hyperparameter-tuning

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

I have been trying to use AutoSklearn with Multi-class classification

so my labels are like this

0 1 2 3 4 ... 200
1 0 1 1 1 ... 1
0 1 0 0 1 ... 0
1 0 0 1 0 ... 0
1 1 0 1 0 ... 1
0 1 1 0 1 ... 0
1 1 1 0 0 ... 1
1 0 1 0 1 ... 0

I used this code

`
y = y[:, (65,67,54,133,122,63,102

Describe the bug
Code could be more conform to pep8 and so forth.

To Reproduce
https://app.codacy.com/gh/Neuraxio/Neuraxle/issues?&filters=W3siaWQiOiJMYW5ndWFnZSIsInZhbHVlcyI6W119LHsiaWQiOiJDYXRlZ29yeSIsInZhbHVlcyI6WyJDb2RlU3R5bGUiXX0seyJpZCI6IkxldmVsIiwidmFsdWVzIjpbXX0seyJpZCI6IlBhdHRlcm4iLCJ2YWx1ZXMiOltdfSx7ImlkIjoiQXV0aG9yIiwidmFsdWVzIjpbXX1d

Expected behavior
Less code st

I am unable to install EvalMl on an M1 Mac in an arm64 terminal via pip or conda. As M1 Macs become more widely used, the install instructions for EvalML might need to provide some guidance on what to do.

pip install evalml - doesn't work
conda install -c conda-forge evalml - doesn't work
conda install -c conda-forge evalml-core - does work

Note: @dvreed77 was able to install EvalML