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]

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Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 97.54%. Comparing base (d750b33) to head (716a796). Report is 31 commits behind head on master.

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Additional details and impacted files

@@            Coverage Diff             @@
##           master     #145      +/-   ##
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+ Coverage   97.41%   97.54%   +0.12%     
==========================================
  Files          63       63              
  Lines        2707     2849     +142     
==========================================
+ Hits         2637     2779     +142     
  Misses         70       70              

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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.