Bug Report

Describe the bug

An ONNX model with QuantizeLinear (opset 13) passes shape inference when it has no input for zero_point, but fails if the input is the empty string. Trailing optional inputs should be able to be expressed in either way.

onnx version 1.10.1

Talking to a user recently, they were confused about how to pass divisions= in set_index. Specifically, they missed that the list had to have N+1 elements in it, and the N+1th element was the upper bound for the last partition.

The API docs currently link to https://docs.dask.org/en/latest/dataframe-design.html#partitions, which does describe this in words, but takes some close reading to g

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 issue linked to the documentation

Some of the classifiers don't include and examples section in their docstring. We should update the docstrings to include the examples section.

• ColumnEnsembleClassifier
• ElasticEnsemble
• ProximityForest
• ProximityTree
• ProximityStump

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.