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Version 0.17.0
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@rasbt rasbt released this 19 Jul 18:16
· 628 commits to master since this release
v0.17.0
8e22fe5
New Features
  • Added an enhancement to the existing 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)
  • Added optional groups parameter to SequentialFeatureSelector and ExhaustiveFeatureSelector fit() methods for forwarding to sklearn CV (#537 via arc12)
  • Added a new plot_pca_correlation_graph function to the mlxtend.plotting submodule for plotting a PCA correlation graph. (#544 via Gabriel-Azevedo-Ferreira)
  • Added a 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)
  • Added a function 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)
  • New heatmap function in mlxtend.plotting. (#552)
  • Added a function fpmax that implements the FP-Max algorithm for mining maximal itemsets as a drop-in replacement for the fpgrowth algorithm. (#553 via Steve Harenberg)
  • New figsize parameter for the plot_decision_regions function in mlxtend.plotting. (#555 via Mirza Hasanbasic)
  • New 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)
Changes
  • Now uses the latest joblib library under the hood for multiprocessing instead of sklearn.externals.joblib. (#547)
  • Changes to StackingCVClassifier and StackingCVRegressor such that first-level models are allowed to generate output of non-numeric type. (#562)
Bug Fixes
  • Fixed documentation of iris_data() under iris.py by adding a note about differences in the iris data in R and UCI machine learning repo.
  • Make sure that if the '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)
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