Dealing with a MissingMetadataException
The problem
Running the fit
, calculate
or estimate
methods of a Calculator
or Estimator
fails by returning
a MissingMetadataException
.
nannyml.exceptions.MissingMetadataException: metadata is still missing values for ['predicted_probability_column_name'].
The solution
The MissingMetadataException
is raised when the model metadata
used to create the Calculator
or Estimator
is not complete, i.e. it is missing some required properties.
The exception will list the properties it is missing, as shown in the problem statement.
Assume md
is the model metadata
object used,
predicted_probability_column_name
is the property missing and in your data the predicted probabilities are located
in the model_probas
column.
The following snippet should help you prevent the exception by completing the metadata manually:
>>> md.is_complete() # just checking
(False, ['predicted_probability_column_name'])
>>> md.predicted_probability_column_name = 'model_probas'
>>> md.is_complete()
(True, [])
Any metadata property can be set or updated.