A library of extension and helper modules for Python's data analysis and machine learning libraries.
iris_data() such that both the UCI Repository version of the Iris dataset as well as the corrected, original
version of the dataset can be loaded, which has a slight difference in two data points (consistent with Fisher's paper; this is also the same as in R). (via #539 via janismdhanbad)groups parameter to SequentialFeatureSelector and ExhaustiveFeatureSelector fit() methods for forwarding to sklearn CV (#537 via arc12)plot_pca_correlation_graph function to the mlxtend.plotting submodule for plotting a PCA correlation graph. (#544 via Gabriel-Azevedo-Ferreira)zoom_factor parameter to the mlxten.plotting.plot_decision_region function that allows users to zoom in and out of the decision region plots. (#545)fpgrowth that implements the FP-Growth algorithm for mining frequent itemsets as a drop-in replacement for the existing apriori algorithm. (#550 via Steve Harenberg)heatmap function in mlxtend.plotting. (#552)fpmax that implements the FP-Max algorithm for mining maximal itemsets as a drop-in replacement for the fpgrowth algorithm. (#553 via Steve Harenberg)figsize parameter for the plot_decision_regions function in mlxtend.plotting. (#555 via Mirza Hasanbasic)low_memory option for the apriori frequent itemset generating function. Setting low_memory=False (default) uses a substantially optimized version of the algorithm that is 3-6x faster than the original implementation (low_memory=True). (#567 via jmayse)sklearn.externals.joblib. (#547)StackingCVClassifier and StackingCVRegressor such that first-level models are allowed to generate output of non-numeric type. (#562)iris_data() under iris.py by adding a note about differences in the iris data in R and UCI machine learning repo.'svd' mode is used in PCA, the number of eigenvalues is the same as when using 'eigen' (append 0's zeros in that case) (#565)StackingCVClassifier and StackingCVRegressor now support random_state parameter, which, together with shuffle, controls the randomness in the cv splitting. (#523 via Qiang Gu)StackingCVClassifier and StackingCVRegressor now have a new drop_last_proba parameter. It drops the last "probability" column in the feature set since if True,
because it is redundant: p(y_c) = 1 - p(y_1) + p(y_2) + ... + p(y_{c-1}). This can be useful for meta-classifiers that are sensitive to perfectly collinear features. (#532)StackingClassifier, StackingCVClassifier and StackingRegressor, support grid search over the regressors and even a single base regressor. (#522 via Qiang Gu)StackingCVClassifier. (#522 via Qiang Gu)StackingCVRegressor. (#512 via Qiang Gu)StackingCVRegressor also enables grid search over the regressors and even a single base regressor. When there are level-mixed parameters, GridSearchCV will try to replace hyperparameters in a top-down order (see the documentation for examples details). (#515 via Qiang Gu)verbose parameter to apriori to show the current iteration number as well as the itemset size currently being sampled. (#519
class_name parameter to the confusion matrix function to display class names on the axis as tick marks. (#487 via sandpiturtle)GridSearchCV, etc.) the StackingCVRegressor's meta regressor is now being accessed via 'meta_regressor__* in the parameter grid. E.g., if a RandomForestRegressor as meta- egressor was previously tuned via 'randomforestregressor__n_estimators', this has now changed to 'meta_regressor__n_estimators'. (#515 via Qiang Gu)StackingClassifier, StackingCVClassifier and StackingRegressor. (#522 via Qiang Gu)feature_selection.ColumnSelector now also supports column names of type int (in addition to str names) if the input is a pandas DataFrame. (#500 via tetrar124
plot_confusion_matrix for imbalanced datasets if show_absolute=True and show_normed=True. (#504)SparseDataFrame is passed to apriori and the dataframe has integer column names that don't start with 0 due to current limitations of the SparseDataFrame implementation in pandas. (#503)mlxtend.evaluate.feature_importance_permutation now correctly accepts scoring functions with proper function signature as metric argument. #528
mlxtend.image, EyepadAlign, that aligns face images based on the location of the eyes. (#466 by Vahid Mirjalili)mlxtend.evaluate.bias_variance_decomp that decomposes the loss of a regressor or classifier into bias and variance terms. (#470)whitening parameter to PrincipalComponentAnalysis, to optionally whiten the transformed data such that the features have unit variance. (#475)PrincipalComponentAnalysis to 'svd' instead of 'eigen' to improve numerical stability. (#474)mlxtend.image.extract_face_landmarks now returns None if no facial landmarks were detected instead of an array of all zeros. (#466)scatterplotmatrix function to the plotting module. (#437)sample_weight option to StackingRegressor, StackingClassifier, StackingCVRegressor, StackingCVClassifier, EnsembleVoteClassifier. (#438)RandomHoldoutSplit class to perform a random train/valid split without rotation in SequentialFeatureSelector, scikit-learn GridSearchCV etc. (#442)PredefinedHoldoutSplit class to perform a train/valid split, based on user-specified indices, without rotation in SequentialFeatureSelector, scikit-learn GridSearchCV etc. (#443)mlxtend.image submodule for working on image processing-related tasks. (#457)extract_face_landmarks based on dlib to mlxtend.image. (#458)method='oob' option to the mlxtend.evaluate.bootstrap_point632_score method to compute the classic out-of-bag bootstrap estimate (#459)method='.632+' option to the mlxtend.evaluate.bootstrap_point632_score method to compute the .632+ bootstrap estimate that addresses the optimism bias of the .632 bootstrap (#459)mlxtend.evaluate.ftest function to perform an F-test for comparing the accuracies of two or more classification models. (#460)mlxtend.evaluate.combined_ftest_5x2cv function to perform an combined 5x2cv F-Test for comparing the performance of two models. (#461)mlxtend.evaluate.difference_proportions test for comparing two proportions (e.g., classifier accuracies) (#462)mlxtend.plotting.plot_confusion_matrix. (#428)SequentialFeatureSelector. (#377)SequentialFeatureSelector now accepts custom feature names via the fit method for more interpretable feature subset reports. (#379)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)ExhaustiveFeatureSelector estimator in mlxtend.feature_selection now is safely stoppable mid-process by control+c. (#380)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)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.StackingRegressor (via use_features_in_secondary) like it is already supported in the other Stacking classes. (#418)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)apriori are now frozensets (#393 by William Laney and #394)apriori contains non 0, 1, True, False values. #419)clone function. (#374)refit=False in StackingRegressor and StackingCVRegressor (#384 and (#385) by selay01)StackingClassifier to work with sparse matrices when use_features_in_secondary=True (#408 by Floris Hoogenbook)StackingCVRegressor to work with sparse matrices when use_features_in_secondary=True (#416)StackingCVClassifier to work with sparse matrices when use_features_in_secondary=True (#417)feature_importance_permuation function to compute the feature importance in classifiers and regressors via the permutation importance method (#358)ExhaustiveFeatureSelector now optionally accepts **fit_params for the estimator that is used for the feature selection. (#354 by Zach Griffith)SequentialFeatureSelector now optionally accepts
**fit_params for the estimator that is used for the feature selection. (#350 by Zach Griffith)plot_decision_regions colors by a colorblind-friendly palette and adds contour lines for decision regions. (#348)NonFittedErrors if any method for inference is called prior to fitting the estimator. (#353)refit parameter of both the StackingClassifier and StackingCVClassifier to use_clones to be more explicit and less misleading. (#368)StackingCVClassifier's meta features were not stored in the original order when shuffle=True (#370)paired_ttest_resampled)
to compare the performance of two models
(also called k-hold-out paired t-test). (#323)paired_ttest_kfold_cv)
to compare the performance of two models
(also called k-hold-out paired t-test). (#324)paired_ttest_5x2cv) proposed by Dieterrich (1998)
to compare the performance of two models. (#325)refit parameter was added to stacking classes (similar to the refit parameter in the EnsembleVoteClassifier), to support classifiers and regressors that follow the scikit-learn API but are not compatible with scikit-learn's clone function. (#325)ColumnSelector now has a drop_axis argument to use it in pipelines with CountVectorizers. (#333)predict or predict_meta_features is called prior to calling the fit method in StackingRegressor and StackingCVRegressor. (#315)plot_decision_regions function now automatically determines the optimal setting based on the feature dimensions and supports anti-aliasing. The old res parameter has been deprecated. (#309 by Guillaume Poirier-Morency)onehot transformation and the amount of candidates generated by the apriori algorithm. (#327 by Jakub Smid)OnehotTransactions class (which is typically often used in combination with the apriori function for association rule mining) is now more memory efficient as it uses boolean arrays instead of integer arrays. In addition, the OnehotTransactions class can be now be provided with sparse argument to generate sparse representations of the onehot matrix to further improve memory efficiency. (#328 by Jakub Smid)OneHotTransactions has been deprecated and replaced by the TransactionEncoder. (#332
plot_decision_regions function now has three new parameters, scatter_kwargs, contourf_kwargs, and scatter_highlight_kwargs, that can be used to modify the plotting style. (#342 by James Bourbeau)EnsembleVoteClassifier when refit was set to false. (#322)plot_decision_regions function. (#337)store_train_meta_features parameter for fit in StackingCVRegressor. if True, train meta-features are stored in self.train_meta_features_.
New pred_meta_features method for StackingCVRegressor. People can get test meta-features using this method. (#294 via takashioya)store_train_meta_features attribute and pred_meta_features method for the StackingCVRegressor were also added to the StackingRegressor, StackingClassifier, and StackingCVClassifier (#299 & #300)evaluate.mcnemar_tables) for creating multiple 2x2 contigency from model predictions arrays that can be used in multiple McNemar (post-hoc) tests or Cochran's Q or F tests, etc. (#307)evaluate.cochrans_q) for performing Cochran's Q test to compare the accuracy of multiple classifiers. (#310)requirements.txt to setup.py. (#304 via Colin Carrol)mlxtend.evaluate.bootstrap_point632_score to evaluate the performance of estimators using the .632 bootstrap. (#283)max_len parameter for the frequent itemset generation via the apriori function to allow for early stopping. (#270)SequentialFeatureSelector or now in sorted order. (#262)SequentialFeatureSelector now runs the continuation of the floating inclusion/exclusion as described in Novovicova & Kittler (1994).
Note that this didn't cause any difference in performance on any of the test scenarios but could lead to better performance in certain edge cases.
(#262)utils.Counter now accepts a name variable to help distinguish between multiple counters, time precision can be set with the 'precision' kwarg and the new attribute end_time holds the time the last iteration completed. (#278 via Mathew Savage)evaluate.permutation_test, a permutation test for hypothesis testing (or A/B testing) to test if two samples come from the same distribution. Or in other words, a procedure to test the null hypothesis that that two groups are not significantly different (e.g., a treatment and a control group). (#250)'leverage' and 'conviction as evaluation metrics to the frequent_patterns.association_rules function. (#246 & #247)loadings_ attribute to PrincipalComponentAnalysis to compute the factor loadings of the features on the principal components. (#251)make_multiplexer_dataset function that creates a dataset generated by a n-bit Boolean multiplexer for evaluating supervised learning algorithms. (#263)BootstrapOutOfBag class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms. (#265)StackingClassifier, StackingCVClassifier, StackingRegressor, StackingCVRegressor, and EnsembleVoteClassifier can now be tuned using scikit-learn's GridSearchCV (#254 via James Bourbeau)'support' column returned by frequent_patterns.association_rules was changed to compute the support of "antecedant union consequent", and new antecedant support' and 'consequent support' column were added to avoid ambiguity. (#245)OnehotTransactions to be cloned via scikit-learn's clone function, which is required by e.g., scikit-learn's FeatureUnion or GridSearchCV (via Iaroslav Shcherbatyi). (#249)self._init_time parameter in _IterativeModel subclasses. (#256)plot_ecdf when run on Python 2.7. (264)PrincipalComponentAnalysis are no being scaled so that the eigenvalues via solver='eigen' and solver='svd' now store eigenvalues that have the same magnitudes. (#251)