pm4py.conformance

The pm4py.conformance module contains the conformance checking algorithms implemented in pm4py

Functions

check_is_fitting(*args[, activity_key])	Checks if a trace object is fit against a process model
conformance_declare(log, declare_model[, ...])	Applies conformance checking against a DECLARE model.
conformance_diagnostics_alignments(log, *args)	Apply the alignments algorithm between a log and a process model.
conformance_diagnostics_footprints(*args)	Provide conformance checking diagnostics using footprints
conformance_diagnostics_token_based_replay(...)	Apply token-based replay for conformance checking analysis.
conformance_log_skeleton(log, log_skeleton)	Performs conformance checking using the log skeleton
conformance_temporal_profile(log, ...[, ...])	Performs conformance checking on the provided log with the provided temporal profile.
fitness_alignments(log, petri_net, ...[, ...])	Calculates the fitness using alignments The output dictionary contains the following keys: - average_trace_fitness (between 0.0 and 1.0; computed as average of the trace fitnesses) - log_fitness (between 0.0 and 1.0) - percentage_of_fitting_traces (the percentage of fit traces (from 0.0 to 100.0)
fitness_footprints(*args)	Calculates fitness using footprints.
fitness_token_based_replay(log, petri_net, ...)	Calculates the fitness using token-based replay.
generalization_tbr(log, petri_net, ...[, ...])	Computes the generalization of the model (against the event log).
precision_alignments(log, petri_net, ...[, ...])	Calculates the precision of the model w.r.t.
precision_footprints(*args)	Calculates precision using footprints
precision_token_based_replay(log, petri_net, ...)	Calculates the precision precision using token-based replay
replay_prefix_tbr(prefix, net, im, fm[, ...])	Replays a prefix (list of activities) on a given accepting Petri net, using Token-Based Replay.