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?

We run doctests on a CI environment with many packages installed:

https://github.com/dask/dask/blob/a87236041ff223363698f65c5b7c153415a41259/.github/workflows/additional.yml#L80:L104

So we probably don't need to skip as many as we do (grep for # doctest: +SKIP).

Bug/Feature Request Description

In [1]: import featuretools as ft                                                                                                                             

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)                                                                                                

In [3]: import pandas as pd    

The problem I want to use auto-sklearn on is a time-series. Can we modify sklearn to include cv with time series?

Describe the bug
when axis has duplicate value , onnxruntime compute result is all same value ,which is different with expect of tensorflow

Urgency
2020.11.18

System information
Linux Ubuntu 16.04

• ONNX Runtime installed from binary
• ONNX Runtime version:1.4.0
• Python version:3.5

Expected behavior
When there are duplicate values, the duplicate can be removed. j

Users currently have to set the seasonal periodicity sp. We should provide an option to estimate it from the data or infer it from the date time index.

There may be some existing related functionality in pycaret and statmodels already.

Hi! I was a bit surprised by the name of CVSplit. The name suggests that it is responsible for cross-validation, but the documentation reveals that it only trains and validates on one split. That doesn't match my understanding of cross-validation. It could [technically](https://en.m.wikipedia.org/wiki/Cross-va

Yes

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 =

• What's wrong?

Description

I'm the creator and only maintainer of the project at the moment. I'm working on adding new features and thus I would like to let this issue open for newcomers who want to contribute to the project.

Basically, I wrote the cli using argparse since it is part of the standard language already. However, I'm starting to rethin

In issue #422/#423, users brought up that it's not clear from the error messages that you must fit before convert for most models.

We suspect that with KNN we could maybe also work if the model is not trained, but in general (e.g., with RandomForests) this won't work.

We need help documenting this, and also generating proper error messages. (You can see an example of an unhelpful error mess