R/approach.R, R/approach_categorical.R, R/approach_copula.R, and 8 more
setup_approach.RdThe different choices of approach takes different (optional) parameters,
which are forwarded from explain().
setup_approach(internal, ...)
# S3 method for categorical
setup_approach(
internal,
categorical.joint_prob_dt = NULL,
categorical.epsilon = 0.001,
...
)
# S3 method for copula
setup_approach(internal, ...)
# S3 method for ctree
setup_approach(
internal,
ctree.mincriterion = 0.95,
ctree.minsplit = 20,
ctree.minbucket = 7,
ctree.sample = TRUE,
...
)
# S3 method for empirical
setup_approach(
internal,
empirical.type = "fixed_sigma",
empirical.eta = 0.95,
empirical.fixed_sigma = 0.1,
empirical.n_samples_aicc = 1000,
empirical.eval_max_aicc = 20,
empirical.start_aicc = 0.1,
empirical.cov_mat = NULL,
model = NULL,
predict_model = NULL,
...
)
# S3 method for gaussian
setup_approach(internal, gaussian.mu = NULL, gaussian.cov_mat = NULL, ...)
# S3 method for independence
setup_approach(internal, ...)
# S3 method for regression_separate
setup_approach(
internal,
regression.model = parsnip::linear_reg(),
regression.tune_values = NULL,
regression.vfold_cv_para = NULL,
regression.recipe_func = NULL,
...
)
# S3 method for regression_surrogate
setup_approach(
internal,
regression.model = parsnip::linear_reg(),
regression.tune_values = NULL,
regression.vfold_cv_para = NULL,
regression.recipe_func = NULL,
regression.surrogate_n_comb = internal$parameters$used_n_combinations - 2,
...
)
# S3 method for timeseries
setup_approach(
internal,
timeseries.fixed_sigma_vec = 2,
timeseries.bounds = c(NULL, NULL),
...
)
# S3 method for vaeac
setup_approach(
internal,
vaeac.depth = 3,
vaeac.width = 32,
vaeac.latent_dim = 8,
vaeac.activation_function = torch::nn_relu,
vaeac.lr = 0.001,
vaeac.n_vaeacs_initialize = 4,
vaeac.epochs = 100,
vaeac.extra_parameters = list(),
...
)Not used.
approach-specific arguments. See below.
Data.table. (Optional)
Containing the joint probability distribution for each combination of feature
values.
NULL means it is estimated from the x_train and x_explain.
Numeric value. (Optional)
If joint_probability_dt is not supplied, probabilities/frequencies are
estimated using x_train. If certain observations occur in x_train and NOT in x_explain,
then epsilon is used as the proportion of times that these observations occurs in the training data.
In theory, this proportion should be zero, but this causes an error later in the Shapley computation.
Numeric scalar or vector. (default = 0.95) Either a scalar or vector of length equal to the number of features in the model. Value is equal to 1 - \(\alpha\) where \(\alpha\) is the nominal level of the conditional independence tests. If it is a vector, this indicates which value to use when conditioning on various numbers of features.
Numeric scalar. (default = 20) Determines minimum value that the sum of the left and right daughter nodes required for a split.
Numeric scalar. (default = 7) Determines the minimum sum of weights in a terminal node required for a split
Boolean. (default = TRUE)
If TRUE, then the method always samples n_samples observations from the leaf nodes (with replacement).
If FALSE and the number of observations in the leaf node is less than n_samples,
the method will take all observations in the leaf.
If FALSE and the number of observations in the leaf node is more than n_samples,
the method will sample n_samples observations (with replacement).
This means that there will always be sampling in the leaf unless
sample = FALSE AND the number of obs in the node is less than n_samples.
Character. (default = "fixed_sigma")
Should be equal to either "independence","fixed_sigma", "AICc_each_k" "AICc_full".
TODO: Describe better what the methods do here.
Numeric. (default = 0.95)
Needs to be 0 < eta <= 1.
Represents the minimum proportion of the total empirical weight that data samples should use.
If e.g. eta = .8 we will choose the K samples with the largest weight so that the sum of the weights
accounts for 80\
eta is the \(\eta\) parameter in equation (15) of Aas et al (2021).
Positive numeric scalar. (default = 0.1)
Represents the kernel bandwidth in the distance computation used when conditioning on all different combinations.
Only used when empirical.type = "fixed_sigma"
Positive integer. (default = 1000)
Number of samples to consider in AICc optimization.
Only used for empirical.type is either "AICc_each_k" or "AICc_full".
Positive integer. (default = 20)
Maximum number of iterations when optimizing the AICc.
Only used for empirical.type is either "AICc_each_k" or "AICc_full".
Numeric. (default = 0.1)
Start value of the sigma parameter when optimizing the AICc.
Only used for empirical.type is either "AICc_each_k" or "AICc_full".
Numeric matrix. (Optional, default = NULL)
Containing the covariance matrix of the data generating distribution used to define the Mahalanobis distance.
NULL means it is estimated from x_train.
Objects.
The model object that ought to be explained.
See the documentation of explain() for details.
Function.
The prediction function used when model is not natively supported.
See the documentation of explain() for details.
Numeric vector. (Optional)
Containing the mean of the data generating distribution.
NULL means it is estimated from the x_train.
Numeric matrix. (Optional)
Containing the covariance matrix of the data generating distribution.
NULL means it is estimated from the x_train.
A tidymodels object of class model_specs. Default is a linear regression model, i.e.,
parsnip::linear_reg(). See tidymodels for all possible models,
and see the vignette for how to add new/own models. Note, to make it easier to call explain() from Python, the
regression.model parameter can also be a string specifying the model which will be parsed and evaluated. For
example, "parsnip::rand_forest(mtry = hardhat::tune(), trees = 100, engine = "ranger", mode = "regression")"
is also a valid input. It is essential to include the package prefix if the package is not loaded.
Either NULL (default), a data.frame/data.table/tibble, or a function.
The data.frame must contain the possible hyperparameter value combinations to try.
The column names must match the names of the tuneable parameters specified in regression.model.
If regression.tune_values is a function, then it should take one argument x which is the training data
for the current combination/coalition and returns a data.frame/data.table/tibble with the properties described above.
Using a function allows the hyperparameter values to change based on the size of the combination. See the regression
vignette for several examples.
Note, to make it easier to call explain() from Python, the regression.tune_values can also be a string
containing an R function. For example,
"function(x) return(dials::grid_regular(dials::mtry(c(1, ncol(x)))), levels = 3))" is also a valid input.
It is essential to include the package prefix if the package is not loaded.
Either NULL (default) or a named list containing
the parameters to be sent to rsample::vfold_cv(). See the regression vignette for
several examples.
Either NULL (default) or a function that that takes in a recipes::recipe()
object and returns a modified recipes::recipe() with potentially additional recipe steps. See the regression
vignette for several examples.
Note, to make it easier to call explain() from Python, the regression.recipe_func can also be a string
containing an R function. For example,
"function(recipe) return(recipes::step_ns(recipe, recipes::all_numeric_predictors(), deg_free = 2))" is also
a valid input. It is essential to include the package prefix if the package is not loaded.
Integer (default is internal$parameters$used_n_combinations) specifying the
number of unique combinations/coalitions to apply to each training observation. Maximum allowed value is
"internal$parameters$used_n_combinations - 2". By default, we use all coalitions, but this can take a lot of memory
in larger dimensions. Note that by "all", we mean all coalitions chosen by shapr to be used. This will be all
\(2^{n_{\text{features}}}\) coalitions (minus empty and grand coalition) if shapr is in the exact mode. If the
user sets a lower value than internal$parameters$used_n_combinations, then we sample this amount of unique
coalitions separately for each training observations. That is, on average, all coalitions should be equally trained.
Numeric. (Default = 2) Represents the kernel bandwidth in the distance computation. TODO: What length should it have? 1?
Numeric vector of length two. (Default = c(NULL, NULL)) If one or both of these bounds are not NULL, we restrict the sampled time series to be between these bounds. This is useful if the underlying time series are scaled between 0 and 1, for example.
Positive integer (default is 3). The number of hidden layers
in the neural networks of the masked encoder, full encoder, and decoder.
Positive integer (default is 32). The number of neurons in each
hidden layer in the neural networks of the masked encoder, full encoder, and decoder.
Positive integer (default is 8). The number of dimensions in the latent space.
An torch::nn_module() representing an activation function such as, e.g.,
torch::nn_relu() (default), torch::nn_leaky_relu(), torch::nn_selu(), or torch::nn_sigmoid().
Positive numeric (default is 0.001). The learning rate used in the torch::optim_adam() optimizer.
Positive integer (default is 4). The number of different vaeac models to initiate
in the start. Pick the best performing one after vaeac.extra_parameters$epochs_initiation_phase
epochs (default is 2) and continue training that one.
Positive integer (default is 100). The number of epochs to train the final vaeac model.
This includes vaeac.extra_parameters$epochs_initiation_phase, where the default is 2.
Named list with extra parameters to the vaeac approach. See
vaeac_get_extra_para_default() for description of possible additional parameters and their default values.