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?

What happened:

When reading an empty parquet file with chunksize argument, the error "IndexError: list index out of range" is raised. While it may seem that using chunksize is irrelevant, the use case here is reading files from an external source where it is not known a priori whether or not the file is empty (or really large).

What you expected to happen:

An empty dataframe

• 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

From issue #1302, it appears autosklearn is a bit unstable when run many times in the same script, i.e. in a for loop.

for i in range(400):
    automodel = AutoSklearn(full_resources)
    automodel.fit(x, y)

We currently have no test for this and it would be good to see if we can reproduce the same connection refused error.

Please add LinkedIn Greykite time series model as a part of sktime.

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

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

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.