CHANGELOG.md

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