rasbt/mlxtend: 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