Published July 21, 2018
| Version 0.13.0
Software
Open
rasbt/mlxtend: Version 0.13.0
Creators
- Sebastian Raschka1
- James Bourbeau2
- Reiichiro Nakano3
- Zach Griffith2
- Will McGinnis4
- JJLWHarrison
- Guillaume Poirier-Morency5
- Daniel
- Floris Hoogenboom
- Colin
- selay01
- Christos Aridas
- Pablo Fernandez6
- Alejandro Correa Bahnsen7
- Marc Abramowitz8
- Konstantinos Paliouras9
- Joshua Görner
- Jelmer Borst
- Ilya10
- Iaroslav Shcherbatyi11
- hsperr
- GILLES Armand12
- Francis T. O'Donovan13
- Eike Dehling14
- Batuhan Bardak15
- Arfon Smith16
- Anton Loss
- Anebi
- Ajinkya Kale
- Adam Erickson
- 1. Michigan State University
- 2. @WIPACrepo
- 3. @infostellarinc
- 4. Predikto Inc.
- 5. IRIC @major-lab
- 6. FANSI Motorsport
- 7. Easy Solutions
- 8. @adobe-platform
- 9. @Workable
- 10. LPI ASC
- 11. Saarland University
- 12. millesime.ai
- 13. @betteroutcomes
- 14. Textkernel
- 15. STM
- 16. @spacetelescope
Description
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 thefit
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 inmlxtend.feature_selection
now is safely stoppable mid-process by control+c. (#380) - Two new functions,
vectorspace_orthonormalization
andvectorspace_dimensionality
were added tomlxtend.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 pandasSparseDataFrame
s 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
(viause_features_in_secondary
) like it is already supported in the other Stacking classes. (#418) - Added a
support_only
to theassociation_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)
- Itemsets generated with
apriori
are nowfrozenset
s (#393 by William Laney and #394) - Now raises an error if a input DataFrame to
apriori
contains non 0, 1, True, False values. #419)
- Allow mlxtend estimators to be cloned via scikit-learn's
clone
function. (#374) - Fixes bug to allow the correct use of
refit=False
inStackingRegressor
andStackingCVRegressor
(#384 and (#385) by selay01) - Allow
StackingClassifier
to work with sparse matrices whenuse_features_in_secondary=True
(#408 by Floris Hoogenbook) - Allow
StackingCVRegressor
to work with sparse matrices whenuse_features_in_secondary=True
(#416) - Allow
StackingCVClassifier
to work with sparse matrices whenuse_features_in_secondary=True
(#417)
Files
rasbt/mlxtend-0.13.0.zip
Files
(11.0 MB)
Name | Size | Download all |
---|---|---|
md5:96373095ba131aeac10b3af210924070
|
11.0 MB | Preview Download |
Additional details
Related works
- Is supplement to
- https://github.com/rasbt/mlxtend/tree/0.13.0 (URL)