b7-4892-af3e-92b79cdcf282') 

# grab the best run and model
best_run, best_model = automl_run.get_output()

# get the test dataframe
from azureml.core import Dataset
test_dataset = Dataset.get_by_name(workspace=ws, name='employee_turnover_test')
df_test = test_dataset.to_pandas_dataframe()
y_col = ['EmployeeLeft']
x_col = ['City', 'EmailDomain', 'HiredthroughSMTP', 'ManagerRatingOfLikelihoodToLeave', 
        'MarkedForPHTProgram', 'MostRecentPerformanceEvaluation', 'SocialMediaActivity',
        'Survey_AttitudeTowardWorkType', 'Survey_AttitudeTowardWorkload', 'Survey_RelativePeerAverageAttitudeTowardManager']
x_test = df_test.loc[:,x_col]
y_test = df_test.loc[:,y_col]

# confirm the model predicts on test dataframe
pred = best_model.predict_proba(x_test)
pred

# try feeding the model's predict_proba method and test dataframe to PartialDependence
from interpret.blackbox import PartialDependence

pdp = PartialDependence(predict_fn=best_model.predict_proba, data=x_test)

---------------------------------------------------------------------------
DataException                             Traceback (most recent call last)
<ipython-input-14-0bf5382bcb1b> in <module>
      1 from interpret.blackbox import PartialDependence
      2 
----> 3 pdp = PartialDependence(predict_fn=best_model.predict_proba, data=x_test)
      4 pdp_global = pdp.explain_global(name='Partial Dependence')

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/interpret/blackbox/partialdependence.py in __init__(self, predict_fn, data, sampler, feature_names, feature_types, num_points, std_coef)
     43             data, None, feature_names, feature_types
     44         )
---> 45         self.predict_fn = unify_predict_fn(predict_fn, self.data)
     46         self.num_points = num_points
     47         self.std_coef = std_coef

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/interpret/utils/all.py in unify_predict_fn(predict_fn, X)
    210 def unify_predict_fn(predict_fn, X):
    211     predictions = predict_fn(X[:1])
--> 212     if predictions.ndim == 2:
    213         new_predict_fn = lambda x: predict_fn(x)[:, 1]  # noqa: E731
    214         return new_predict_fn

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/sklearn/utils/metaestimators.py in <lambda>(*args, **kwargs)
    114 
    115         # lambda, but not partial, allows help() to work with update_wrapper
--> 116         out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
    117         # update the docstring of the returned function
    118         update_wrapper(out, self.fn)

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/sklearn/pipeline.py in predict_proba(self, X)
    469         Xt = X
    470         for _, name, transform in self._iter(with_final=False):
--> 471             Xt = transform.transform(Xt)
    472         return self.steps[-1][-1].predict_proba(Xt)
    473 

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/automl/core/shared/logging_utilities.py in debug_log_wrapped(self, *args, **kwargs)
    299         def debug_log_wrapped(self: Any, *args: Any, **kwargs: Any) -> Any:
    300             self._logger_wrapper(log_level, "Starting {} operation of {}.".format(f.__name__, self.__class__.__name__))
--> 301             r = f(self, *args, **kwargs)
    302             self._logger_wrapper(log_level, "{} {} operation complete.".format(self.__class__.__name__, f.__name__))
    303             return r

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/automl/runtime/featurization/data_transformer.py in transform(self, df)
    406         if self._columns_types_mapping is not None:
    407             df = self._check_columns_names_and_convert_types(
--> 408                 df, self._columns_types_mapping
    409             )
    410 

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/automl/runtime/featurization/data_transformer.py in _check_columns_names_and_convert_types(df, columns_types_mapping)
    697                         columns=col,
    698                         data_object_name="fitted data",
--> 699                         reference_code=ReferenceCodes._DATA_TRANSFORMER_TRANSFROM_COLUMN_NOT_FOUND,
    700                     )
    701                 )

DataException: DataException:
	Message: Expected column(s) 0 not found in fitted data.
	InnerException: None
	ErrorResponse 
{
    "error": {
        "code": "UserError",
        "message": "Expected column(s) 0 not found in fitted data.",
        "target": "X",
        "inner_error": {
            "code": "BadArgument",
            "inner_error": {
                "code": "MissingColumnsInData"
            }
        },
        "reference_code": "17049f70-3bbe-4060-a63f-f06590e784e5"
    }
}