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

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
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
from pm4py.objects.log.obj import EventLog




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"





class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY





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
        ),
    }