explain: Fast approximate Shapley values
In fastshap: Fast Approximate Shapley Values

View source: R/explain.R

explain

R Documentation

Fast approximate Shapley values

Description

Compute fast (approximate) Shapley values for a set of features using the Monte Carlo algorithm described in Strumbelj and Igor (2014). An efficient algorithm for tree-based models, commonly referred to as Tree SHAP, is also supported for lightgbm and xgboost models; see Lundberg et. al. (2020) for details.

Usage

explain(object, ...)

## Default S3 method:
explain(
  object,
  feature_names = NULL,
  X = NULL,
  nsim = 1,
  pred_wrapper = NULL,
  newdata = NULL,
  adjust = FALSE,
  baseline = NULL,
  shap_only = TRUE,
  parallel = FALSE,
  ...
)

## S3 method for class 'lm'
explain(
  object,
  feature_names = NULL,
  X,
  nsim = 1,
  pred_wrapper,
  newdata = NULL,
  adjust = FALSE,
  exact = FALSE,
  baseline = NULL,
  shap_only = TRUE,
  parallel = FALSE,
  ...
)

## S3 method for class 'xgb.Booster'
explain(
  object,
  feature_names = NULL,
  X = NULL,
  nsim = 1,
  pred_wrapper,
  newdata = NULL,
  adjust = FALSE,
  exact = FALSE,
  baseline = NULL,
  shap_only = TRUE,
  parallel = FALSE,
  ...
)

## S3 method for class 'lgb.Booster'
explain(
  object,
  feature_names = NULL,
  X = NULL,
  nsim = 1,
  pred_wrapper,
  newdata = NULL,
  adjust = FALSE,
  exact = FALSE,
  baseline = NULL,
  shap_only = TRUE,
  parallel = FALSE,
  ...
)

Arguments

`object`	A fitted model object (e.g., a `ranger::ranger()`, `xgboost::xgboost()`, or `earth::earth()` object, to name a few).
`...`	Additional optional arguments to be passed on to `foreach::foreach()` whenever `parallel = TRUE`. For example, you may need to supply additional packages that the parallel task depends on via the `.packages` argument to `foreach::foreach()`. NOTE: `foreach::foreach()`'s `.combine` argument is already set internally by `explain()`, so passing it via the `...` argument would likely result in an error.
`feature_names`	Character string giving the names of the predictor variables (i.e., features) of interest. If `NULL` (default) they will be taken from the column names of `X`.
`X`	A matrix-like R object (e.g., a data frame or matrix) containing ONLY the feature columns from the training data (or suitable background data set). NOTE: This argument is required whenever `exact = FALSE`.
`nsim`	The number of Monte Carlo repetitions to use for estimating each Shapley value (only used when `exact = FALSE`). Default is 1. NOTE: To obtain the most accurate results, `nsim` should be set as large as feasibly possible.
`pred_wrapper`	Prediction function that requires two arguments, `object` and `newdata`. NOTE: This argument is required whenever `exact = FALSE`. The output of this function should be determined according to: Regression A numeric vector of predicted outcomes. Binary classification A vector of predicted class probabilities for the reference class. Multiclass classification A vector of predicted class probabilities for the reference class.
`newdata`	A matrix-like R object (e.g., a data frame or matrix) containing ONLY the feature columns for the observation(s) of interest; that is, the observation(s) you want to compute explanations for. Default is `NULL` which will produce approximate Shapley values for all the rows in `X` (i.e., the training data).
`adjust`	Logical indicating whether or not to adjust the sum of the estimated Shapley values to satisfy the local accuracy property; that is, to equal the difference between the model's prediction for that sample and the average prediction over all the training data (i.e., `X`). Default is `FALSE` and setting to `TRUE` requires `nsim` > 1.
`baseline`	Numeric baseline to use when adjusting the computed Shapley values to achieve local accuracy. Adjusted Shapley values for a single prediction (`fx`) will sum to the difference `fx - baseline`. Defaults to `NULL`, which corresponds to the average predictions computed from `X`, and zero otherwise (i.e., no additional predictions will be computed and the baseline attribute of the output will be set to zero).
`shap_only`	Logical indicating whether or not to include additional output useful for plotting (i.e., `newdata` and the `baseline` value.). This is convenient, for example, when using `shapviz::shapviz()` for plotting. Default is `TRUE`.
`parallel`	Logical indicating whether or not to compute the approximate Shapley values in parallel across features; default is `FALSE`. NOTE: setting `parallel = TRUE` requires setting up an appropriate (i.e., system-specific) parallel backend as described in the foreach; for details, see `vignette("foreach", package = "foreach")` in R.
`exact`	Logical indicating whether to compute exact Shapley values. Currently only available for `stats::lm()`, `xgboost::xgboost()`, and `lightgbm::lightgbm()` objects. Default is `FALSE`. Note that setting `exact = TRUE` will return explanations for each of the `stats::terms()` in an `stats::lm()` object. Default is `FALSE`.

Value

If shap_only = TRUE (the default), a matrix is returned with one column for each feature specified in feature_names (if feature_names = NULL, the default, there will be one column for each feature in X) and one row for each observation in newdata (if newdata = NULL, the default, there will be one row for each observation in X). Additionally, the returned matrix will have an attribute called "baseline" containing the baseline value. If shap_only = FALSE, then a list is returned with three components:

shapley_values - a matrix of Shapley values (as described above);
feature_values - the corresponding feature values (for plotting with shapviz::shapviz());
baseline - the corresponding baseline value (for plotting with shapviz::shapviz()).

Note

Setting exact = TRUE with a linear model (i.e., an stats::lm() or stats::glm() object) assumes that the input features are independent. Also, setting adjust = TRUE is experimental and we follow the same approach as in shap.

References

Strumbelj, E., and Igor K. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems, 41(3), 647-665.

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, Su-In (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 2522–5839.

Examples

#
# A projection pursuit regression (PPR) example
#

# Load the sample data; see ?datasets::mtcars for details
data(mtcars)

# Fit a projection pursuit regression model
fit <- ppr(mpg ~ ., data = mtcars, nterms = 5)

# Prediction wrapper
pfun <- function(object, newdata) {  # needs to return a numeric vector
  predict(object, newdata = newdata)  
}

# Compute approximate Shapley values using 10 Monte Carlo simulations
set.seed(101)  # for reproducibility
shap <- explain(fit, X = subset(mtcars, select = -mpg), nsim = 10, 
                pred_wrapper = pfun)
head(shap)

fastshap documentation built on June 7, 2023, 5:48 p.m.