Exact permutation SHAP algorithm with respect to a background dataset,
see Strumbelj and Kononenko. The function works for up to 14 features.
For more than eight features, we recommend kernelshap() due to its higher speed.
permshap(object, ...)
# Default S3 method
permshap(
object,
X,
bg_X = NULL,
pred_fun = stats::predict,
feature_names = colnames(X),
bg_w = NULL,
bg_n = 200L,
parallel = FALSE,
parallel_args = NULL,
verbose = TRUE,
...
)
# S3 method for class 'ranger'
permshap(
object,
X,
bg_X = NULL,
pred_fun = NULL,
feature_names = colnames(X),
bg_w = NULL,
bg_n = 200L,
parallel = FALSE,
parallel_args = NULL,
verbose = TRUE,
survival = c("chf", "prob"),
...
)Fitted model object.
Additional arguments passed to pred_fun(object, X, ...).
\((n \times p)\) matrix or data.frame with rows to be explained.
The columns should only represent model features, not the response
(but see feature_names on how to overrule this).
Background data used to integrate out "switched off" features,
often a subset of the training data (typically 50 to 500 rows).
In cases with a natural "off" value (like MNIST digits),
this can also be a single row with all values set to the off value.
If no bg_X is passed (the default) and if X is sufficiently large,
a random sample of bg_n rows from X serves as background data.
Prediction function of the form function(object, X, ...),
providing \(K \ge 1\) predictions per row. Its first argument
represents the model object, its second argument a data structure like X.
Additional (named) arguments are passed via ....
The default, stats::predict(), will work in most cases.
Optional vector of column names in X used to calculate
SHAP values. By default, this equals colnames(X). Not supported if X
is a matrix.
Optional vector of case weights for each row of bg_X.
If bg_X = NULL, must be of same length as X. Set to NULL for no weights.
If bg_X = NULL: Size of background data to be sampled from X.
If TRUE, use parallel foreach::foreach() to loop over rows
to be explained. Must register backend beforehand, e.g., via 'doFuture' package,
see README for an example. Parallelization automatically disables the progress bar.
Named list of arguments passed to foreach::foreach().
Ideally, this is NULL (default). Only relevant if parallel = TRUE.
Example on Windows: if object is a GAM fitted with package 'mgcv',
then one might need to set parallel_args = list(.packages = "mgcv").
Set to FALSE to suppress messages and the progress bar.
Should cumulative hazards ("chf", default) or survival
probabilities ("prob") per time be predicted? Only in ranger() survival models.
An object of class "kernelshap" with the following components:
S: \((n \times p)\) matrix with SHAP values or, if the model output has
dimension \(K > 1\), a list of \(K\) such matrices.
X: Same as input argument X.
baseline: Vector of length K representing the average prediction on the
background data.
bg_X: The background data.
bg_w: The background case weights.
m_exact: Integer providing the effective number of exact on-off vectors used.
exact: Logical flag indicating whether calculations are exact or not
(currently always TRUE).
txt: Summary text.
predictions: \((n \times K)\) matrix with predictions of X.
algorithm: "permshap".
permshap(default): Default permutation SHAP method.
permshap(ranger): Permutation SHAP method for "ranger" models, see Readme for an example.
Erik Strumbelj and Igor Kononenko. Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems 41, 2014.
# MODEL ONE: Linear regression
fit <- lm(Sepal.Length ~ ., data = iris)
# Select rows to explain (only feature columns)
X_explain <- iris[-1]
# Calculate SHAP values
s <- permshap(fit, X_explain)
#> Exact permutation SHAP
#>
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|==== | 6%
|
|===== | 7%
|
|====== | 8%
|
|====== | 9%
|
|======= | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 11%
|
|======== | 12%
|
|========= | 13%
|
|========== | 14%
|
|========== | 15%
|
|=========== | 15%
|
|=========== | 16%
|
|============ | 17%
|
|============= | 18%
|
|============= | 19%
|
|============== | 19%
|
|============== | 20%
|
|============== | 21%
|
|=============== | 21%
|
|=============== | 22%
|
|================ | 23%
|
|================= | 24%
|
|================= | 25%
|
|================== | 25%
|
|================== | 26%
|
|=================== | 27%
|
|==================== | 28%
|
|==================== | 29%
|
|===================== | 29%
|
|===================== | 30%
|
|===================== | 31%
|
|====================== | 31%
|
|====================== | 32%
|
|======================= | 33%
|
|======================== | 34%
|
|======================== | 35%
|
|========================= | 35%
|
|========================= | 36%
|
|========================== | 37%
|
|=========================== | 38%
|
|=========================== | 39%
|
|============================ | 39%
|
|============================ | 40%
|
|============================ | 41%
|
|============================= | 41%
|
|============================= | 42%
|
|============================== | 43%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 45%
|
|================================ | 46%
|
|================================= | 47%
|
|================================== | 48%
|
|================================== | 49%
|
|=================================== | 49%
|
|=================================== | 50%
|
|=================================== | 51%
|
|==================================== | 51%
|
|==================================== | 52%
|
|===================================== | 53%
|
|====================================== | 54%
|
|====================================== | 55%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 57%
|
|========================================= | 58%
|
|========================================= | 59%
|
|========================================== | 59%
|
|========================================== | 60%
|
|========================================== | 61%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================= | 64%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 67%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 69%
|
|================================================= | 70%
|
|================================================= | 71%
|
|================================================== | 71%
|
|================================================== | 72%
|
|=================================================== | 73%
|
|==================================================== | 74%
|
|==================================================== | 75%
|
|===================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 77%
|
|======================================================= | 78%
|
|======================================================= | 79%
|
|======================================================== | 79%
|
|======================================================== | 80%
|
|======================================================== | 81%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 83%
|
|=========================================================== | 84%
|
|=========================================================== | 85%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|============================================================= | 87%
|
|============================================================== | 88%
|
|============================================================== | 89%
|
|=============================================================== | 89%
|
|=============================================================== | 90%
|
|=============================================================== | 91%
|
|================================================================ | 91%
|
|================================================================ | 92%
|
|================================================================= | 93%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 99%
|
|======================================================================| 100%
s
#> SHAP values of first observations:
#> Sepal.Width Petal.Length Petal.Width Species
#> [1,] 0.21951350 -1.955357 0.3149451 0.5823533
#> [2,] -0.02843097 -1.955357 0.3149451 0.5823533
# MODEL TWO: Multi-response linear regression
fit <- lm(as.matrix(iris[, 1:2]) ~ Petal.Length + Petal.Width + Species, data = iris)
s <- permshap(fit, iris[3:5])
#> Exact permutation SHAP
#>
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|==== | 6%
|
|===== | 7%
|
|====== | 8%
|
|====== | 9%
|
|======= | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 11%
|
|======== | 12%
|
|========= | 13%
|
|========== | 14%
|
|========== | 15%
|
|=========== | 15%
|
|=========== | 16%
|
|============ | 17%
|
|============= | 18%
|
|============= | 19%
|
|============== | 19%
|
|============== | 20%
|
|============== | 21%
|
|=============== | 21%
|
|=============== | 22%
|
|================ | 23%
|
|================= | 24%
|
|================= | 25%
|
|================== | 25%
|
|================== | 26%
|
|=================== | 27%
|
|==================== | 28%
|
|==================== | 29%
|
|===================== | 29%
|
|===================== | 30%
|
|===================== | 31%
|
|====================== | 31%
|
|====================== | 32%
|
|======================= | 33%
|
|======================== | 34%
|
|======================== | 35%
|
|========================= | 35%
|
|========================= | 36%
|
|========================== | 37%
|
|=========================== | 38%
|
|=========================== | 39%
|
|============================ | 39%
|
|============================ | 40%
|
|============================ | 41%
|
|============================= | 41%
|
|============================= | 42%
|
|============================== | 43%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 45%
|
|================================ | 46%
|
|================================= | 47%
|
|================================== | 48%
|
|================================== | 49%
|
|=================================== | 49%
|
|=================================== | 50%
|
|=================================== | 51%
|
|==================================== | 51%
|
|==================================== | 52%
|
|===================================== | 53%
|
|====================================== | 54%
|
|====================================== | 55%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 57%
|
|========================================= | 58%
|
|========================================= | 59%
|
|========================================== | 59%
|
|========================================== | 60%
|
|========================================== | 61%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================= | 64%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 67%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 69%
|
|================================================= | 70%
|
|================================================= | 71%
|
|================================================== | 71%
|
|================================================== | 72%
|
|=================================================== | 73%
|
|==================================================== | 74%
|
|==================================================== | 75%
|
|===================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 77%
|
|======================================================= | 78%
|
|======================================================= | 79%
|
|======================================================== | 79%
|
|======================================================== | 80%
|
|======================================================== | 81%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 83%
|
|=========================================================== | 84%
|
|=========================================================== | 85%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|============================================================= | 87%
|
|============================================================== | 88%
|
|============================================================== | 89%
|
|=============================================================== | 89%
|
|=============================================================== | 90%
|
|=============================================================== | 91%
|
|================================================================ | 91%
|
|================================================================ | 92%
|
|================================================================= | 93%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 99%
|
|======================================================================| 100%
s
#> SHAP values of first observations:
#> $Sepal.Length
#> Petal.Length Petal.Width Species
#> [1,] -2.13622 0.005991405 1.237003
#> [2,] -2.13622 0.005991405 1.237003
#>
#> $Sepal.Width
#> Petal.Length Petal.Width Species
#> [1,] -0.3647252 -0.62303 1.320153
#> [2,] -0.3647252 -0.62303 1.320153
#>
# Note 1: Feature columns can also be selected 'feature_names'
# Note 2: Especially when X is small, pass a sufficiently large background data bg_X
s <- permshap(
fit,
iris[1:4, ],
bg_X = iris,
feature_names = c("Petal.Length", "Petal.Width", "Species")
)
#> Exact permutation SHAP
#>
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
s
#> SHAP values of first observations:
#> $Sepal.Length
#> Petal.Length Petal.Width Species
#> [1,] -2.13622 0.005991405 1.237003
#> [2,] -2.13622 0.005991405 1.237003
#>
#> $Sepal.Width
#> Petal.Length Petal.Width Species
#> [1,] -0.3647252 -0.62303 1.320153
#> [2,] -0.3647252 -0.62303 1.320153
#>