'''
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 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, pandas_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 pandas_utils.check_is_pandas_dataframe(log):
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,
)
