SHAP (SHapley Additive exPlanations, see Lundberg and Lee (2017)) is an ingenious way to study black box models. SHAP values decompose - as fair as possible - predictions into additive feature contributions. Crunching SHAP values requires clever algorithms by clever people. Analyzing them, however, is super easy with the right visualizations. {shapviz} offers the latter:

`sv_importance()`

: Importance plots (bar plots and/or beeswarm plots) to study variable importance.`sv_dependence()`

and`sv_dependence2D()`

: Dependence plots to study feature effects and interactions.`sv_interaction()`

: Interaction plots.`sv_waterfall()`

: Waterfall plots.`sv_force()`

: Force plots as an alternative to waterfall plots.

These plots require a “shapviz” object, which is built from two things only:

`S`

: Matrix of SHAP values`X`

: Dataset that includes the corresponding feature values

Optionally, a `baseline`

can be passed to represent an
average prediction on the scale of the SHAP values. Also a 3D array of
SHAP interaction values can be passed as `S_inter`

.

A key feature of “shapviz” is that `X`

is used for
visualization only. Thus it is perfectly fine to use factor variables,
even if the underlying model would not accept these. Additionally, in
order to improve visualization, it can sometimes make sense to clip
gross outliers, take logarithms for certain columns, or replace missing
values by some explicit value.

To further simplify the use of {shapviz}, we added direct connectors to:

For XGBoost, LightGBM, and H2O, the SHAP values are directly calculated from the fitted model.

`CatBoost`

is
not included, but see Section “Any other package” how to use its SHAP
calculation backend with {shapviz}.

See vignette “Multiple shapviz objects” for how to deal with multiple models or multiclass models.

We start by fitting an XGBoost model to predict diamond prices based on the four “C” features.

```
library(shapviz)
library(ggplot2)
library(xgboost)
set.seed(3653)
# Turn ordinal factors into normal ones (required for some of the examples)
ord <- c("clarity", "cut", "color")
diamonds[, ord] <- lapply(diamonds[, ord], factor, ordered = FALSE)
# Fit XGBoost model
x <- c("carat", "clarity", "cut", "color")
dtrain <- xgb.DMatrix(data.matrix(diamonds[x]), label = diamonds$price)
fit <- xgb.train(
params = list(learning_rate = 0.1, nthread = 1), data = dtrain, nrounds = 65
)
```

One line of code creates a “shapviz” object. It contains SHAP values
and feature values for the set of observations we are interested in.
Note again that `X`

is solely used as explanation dataset,
not for calculating SHAP values.

In this example we construct the “shapviz” object directly from the
fitted XGBoost model. Thus we also need to pass a corresponding
prediction dataset `X_pred`

used for calculating SHAP values
by XGBoost.

```
# Explanation data
dia_small <- diamonds[sample(nrow(diamonds), 2000), ]
shp <- shapviz(fit, X_pred = data.matrix(dia_small[x]), X = dia_small)
```

Note: If `X_pred`

would contain one-hot-encoded dummy
variables, their SHAP values (and also SHAP interaction values) could be
collapsed by the `collapse`

argument of
`shapviz()`

.

The main idea behind SHAP values is to decompose, in a fair way, a prediction into additive contributions of each feature. Typical visualizations include waterfall plots and force plots:

Works pretty sweet, and factor input is respected!

Alternatively, we can study a force plot:

Also multiple `row_id`

can be passed: The SHAP values of
the selected observations are averaged and then plotted as
**aggregated SHAP values**: The prediction profile for
beautiful color “D” diamonds:

We have decomposed 2000 predictions, not just one. This allows us to study variable importance at a global model level by studying average absolute SHAP values or by looking at beeswarm “summary” plots of SHAP values.

A SHAP beeswarm importance plot gives first hints on whether high feature values tend to high or low predictions. This impression can be substantiated by studying simple scatterplots of SHAP values of a feature against its feature values.

On the color axis, the feature with (heuristically) strongest
interaction is shown by default. Use `color_var`

to use
another feature (or `NULL`

for no coloring).

```
sv_dependence(shp, v = "carat", alpha = 0.2, size = 1) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size = 2)))
```

Using {patchwork}, we can also plot multiple features at the same time.

```
library(patchwork) # to use the & operator
sv_dependence(shp, v = x) &
theme_gray(base_size = 9) &
ylim(-5000, 15000)
```

To show the combined effects of two features (sum of their SHAP values), 2-dimensional dependence plots are available:

This is especially interesting for geographic components in a model.

If SHAP interaction values have been computed (via {xgboost} or {treeshap}), the dependence plot can focus on main effects or SHAP interaction effects (multiplied by two due to symmetry):

```
shp_i <- shapviz(
fit, X_pred = data.matrix(dia_small[x]), X = dia_small, interactions = TRUE
)
sv_dependence(shp_i, v = "color", color_var = "cut", interactions = TRUE)
```

Using {patchwork}, we can show multiple colors at the same time:

We can also study all interactions and main effects together using the following beeswarm visualization:

The above example uses XGBoost to calculate SHAP values. In the following sections, we show (without running the code), how other packages work together with {shapviz}.

```
library(fastshap)
fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length, data = iris)
# fastshap > 0.0.7 and shap_only = FALSE
shap <- fastshap::explain(
fit,
X = iris[c("Sepal.Width", "Petal.Length")],
nsim = 100,
pred_wrapper = predict,
shap_only = FALSE
)
sv <- shapviz(shap)
sv_dependence(sv, "Sepal.Width", color_var = NULL)
# fastshap <= 0.0.7 or shap_only = TRUE
shap <- fastshap::explain(
fit,
X = iris[c("Sepal.Width", "Petal.Length")],
nsim = 100,
pred_wrapper = predict
)
sv <- shapviz(shap, X = iris, baseline = mean(iris$Sepal.Length))
sv_dependence(sv, "Sepal.Width", color_var = NULL)
```

```
library(shapr)
fit <- lm(price ~ carat + clarity + cut + color, data = diamonds)
background <- diamonds[sample(nrow(diamonds), 100), ]
explainer <- shapr(background, fit)
explanation <- shapr::explain(
as.data.frame(dia_small)[1:200, ],
approach = "ctree",
explainer = explainer,
prediction_zero = mean(diamonds$price)
)
shp <- shapviz(explanation)
sv_dependence(shp, "carat")
```

If you work with a tree-based H2O model:

```
library(treeshap)
library(ranger)
fit <- ranger(
y = diamonds$price, x = data.matrix(diamonds[x]), max.depth = 6, num.trees = 100
)
unified_model <- ranger.unify(fit, data.matrix(diamonds[x]))
shaps <- treeshap(unified_model, data.matrix(dia_small[x]), interactions = TRUE)
shp <- shapviz(shaps, X = dia_small)
sv_importance(shp)
sv_dependence(shp, "carat", alpha = 0.7, interactions = TRUE)
```

Decompositions of single predictions obtained by
`DALEX::predict_parts()`

:

```
library(DALEX)
library(ranger)
fit <- ranger(reformulate(x, "price"), data = diamonds, max.depth = 6, num.trees = 100)
explainer <- DALEX::explain(fit, data = diamonds[x], y = diamonds$price, label = "RF")
breakdown <- predict_parts(explainer, diamonds[10000, ], keep_distributions = FALSE)
sv_force(shapviz(breakdown)) +
xlab("Breakdown")
```

The most general interface is to provide a matrix of SHAP values and corresponding feature values (and optionally, a baseline value):

```
S <- matrix(c(1, -1, -1, 1), ncol = 2, dimnames = list(NULL, c("x", "y")))
X <- data.frame(x = c("a", "b"), y = c(100, 10))
shp <- shapviz(S, X, baseline = 4)
```

An example is CatBoost: it is not on CRAN and requires
`catboost.*()`

functions to calculate SHAP values, so we
cannot directly add it to {shapviz} for now. Just use a wrapper like
this:

```
library(catboost)
shapviz.catboost.Model <- function(object, X_pred, X = X_pred, collapse = NULL, ...) {
if (!requireNamespace("catboost", quietly = TRUE)) {
stop("Package 'catboost' not installed")
}
stopifnot(
"X must be a matrix or data.frame. It can't be an object of class catboost.Pool" =
is.matrix(X) || is.data.frame(X),
"X_pred must be a matrix, a data.frame, or a catboost.Pool" =
is.matrix(X_pred) || is.data.frame(X_pred) || inherits(X_pred, "catboost.Pool"),
"X_pred must have column names" = !is.null(colnames(X_pred))
)
if (!inherits(X_pred, "catboost.Pool")) {
X_pred <- catboost.load_pool(X_pred)
}
S <- catboost.get_feature_importance(object, X_pred, type = "ShapValues", ...)
# Call matrix method
pp <- ncol(X_pred) + 1
baseline <- S[1, pp]
S <- S[, -pp, drop = FALSE]
colnames(S) <- colnames(X_pred)
shapviz(S, X = X, baseline = baseline, collapse = collapse)
}
# Example
X_pool <- catboost.load_pool(diamonds[x], label = diamonds$price)
fit <- catboost.train(
X_pool,
params = list(
loss_function = "RMSE",
iterations = 165,
logging_level = "Silent",
allow_writing_files = FALSE
)
)
shp <- shapviz(fit, X_pred = dia_small[x])
sv_importance(shp)
sv_dependence(shp, "clarity", alpha = 0.2, size = 1)
```

Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to
Interpreting Model Predictions.” In *Advances in Neural
Information Processing Systems 30*, edited by I. Guyon, U. V.
Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R.
Garnett, 4765–74. Curran Associates, Inc. https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf.