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?

This may seem like a rather silly thing to validate, but it's easy to accidentally pass in empty divisions if you're using a divisions list generated elsewhere, or mess up typing range(1000, 2) instead of range(0, 1000, 2) like I did.

Would be nice to have a short, informative traceback in this case instead of this inscrutable error.

result.set_index("id", divisions=[])

It seems there is no validation on fit_ensemble when ensemble size is 0, causing an issue to appear as seen in #1327

• transform_primitive.py is becoming very large. I suggest splitting out into separate files.
• We could split this file up by groups (such as LatLong transform primitives in 1 file).

A number of sklearn calls raise deprecation warnings and will turn into bugs when sklearn releases version 1.2.
These should be fixed.

• replacing float variable names by str in pandas.DataFrame passed:

classification/feature_based/_summary_classifier.py: 2 warnings
classification/feature_based/tests/test_summary_classifier.py: 2 warnings
classification/tests/test_all_c

Yes

readthedocs analytics says that we have several search results that yield little or no useful results. Let's improvethose:

• gpu (only 2 results): make sure that explanation of device parameter mentions gpu as well
• gridsearch (0 results): make sure to include the term gridsearch in the meta data of

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning