Description
SHAP Visualizations.
Description
Visualizations for SHAP (SHapley Additive exPlanations), such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a 'shapviz' object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages 'xgboost', 'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and 'kernelshap' are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the 'shap' package in Python, but there is no dependency on it.
README.md
{shapviz} 
Overview
{shapviz} provides typical SHAP plots:
sv_importance(): Importance plots (bar plots and/or beeswarm plots).sv_dependence()andsv_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:
- Models that know how to calculate SHAP values: XGBoost, LightGBM, h2o, or
- SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, {DALEX}, or simply from a
- 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).