Adding PCA correlation circle graph

[Gabriel-Azevedo-Ferreira]

Description

Adding a function to enable the plotting of PCA correlation circles, similarly to what is done by factomineR in R.

Related issues or pull requests

Implements the feature proposed in #543 (#543)

Pull Request Checklist

• [DONE] Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• [DONE ] Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• [DONE] Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• [DONE] Ran nosetests ./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., nosetests ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• [DONE] Checked for style issues by running flake8 ./mlxtend

[rasbt]

Just had a look at the good, which looks great so far. Here are just a few minor issues:

[pep8speaks]

Hello @Gabriel-Azevedo-Ferreira! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2019-05-29 02:19:11 UTC

[rasbt]

I think the failing unit tests could be because a new scikit-learn version was just recently uploaded to conda ... I will change the travis tests (here) to test on 0.20 and then carefully go through the 0.21 changes later

[rasbt]

There were some odd things with the unit tests regarding nosetests; I think it's easier to get this all to work here instead of having to rebase after I do it in master. I switched to pytest now which doesn't seem to have issues with regard to the latest changes in sklearn for some of the things nosetests complained about.

[coveralls]

Coverage increased (+0.4%) to 92.087% when pulling 0a53af6 on Gabriel-Azevedo-Ferreira:adding_pca_corr_circle_graph into 92753e0 on rasbt:master.

[rasbt]

Sorry for the mess, it was a bit of a coincidence that the new scikit-learn version was just pushed to conda when you created this PR so that this caused problems with some of the unit tests.

In any case, I made some minor adjustments to the documentation, and it should be all ok for merge now when the unit tests pass. Thanks a lot for the contribution!

[Gabriel-Azevedo-Ferreira]

Thanks!
Glad I could contribute!

No problem and thank you for the discussions!