# SHAP package in R

I built an R package SHAPforxgboost that wraps all the R plotting functions illustrated in this post. It serves for now as the vignette for the package.

Please install from cran or Github page.

install.packages("SHAPforxgboost")
# or
devtools::install_github("liuyanguu/SHAPforxgboost")

# Why SHAP values

SHAP’s main advantages are local explanation and consistency in global model structure.

Tree-based machine learning models (random forest, gradient boosted trees, XGBoost) are the most popular non-linear models today. SHAP (SHapley Additive exPlnation) values is claimed to be the most advanced method to interpret results from tree-based models. It was based on Shaply values from game theory.

The github page that explains the Python package developed by Scott Lundberg. Here we show all the visualizations in R. In r package xgboost there is only one function xgb.shap.plot that can make some simple dependence plots.

## Local Explanation

# run the model with built-in data
suppressPackageStartupMessages({
library("SHAPforxgboost"); library("ggplot2"); library("xgboost")
library("data.table"); library("here")
})

y_var <-  "diffcwv"
dataX <- as.matrix(dataXY_df[,-..y_var])
# hyperparameter tuning results
param_dart <- list(objective = "reg:linear",  # For regression
# booster = "dart",
nrounds = 10,
eta = 0.018,
max_depth = 10,
gamma = 0.009,
subsample = 0.98,
colsample_bytree = 0.86
)

mod <- xgboost::xgboost(data = dataX,
label = as.matrix(dataXY_df[[y_var]]),
xgb_param = param_dart, nrounds = param_dart$nrounds, verbose = FALSE, nthread = parallel::detectCores() - 2, early_stopping_rounds = 8) # To return the SHAP values and ranked features by mean|SHAP| shap_values <- shap.values(xgb_model = mod, X_train = dataX) # The ranked features by mean |SHAP| shap_values$mean_shap_score
##          dayint       Column_WV AOT_Uncertainty             aod            elev
##     0.083414630     0.059935008     0.048251554     0.029121900     0.024288072
##   dist_water_km     DevAll_P1km  forestProp_1km           RelAZ
##     0.024066129     0.009241162     0.008385689     0.006239526

Shapley values are caculated across the entire dataset. The SHAP values dataset has the same dimention (10148,9) as the dataset of the independent variables (10148,9) fit into the xgboost model.

The sum of each row’s SHAP values (plus the BIAS, which is like an intercept) is the predicted model output. As in the following table of SHAP values, rowSum equals the output predict(xgb_mod). I.e., the explanation’s attribution values sum up to the model output (last column in the table below).

# show that rowSum is the output
shap_data <- shap_values$shap_score shap_data[, BIAS := shap_values$BIAS0]
m2 = xgboost(
data = X2, label = d$y,base_score = 0, gamma = 0, eta = 1, lambda = 0,nrounds = 1,objective = "reg:linear",verbose = F) xgb.importance(model = m1) ## Feature Gain Cover Frequency ## 1: Cough 0.6666667 0.3333333 0.5 ## 2: Fever 0.3333333 0.6666667 0.5 xgb.importance(model = m2) ## Feature Gain Cover Frequency ## 1: Fever 0.6666667 0.3333333 0.5 ## 2: Cough 0.3333333 0.6666667 0.5 So one key message is, the order/structure of how the tree is built doesn’t matter for SHAP, but matters for Gain, since the mean absolute SHAP is always the same (20 vs. 20). The explanation in the paper is a little bit confusing. knitr::kable(output_simple) x.Fever x.Cough y.actual y.pred SHAP.Fever SHAP.Cough BIAS.BIAS 0 0 0 0 -10 -10 20 0 1 0 0 -30 10 20 1 0 0 0 10 -30 20 1 1 80 80 30 30 20 Moreover, comparing Model B to Model A, Model B was revised in a way that it relies more on a given feature (Cough), so cough should be a more important feature. But of course Gain still get it wrong, and only SHAP gives the correct global feature importance. # SHAP plots ## Summary plot The summary plot shows global feature importance. The sina plots show the distribution of feature contributions to the model output (in this example, the predictions of CWV measurement error) using SHAP values of each feature for every observation. Each dot is an observation (station-day). # To prepare the long-format data: shap_long <- shap.prep(xgb_model = mod, X_train = dataX) # is the same as: using given shap_contrib shap_long <- shap.prep(shap_contrib = shap_values$shap_score, X_train = dataX)
# **SHAP summary plot**
shap.plot.summary(shap_long)

# diluted points:
shap.plot.summary(shap_long, x_bound  = 1.2, dilute = 10)

Alternatives ways to make the same plot:

# option 1: from the xgboost model
shap.plot.summary.wrap1(mod, X = dataX)

# option 2: supply a self-made SHAP values dataset (e.g. sometimes as output from cross-validation)
shap.plot.summary.wrap2(shap_values$shap_score, dataX) The summary function offers better visualization compared to its Python counterpart using shap.summary_plot(shap_values, dataX): ## Dependence plot It plots the SHAP values against the feature values for each variable. Again, each dot is a station-day observation. g1 <- shap.plot.dependence(data_long = shap_long, x = 'dayint', y = 'dayint', color_feature = 'Column_WV') + ggtitle("(A) SHAP values of Time trend vs. Time trend") g2 <- shap.plot.dependence(data_long = shap_long, x = 'dayint', y = 'Column_WV', color_feature = 'Column_WV') + ggtitle("(B) SHAP values of CWV vs. Time trend") gridExtra::grid.arrange(g1, g2, ncol = 2) 1. SHAP values showing the contribution of the time trend to predictions. The color represents the MAIAC CWV for each observation (purple high, yellow low). The LOESS (locally estimated scatterplot smoothing) curve is overlaid in red. 2. SHAP values showing the contribution of the MAIAC CWV to predictions of CWV measurement error shown across the time period of the study. Note distinct y-axis scales for Terra and Aqua datasets. The color represents the MAIAC CWV for each observation (purple high, yellow low). Here I choose to plot top 4 features using function shap.plot.dependence: # without color version but has marginal distribution, # just plot SHAP value against feature value: fig_list <- lapply(names(shap_values$mean_shap_score)[1:4],
shap.plot.dependence, data_long = shap_long)
gridExtra::grid.arrange(grobs = fig_list, ncol = 2)

## Interaction effects

SHAP interaction values separate the impact of variable into main effects and interaction effects. They add up roughly to the dependence plot.

Quote paper 2: “SHAP interaction values can be interpreted as the difference between the SHAP values for feature i when feature j is present and the SHAP values for feature i when feature j is absent.”

The SHAP interaction values take time since it calculates all the combinations.

# prepare the data using either:
# (this step is slow since it calculates all the combinations of features.)
shap_int <- shap.prep.interaction(xgb_mod = mod, X_train = dataX)
# or:
shap_int <- predict(mod, dataX, predinteraction = TRUE) # (the same)
# **SHAP interaction effect plot **
# if data_int is supplied, the same function will plot the interaction effect:
g3 <- shap.plot.dependence(data_long = shap_long,
data_int = shap_int,
x= "dayint", y = "Column_WV",
color_feature = "Column_WV")
g4 <- shap.plot.dependence(data_long = shap_long,
data_int = shap_int,
x= "Column_WV", y = "AOT_Uncertainty",
color_feature = "AOT_Uncertainty")
gridExtra::grid.arrange(g3, g4, ncol=2)

Here I show the interaction effects between Time trend and CWV (LEFT), and between Blue band uncertainty and CWV (RIGHT).

## SHAP force plot

The SHAP force plot basically stacks these SHAP values for each observation, and show how the final output was obtained as a sum of each predictor’s attribution.

# choose to show top 4 features by setting top_n = 4,
# set 6 clustering groups of observations.
plot_data <- shap.prep.stack.data(shap_contrib = shap_values\$shap_score, top_n = 4, n_groups = 6)
# choose to zoom in at location 500, set y-axis limit using y_parent_limit
# it is also possible to set y-axis limit for zoom-in part alone using y_zoomin_limit
shap.plot.force_plot(plot_data, zoom_in_location = 500, y_parent_limit = c(-1.5,1.5))

# plot the 6 clusters
shap.plot.force_plot_bygroup(plot_data)

# References

Recent submitted paper from my lab that applies these figures: Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor Measurement Error