evalml
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alteryx
EvalML is an AutoML library written in python.
Change `_schema_is_equal` check to `_schema_is_compatible` and use training schema for predict data
Currently, at `ComponentGraph._transform_features`, when the graph is not already fit, we do a check for whether or not X's woodwork schema is equal to the `ComponentGraph.input_types`. If the types do...
- As a user of EvalML, I expect EvalML to check the Logical Type of my target column to determine if it is valid. - If it is not valid,...
- As a developer, I want to calculate the residuals per training fold. To do that, i need the true target values along with the training predictions. I can not...
In https://github.com/alteryx/evalml/issues/4103 we will have added a call to `_handle_nullable_types` in `determine_periodicity` to support IntegerNullable for the decomposer. Once the [pandas bug](https://github.com/pandas-dev/pandas/issues/52127) causing this issue is resolved and released, we...
With https://github.com/alteryx/evalml/issues/4095, we made the handling of integer grid values for partial dependence explicit, rounding the fractional values produced in `_grid_from_X` to allow IntegerNullable types to be used for partial...
Currently, the `TargetDistributionDataCheck` does not allow nullable logical types. This doesn't match the behavior of `InvalidTargetDataCheck`, which does allow nullable types. With the new nullable type support across automl search,...
As a user, I wish I could train a pipeline on data that might not have nans and has non nullable types and then predict/`transform_all_but_final` /score data that has nans...
Woodwork 0.22.0 supports numeric logical type inference for `object` dtypes. This issue tracks updating any failing tests due to this change.
In #4071 we capped `nbsphinx` as it was breaking our test builds. https://readthedocs.com/projects/feature-labs-inc-evalml/builds/1347489/ t is an example run. This issue tracks investigating this failure and unpinning `nbsphinx`.
Once https://github.com/scikit-learn/scikit-learn/issues/25798 has been implemented and released, we should upgrade imblearn to that version, which will allow us to remove the nullable type handling put in place by https://github.com/alteryx/evalml/issues/3974.