New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

Functions which accept a numerical value and an optional dtype try to determine the dtype from the value if not explicitely provided.
Specifically, da.full(shape, fill_value) works for a literal scalar, but not for a Dask array that later produces a scalar.
This worked in dask 2021.7 but broke in 2021.8

What happened:

The example raises NotImplementedError "Can not use auto rechun

The components part of our codebase was written sometime ago, with older sklearn versions and before python typing was production ready.

In general, some of these files need to be cleaned up. Mostly typing of parameters and functions, adding documentation a bout these parameters and finally double checking with scikit learn that there aren't some new or deprecated parameters we still use.

To

• 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

In issue #1845, an instance of a statsmodels interfacing estimator was discovered which was missing crucial parameters.

The reason was that in statsmodels, model parameteres are spread out across constructor (__init__), fit, and potentially other functions.

Due to this, it would be important to look at other statsmodels interfaces to check whether we are missing useful parameters f

Yes

The current History class has some limitations: (ver 0.10.0)

1. Currently the history is saved as JSON, as a result, those recorded values are limited to simple numbers and strings. Other objects can not be saved in history files directly.
2. Saving as JSON takes lots of time and space because numbers are stored in decimal. It's getting worse when the training epoch is increasing.
3. In some

Could FeatureTools be implemented as an automated preprocessor to Autogluon, adding the ability to handle multi-entity problems (i.e. Data split across multiple normalised database tables)? So if you supply Autogluon with a list of Dataframes instead of a single Dataframe it would first invoke FeatureTools:

• take the multiple Dataframes (entities) and try to auto-infer the relationship betwee

Can we have an example of REST API calls in the documentation?
Examples with CURL, HTTPie or another client and the results would be better for newbies.
Thanks again for your good work.