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

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from enum import Enum
from pm4py.util import constants
from pm4py.algo.discovery.dfg import algorithm as dfg_discovery
from pm4py.objects.conversion.log import converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import xes_constants
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, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]: """ Discovers a footprint object from an event log (the footprints are returned case-by-case) Parameters -------------- log Log parameters Parameters of the algorithm: - Parameters.ACTIVITY_KEY Returns -------------- footprints_obj List of footprints for the cases of the log """ 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) ret = [] for trace in log: dfg = dfg_discovery.apply(EventLog([trace]), parameters=parameters) parallel = {(x, y) for (x, y) in dfg if (y, x) in dfg} sequence = {(x, y) for (x, y) in dfg if not (y, x) in dfg} trace = tuple(x[activity_key] for x in trace) activities = set(trace) if len(trace) > 0: start_activities = {trace[0]} end_activities = {trace[-1]} else: start_activities = set() end_activities = set() ret.append( {Outputs.DFG.value: dfg, Outputs.SEQUENCE.value: sequence, Outputs.PARALLEL.value: parallel, Outputs.ACTIVITIES.value: activities, Outputs.START_ACTIVITIES.value: start_activities, Outputs.END_ACTIVITIES.value: end_activities, Outputs.MIN_TRACE_LENGTH.value: len(trace), Outputs.TRACE.value: trace}) return ret