Bug Report

Is the issue related to model conversion? No

Describe the bug

DynamicQuantizeLinear function op does not have shape inference function defined. In absence of shape inference, function body is used to get the shape inference for the function op and although it works as a fallback option it hurts perf.

Expected behavior

Add shape inference function for DynamicQuan

The dask.delayed decorator should have type annotations.

from dask import delayed

@delayed(pure=True)
def well_typed_function() -> int:
    return 1

mypy: Untyped decorator makes function "well_typed_function" untyped

Ideally, the type of delayed would be something like

from typing import TypeVar
from dask.delayed import Delayed

T = TypeVar("T")

delayed(o

• As a user of Featuretools, I wish there was a utility function in Featuretools, which given an EntitySet, which provide me with a list of valid/applicable primitives.
• For an EntitySet with a single DataFrame, this should be possible by looking at the typing information

def get_valid_primitives(entityset, target_entity, max_depth=2, selected_primitives=None):
    """
       

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

The classifiers have many and varied parameters. Most could be improved with

1. Clearer description of the parameter meaning
2. Action/exceptions thrown when the classifier is parameterised in a way that will make it crash

For example, see this issue for the RISE classifier.
alan-turing-institute/sktime#895

some checks can be done when the classifier is constr

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 your use case?

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!

Due to my actual schedule, I'm very busy and I will not find much time to work fully (100%) on my open-source projects in the meantime.

I would like this project to continue improving, so I would be accepting maintainer(s) that would help mo

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