• With Featuretools 1.0.0 we add a dataframe to an EntitySet with the following:

es = ft.EntitySet('new_es')

es.add_dataframe(dataframe=orders_df,
                 dataframe_name='orders',
                 index='order_id',
                 time_index='order_date')

Improvement

• However, you could also change the EntitySet setter to add it with this approach:

es = ft.Ent

When specifying on demand feature views at retrieval time (e.g. get_X_features), the output feature vectors include e.g. request data or dependent feature vectors, even if users did not specify said features.

Expected Behavior

Non-specified dependent feature values are not returned in output

Current Behavior

Non-specified dependent feature values are in output

Steps to reprodu

Now we are using default spark catalog to load tables from hive metastore.

We should test and use the non-default spark catalog to do that and make sure all the user tables can be loaded for OpenMLDB session.

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

Details in discussion mljar/mljar-supervised#421

At the moment, in the categorical tree encoder and the tree discretiser, we have an argument is_regression that the user needs to fill in in order to detect if the user is aiming to perform classification or regression.

Sklearn has an automated process with the is_classification (see Decision tree source code).

Can we bring this functionality to feature-engine?

I think we can :p

Just reviewing the docs and found this under the AutoML User Guide:

We should figure out a way to deal with this kind of thing. I think a couple of options here are:

1. Modifying the cell to only show the first few keys or so of the output.
2. Modifying the output cell so that