Source code for pm4py.algo.discovery.inductive.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/>.
'''
from enum import Enum

from pm4py.algo.discovery.inductive.variants.im import algorithm as im_algo
from pm4py.algo.discovery.inductive.variants.im_clean import algorithm as im_clean
from pm4py.algo.discovery.inductive.variants.im_d import dfg_based
from pm4py.algo.discovery.inductive.variants.im_f import algorithm as im_f_algo
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.process_tree.obj import ProcessTree


[docs]class Variants(Enum): IMd = dfg_based IM = im_algo IMf = im_f_algo IM_CLEAN = im_clean
IM = Variants.IM IMf = Variants.IMf IMd = Variants.IMd IM_CLEAN = Variants.IM_CLEAN VERSIONS = {Variants.IMd, Variants.IM, Variants.IMf, Variants.IM_CLEAN} DEFAULT_VARIANT_LOG = IM_CLEAN DEFAULT_VARIANT_VARIANTS = IM_CLEAN DEFAULT_VARIANT_DFG = IM_CLEAN
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT_LOG) -> Tuple[PetriNet, Marking, Marking]: """ Apply the chosen IM algorithm to a log obtaining a Petri net along with an initial and final marking Parameters ------------- log Log variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ----------- net Petri net initial_marking Initial marking final_marking Final marking """ return exec_utils.get_variant(variant).apply(log, parameters=parameters)
[docs]def apply_dfg(dfg: Dict[Tuple[str, str], int], start_activities: Dict[str, int], end_activities: Dict[str, int], activities: Dict[str, int], parameters=None, variant=DEFAULT_VARIANT_DFG) -> Tuple[PetriNet, Marking, Marking]: """ Apply the chosen IM algorithm to a DFG graph obtaining a Petri net along with an initial and final marking Parameters ----------- dfg Directly-Follows graph variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ----------- net Petri net initial_marking Initial marking final_marking Final marking """ return exec_utils.get_variant(variant).apply_dfg(dfg, start_activities=start_activities, end_activities=end_activities, activities=activities, parameters=parameters)
[docs]def apply_tree(log: Union[EventLog, EventStream, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT_LOG) -> ProcessTree: """ Apply the chosen IM algorithm to a log obtaining a process tree Parameters ---------- log Log variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ---------- tree Process tree """ return exec_utils.get_variant(variant).apply_tree(log, parameters=parameters)
[docs]def apply_tree_dfg(dfg: Dict[Tuple[str, str], int], start_activities: Dict[str, int], end_activities: Dict[str, int], activities: Dict[str, int], parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT_DFG) -> ProcessTree: """ Apply the chosen IM algorithm to a DFG graph obtaining a process tree Parameters ---------- dfg Directly-follows graph variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ---------- tree Process tree """ return exec_utils.get_variant(variant).apply_tree_dfg(dfg, start_activities=start_activities, end_activities=end_activities, activities=activities, parameters=parameters)
[docs]def apply_variants(variants: Dict[Union[str, List[str]], int], parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT_VARIANTS) -> Tuple[PetriNet, Marking, Marking]: """ Apply the chosen IM algorithm to a dictionary/list/set of variants obtaining a Petri net along with an initial and final marking Parameters ----------- variants Dictionary/list/set of variants in the log variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ----------- net Petri net initial_marking Initial marking final_marking Final marking """ return exec_utils.get_variant(variant).apply_variants(variants, parameters=parameters)
[docs]def apply_tree_variants(variants: Dict[Union[str, List[str]], int], parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT_VARIANTS) -> ProcessTree: """ Apply the chosen IM algorithm to a dictionary/list/set of variants a log obtaining a process tree Parameters ---------- variants Dictionary/list/set of variants in the log variant Variant of the algorithm to apply, possible values: - Variants.IMd parameters Parameters of the algorithm, including: Parameters.ACTIVITY_KEY -> attribute of the log to use as activity name (default concept:name) Returns ---------- tree Process tree """ return exec_utils.get_variant(variant).apply_tree_variants(variants, parameters=parameters)