- Removed dependency “dorng”. This might have an impact on the seeding if in parallel mode.
- Removed dependency “MASS”

- Rewritten README and examples to better show the role of the background data.

- When
`bg_X`

contained more columns than`X`

, unflexible prediction functions could fail when being applied to`bg_X`

.

- New argument
`feature_names`

allows to specify the features to calculate SHAP values for. The default equals to`colnames(X)`

. This should be changed only in situations when`X`

(the dataset to be explained) contains non-feature columns. - The background dataset can now consist of a single row only. This is useful in situations with natural “off” value such as for image data or for models that can naturally deal with missing values.

Thanks to David Watson, exact calculations are now also possible for \(p>5\) features. By default, the algorithm uses exact calculations for \(p \le 8\) and a hybrid strategy otherwise, see the next section. At the same time, the exact algorithm became much more efficient.

A word of caution: Exact calculations mean to create \(2^p-2\) on-off vectors \(z\) (cheap step) and evaluating the model on a whopping \((2^p-2)N\) rows, where \(N\) is the number of rows of the background data (expensive step). As this explodes with large \(p\), we do not recommend the exact strategy for \(p > 10\).

The iterative Kernel SHAP sampling algorithm of Covert and Lee (2021) [1] works by randomly sample \(m\) on-off vectors \(z\) so that their sum follows the SHAP Kernel weight distribution (renormalized to the range from \(1\) to \(p-1\)). Based on these vectors, many predictions are formed. Then, Kernel SHAP values are derived as the solution of a constrained linear regression, see [1] for details. This is done multiple times until convergence.

A drawback of this strategy is that many (at least 75%) of the \(z\) vectors will have \(\sum z \in \{1, p-1\}\), producing many duplicates. Similarly, at least 92% of the mass will be used for the \(p(p+1)\) possible vectors with \(\sum z \in \{1, 2, p-1, p-2\}\) etc. This inefficiency can be fixed by a hybrid strategy, combining exact calculations with sampling. The hybrid algorithm has two steps:

- Step 1 (exact part): There are \(2p\) different on-off vectors \(z\) with \(\sum z \in \{1, p-1\}\), covering a large proportion of the Kernel SHAP distribution. The degree 1 hybrid will list those vectors and use them according to their weights in the upcoming calculations. Depending on \(p\), we can also go a step further to a degree 2 hybrid by adding all \(p(p-1)\) vectors with \(\sum z \in \{2, p-2\}\) to the process etc. The necessary predictions are obtained along with other calculations similar to those in [1].
- Step 2 (sampling part): The remaining weight is filled by sampling vectors \(z\) according to Kernel SHAP weights renormalized to the values not yet covered by Step 1. Together with the results from Step 1 - correctly weighted - this now forms a complete iteration as in Covert and Lee (2021). The difference is that most mass is covered by exact calculations. Afterwards, the algorithm iterates until convergence. The output of Step 1 is reused in every iteration, leading to an extremely efficient strategy.

The default behaviour of `kernelshap()`

is as follows:

- \(p \le 8\): Exact Kernel SHAP (with respect to the background data)
- \(9 \le p \le 16\): Degree 2 hybrid
- \(p > 16\): Degree 1 hybrid
- \(p = 1\): Exact Shapley values

It is also possible to use a pure sampling strategy, see Section
“User visible changes” below. While this is usually not advisable
compared to a hybrid approach, the options of `kernelshap()`

allow to study different properties of Kernel SHAP and doing empirical
research on the topic.

Kernel SHAP in the Python implementation “shap” uses a quite similar hybrid strategy, but without iterating. The new logic in the R package thus combines the efficiency of the Python implementation with the convergence monitoring of [1].

[1] Ian Covert and Su-In Lee. Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:3457-3465, 2021.

- The default value of
`m`

is reduced from \(8p\) to \(2p\) except when`hybrid_degree = 0`

(pure sampling). - The default value of
`exact`

is now`TRUE`

for \(p \le 8\) instead of \(p \le 5\). - A new argument
`hybrid_degree`

is introduced to control the exact part of the hybrid algorithm. The default is 2 for \(4 \le p \le 16\) and degree 1 otherwise. Set to 0 to force a pure sampling strategy (not recommended but useful to demonstrate superiority of hybrid approaches). - The default value of
`tol`

was reduced from 0.01 to 0.005. - The default of
`max_iter`

was reduced from 250 to 100. - The order of some of the arguments behind the first four has been changed.
- Paired sampling no longer duplicates
`m`

. - Thanks to Mathias Ambuehl, the random sampling of z vectors is now fully vectorized.
- The output of
`print()`

is now more slim. - A new
`summary()`

function shows more infos.

- The resulting object now contains
`m_exact`

(the number of on-off vectors used for the exact part),`prop_exact`

(proportion of mass treated in exact fashion),`exact`

flag, and`txt`

(the info message when starting the algorithm).

- 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).

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)))`

`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.

- 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.

- 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()`

.

- David Watson

This is the initial release.