'''
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)