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@mayer79 mayer79 released this 06 Sep 06:31
0.2.0
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kernelshap 0.2.0

Breaking change

The interface of kernelshap() has been revised. Instead of specifying a prediction function, it suffices now to pass the fitted model object. The default pred_fun is now stats::predict, which works in most cases. Some other cases are catched via model class ("ranger" and mlr3 "Learner"). The pred_fun can be overwritten by a function of the form function(object, X, ...). Additional arguments to the prediction function are passed via ... of kernelshap().

Some examples:

  • Logistic regression (logit scale): kernelshap(fit, X, bg_X)
  • Logistic regression (probabilities): kernelshap(fit, X, bg_X, type = "response")
  • Linear regression with logarithmic response, but evaluated on original scale: Here, the default predict function needs to be overwritten: kernelshap(fit, X, bg_X, pred_fun = function(m, X) exp(predict(m, X)))

Major improvements

  • kernelshap() has received a more intuitive interface, see breaking change above.
  • The package now supports multidimensional predictions. Hurray!
  • Thanks to David Watson, parallel computing is now supported. The user needs to set up the parallel backend before calling kernelshap(), e.g., using the "doFuture" package, and then set parallel = TRUE. Especially on Windows, sometimes not all global variables or packages are loaded in the parallel instances. These can be specified by parallel_args, a list of arguments passed to foreach().
  • Even without parallel computing, kernelshap() has become much faster.
  • For $2 \le p \le 5$ features, the algorithm now returns exact Kernel SHAP values with respect to the given background data. (For $p = 1$, exact Shapley values are returned.)
  • Direct handling of "tidymodels" models.

User visible changes

  • Besides matrix, data.frames, and tibbles, the package now also accepts data.tables (if the prediction function can deal with them).
  • kernelshap() is less picky regarding the output structure of pred_fun().
  • kernelshap() is less picky about the column structure of the background data bg_X. It should simply contain the columns of X (but can have more or in different order). The old behaviour was to launch an error if colnames(X) != colnames(bg_X).
  • The default m = "auto" has been changed from trunc(20 * sqrt(p)) to max(trunc(20 * sqrt(p)), 5 * p. This will have an effect for cases where the number of features $p > 16$. The change will imply more robust results for large p.
  • There were too many "ks_*()" functions to extract elements of a "kernelshap" object. They are now all deprecated and replaced by ks_extract(, what = "S").
  • Added "MASS", "doRNG", and "foreach" to dependencies.

Bug fixes

  • Depending on $m$ and $p$, the matrix inversion required in the constrained least-squares solution could fail. It is now replaced by MASS::ginv(), the Moore-Penrose pseudoinverse using svd().

New contributor

  • David Watson
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