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@mayer79 mayer79 released this 12 Jan 12:41
· 46 commits to main since this release
0.9.3
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Learn about vigilant mode.

sv_dependence(): Control over automatic color feature selection

How is the color feature selected, anyway?

If no SHAP interaction values are available, by default, the color feature v' is selected by the heuristic potential_interaction(), which works as follows:

  1. If the feature v (the on the x-axis) is numeric, it is binned into nbins bins.
  2. Per bin, the SHAP values of v are regressed onto v' and the R-squared is calculated. Rows with missing v' are discarded.
  3. The R-squared are averaged over bins, weighted by the number of non-missing v' values.

This measures how much variability in the SHAP values of v is explained by v', after accounting for v.

We have introduced four parameters to control the heuristic. Their defaults are in line with the old behaviour.

  • nbin = NULL: Into how many quantile bins should a numeric v be binned? The default NULL equals the smaller of $n/20$ and $\sqrt n$ (rounded up), where $n$ is the sample size.
  • color_num Should color features be converted to numeric, even if they are factors/characters? Default is TRUE.
  • scale = FALSE: Should R-squared be multiplied with the sample variance of
    within-bin SHAP values? If TRUE, bins with stronger vertical scatter will get higher weight. The default is FALSE.
  • adjusted = FALSE: Should adjusted R-squared be calculated?

If SHAP interaction values are available, these parameters have no effect. In sv_dependence() they are called ih_nbin etc.

This partly implements the ideas in #119 of Roel Verbelen, thanks a lot for your patient explanations!

Further plans?

We will continue to experiment with the defaults, which might change in the future. A good alternative to the current (naive) defaults could be:

  • nbins = 7: Smaller than now to not overfit too strongly with factor/character color features.
  • color_num = FALSE: To not naively integer encode factors/characters.
  • scale = TRUE: To account for non-equal spread in bins.
  • adjusted = TRUE: To not put too much weight on factors with many categories.

Other user-visible changes

  • sv_dependence(): If color_var = "auto" (default) and no color feature seems to be relevant (SHAP interaction is NULL, or heuristic returns no positive value), there won't be any color scale. Furthermore, in some edge cases, a different
    color feature might be selected.
  • mshapviz() objects can now be rowbinded via rbind() or +. Implemented by @jmaspons in #110.
  • mshapviz() is more strict when combining multiple "shapviz" objects. These now need to have identical column names, see #114.

Small changes

  • The README is shorter and easier.
  • Updated vignettes.
  • print.shapviz() now shows top two rows of SHAP matrix.
  • Re-activate all unit tests.
  • Setting nthread = 1 in all calls to xgb.DMatrix() as suggested by @jmaspons in #109.
  • Added "How to contribute" to README.
  • permshap() connector is now part of {kerneshap} #122.

Bug fixes

  • sv_dependence2D(): In case add_vars are passed, x and/or y are removed from it in order to not use any variable twice. #116.
  • split.shapviz() now drops empty levels. They launched an error because empty "shapviz" objects are currently not supported. #117, #118
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