Source code for pm4py.visualization.petrinet.variants.greedy_decoration_performance

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from pm4py.algo.discovery.dfg.variants import native, performance
from pm4py.statistics.attributes.log import get as attr_get
from pm4py.util import xes_constants as xes
from pm4py.visualization.petrinet.common import visualize
from pm4py.visualization.petrinet.util.vis_trans_shortest_paths import get_decorations_from_dfg_spaths_acticount
from pm4py.visualization.petrinet.util.vis_trans_shortest_paths import get_shortest_paths
from pm4py.visualization.petrinet.parameters import Parameters
from pm4py.util import exec_utils

[docs]def get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant="frequency"): """ Get a decorated net according to the specified variant (decorate Petri net based on DFG) Parameters ------------ net Petri net initial_marking Initial marking final_marking Final marking log Log to use to decorate the Petri net parameters Algorithm parameters variant Specify if the decoration should take into account the frequency or the performance Returns ------------ gviz GraphViz object """ if parameters is None: parameters = {} aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters, "sum" if "frequency" in variant else "mean") activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY) stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {}) # we find the DFG if variant == "performance": dfg = performance.performance(log, parameters=parameters) else: dfg = native.native(log, parameters=parameters) # we find shortest paths spaths = get_shortest_paths(net) # we find the number of activities occurrences in the log activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters) aggregated_statistics = get_decorations_from_dfg_spaths_acticount(net, dfg, spaths, activities_count, variant=variant, aggregation_measure=aggregation_measure, stat_locale=stat_locale) return visualize.apply(net, initial_marking, final_marking, parameters=parameters, decorations=aggregated_statistics)
[docs]def apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None): """ Apply performance decoration through greedy algorithm (decorate Petri net based on DFG) Parameters ------------ net Petri net initial_marking Initial marking final_marking Final marking log Log to use to decorate the Petri net aggregated_statistics Dictionary containing the frequency statistics parameters Algorithm parameters Returns ------------ gviz GraphViz object """ del aggregated_statistics return get_decorated_net(net, initial_marking, final_marking, log, parameters=parameters, variant="performance")