# Diagnostics token-based replay ------------------------------------------
#' @title Apply __the token-based replay__ algorithm between a log and a process model
#'
#' @description Apply token-based replay for conformance checking analysis. The methods return the full token-based-replay diagnostics.
#'
#' @inheritParams discover_inductive
#' @inheritParams fitness_alignments
#'
#' @return Token-based-replay diagnostics.
#'
#' @export
diagnostics_token_based_replay <- function(log,
marked_petrinet,
convert = TRUE) {
UseMethod("diagnostics_token_based_replay")
}
#' @export
diagnostics_token_based_replay.log <- function(log,
marked_petrinet,
convert = TRUE) {
pm4py_conformance <- reticulate::import("pm4py.conformance", convert = convert)
if(n_events(log) > n_activity_instances(log)) {
cli::cli_warn("Activity instances with multiple events found in log: using only complete events.")
if("eventlog" %in% class(log)) {
log %>%
filter(.data[[lifecycle_id(log)]] == "complete") -> log
}
}
log %>%
mutate(across(activity_id(log), as.character)) -> log
# prepare arguments for pm4py module
py_log <- r_to_py(log)
py_pn <- as_py_value(marked_petrinet$petrinet)
im <- as_pm4py_marking(marked_petrinet$initial_marking, py_pn)
fm <- as_pm4py_marking(marked_petrinet$final_marking, py_pn)
activity_key <- bupaR::activity_id(log)
timestamp_key <- bupaR::timestamp(log)
case_id_key <- bupaR::case_id(log)
pm4py_conformance$conformance_diagnostics_token_based_replay(log = py_log, petri_net = py_pn,
initial_marking = im,
final_marking = fm,
activity_key = activity_key,
timestamp_key = timestamp_key,
case_id_key = case_id_key)
}
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