Added Hmine algorithm

[fatihsen20]

Code of Conduct

New feature.

Description

In order to enrich the library and to compare the speed and memory costs of certain algorithms, I added the hmine algorithm to the library.

Related issues or pull requests

None

Pull Request Checklist

• Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• Ran PYTHONPATH='.' pytest ./mlxtend -sv and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• Checked for style issues by running flake8 ./mlxtend

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[rasbt]

Awesome, thanks so much for the contribution!

H-Mine is guaranteed to find the same frequent itemsets as apriori and fp-growth, correct? Would it be possible to add a unit test to test for equivalence with the existing algos?

[rasbt]

I think you accidentally added this to the wrong version in the Changelog

[rasbt]

PS: Don't worry about the linter above, we can address that (happy to take care of that) at the very end.

[codecov]

Codecov Report

Patch coverage: 90.59% and project coverage change: +0.13 🎉

Comparison is base (11b61b9) 77.33% compared to head (21e038d) 77.46%.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1020      +/-   ##
==========================================
+ Coverage   77.33%   77.46%   +0.13%     
==========================================
  Files         198      200       +2     
  Lines       11171    11287     +116     
  Branches     1461     1480      +19     
==========================================
+ Hits         8639     8744     +105     
- Misses       2318     2324       +6     
- Partials      214      219       +5     

Impacted Files	Coverage Δ
mlxtend/frequent_patterns/tests/test_hmine.py	88.46% <88.46%> (ø)
mlxtend/frequent_patterns/hmine.py	92.06% <92.06%> (ø)
mlxtend/frequent_patterns/__init__.py	100.00% <100.00%> (ø)

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[rasbt]

Thanks a lot for the update! I will do my best to review it this weekend!

[fatihsen20]

I always overlook the isort rules. I am sorry 🥲

[rasbt]

No worries about that! The style checks are really not a big deal, we can address those at the very end upon merging.

[fatihsen20]

Hello, will my pull request be merge? I am very excited about this 😊 @rasbt

[rasbt]

This is a fantastic PR! And sorry, will merge soon! Haven't had a chance to go over the docs yet! I have a question here:

You mentioned

on a large dataset.

but did you mean

on a small dataset.

I.e., the dataset from the example above? No worries about fixing it. I did some other tweaks in the docs and will update soon (pls don't modify the Nb in the meantime due to merge conflicts, Jupyter NB's are still a bit tricky on GitHub 😅)

[fatihsen20]

This is a fantastic PR! And sorry, will merge soon! Haven't had a chance to go over the docs yet! I have a question here:

You mentioned

on a large dataset.

but did you mean

on a small dataset.

I.e., the dataset from the example above? No worries about fixing it. I did some other tweaks in the docs and will update soon (pls don't modify the Nb in the meantime due to merge conflicts, Jupyter NB's are still a bit tricky on GitHub 😅)

Sorry, I probably missed it while preparing the document. Yes, there will be a small dataset in the example there. Thank you so much for your hard work 😊

[rasbt]

Oh I thought it was based on the small example at the top. So you ran the benchmark on a small hands-on dataset if I understand it correctly? I think we can leave things as is unless it's not too large and we can add it to the doc repo. What do you think?

[fatihsen20]

Oh I thought it was based on the small example at the top. So you ran the benchmark on a small hands-on dataset if I understand it correctly? I think we can leave things as is unless it's not too large and we can add it to the doc repo. What do you think?

Yes it was based on the small example above. Since we are testing with small dataset on Doc, it may remain small. I don't think it will be a big problem if we add it this way.

[rasbt]

Ok perfect, then I'd say it's fine as is because the example is already in the notebook.

Should be good to merge then, correct?

[fatihsen20]

Ok perfect, then I'd say it's fine as is because the example is already in the notebook.

Should be good to merge then, correct?

Okey sir, please 😊

[rasbt]

Merged it @fatihsen20 . Thanks again for this awesome PR!
I am hoping to make a new release version on the upcoming weekend