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{shapviz}

Overview

{shapviz} provides typical SHAP plots:

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

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, or
2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, {DALEX}, or simply from a
3. SHAP matrix and its corresponding feature values.

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)

# Build model
x <- c("carat", "cut", "color", "clarity")
dtrain <- xgb.DMatrix(data.matrix(diamonds[x]), label = diamonds$price)
fit <- xgb.train(params = list(learning_rate = 0.1), data = dtrain, nrounds = 65)

# SHAP analysis: X can even contain factors
dia_2000 <- diamonds[sample(nrow(diamonds), 2000), x]
shp <- shapviz(fit, X_pred = data.matrix(dia_2000), X = dia_2000)

sv_importance(shp, show_numbers = TRUE)
sv_dependence(shp, v = x)

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

sv_waterfall(shp, row_id = 1)
sv_force(shp, row_id = 1)

More to Discover

Check-out the vignettes for topics like:

• How to work with other SHAP packages like {fastshap}, {kernelshap} or {treeshap}?
• SHAP interactions.
• Multiple models, multi-output models, and subgroup analyses.
• Plotting geographic effects.

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.
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