This Pull Request is for HacktoberFest 2020

Description of Change

Checklist

• Added description

I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

Description of Change

References

Checklist

Today, I can manipulate ListDtype columns and execute operations like segmented sort and unique. Because these operations do not have cross-row (or cross-partition) dependencies, they can be executed in Dask by passing a lambda function to map_partitions.

It would be nice to expose the list accessor on dask-cuDF objects like we do for other accessors. As this is not supported by pandas, per

https://igel.readthedocs.io/en/latest/_sources/readme.rst.txt includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.

readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif

The sphinx build needs to point to the asset directory, absolutely:

.. image:: /assets/igel-help.gif

I haven't made a patch, because I haven't

Describe the bug
Not a proper bug. The UMAP implementation has a typo in the target_weights argument, where the original UMAP uses target_weight. This creates issues of compatibility when working with both libraries.

Steps/Code to reproduce bug

from cuml import UMAP

mapper = UMAP(n_neighbors=15, n_components=2, target_weights=0.5)
# no error here

mapper2 = UMAP(n_neigh

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

Our xgboost models use the binary:logistic' objective function, however the m2cgen converted version of the models return raw scores instead of the transformed scores.

This is fine as long as the user knows this is happening! I didn't, so it took a while to figure out what was going on. I'm wondering if perhaps a useful warning could be raised for users to alert them of this issue? A warning

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually