Implement semi-deterministic sampling of coalitions similar to
the default in the shap python library and described and
discussed as the PySHAP* strategy in Olsen & Jullum (2024).
It is disabled by default, but can be set via
extra_computation_args = list(semi_deterministic_sampling = TRUE)
in explain(). The functionality is available when paired
coalition sampling (the default) is enabled. See #449 for
details.
Deletes the regression-surrogate parsnip object when testing to avoid future conflicts with model object changes. (Second last commit in #447).
Improve and update the logic and print for setting the number of
coalitions in the next iteration for iterative = TRUE (#452)
Allow passing vS_batching_method to
explain()/explain_forecast() to specify the batch
computation method (default is "future" for both,
"forloop" available mainly for dev purposes) (#452)
Transform to use the cli and rlang
packages to provide all messages/warnings/stops with nicer formatting
and layout. The messages (via cli_inform()) now also obey
the verbose argument and are displayed only if
'basic' %in% verbose is TRUE. The header printout also
differs between explain()/explain_forecast()
and whether called from Python. This also adds cli and
rlang to imports. (#453)
Now using testthat::skip_if_not_installed for all
tests requiring suggested packages to ensure they are skipped gracefully
when dependencies are unavailable (#451)
KernelSHAP_reweighing() (#448)seed argument, and only pass to
torch if not NULL (#452)explain_forecast() use future for
batch computation as well (by default) (#452)approach = 'empirical' occurring when
n_features < n_explain (#453)Fix documentation issues detected during shapr 1.0.2 release (#442)
print() by warning() on
two occasionsFix issue with
Expected <nn_module> but got object of type <NULL>
for approach='vaeac' after recent torch update
broke it (#444)
Changes default seed in explain() and
explain_forecast() from 1 to NULL to avoid set.seed() to
conflict with later called code (#445)
Other minor fixes
expect_snapshot_rds() to reduce false positive
roundoff-errors between platforms (#444)explain_forecast() (#433)by=.I (#434)paired_shap_sampling and
kernelSHAP_reweighting into
extra_computation_args (#428)iterative = TRUE for
explain_forecast() which was not using coalitions from
previous iterations (#426)verbose
argument for explain_forecast() (#425)party package returns a
constparty object (#423)keep_samp_for_vS with iterative approach (#417)explain() in R (#416)shapr() for initial setup + explain() for
explanation for specific observations), to a single function
call (also named explain()). The data used for training and
to be explained have gotten explicit names (x_train and
x_explain). The order of the input arguments has also been
slightly changed (model is now the first argument).explain() instead of being defined
as functions of a specific class in the global env.make_dummies used to
explain xgboost models with categorical data, is removed to
simplify the code base. This is rather handled with a custom prediction
model.explain.ctree_comb_mincrit, which allowed
combining models with approch=ctree with different
mincrit parameters, has been removed to simplify the code
base. It may return in a completely general manner in later version of
shapr.shaprpyr, #325) for
explaining predictions from Python models (from Python) utilizing almost
all functionality of shapr. The wrapper moves back and
forth back and forth between Python and R, doing the prediction in
Python, and almost everything else in R. This simplifies maintenance of
shaprpy significantly. The wrapper is available here.progressr
package. Must be activated by the user with
progressr::handlers(global = TRUE) or wrapping the call to
explain() around
progressr::with_progress({})approach = 'categorical' (#256,
#307)
used to explain models with solely categorical features by directly
using/estimating the joint distribution of all feature
combinations.approch='timeseries' (#314) for
explaining classifications based on time series data/models with the
method described in Sec 4.3 of the groupShapley
paper.explain_forecast to explain
forecasts from time series models, at various prediction horizons (#328).
Uses a different set of input argument which is more appropriate for
these models.approach = 'independence' method
providing significantly faster computation (no longer as a special case
of the empirical method). Also allow the method to be used
on models with categorical data (#315).explain, also using vdiffr for plot tests. Test
functions are only written for exported core functions. Internal
functions are only tested through the exported ones.datasets::airquality dataset. This avoids including a new
package just for the dataset (#248).shapr(data[,1:5],model...)attach(): Fixed by changing how we simulate adding a
function to .GlobalEnv in the failing test. Actual package not
affected.