Calculate Contextual Importance (CI) and Contextual Utility (CU)
for an instance (Context) using the given "black-box" model. First get a
CIU object by calling ciu.new as e.g. ciu <- ciu.new(...),
then call as ciu.res <- ciu$explain(...). "Usage" section is in
"Details" section because Roxygen etc. don't support documentation of functions
within functions.
A ciu.result object as returned by ciu.result.new
Input values for the instance to explain.
Should be a data.frame even though a vector or
matrix might
work too if input names and other needed metadata can be deduced from the
dataset or other parameters given to ciu.new.
vector of indices for the
inputs to be explained, i.e. for which CIU should be calculated.
If NULL, then all inputs will be included.
data.frame or matrix
with one row per output and
two columns, where the first column indicates the minimal value and the
second column the maximal value for that output. ONLY NEEDED HERE IF not
given as parameter to ciu.new or if the limits are different
for this specific instance than the default ones.
How many instances to generate for estimating CI and CU.
For inputs of type factor, all possible combinations of input values
is generated, so this parameter only influences how many instances are
(at least) generated for continuous-valued inputs.
If provided, then calculate CIU of inputs
ind.inputs.to.explain relative to the given concept rather than
relative to the actual output(s). ind.inputs.to.explain should
normally be a subset (or all) of the inputs that target.concept
consists of, even though that not required by the CIU calculation.
If a "target.ciu" is provided, then the "target.concept" doesn't have to
be included in the vocabulary gives as parameter to
ciu.new (at least for the moment).
ciu.result object previously calculated for
target.concept. If a target.concept
is provided but target.ciu=NULL, then target.ciu is
estimated by a call to explain with the n.samples value given as
a parameter to this call. It may be useful to provide target.ciu
if it should be estimated using some other (typically greater) value for
n.samples than the default one, or if it has already been calculated
for some reason.
Kary Främling
Usage
explain(
instance,
ind.inputs.to.explain,
in.min.max.limits=NULL,
n.samples=100,
target.concept=NULL,
target.ciu=NULL
)
ciu.result.new