Merge pull request #41 from mayer79/lost_news

added changelog of last release 0.2.0 that was lost

Author: mayer79

NEWS.md

@@ -53,6 +53,40 @@ Kernel SHAP in the Python implementation "shap" uses a quite similar hybrid stra
 - Predictions of `mgcv::gam()` would cause an error in `check_pred()` (they are 1D-arrays).
 - Fixed small mistakes in the examples of the README (mlr3 and mgcv).
 
+# 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.frame`s, and `tibble`s, the package now also accepts `data.table`s (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