Source code for pm4py.algo.discovery.heuristics.algorithm

'''
    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/>.
'''
import pkgutil
from enum import Enum

from pm4py.algo.discovery.heuristics.variants import classic, plusplus
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.objects.heuristics_net.obj import HeuristicsNet
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.objects.petri_net.obj import PetriNet, Marking


[docs]class Variants(Enum): CLASSIC = classic PLUSPLUS = plusplus
CLASSIC = Variants.CLASSIC DEFAULT_VARIANT = CLASSIC VERSIONS = {CLASSIC}
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None, variant=CLASSIC) -> Tuple[PetriNet, Marking, Marking]: """ Discovers a Petri net using Heuristics Miner Parameters ------------ log Event log parameters Possible parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.DFG_PRE_CLEANING_NOISE_THRESH - Parameters.LOOP_LENGTH_TWO_THRESH variant Variant of the algorithm: - Variants.CLASSIC - Variants.PLUSPLUS Returns ------------ net Petri net im Initial marking fm Final marking """ if pkgutil.find_loader("pandas"): import pandas if isinstance(log, pandas.core.frame.DataFrame): return exec_utils.get_variant(variant).apply_pandas(log, parameters=parameters) return exec_utils.get_variant(variant).apply(log_conversion.apply(log, variant=log_conversion.Variants.TO_EVENT_LOG, parameters=parameters), parameters=parameters)
[docs]def apply_dfg(dfg: Dict[Tuple[str, str], int], activities=None, activities_occurrences=None, start_activities=None, end_activities=None, parameters=None, variant=CLASSIC) -> Tuple[PetriNet, Marking, Marking]: """ Discovers a Petri net using Heuristics Miner Parameters ------------ dfg Directly-Follows Graph activities (If provided) list of activities of the log activities_occurrences (If provided) dictionary of activities occurrences start_activities (If provided) dictionary of start activities occurrences end_activities (If provided) dictionary of end activities occurrences parameters Possible parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.DFG_PRE_CLEANING_NOISE_THRESH - Parameters.LOOP_LENGTH_TWO_THRESH variant Variant of the algorithm: - Variants.CLASSIC Returns ------------ net Petri net im Initial marking fm Final marking """ return exec_utils.get_variant(variant).apply_dfg(dfg, activities=activities, activities_occurrences=activities_occurrences, start_activities=start_activities, end_activities=end_activities, parameters=parameters)
[docs]def apply_heu(log: Union[EventLog, EventStream, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None, variant=CLASSIC) -> HeuristicsNet: """ Discovers an Heuristics Net using Heuristics Miner Parameters ------------ log Event log parameters Possible parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.DFG_PRE_CLEANING_NOISE_THRESH - Parameters.LOOP_LENGTH_TWO_THRESH variant Variant of the algorithm: - Variants.CLASSIC Returns ------------ net Petri net im Initial marking fm Final marking """ return exec_utils.get_variant(variant).apply_heu(log_conversion.apply(log, variant=log_conversion.Variants.TO_EVENT_LOG, parameters=parameters), parameters=parameters)
[docs]def apply_heu_dfg(dfg: Dict[Tuple[str, str], int], activities=None, activities_occurrences=None, start_activities=None, end_activities=None, parameters=None, variant=CLASSIC) -> HeuristicsNet: """ Discovers an Heuristics Net using Heuristics Miner Parameters ------------ dfg Directly-Follows Graph activities (If provided) list of activities of the log activities_occurrences (If provided) dictionary of activities occurrences start_activities (If provided) dictionary of start activities occurrences end_activities (If provided) dictionary of end activities occurrences parameters Possible parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.DFG_PRE_CLEANING_NOISE_THRESH - Parameters.LOOP_LENGTH_TWO_THRESH variant Variant of the algorithm: - Variants.CLASSIC Returns ------------ net Petri net im Initial marking fm Final marking """ return exec_utils.get_variant(variant).apply_heu_dfg(dfg, activities=activities, activities_occurrences=activities_occurrences, start_activities=start_activities, end_activities=end_activities, parameters=parameters)