Description Usage Arguments Details Value References Examples
View source: R/xgb.plot.shap.R
Visualizing the SHAP feature contribution to prediction dependencies on feature value.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  xgb.plot.shap(
data,
shap_contrib = NULL,
features = NULL,
top_n = 1,
model = NULL,
trees = NULL,
target_class = NULL,
approxcontrib = FALSE,
subsample = NULL,
n_col = 1,
col = rgb(0, 0, 1, 0.2),
pch = ".",
discrete_n_uniq = 5,
discrete_jitter = 0.01,
ylab = "SHAP",
plot_NA = TRUE,
col_NA = rgb(0.7, 0, 1, 0.6),
pch_NA = ".",
pos_NA = 1.07,
plot_loess = TRUE,
col_loess = 2,
span_loess = 0.5,
which = c("1d", "2d"),
plot = TRUE,
...
)

data 
data as a 
shap_contrib 
a matrix of SHAP contributions that was computed earlier for the above

features 
a vector of either column indices or of feature names to plot. When it is NULL,
feature importance is calculated, and 
top_n 
when 
model 
an 
trees 
passed to 
target_class 
is only relevant for multiclass models. When it is set to a 0based class index, only SHAP contributions for that specific class are used. If it is not set, SHAP importances are averaged over all classes. 
approxcontrib 
passed to 
subsample 
a random fraction of data points to use for plotting. When it is NULL, it is set so that up to 100K data points are used. 
n_col 
a number of columns in a grid of plots. 
col 
color of the scatterplot markers. 
pch 
scatterplot marker. 
discrete_n_uniq 
a maximal number of unique values in a feature to consider it as discrete. 
discrete_jitter 
an 
ylab 
a yaxis label in 1D plots. 
plot_NA 
whether the contributions of cases with missing values should also be plotted. 
col_NA 
a color of marker for missing value contributions. 
pch_NA 
a marker type for NA values. 
pos_NA 
a relative position of the xlocation where NA values are shown:

plot_loess 
whether to plot loesssmoothed curves. The smoothing is only done for features with more than 5 distinct values. 
col_loess 
a color to use for the loess curves. 
span_loess 
the 
which 
whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far. 
plot 
whether a plot should be drawn. If FALSE, only a lits of matrices is returned. 
... 
other parameters passed to 
These scatterplots represent how SHAP feature contributions depend of feature values. The similarity to partial dependency plots is that they also give an idea for how feature values affect predictions. However, in partial dependency plots, we usually see marginal dependencies of model prediction on feature value, while SHAP contribution dependency plots display the estimated contributions of a feature to model prediction for each individual case.
When plot_loess = TRUE
is set, feature values are rounded to 3 significant digits and
weighted LOESS is computed and plotted, where weights are the numbers of data points
at each rounded value.
Note: SHAP contributions are shown on the scale of model margin. E.g., for a logistic binomial objective, the margin is prediction before a sigmoidal transform into probabilitylike values. Also, since SHAP stands for "SHapley Additive exPlanation" (model prediction = sum of SHAP contributions for all features + bias), depending on the objective used, transforming SHAP contributions for a feature from the marginal to the prediction space is not necessarily a meaningful thing to do.
In addition to producing plots (when plot=TRUE
), it silently returns a list of two matrices:
data
the values of selected features;
shap_contrib
the contributions of selected features.
Scott M. Lundberg, SuIn Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, https://arxiv.org/abs/1705.07874
Scott M. Lundberg, SuIn Lee, "Consistent feature attribution for tree ensembles", https://arxiv.org/abs/1706.06060
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
bst < xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
eta = 0.1, max_depth = 3, subsample = .5,
method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
contr < predict(bst, agaricus.test$data, predcontrib = TRUE)
xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12) # Summary plot
# multiclass example  plots for each class separately:
nclass < 3
nrounds < 20
x < as.matrix(iris[, 5])
set.seed(123)
is.na(x[sample(nrow(x) * 4, 30)]) < TRUE # introduce some missing values
mbst < xgboost(data = x, label = as.numeric(iris$Species)  1, nrounds = nrounds,
max_depth = 2, eta = 0.3, subsample = .5, nthread = 2,
objective = "multi:softprob", num_class = nclass, verbose = 0)
trees0 < seq(from=0, by=nclass, length.out=nrounds)
col < rgb(0, 0, 1, 0.5)
xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4,
n_col = 2, col = col, pch = 16, pch_NA = 17)
xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
n_col = 2, col = col, pch = 16, pch_NA = 17)
xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
n_col = 2, col = col, pch = 16, pch_NA = 17)
xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4) # Summary plot

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