Permutation SHAP

Source: R/permshap.R

Exact permutation SHAP algorithm with respect to a background dataset, see Strumbelj and Kononenko. The function works for up to 14 features.

permshap(object, ...)

# S3 method for default
permshap(
  object,
  X,
  bg_X,
  pred_fun = stats::predict,
  feature_names = colnames(X),
  bg_w = NULL,
  parallel = FALSE,
  parallel_args = NULL,
  verbose = TRUE,
  ...
)

# S3 method for ranger
permshap(
  object,
  X,
  bg_X,
  pred_fun = function(m, X, ...) stats::predict(m, X, ...)$predictions,
  feature_names = colnames(X),
  bg_w = NULL,
  parallel = FALSE,
  parallel_args = NULL,
  verbose = TRUE,
  ...
)

Arguments

object

Fitted model object.

...

Additional arguments passed to pred_fun(object, X, ...).

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).

bg_X

Background data used to integrate out "switched off" features, often a subset of the training data (typically 50 to 500 rows) It should contain the same columns as X. 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.

pred_fun

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.

feature_names

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.

bg_w

Optional vector of case weights for each row of bg_X.

parallel

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.

parallel_args

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").

verbose

Set to FALSE to suppress messages and the progress bar.

Value

An object of class "permshap" 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.

  • m_exact: Integer providing the effective number of exact on-off vectors used.

  • exact: Logical flag indicating whether calculations are exact or not (currently TRUE).

  • txt: Summary text.

  • predictions: \((n \times K)\) matrix with predictions of X.

Methods (by class)

  • permshap(default): Default permutation SHAP method.

  • permshap(ranger): Permutation SHAP method for "ranger" models, see Readme for an example.

References

  1. Erik Strumbelj and Igor Kononenko. Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems 41, 2014.

Examples

# MODEL ONE: Linear regression
fit <- lm(Sepal.Length ~ ., data = iris)

# Select rows to explain (only feature columns)
X_explain <- iris[1:2, -1]

# Select small background dataset (could use all rows here because iris is small)
set.seed(1)
bg_X <- iris[sample(nrow(iris), 100), ]

# Calculate SHAP values
s <- permshap(fit, X_explain, bg_X = bg_X)
#> Exact permutation SHAP
#> 
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s
#> SHAP values of first observations:
#>      Sepal.Width Petal.Length Petal.Width   Species
#> [1,]  0.21571169    -1.981893   0.3157855 0.5825284
#> [2,] -0.03223278    -1.981893   0.3157855 0.5825284

# MODEL TWO: Multi-response linear regression
fit <- lm(as.matrix(iris[, 1:2]) ~ Petal.Length + Petal.Width + Species, data = iris)
s <- permshap(fit, iris[1:4, 3:5], bg_X = bg_X)
#> Exact permutation SHAP
#> 
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s
#> SHAP values of first observations:
#> $Sepal.Length
#>      Petal.Length Petal.Width  Species
#> [1,]    -2.165211 0.006007392 1.234918
#> [2,]    -2.165211 0.006007392 1.234918
#> 
#> $Sepal.Width
#>      Petal.Length Petal.Width  Species
#> [1,]   -0.3696749  -0.6246925 1.315597
#> [2,]   -0.3696749  -0.6246925 1.315597
#> 

# Non-feature columns can be dropped via 'feature_names'
s <- permshap(
  fit,
  iris[1:4, ],
  bg_X = bg_X,
  feature_names = c("Petal.Length", "Petal.Width", "Species")
)
#> Exact permutation SHAP
#> 
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s
#> SHAP values of first observations:
#> $Sepal.Length
#>      Petal.Length Petal.Width  Species
#> [1,]    -2.165211 0.006007392 1.234918
#> [2,]    -2.165211 0.006007392 1.234918
#> 
#> $Sepal.Width
#>      Petal.Length Petal.Width  Species
#> [1,]   -0.3696749  -0.6246925 1.315597
#> [2,]   -0.3696749  -0.6246925 1.315597
#>