Fit transform

[tomlincr]

Added .fit_transform method to all node embedding algorithms, primarily motivated by desire to use karateclub algorithms in a scikit-learn pipeline.

Adds:

• y=None argument, for scikit-learn compatibility
• Passthrough if y is not None to allow passing e.g. node attributes through for a downstream task in the pipeline

Tests:

• Method tested for each algorithm
• Generally testing that output matches that of .get_embedding()
• Unless stochastic method, when testing that shapes match

[tomlincr]

Apologies, long day and thought I'd opened this PR on my fork to test coverage, CI etc.

[tomlincr]

Interesting, all passes locally.
Seems to be some variation in the embeddings generated by multiple fits when run by actions.
Will test shape matches instead for these offenders

[codecov-commenter]

Codecov Report

Merging #145 (c7ceb75) into master (d750b33) will increase coverage by 0.12%.
The diff coverage is 100.00%.

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@@            Coverage Diff             @@
##           master     #145      +/-   ##
==========================================
+ Coverage   97.41%   97.53%   +0.12%     
==========================================
  Files          63       63              
  Lines        2707     2845     +138     
==========================================
+ Hits         2637     2775     +138     
  Misses         70       70              

Files Changed	Coverage Δ
karateclub/estimator.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/ae.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/asne.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/bane.py	100.00% <100.00%> (ø)
...arateclub/node_embedding/attributed/feathernode.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/fscnmf.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/musae.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/sine.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/tadw.py	100.00% <100.00%> (ø)
karateclub/node_embedding/attributed/tene.py	100.00% <100.00%> (ø)
... and 18 more

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[LucaCappelletti94]

I have tried to run the test suite of this pull request, but it is currently failing at the HOPE model test. I see that you are comparing the two embeddings - maybe there are numerical instabilities that lead to different results over different runs? I am not familiar with the internals of numpy & scipy that much.