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rasbt/mlxtend: Version 0.13.0

Sebastian Raschka; James Bourbeau; Reiichiro Nakano; Zach Griffith; Will McGinnis; JJLWHarrison; Guillaume Poirier-Morency; Daniel; Floris Hoogenboom; Colin; selay01; Christos Aridas; Pablo Fernandez; Alejandro Correa Bahnsen; Marc Abramowitz; Konstantinos Paliouras; Joshua Görner; Jelmer Borst; Ilya; Iaroslav Shcherbatyi; hsperr; GILLES Armand; Francis T. O'Donovan; Eike Dehling; Batuhan Bardak; Arfon Smith; Anton Loss; Anebi; Ajinkya Kale; Adam Erickson

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)

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