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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

[0.3.0]

Changed

  • Refactor. Please refer to the Design Overview session in docs for more details.
  • Support both matplotlib and plotly.
  • Update tutorials according to the refactor codes.
  • Better unit test.
  • Semi-automate dosctring generation.

[0.2.0]

Added

  • Formal documentation hosted on readthedocs.org
  • Keep trace of historical documentations
  • Unit tests
  • info_plots.target_plot_interact: visualise average target value across interaction between two features
  • info_plots.actual_plot_interact: visualise prediction distribution across interaction between two features
  • get_dataset: store models and datasets for three different problems (binary classification, multi-class classification, regression)
  • Tutorials in jupyter notebook format

Changed

  • Move all information related plots under info_plots, including
    • info_plots.target_plot
    • info_plots.target_plot_interact
    • info_plots.actual_plot
    • info_plots.actual_plot_interact
  • Move all utility functions under xx_utils.py
    • utils.py: general utility functions
    • info_plot_utils.py: utility functions for information plots
    • pdp_calc_utils.py: utility functions for pdp related calculation
    • pdp_plot_utils.py: utility functions for pdp related plots
  • class PDPIsolate
    • Rename class pdp_isolate_obj as class PDPIsolate
    • Remove self.classifier, self.model_features, self.actual_columns: useless
    • Add self.which_class, self.percentile_info, self.count_data, self.hist_data: store class information for multi-class problem, store percentile information for grid points, store value count information as well as feature values for numeric feature
  • class PDPInteract
    • Rename class pdp_interact_obj as class PDPInteract
    • Remove self.classifier, self.model_features: useless
    • Add self.which_class: store class information for multi-class problem
    • Combine self.pdp_isolate_out1 and self.pdp_isolate_out2 into self.pdp_isolate_outs
  • pdp.pdp_isolate
    • Replace train_X as dataset to store whole dataset instead of only the subset for model training, thus add model_features to indicate features used for model training
    • Add grid_type, grid_range: define type and range for grid points
    • Add memory_limit, n_jobs: limit memory usage, support parallel processing
    • Set predict_kwds default value into None instead of {}
    • Add data_transformer: support dataset transformation
  • pdp.pdp_plot
    • Add plot_pts_dist: enable to plot distribution of data points
    • Remove plot_org_pts: no longer support plotting original data points
    • Set cluster_method default value as 'accurate' instead of None
    • Add show_percentile: show percentile information of grid points
    • Set ncols default value as 2 instead of None
    • Add which_classes, remove multi_flag, which_class: plot for a single class is now supported by which_classes
  • pdp.pdp_interact
    • Replace train_X as dataset to store whole dataset instead of only the subset for model training, thus add model_features to indicate features used for model training
    • Set num_grid_points default value as None instead of [10, 10]
    • Add grid_type, grid_range: define type and range for grid points
    • Set percentile_ranges default value as None instead of [None, None]
    • Set cust_grid_points default value as None instead of [None, None]
    • Set predict_kwds default value into None instead of {}
  • pdp.pdp_interact_plot
    • Add plot_type, plot_pdp, remove only_inter: define plot type and whether to plot pdp for both features, only showing contour plot now is supported by plot_type and plot_pdp
    • Add which_classes, remove multi_flag, which_class: plot for a single class is now supported by which_classes
    • Set ncols default value as 2 instead of None
    • Remove center, plot_org_pts, plot_lines, frac_to_plot, cluster, n_cluster_centers, cluster_method: no longer support plotting separate pdp plots
  • info_plots.target_plot
    • Add grid_type, grid_range: define type and range for grid points
    • Add show_percentile: show percentile information of grid points
    • Add show_outliers: whether to show data points outside the grid range
    • Add endpoint: whether stop is the last grid point
    • Add ncols: define number of columns for multiple plots
  • info_plots.actual_plot
    • Add model, X, feature, remove pdp_isolate_out: no longer depend on pdp.pdp_isolate, thus need to define all necessary parameters for calculating the results
    • Add num_grid_points, grid_type, percentile_range, grid_range, cust_grid_points, show_percentile, show_outliers, endpoint, which_classes, predict_kwds
    • Set ncols default value as 2 instead of None
    • Add which_classes, remove multi_flag, which_class: plot for a single class is now supported by which_classes
    • Set predict_kwds default value into None instead of {}

Fixed

  • Python3 compatibility
  • All plotting related functions would return a matplotlib.figure.Figure object as well as Matplotlib.axes for further modification