Package 'fastshap'

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.

See Also

You can find more examples (with larger and more realistic data sets) on the fastshap GitHub repository: https://github.com/bgreenwell/fastshap.

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)

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Friedman benchmark data

Description

Simulate data from the Friedman 1 benchmark problem. These data were originally described in Friedman (1991) and Breiman (1996). For details, see sklearn.datasets.make_friedman1.

Usage

gen_friedman(
  n_samples = 100,
  n_features = 10,
  n_bins = NULL,
  sigma = 0.1,
  seed = NULL
)

Arguments

n_samples	Integer specifying the number of samples (i.e., rows) to generate. Default is 100.
n_features	Integer specifying the number of features to generate. Default is 10.
n_bins	Integer specifying the number of (roughly) equal sized bins to split the response into. Default is NULL for no binning. Setting to a positive integer > 1 effectively turns this into a classification problem where n_bins gives the number of classes.
sigma	Numeric specifying the standard deviation of the noise.
seed	Integer specifying the random seed. If NULL (the default) the results will be different each time the function is run.

Note

This function is mostly used for internal testing.

References

Breiman, Leo (1996) Bagging predictors. Machine Learning 24, pages 123-140.

Friedman, Jerome H. (1991) Multivariate adaptive regression splines. The Annals of Statistics 19 (1), pages 1-67.

Examples

gen_friedman()

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Survival of Titanic passengers

Description

A data set containing the survival outcome, passenger class, age, sex, and the number of family members for a large number of passengers aboard the ill-fated Titanic.

Usage

titanic

Format

A data frame with 1309 observations on the following 6 variables:

survived

binary with levels "yes" for survived and "no" otherwise;

pclass

integer giving the corresponding passenger (i.e., ticket) class with values 1–3;

age

the age in years of the corresponding passenger (with 263 missing values);

sex

factor giving the sex of each passenger with levels "male" and "female";

sibsp

integer giving the number of siblings/spouses aboard for each passenger (ranges from 0–8);

parch

integer giving the number of parents/children aboard for each passenger (ranges from 0–9).

Note

As mentioned in the column description, age contains 263 NAs (or missing values). For a complete version (or versions) of the data set, see titanic_mice.

Source

https://hbiostat.org/data/.

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Survival of Titanic passengers

Description

The titanic data set contains 263 missing values (i.e., NA's) in the age column. This version of the data contains imputed values for the age column using multivariate imputation by chained equations via the mice package. Consequently, this is a list containing 11 imputed versions of the observations containd in the titanic data frame; each completed data sets has the same dimension and column structure as titanic.

Usage

titanic_mice

Format

An object of class mild (inherits from list) of length 21.

Source

Greenwell, Brandon M. (2022). Tree-Based Methods for Statistical Learning in R. CRC Press.

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