get_results: Extract Components from a Shapr Object

Description

Extract Components from a Shapr Object

Usage

get_results(
  x,
  what = c("calling_function", "proglang", "approach", "shapley_est", "shapley_sd",
    "pred_explain", "MSEv", "MSEv_explicand", "MSEv_coalition", "iterative_info",
    "iterative_shapley_est", "iterative_shapley_sd", "saving_path", "timing_summary",
    "timing_details", "parameters", "x_train", "x_explain", "dt_vS", "dt_samp_for_vS",
    "dt_used_coalitions", "dt_valid_causal_coalitions", "dt_coal_samp_info"),
  ...
)

Value

If a single component is requested, returns that object. If multiple are requested, returns a named list.

Arguments

x: A shapr object
what: Character vector specifying one or more components to extract. Options: "calling_function", "proglang", "approach", "shapley_est", "shapley_sd", "pred_explain", "MSEv", "MSEv_explicand", "MSEv_coalition", "iterative_info", "iterative_shapley_est", "iterative_shapley_sd", "saving_path", "timing_summary", "timing_details", "parameters", "x_train", "x_explain", "dt_vS", "dt_samp_for_vS", "dt_used_coalitions", "dt_valid_causal_coalitions", "dt_coal_samp_info". The default is to return all components. See details for what each component contains.
...: Not used

Details

The function extracts a full suite of information related to the computation of the Shapley values from a shapr object. The allowed characters in what provides information as follows:

calling_function: Name of function called to create the shapr object, (explain() or explain_forecast()).
proglang: Programming language used to initiate the computations (R or Python).
approach: Approach used to estimate the conditional expectations.
shapley_est: data.table with the estimated Shapley values.
shapley_sd: data.table with the standard deviation of the Shapley values reflecting the uncertainty in the coalition sampling part of the kernelSHAP procedure.
pred_explain: Numeric vector with the predictions for the explained observations.
MSEv/MSEv_explicand/MSEv_coalition: Data.tables with MSEv evaluation criterion values overall/ per explicand/per coalition. Smaller values indicate better estimates of v(S). See the MSEv evaluation section in the general usage vignette for details.
iterative_info: Data.table with information about the iterative estimation procedure.
iterative_shapley_est/iterative_shapley_sd: Data.tables with the estimated Shapley values/their standard deviation for each iteration (when using the iterative estimation procedure).
saving_path: Character string with the path where the (temporary) results are saved.
timing_summary: Data.table with one row and three columns: init_time and end_time give the time stamps for the start and end of the computation, respectively, while total_time_secs gives the total time in seconds for the full computation.
timing_details: List containing timing information for the different parts of the computation. summary contains the information from timing_summary. overall_timing_secs gives the time spent on the different parts of the explanation computation. main_computation_timing_secs further decomposes the main computation time into the different parts of the computation for each iteration of the iterative estimation routine, if used.
parameters: List with the parameters used in the computation.
x_train/x_explain: Data.tables with the training data used in the computation/observations to explain.
dt_vS: Data.table with the contribution function (v(S)) estimates for each coalition.
dt_samp_for_vS: Data.table with the samples used in the Monte Carlo estimation of the contribution function (v(S)). This is only available if output_args_default$keep_samp_for_vS = TRUE (defaults to FALSE) in explain().
dt_used_coalitions: Data.table with an overview of the coalitions used in the computation.
dt_valid_causal_coalitions: Data.table with the valid causal coalitions used in the computation.
dt_coal_samp_info: Data.table with information related to the coalition sampling procedure being used.

Note that the summary.shapr() function provides a nicely formatted printout with the most important information, to then invisibly return the output of the present function. The print.shapr() allows direct printing of the main results.