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

Describe the bug
After applying the unstack function, the variable names change to numeric format.

Steps/Code to reproduce bug

def get_df(length, num_cols, num_months, acc_offset):
    cols = [ 'var_{}'.format(i) for i in range(num_cols)]
    df = cudf.DataFrame({col: cupy.random.rand(length * num_months) for col in cols})
    df['acc_id'] = cupy.repeat(cupy.arange(length), nu

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

confusion_matrix should automatically convert dtypes as appropriate in order to avoid failing, like other metric functions.

from sklearn.metrics import confusion_matrix
import numpy as np
import cuml
​
y = np.array([0.0, 1.0, 0.0])
y_pred = np.array([0.0, 1.0, 1.0])
print(confusion_matrix(y, y_pred))
cuml.metrics.confusion_matrix(y, y_pred)
[[1 1]
 [0 1]]
----------------

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/

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

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