Version 0.13.0

Version 0.13.0 (07/20/2018)

New Features

• A meaningful error message is now raised when a cross-validation generator is used with SequentialFeatureSelector. (#377)
• The SequentialFeatureSelector now accepts custom feature names via the fit method for more interpretable feature subset reports. (#379)
• The SequentialFeatureSelector is now also compatible with Pandas DataFrames and uses DataFrame column-names for more interpretable feature subset reports. (#379)
• ColumnSelector now works with Pandas DataFrames columns. (#378 by Manuel Garrido)
• The ExhaustiveFeatureSelector estimator in mlxtend.feature_selection now is safely stoppable mid-process by control+c. (#380)
• Two new functions, vectorspace_orthonormalization and vectorspace_dimensionality were added to mlxtend.math to use the Gram-Schmidt process to convert a set of linearly independent vectors into a set of orthonormal basis vectors, and to compute the dimensionality of a vectorspace, respectively. (#382)
• mlxtend.frequent_patterns.apriori now supports pandas SparseDataFrames to generate frequent itemsets. (#404 via Daniel Morales)
• The plot_confusion_matrix function now has the ability to show normalized confusion matrix coefficients in addition to or instead of absolute confusion matrix coefficients with or without a colorbar. The text display method has been changed so that the full range of the colormap is used. The default size is also now set based on the number of classes.
• Added support for merging the meta features with the original input features in StackingRegressor (via use_features_in_secondary) like it is already supported in the other Stacking classes. (#418)
• Added a support_only to the association_rules function, which allow constructing association rules (based on the support metric only) for cropped input DataFrames that don't contain a complete set of antecedent and consequent support values. (#421)

Changes

• Itemsets generated with apriori are now frozensets (#393 by William Laney and #394)
• Now raises an error if a input DataFrame to apriori contains non 0, 1, True, False values. #419)

Bug Fixes

• Allow mlxtend estimators to be cloned via scikit-learn's clone function. (#374)
• Fixes bug to allow the correct use of refit=False in StackingRegressor and StackingCVRegressor (#384 and (#385) by selay01)
• Allow StackingClassifier to work with sparse matrices when use_features_in_secondary=True (#408 by Floris Hoogenbook)
• Allow StackingCVRegressor to work with sparse matrices when use_features_in_secondary=True (#416)
• Allow StackingCVClassifier to work with sparse matrices when use_features_in_secondary=True (#417)