pm4py.analysis

Functions

behavioral_similarity(*args)	Computes the behavioral similarity (footprints-based) between two process models.
check_is_workflow_net(net)	Checks if the input Petri net satisfies the WF-net (Workflow net) conditions: 1.
check_soundness(petri_net, initial_marking, ...)	Checks if a given Petri net is a sound Workflow net (WF-net).
cluster_log(log[, sklearn_clusterer, ...])	Applies clustering to the provided event log by extracting profiles for the log's traces and clustering them using a Scikit-Learn clusterer (default is K-Means with two clusters).
compute_emd(language1, language2)	Computes the Earth Mover Distance (EMD) between two stochastic languages.
construct_synchronous_product_net(trace, ...)	Constructs the synchronous product net between a trace and a Petri net process model.
embeddings_similarity(*args)	Computes the embeddings similarity between two process models, following the approach described in:
generate_marking(net, place_or_dct_places)	Generates a marking for a given Petri net based on specified places and token counts.
get_activity_labels(*args)	Gets the activity labels from the specified event log / process model.
get_enabled_transitions(net, marking)	Retrieves the set of transitions that are enabled in a given marking of a Petri net.
insert_artificial_start_end(log[, ...])	Inserts artificial start and end activities into an event log or a Pandas DataFrame.
insert_case_arrival_finish_rate(log[, ...])	Inserts arrival and finish rate information for each case into a Pandas DataFrame.
insert_case_service_waiting_time(log[, ...])	Inserts service time, waiting time, and sojourn time information for each case into a Pandas DataFrame.
label_sets_similarity(*args[, threshold])	Computes the label sets similarity between two process models.
map_labels_from_second_model(*args[, threshold])	Maps the labels from the second process model into the first.
maximal_decomposition(net, im, fm)	Calculates the maximal decomposition of an accepting Petri net into its maximal components.
reduce_petri_net_implicit_places(net, im, fm)	Reduces the number of implicit places in the provided Petri net.
reduce_petri_net_invisibles(net)	Reduces the number of invisible transitions in the provided Petri net.
replace_activity_labels(string_dictio, *args)	Replace the activity labels in the specified process model.
simplicity_petri_net(net, im, fm[, variant])	Computes the simplicity metric for a given Petri net model.
solve_extended_marking_equation(trace, ...)	Computes a heuristic value (an underestimation of the cost of an alignment) between a trace and a synchronous product net using the extended marking equation with the standard cost function.
solve_marking_equation(petri_net, ...[, ...])	Solves the marking equation of a Petri net using an Integer Linear Programming (ILP) approach.
structural_similarity(*args)	Computes the structural similarity between two semi-block-structured process models, following an approach similar to: