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rasbt/mlxtend: Version 0.17.1

Sebastian Raschka; James Bourbeau; Reiichiro Nakano; Zach Griffith; Kota Mori; Will McGinnis; JJLWHarrison; Jakub Šmíd; Qiang Gu; Guillaume Poirier-Morency; Daniel; Floris Hoogenboom; Colin; Vahid Mirjalili; Steve Harenberg; Denis Barbier; Selay; Christos Aridas; Pablo Fernandez; Oliver Tomic; Laurens Geffert; Janpreet Singh; Gabriel Azevedo Ferreira; Alejandro Correa Bahnsen; Anton Loss; Anebi; Ajinkya Kale; Adam Erickson; Adam Cooper; Ackerley Tng

New Features
  • The SequentialFeatureSelector now supports using pre-specified feature sets via the fixed_features parameter. (#578)
  • Adds a new accuracy_score function to mlxtend.evaluate for computing basic classifcation accuracy, per-class accuracy, and average per-class accuracy. (#624 via Deepan Das)
  • StackingClassifier and StackingCVClassifiernow have a decision_function method, which serves as a preferred choice over predict_proba in calculating roc_auc and average_precision scores when the meta estimator is a linear model or support vector classifier. (#634 via Qiang Gu)
  • Improve the runtime performance for the apriori frequent itemset generating function when low_memory=True. Setting low_memory=False (default) is still faster for small itemsets, but low_memory=True can be much faster for large itemsets and requires less memory. Also, input validation for apriori, ̀ fpgrowthandfpmaxtakes a significant amount of time when input pandas DataFrame is large; this is now dramatically reduced when input contains boolean values (and not zeros/ones), which is the case when usingTransactionEncoder`. (#619 via Denis Barbier)
  • Add support for newer sparse pandas DataFrame for frequent itemset algorithms. Also, input validation for apriori, ̀ fpgrowthandfpmax` runs much faster on sparse DataFrame when input pandas DataFrame contains integer values. (#621 via Denis Barbier)
  • Let fpgrowth and fpmax directly work on sparse DataFrame, they were previously converted into dense Numpy arrays. (#622 via Denis Barbier)
Bug Fixes
  • Fixes a bug in mlxtend.plotting.plot_pca_correlation_graph that caused the explaind variances not summing up to 1. Also, improves the runtime performance of the correlation computation and adds a missing function argument for the explained variances (eigenvalues) if users provide their own principal components. (#593 via Gabriel Azevedo Ferreira)
  • Behavior of fpgrowth and apriori consistent for edgecases such as min_support=0. (#573 via Steve Harenberg)
  • fpmax returns an empty data frame now instead of raising an error if the frequent itemset set is empty. (#573 via Steve Harenberg)
  • Fixes and issue in mlxtend.plotting.plot_confusion_matrix, where the font-color choice for medium-dark cells was not ideal and hard to read. #588 via sohrabtowfighi)
  • The svd mode of mlxtend.feature_extraction.PrincipalComponentAnalysis now also n-1 degrees of freedom instead of n d.o.f. when computing the eigenvalues to match the behavior of eigen. #595
  • Disable input validation for StackingCVClassifier because it causes issues if pipelines are used as input. #606
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