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sv_waterfall() and sv_force(): The x label
has been changed from “SHAP value” to “Prediction”.sort_features = TRUE in
sv_importance() and sv_interaction(). Set to
FALSE to show the features as they appear in your SHAP
matrix. In that case, the plots will show the first
max_display features, not the most important
features. Implements #137.shapviz.xgboost() would fail if a single row is passed.
This has been fixed in #142.
Thanks @sebsilas for
reporting.sv_dependence():
Control over automatic color feature selectionIf no SHAP interaction values are available, by default, the color
feature v' is selected by the heuristic
potential_interaction(), which works as follows:
v (the on the x-axis) is numeric, it is
binned into nbins bins.v are regressed onto
v' and the R-squared is calculated. Rows with missing
v' are discarded.v' values.This measures how much variability in the SHAP values of
v is explained by v', after accounting for
v.
We have introduced four parameters to control the heuristic. Their defaults are in line with the old behaviour.
nbin = NULL: Into how many quantile bins should a
numeric v be binned? The default NULL equals
the smaller of \(n/20\) and \(\sqrt n\) (rounded up), where \(n\) is the sample size.color_num Should color features be converted to
numeric, even if they are factors/characters? Default is
TRUE.scale = FALSE: Should R-squared be multiplied with the
sample variance of within-bin SHAP values? If TRUE, bins
with stronger vertical scatter will get higher weight. The default is
FALSE.adjusted = FALSE: Should adjusted R-squared be
calculated?If SHAP interaction values are available, these parameters have no
effect. In sv_dependence() they are called
ih_nbin etc.
This partly implements the ideas in #119 of Roel Verbelen, thanks a lot for your patient explanations!
We will continue to experiment with the defaults, which might change in the future. A good alternative to the current (naive) defaults could be:
nbins = 7: Smaller than now to not overfit too strongly
with factor/character color features.color_num = FALSE: To not naively integer encode
factors/characters.scale = TRUE: To account for non-equal spread in
bins.adjusted = TRUE: To not put too much weight on factors
with many categories.sv_dependence(): If color_var = "auto"
(default) and no color feature seems to be relevant (SHAP interaction is
NULL, or heuristic returns no positive value), there won’t
be any color scale. Furthermore, in some edge cases, a different color
feature might be selected.mshapviz() objects can now be rowbinded via
rbind() or +. Implemented by @jmaspons in #110.mshapviz() is more strict when combining multiple
“shapviz” objects. These now need to have identical column names, see #114.print.shapviz() now shows top two rows of SHAP
matrix.nthread = 1 in all calls to
xgb.DMatrix() as suggested by @jmaspons in #109.permshap() connector is now part of {kerneshap} #122.sv_dependence2D(): In case add_vars are
passed, x and/or y are removed from it in
order to not use any variable twice. #116.split.shapviz() now drops empty levels. They launched
an error because empty “shapviz” objects are currently not supported. #117, #118sv_importance() of a “mshapviz” object now returns a
dodged barplot instead of separate barplots via {patchwork}. Use the new
argument bar_type to switch to a stacked barplot
(bar_type = "stack"), to “facets” (via {ggplot2}), or
“separate” for the old behaviour.dimnames.shapviz() has received a replacement method.
You can thus change the column names of SHAP matrix and feature data (as
well as SHAP interactions) by colnames(x) <- ..., see
https://github.com/ModelOriented/shapviz/issues/98package_version() applied to numeric value will be
deprecated in the future)sv_dependence2D(): x and y
coordinates are two features, while their summed SHAP values are shown
on the color scale. If interaction = TRUE, SHAP interaction
values are shown on the color scale instead. The function is vectorized
in x and/or y. This visualization is
especially useful for models with geographic components.split(x, f) splits a “shapviz” object x
into a “mshapviz” object.fastshap::explain() offers the option
shap_only. To conveniently construct the “shapviz” object,
use shapviz(fastshap::explain(..., shap_only = FALSE)).
This not only passes the SHAP matrix but also the feature data and the
baseline. Thanks, Brandon Greenwell!Sometimes, you will find it necessary to work with several “shapviz” objects at the same time:
To simplify the workflow, {shapviz} introduces the “mshapviz” object (“m” like “multi”). You can create it in different ways:
shapviz() on multiclass XGBoost or LightGBM
models.shapviz() on “kernelshap” objects created from
multiclass/multioutput models.c(Mod_1 = s1, Mod_2 = s2, ...) on “shapviz” objects
s1, s2, …mshapviz(list(Mod_1 = s1, Mod_2 = s2, ...))The sv_*() functions use the {patchwork} package to glue
the individual plots together.
See the new vignette for more info and specific examples.
sv_dependence() now allows multiple v
and/or color_var to be plotted (glued via
{patchwork}).row_id of
sv_waterfall() and sv_force() now also allows
a vector of integers or a logical vector. If more than one row is
selected, SHAP values and predictions are averaged before plotting
(aggregated SHAP values in {DALEX}).x1, x2 can now
be concatenated in rowwise manner using x1 + x2 or
rbind(x1, x2), again thanks to Adrian.colnames(): “shapviz” objects x have
received a dimnames() function, so you can now, e.g., use
colnames(x) to see the feature names.x can now be subsetted using
x[cond, features].sv_dependence(), sv_importance(kind="bee"),
and sv_interaction().sv_dependence() has been shortened to “SHAP
interaction”.show_other of
sv_importance() has been removed.S_inter.print.shapviz() is much more compact, use
summary.shapviz() for more info.sv_waterfall(): Using order_fun() would
not work as expected with max_display. This has been
fixed.sv_dependence(): Passing
viridis_args = NULL would hide the color guide title. This
has been fixed. But please pass viridis_args = list()
instead.sv_dependence() now uses
color_var = "auto" instead of
color_var = NULL.sv_dependence() now uses “SHAP value” as y label
(instead of the more verbose “SHAP value of [feature]”).S_inter (3D
array):
shapviz(object, ..., S_inter = NULL)shapviz(object, ..., interactions = TRUE)shapviz(object, ...)sv_interaction(x) shows matrix of beeswarm plots.sv_dependence(x, v = "x1", color_var = "x2", interactions = TRUE)
plots SHAP interaction values.sv_dependence(x, v = "x1", interactions = TRUE) plots
pure main effects of “x1”.sv_dependence(..., color_var = "auto") uses those to
determine the most interacting color variable.collapse_shap() also works for SHAP interaction
arrays.get_shap_interactions().sv_importance(): In case of too many features,
sv_importance() used to collapse the remaining features
into an additional bar/beeswarm. This logic has been removed, and the
show_other argument has been deprecated.sv_dependence() automatically adds
horizontal jitter for discrete v. This now also works if
v is numeric with at most seven unique values, not only for
logicals, factors, and character v.sv_importance() does not use a flipped coordinate
system anymore.sv_importance() has received a new
argument show_others = TRUE. Set to FALSE to
hide the “other” bar/beeswarm.The following dependencies have been removed:
sv_importance()bee_width: Relative width of the
beeswarms. The default is 0.4. It replaces the width
argument passed via ....bee_adjust: Relative adjustment factor of
the bandwidth used in estimating the density of the beeswarms. Default
is 0.5.... arguments are now
passed to geom_point().plotly::ggplotly() now works for most functionalities
of sv_importance(), including beeswarms.X of the constructor of
shapviz() is now less picky. If it contains columns not
present in the SHAP matrix, they are silently dropped. Furthermore, the
column order of the SHAP matrix and X is now determined by
the SHAP matrix.shapviz_from_lgb_predict() and
shapviz_from_xgb_predict()format_fun argument in sv_force() and
sv_waterfall()sort_fun argument in sv_waterfall()collapse_shap() is not anymore an S3 method. It is just
a normal function that can be applied to a matrix.sv_importance() would return
an error.X_pred from
matrix to xgb.DMatrix in
shapviz.xgb.Booster().treeshap() example to a ranger() model.collapse argument in
shapviz(). This is named list specifying which columns in
the SHAP matrix are to be collapsed by rowwise summation. A typical
application will be to combine the SHAP values of one-hot-encoded
dummies and explain them by the corrsponding factor variable.sv_importance(), see next section.sv_importance()The calculations behind sv_importance() are unchanged,
but defaults and some plot aspects have been reworked.
sv_importance() now shows a
bar plot by default. Use kind = "beeswarm" to get a
beeswarm plot.sv_importance() does not show SHAP
feature importances as text anymore. Use
show_numbers = TRUE to get them back. Furthermore, the
numbers are now printed on top of the bars instead on their bottom.show_numbers can be used to to add
SHAP feature importance values for all plot types.max_display has been increased from 10
to 15.bar_width.color_bar_title. Set to
NULL to remove the color bar altogether.format_fun now uses a right-aligned number
formatter with aligned decimal separator by default.dim() method for “shapviz” object, implying
nrow() and ncol().format_fun
argument of sv_waterfall() and sv_force() has
been replaced by format_shap to format SHAP values and
format_feat to format numeric feature values. By default,
they use the new global options “shapviz.format_shap” and
“shapviz.format_feat”, both with default
function(z) prettyNum(z, digits = 3, scientific = FALSE).sv_waterfall() now uses the more consistent argument
order_fun = function(s) order(abs(s)) instead of the
original sort_fun = function(shap) abs(shap) that was then
passed to order().viridis_args = getOption("shapviz.viridis_args") to
sv_dependence() and sv_importance() to control
the viridis color scale options. The default global option equals
list(begin = 0.25, end = 0.85, option = "inferno"). For
example, to switch to a standard viridis scale, you can either change
the default with options(shapviz.viridis_args = NULL) or
set viridis_args = NULL.shapviz_from_lgb_predict()
and shapviz_from_xgb_predict in favour of the collapsing
logic (see above). The functions will be removed in version 0.3.0.predict() arguments of LightGBM
(data -> newdata, predcontrib = TRUE -> type = “contrib”).This is the initial CRAN release.
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.