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{shapviz}

Overview

{shapviz} provides typical SHAP plots:

• sv_importance(): Importance plot (bar/beeswarm).
• sv_dependence() and sv_dependence2D(): Dependence plots to study feature effects and interactions.
• sv_interaction(): Interaction plot (beeswarm).
• sv_waterfall(): Waterfall plot to study single or average predictions.
• sv_force(): Force plot as alternative to waterfall plot.

SHAP and feature values are stored in a “shapviz” object that is built from:

1. Models that know how to calculate SHAP values: XGBoost, LightGBM, H2O (boosted trees).
2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, and {DALEX}.
3. SHAP matrix and corresponding feature values.

We use {patchwork} to glue together multiple plots with (potentially) inconsistent x and/or color scale.

Installation

# From CRAN
install.packages("shapviz")

# Or the newest version from GitHub:
# install.packages("devtools")
devtools::install_github("ModelOriented/shapviz")

Usage

Shiny diamonds… let’s use XGBoost to model their prices by the four “C” variables:

library(shapviz)
library(ggplot2)
library(xgboost)

set.seed(1)

xvars <- c("log_carat", "cut", "color", "clarity")
X <- diamonds |> 
  transform(log_carat = log(carat)) |> 
  subset(select = xvars)

# Fit (untuned) model
fit <- xgb.train(
  params = list(learning_rate = 0.1), 
  data = xgb.DMatrix(data.matrix(X), label = log(diamonds$price)),
  nrounds = 65
)

# SHAP analysis: X can even contain factors
X_explain <- X[sample(nrow(X), 2000), ]
shp <- shapviz(fit, X_pred = data.matrix(X_explain), X = X_explain)

sv_importance(shp, show_numbers = TRUE)
sv_importance(shp, kind = "bee")
sv_dependence(shp, v = xvars)  # multiple plots -> patchwork

Decompositions of individual predictions can be visualized as waterfall or force plot:

sv_waterfall(shp, row_id = 2) +
  ggtitle("Waterfall plot for second prediction")
  
sv_force(shp, row_id = 2) +
  ggtitle("Force plot for second prediction")

More to Discover

Check-out the vignettes for topics like:

• Basic use (includes working with other packages and SHAP interactions).
• Multiple models, multi-output models, and subgroup analyses.
• Plotting geographic effects.
• Working with Tidymodels.

References

[1] Scott M. Lundberg and Su-In Lee. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (2017).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.