Source code for pm4py.algo.discovery.footprints.log.variants.entire_event_log

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from enum import Enum
from pm4py.util import xes_constants
from pm4py.util import constants
from pm4py.algo.discovery.dfg import algorithm as dfg_discovery
from pm4py.algo.discovery.causal import algorithm as causal_discovery
from pm4py.statistics.start_activities.log import get as get_start_activities
from pm4py.statistics.end_activities.log import get as get_end_activities
from pm4py.objects.conversion.log import converter
from pm4py.util import exec_utils
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream

[docs]class Outputs(Enum): DFG = "dfg" SEQUENCE = "sequence" PARALLEL = "parallel" START_ACTIVITIES = "start_activities" END_ACTIVITIES = "end_activities" ACTIVITIES = "activities" SKIPPABLE = "skippable" ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening" MIN_TRACE_LENGTH = "min_trace_length" TRACE = "trace"
[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
[docs]def apply(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]: """ Discovers a footprint object from an event log (the footprints of the event log are returned) Parameters -------------- log Log parameters Parameters of the algorithm: - Parameters.ACTIVITY_KEY Returns -------------- footprints_obj Footprints object """ if parameters is None: parameters = {} activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) log = converter.apply(log, variant=converter.TO_EVENT_LOG, parameters=parameters) dfg = dfg_discovery.apply(log, parameters=parameters) parallel = {(x, y) for (x, y) in dfg if (y, x) in dfg} sequence = set(causal_discovery.apply(dfg, causal_discovery.Variants.CAUSAL_ALPHA)) start_activities = set(get_start_activities.get_start_activities(log, parameters=parameters)) end_activities = set(get_end_activities.get_end_activities(log, parameters=parameters)) activities = set(y[activity_key] for x in log for y in x) return {Outputs.DFG.value: dfg, Outputs.SEQUENCE.value: sequence, Outputs.PARALLEL.value: parallel, Outputs.START_ACTIVITIES.value: start_activities, Outputs.END_ACTIVITIES.value: end_activities, Outputs.ACTIVITIES.value: activities, Outputs.MIN_TRACE_LENGTH.value: min(len(x) for x in log) if len(log) > 0 else 0}