Evalml: PipelineBase Errors Without Estimator

Created on 19 Nov 2019  ·  2Comments  ·  Source: alteryx/evalml

Currently we only check that if there is an estimator it is the last component.
However, the pipeline breaks on fit on the following line without an estimator:

self.input_feature_names.update({self.estimator.name: list(pd.DataFrame(X_t))})

We should either enforce that all pipelines must have an estimator or fix _fit to allow this case.

bug

All 2 comments

I filed #273 to track discussing the long-term plan around this (i.e., do we want to support pipelines with more than one estimator? etc.).

My suggestion: we continue discussion there, but in the meantime, we resolve this ticket by having PipelineBase::__init__ error out if an estimator is not specified as the final component. Does that seem reasonable?

Tagging @angela97lin @jeremyliweishih because we were just discussing this in slack :)

that seems like a reasonable solution for now until we have the long term plan

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