machine-learning-algorithms

Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct

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!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning

Describe the bug

We should raise better error messages in the scenario when users pass stuff like pandas.Series/list etc to the vectorizer.

Steps/Code to reproduce bug

import cudf
import pandas
from cuml.feature_extraction.text import TfidfVectorizer

vec =  TfidfVectorizer()
text_s = pandas.Series(["apple", "is", "great"])

 vec.fit_transform(text_s)

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/

On MacOS, the tslearn.datasets does not work out-of-the-box.

In order to make it work, you need to apply the following steps:

1. Go to your finder
2. run "/Apps/Python/Install Certificates.command". This basically installs the certifi package with pip.

Perhaps we should add this to the documentation page of our datasets module?