v0.23.4

What's Changed

• fix(test): replace np.float_ to np.float64 by @Bot-wxt1221 in #1119
• Fixing of association rule lastest version by @zazass8 in #1121
• v0.23.4 release prep work by @rasbt in #1122

New Contributors

• @Bot-wxt1221 made their first contribution in #1119

Full Changelog: v0.23.3...v0.23.4

v0.23.3

What's Changed

• Improved plot_splits for time series splits by @d-kleine in #1113
• Updated publish CI/CD workflow by @d-kleine in #1111
• Update exhaustive_feature_selector.py by @Haider010 in #1104
• fix: Code examples for the association_rules method have an execution… by @MarceloNunesAlves in #1115
• V0.23.3 by @rasbt in #1116

New Contributors

• @Haider010 made their first contribution in #1104
• @MarceloNunesAlves made their first contribution in #1115

Full Changelog: v0.23.2...v0.23.3

v0.23.2

What's Changed

• Don't include tests in built wheel by @carlsmedstad in #1076
• Fix typo in OneRClassifier notebook by @danickblouin in #1084
• Update CI by @rasbt in #1089
• Most recent scikit-learn results in several failed unit tests by @it176131 in #1091
• Integrate scikit-learn's set_output method into TransactionEncoder by @it176131 in #1087
• Refactor bias_variance_decomposition test. by @fkdosilovic in #1081
• Update minor typos in fpgrowth.ipynb by @tanmaychimurkar in #1057
• Use scipy's decompositions in PCA. by @fkdosilovic in #1080
• Add Jaccard, Certainty, and Kulczynski association rules metrics by @UltraArceus3 in #1099
• FPGrowth/FPMax and Association Rules with the existence of missing values (#1004) by @zazass8 in #1106
• SFS finalize_fit() support for numpy >= 2.0 by @d-kleine in #1107
• Fixed _calc_score for scikit-learn version compatibility by @d-kleine in #1109
• updated CI/CD workflows by @d-kleine in #1108
• Add PyPI deploy workflow by @rasbt in #1110

New Contributors

• @carlsmedstad made their first contribution in #1076
• @danickblouin made their first contribution in #1084
• @it176131 made their first contribution in #1091
• @fkdosilovic made their first contribution in #1081
• @tanmaychimurkar made their first contribution in #1057
• @UltraArceus3 made their first contribution in #1099
• @zazass8 made their first contribution in #1106
• @d-kleine made their first contribution in #1107

Full Changelog: v0.23.1...v0.23.2

Version 0.23.1

Version 0.23.1 (5 Jan 2024)

Changes

• Updated dependency on distutils for python 3.12 and above ([#1072](#1072) via [peanutsee](https://github.com/peanutsee))

Version 0.23.0

Downloads

• [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.21.1.zip)

• [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.22.1.tar.gz)

Changes

• Address NumPy deprecations to make mlxtend compatible to NumPy 1.24
• Changed the signature of the LinearRegression model of sklearn in the test removing the normalize parameter as it is deprecated. ([#1036](#1036))
• Add pyproject.toml to support PEP 518 builds ([#1065](#1065) via [jmahlik](https://github.com/jmahlik))
• Fixed installation from sdist failing ([#1065](#1065) via [jmahlik](https://github.com/jmahlik))
• Converted configuration to pyproject.toml ([#1065](#1065) via [jmahlik](https://github.com/jmahlik))
• Remove mlxtend.image submodule with face recognition functions due to poor dlib support in modern environments.

New Features and Enhancements

• Document how to use SequentialFeatureSelector and multiclass ROC AUC.

Version 0.22.0

Changes

• When ExhaustiveFeatureSelector is run with n_jobs == 1, joblib is now disabled, which enables more immediate (live) feedback when the verbose mode is enabled. (#985 via Nima Sarajpoor)
• Disabled unnecessary warning in EnsembleVoteClassifier (#941)
• Fixed various documentation issues (#849 and #951 via Lekshmanan Natarajan)
• Fixed "Edit on GitHub" button (#1024)

New Features and Enhancements

• The mlxtend.frequent_patterns.association_rules function has a new metric - Zhang's Metric, which measures both association and dissociation. (#980)
• Internal mlxtend.frequent_patterns.fpmax code improvement that avoids casting a sparse DataFrame into a dense NumPy array. (#1000 via Tim Kellogg)
• The plot_decision_regions function now has a n_jobs parameter to parallelize the computation. (In a particular use case, on a small dataset, there was a 21x speed-up (449 seconds vs 21 seconds on local HPC instance of 36 cores). (#998 via Khalid ElHaj)
• Added mlxtend.frequent_patterns.hmine algorithm and documentation for mining frequent itemsets using the H-Mine algorithm. (#1020 via Fatih Sen)

Version 0.21.0

New Features and Enhancements

• The mlxtend.evaluate.feature_importance_permutation function has a new feature_groups argument to treat user-specified feature groups as single features, which is useful for one-hot encoded features. (#955)
• The mlxtend.feature_selection.ExhaustiveFeatureSelector and SequentialFeatureSelector also gained support for feature_groups with a behavior similar to the one described above. (#957 and #965 via Nima Sarajpoor)

Changes

• The custom_feature_names parameter was removed from the ExhaustiveFeatureSelector due to redundancy and to simplify the code base. The ExhaustiveFeatureSelector documentation illustrates how the same behavior and outcome can be achieved using pandas DataFrames. (#957)

Bug Fixes

• None

Version 0.20.0

New Features and Enhancements

Downloads

• Source code (zip)

• Source code (tar.gz)

New Features and Enhancements

• The mlxtend.evaluate.bootstrap_point632_score now supports fit_params. (#861)
• The mlxtend/plotting/decision_regions.py function now has a contourf_kwargs for matplotlib to change the look of the decision boundaries if desired. (#881 via [pbloem])
• Add a norm_colormap parameter to mlxtend.plotting.plot_confusion_matrix, to allow normalizing the colormap, e.g., using matplotlib.colors.LogNorm() (#895)
• Add new GroupTimeSeriesSplit class for evaluation in time series tasks with support of custom groups and additional parameters in comparison with scikit-learn's TimeSeriesSplit. (#915 via Dmitry Labazkin)

Changes

• Due to compatibility issues with newer package versions, certain functions from six.py have been removed so that mlxtend may not work anymore with Python 2.7.
• As an internal change to speed up unit testing, unit testing is now faciliated by GitHub workflows, and Travis CI and Appveyor hooks have been removed.
• Improved axis label rotation in mlxtend.plotting.heatmap and mlxtend.plotting.plot_confusion_matrix (#872)
• Fix various typos in McNemar guides.
• Raises a warning if non-bool arrays are used in the frequent pattern functions apriori, fpmax, and fpgrowth. (#934 via NimaSarajpoor)

Bug Fixes

• Fix unreadable labels in heatmap for certain colormaps. (#852)
• Fix an issue in mlxtend.plotting.plot_confusion_matrix when string class names are passed (#894)

Version 0.19.0

Version 0.19.0 (09/02/2021)

New Features

• Adds a second "balanced accuracy" interpretation ("balanced") to evaluate.accuracy_score in addition to the existing "average" option to compute the scikit-learn-style balanced accuracy. (#764)
• Adds new scatter_hist function to mlxtend.plotting for generating a scattered histogram. (#757 via Maitreyee Mhasaka)
• The evaluate.permutation_test function now accepts a paired argument to specify to support paired permutation/randomization tests. (#768)
• The StackingCVRegressor now also supports multi-dimensional targets similar to StackingRegressor via StackingCVRegressor(..., multi_output=True). (#802 via Marco Tiraboschi)

Changes

• Updates unit tests for scikit-learn 0.24.1 compatibility. (#774)
• StackingRegressor now requires setting StackingRegressor(..., multi_output=True) if the target is multi-dimensional; this allows for better input validation. (#802)
• Removes deprecated res argument from plot_decision_regions. (#803)
• Adds a title_fontsize parameter to plot_learning_curves for controlling the title font size; also the plot style is now the matplotlib default. (#818)
• Internal change using 'c': 'none' instead of 'c': '' in mlxtend.plotting.plot_decision_regions's scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. (#822)
• Adds a fontcolor_threshold parameter to the mlxtend.plotting.plot_confusion_matrix function as an additional option for determining the font color cut-off manually. (#827)
• The frequent_patterns.association_rules now raises a ValueError if an empty frequent itemset DataFrame is passed. (#843)
• The .632 and .632+ bootstrap method implemented in the mlxtend.evaluate.bootstrap_point632_score function now use the whole training set for the resubstitution weighting term instead of the internal training set that is a new bootstrap sample in each round. (#844)

Bug Fixes

• Fixes a typo in the SequentialFeatureSelector documentation (#835 via João Pedro Zanlorensi Cardoso)

Version 0.18.0

New Features

• The bias_variance_decomp function now supports optional fit_params for the estimators that are fit on bootstrap samples. (#748)
• The bias_variance_decomp function now supports Keras estimators. (#725 via @hanzigs)
• Adds new mlxtend.classifier.OneRClassifier (One Rule Classfier) class, a simple rule-based classifier that is often used as a performance baseline or simple interpretable model. (#726
• Adds new create_counterfactual method for creating counterfactuals to explain model predictions. (#740)

Changes

• permutation_test (mlxtend.evaluate.permutation) ìs corrected to give the proportion of permutations whose statistic is at least as extreme as the one observed. (#721 via Florian Charlier)
• Fixes the McNemar confusion matrix layout to match the convention (and documentation), swapping the upper left and lower right cells. (#744 via mmarius)

Bug Fixes

• The loss in LogisticRegression for logging purposes didn't include the L2 penalty for the first weight in the weight vector (this is not the bias unit). However, since this loss function was only used for logging purposes, and the gradient remains correct, this does not have an effect on the main code. (#741)
• Fixes a bug in bias_variance_decomp where when the mse loss was used, downcasting to integers caused imprecise results for small numbers. (#749)