CRAN release 0.5.0

shapviz 0.5.0

Major improvement: SHAP interaction values

• Introduced API for SHAP interaction values S_inter (3D array):
  • Matrix method: shapviz(object, ..., S_inter = NULL)
  • XGBoost method: shapviz(object, ..., interactions = TRUE)
  • treeshap method: shapviz(object, ...)
• sv_interaction(x) shows matrix of beeswarm plots.
• sv_dependence(x, v = "x1", color_var = "x2", interactions = TRUE) plots SHAP interaction values.
• sv_dependence(x, v = "x1", interactions = TRUE) plots pure main effects of "x1".
• If SHAP interaction values are available, sv_dependence(..., color_var = "auto") uses those to determine the most interacting color variable.
• collapse_shap() also works for SHAP interaction arrays.
• SHAP interaction values can be extracted by get_shap_interactions().

User visible changes

• sv_importance(): In case of too many features, sv_importance() used to collapse the remaining features into an additional bar/beeswarm. This logic has been removed, and the show_other argument has been deprecated.
• By default, sv_dependence() automatically adds horizontal jitter for discrete v. This now also works if v is numeric with at most seven unique values, not only for logicals, factors, and character v.

Compatibility with "ggplot2"

• "ggplot2" 3.4 has replaced the "size" aesthetic in line-based geoms by "linewidth". This has been adapted. "shapviz" now depends on ggplot2 >= 3.4.

Technical changes

• sv_importance() does not use a flipped coordinate system anymore.

CRAN release 0.4.1

New functionality

• Hide "other": sv_importance() has received a new argument show_others = TRUE. Set to FALSE to hide the "other" bar/beeswarm.

CRAN release 0.4.0

shapviz 0.4.0

Removed dependencies

The following dependencies have been removed:

• "ggbeeswarm"
• "vipor"
• "beeswarm"

Changes in sv_importance()

• New argument bee_width: Relative width of the beeswarms. The default is 0.4. It replaces the width argument passed via ....
• New argument bee_adjust: Relative adjustment factor of the bandwidth used in estimating the density of the beeswarms. Default is 0.5.
• In case a beeswarm is shown: the ... arguments are now passed to geom_point().

Improvement with Plotly

• plotly::ggplotly() now works for most functionalities of sv_importance(), including beeswarms.

CRAN release 0.3.0

shapviz 0.3.0

Less picky interface

• The argument X of the constructor of shapviz() is now less picky. If it contains columns not present in the SHAP matrix, they are silently dropped. Furthermore, the column order of the SHAP matrix and X is now determined by the SHAP matrix.

Removed (according to depreciation cycle)

• Functions shapviz_from_lgb_predict() and shapviz_from_xgb_predict()
• format_fun argument in sv_force() and sv_waterfall()
• sort_fun argument in sv_waterfall()

Minor changes

• collapse_shap() is not anymore an S3 method. It is just a normal function that can be applied to a matrix.

CRAN release 0.2.2

shapviz 0.2.2

Bug fix

• For R versions < 4.1, sv_importance() would return an error.

Minor improvements

• kernelshap wrapper now also can deal with multioutput models.

CRAN release 0.2.1

shapviz 0.2.1

Major improvements

• Added kernelshap wrapper.

Minor changes

• Removed unnecessary conversion of X_pred from matrix to xgb.DMatrix in shapviz.xgb.Booster().
• Vignette: Added a CatBoost wrapper to the vignette and changed the treeshap() example to a ranger() model.

Maintainance

• Fixed CRAN notes on html5.

CRAN release 0.2.0

Major improvements

• Added H2O wrapper.
• Added shapr wrapper.
• Added an optional collapse argument in shapviz(). This is named list specifying which columns in the SHAP matrix are to be collapsed by rowwise summation. A typical application will be to combine the SHAP values of one-hot-encoded dummies and explain them by the corrsponding factor variable.
• Major rework of sv_importance(), see next section.

Major rework of sv_importance()

The calculations behind sv_importance() are unchanged, but defaults and some plot aspects have been reworked.

• Instead of a beeswarm plot, sv_importance() now shows a bar plot by default. Use kind = "beeswarm" to get a beeswarm plot.
• The bar plot of sv_importance() does not show SHAP feature importances as text anymore. Use show_numbers = TRUE to get them back. Furthermore, the numbers are now printed on top of the bars instead on their bottom.
• The new argument show_numbers can be used to to add SHAP feature importance values for all plot types.
• The default of max_display has been increased from 10 to 15.
• The bar width has been reduced from 0.9 to 2/3 relative width. It can be controlled by the new argument bar_width.
• The color bar title of the beeswarm plot can now be manually chosen by the new argument color_bar_title. Set to NULL to remove the color bar altogether.
• The argument format_fun now uses a right-aligned number formatter with aligned decimal separator by default.

Minor changes

• Added dim() method for "shapviz" object, implying nrow() and ncol().
• To allow more flexible formatting, the format_fun argument of sv_waterfall() and sv_force() has been replaced by format_shap to format SHAP values and format_feat to format numeric feature values. By default, they use the new global options "shapviz.format_shap" and "shapviz.format_feat", both with default function(z) prettyNum(z, digits = 3, scientific = FALSE).
• sv_waterfall() now uses the more consistent argument order_fun = function(s) order(abs(s)) instead of the original sort_fun = function(shap) abs(shap) that was then passed to order().
• Added argument viridis_args = getOption("shapviz.viridis_args") to sv_dependence() and sv_importance() to control the viridis color scale options. The default global option equals list(begin = 0.25, end = 0.85, option = "inferno"). For example, to switch to a standard viridis scale, you can either change the default with options(shapviz.viridis_args = NULL)
  or set viridis_args = NULL.
• Deprecated helper functions shapviz_from_lgb_predict() and shapviz_from_xgb_predict in favour of the collapsing logic (see above). The functions will be removed in version 0.3.0.
• Added 'lightgbm' as "Enhances" dependency.
• Added 'h2o' as "Enhances" dependency.
• Anticipated changes in predict() arguments of LightGBM (data -> newdata, predcontrib = TRUE -> type = "contrib").
• More unit tests.
• Improved documentation.
• Fixed github installation instruction in README and vignette.

CRAN release 0.1.0

This is the initial release.