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Description
New version release
New Features
SequentialFeatureSelector
now supports using pre-specified feature sets via thefixed_features
parameter. (#578)accuracy_score
function tomlxtend.evaluate
for computing basic classifcation accuracy, per-class accuracy, and average per-class accuracy. (#624 via Deepan Das)StackingClassifier
andStackingCVClassifier
now have adecision_function
method, which serves as a preferred choice overpredict_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)Changes
apriori
frequent itemset generating function whenlow_memory=True
. Settinglow_memory=False
(default) is still faster for small itemsets, butlow_memory=True
can be much faster for large itemsets and requires less memory. Also, input validation forapriori
, ̀ fpgrowthand
fpmaxtakes 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 using
TransactionEncoder`. (#619 via Denis Barbier)apriori
, ̀ fpgrowthand
fpmax` runs much faster on sparse DataFrame when input pandas DataFrame contains integer values. (#621 via Denis Barbier)fpgrowth
andfpmax
directly work on sparse DataFrame, they were previously converted into dense Numpy arrays. (#622 via Denis Barbier)Bug Fixes
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)fpgrowth
andapriori
consistent for edgecases such asmin_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)mlxtend.plotting.plot_confusion_matrix
, where the font-color choice for medium-dark cells was not ideal and hard to read. #588 via sohrabtowfighi)svd
mode ofmlxtend.feature_extraction.PrincipalComponentAnalysis
now also n-1 degrees of freedom instead of n d.o.f. when computing the eigenvalues to match the behavior ofeigen
. #595StackingCVClassifier
because it causes issues if pipelines are used as input. #606