See the pkgdown site at norskregnesentral.github.io/shapr/ for a complete introduction with examples and documentation of the package.
With shapr version 1.0.0 (GitHub only, Nov 2024) and version 1.0.1
(CRAN, Jan 2025), the package was subject to a major update, providing a
full restructuring of the code based, and a full suit of new
functionality, including:
- A long list of approaches for estimating the contribution/value
function
$v(S)$ , including Variational Autoencoders, and regression-based methods - Iterative Shapley value estimation with convergence detection
- Parallelized computations with progress updates
- Reweighted Kernel SHAP for faster convergence
- New function
explain_forecast()for explaining forecasts - Several other methodological, computational and user-experience improvements
- Python wrapper making the core functionality of
shapravailable in Python
See the NEWS for a complete list.
shapr version >= 1.0.0 comes with a number of breaking changes. Most
notably, we moved from using two function (shapr() and explain()) to
a single function (explain()). In addition, custom models are now
explained by passing the prediction function directly to explain(),
quite a few input arguments got new names, and a few functions for edge
cases was removed to simplify the code base.
Click here to view a version of this README with old syntax (v0.2.2).
We provide an (experimental) Python wrapper (shaprpy) which allows
explaining Python models with the methodology implemented in shapr,
directly from Python. The wrapper calls R internally, and therefore
requires an installation of R. See
here for
installation instructions and examples.
The shapr R package implements an enhanced version of the Kernel SHAP
method, for approximating Shapley values, with a strong focus on
conditional Shapley values. The core idea is to remain completely
model-agnostic while offering a variety of methods for estimating
contribution functions, enabling accurate computation of conditional
Shapley values across different feature types, dependencies, and
distributions. The package also includes evaluation metrics to compare
various approaches. With features like parallelized computations,
convergence detection, progress updates, and extensive plotting options,
shapr is as a highly efficient and user-friendly tool, delivering
precise estimates of conditional Shapley values, which are critical for
understanding how features truly contribute to predictions.
A basic example is provided below. Otherwise we refer to the pkgdown website and the different vignettes there for details and further examples.
shapr is available on CRAN
and can be installed in R as:
install.packages("shapr")To install the development version of shapr, available on GitHub, use
remotes::install_github("NorskRegnesentral/shapr")To also install all dependencies, use
remotes::install_github("NorskRegnesentral/shapr", dependencies = TRUE)shapr supports computation of Shapley values with any predictive model
which takes a set of numeric features and produces a numeric outcome.
The following example shows how a simple xgboost model is trained
using the airquality dataset, and how shapr explains the individual
predictions.
We first enable parallel computation and progress updates with the following code chunk. These are optional, but recommended for improved performance and user friendliness, particularly for problems with many features.
# Enable parallel computation
# Requires the future and future_lapply packages
future::plan("multisession", workers = 2) # Increase the number of workers for increased performance with many features
# Enable progress updates of the v(S)-computations
# Requires the progressr package
progressr::handlers(global = TRUE)
progressr::handlers("cli") # Using the cli package as backend (recommended for the estimates of the remaining time)Here comes the actual example
library(xgboost)
library(shapr)
data("airquality")
data <- data.table::as.data.table(airquality)
data <- data[complete.cases(data), ]
x_var <- c("Solar.R", "Wind", "Temp", "Month")
y_var <- "Ozone"
ind_x_explain <- 1:6
x_train <- data[-ind_x_explain, ..x_var]
y_train <- data[-ind_x_explain, get(y_var)]
x_explain <- data[ind_x_explain, ..x_var]
# Looking at the dependence between the features
cor(x_train)
#> Solar.R Wind Temp Month
#> Solar.R 1.0000000 -0.1243826 0.3333554 -0.0710397
#> Wind -0.1243826 1.0000000 -0.5152133 -0.2013740
#> Temp 0.3333554 -0.5152133 1.0000000 0.3400084
#> Month -0.0710397 -0.2013740 0.3400084 1.0000000
# Fitting a basic xgboost model to the training data
model <- xgboost(
data = as.matrix(x_train),
label = y_train,
nround = 20,
verbose = FALSE
)
# Specifying the phi_0, i.e. the expected prediction without any features
p0 <- mean(y_train)
# Computing the Shapley values with kernelSHAP accounting for feature dependence using
# the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default)
explanation <- explain(
model = model,
x_explain = x_explain,
x_train = x_train,
approach = "empirical",
phi0 = p0
)
#> Note: Feature classes extracted from the model contains NA.
#> Assuming feature classes from the data are correct.
#> Success with message:
#> max_n_coalitions is NULL or larger than or 2^n_features = 16,
#> and is therefore set to 2^n_features = 16.
#>
#> ── Starting `shapr::explain()` at 2025-01-22 10:22:36 ──────────────────────────
#> • Model class: <xgb.Booster>
#> • Approach: empirical
#> • Iterative estimation: FALSE
#> • Number of feature-wise Shapley values: 4
#> • Number of observations to explain: 6
#> • Computations (temporary) saved at:
#> '/tmp/RtmpAnmtGl/shapr_obj_371a1a52bf4cce.rds'
#>
#> ── Main computation started ──
#>
#> ℹ Using 16 of 16 coalitions.
# Printing the Shapley values for the test data.
# For more information about the interpretation of the values in the table, see ?shapr::explain.
print(explanation$shapley_values_est)
#> explain_id none Solar.R Wind Temp Month
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 43.08571 13.2117337 4.785645 -25.57222 -5.599230
#> 2: 2 43.08571 -9.9727747 5.830694 -11.03873 -7.829954
#> 3: 3 43.08571 -2.2916185 -7.053393 -10.15035 -4.452481
#> 4: 4 43.08571 3.3254595 -3.240879 -10.22492 -6.663488
#> 5: 5 43.08571 4.3039571 -2.627764 -14.15166 -12.266855
#> 6: 6 43.08571 0.4786417 -5.248686 -12.55344 -6.645738
# Finally we plot the resulting explanations
plot(explanation)
See the general usage vignette for further basic usage examples and brief introductions to the methodology. For more thorough information about the underlying methodology, see Aas, Jullum, and Løland (2021), Redelmeier, Jullum, and Aas (2020), Jullum, Redelmeier, and Aas (2021), Olsen et al. (2022), Olsen et al. (2024) . See also Sellereite and Jullum (2019) for a brief paper about the previous (< 1.0.0) version of the package.
All feedback and suggestions are very welcome. Details on how to contribute can be found here. If you have any questions or comments, feel free to open an issue here.
Please note that the ‘shapr’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Aas, Kjersti, Martin Jullum, and Anders Løland. 2021. “Explaining Individual Predictions When Features Are Dependent: More Accurate Approximations to Shapley Values.” Artificial Intelligence 298.
Jullum, Martin, Annabelle Redelmeier, and Kjersti Aas. 2021. “Efficient and Simple Prediction Explanations with groupShapley: A Practical Perspective.” In Proceedings of the 2nd Italian Workshop on Explainable Artificial Intelligence, 28–43. CEUR Workshop Proceedings.
Olsen, Lars Henry Berge, Ingrid Kristine Glad, Martin Jullum, and Kjersti Aas. 2022. “Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features.” Journal of Machine Learning Research 23 (213): 1–51.
———. 2024. “A Comparative Study of Methods for Estimating Model-Agnostic Shapley Value Explanations.” Data Mining and Knowledge Discovery, 1–48.
Redelmeier, Annabelle, Martin Jullum, and Kjersti Aas. 2020. “Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees.” In International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 117–37. Springer.
Sellereite, N., and M. Jullum. 2019. “Shapr: An r-Package for Explaining Machine Learning Models with Dependence-Aware Shapley Values.” Journal of Open Source Software 5 (46): 2027. https://doi.org/10.21105/joss.02027.