'''
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 import util as pmutil
from pm4py.algo.discovery.alpha import variants
from pm4py.algo.discovery.dfg.adapters.pandas import df_statistics
from pm4py.statistics.start_activities.pandas import (
get as start_activities_get,
)
from pm4py.statistics.end_activities.pandas import get as end_activities_get
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.util import exec_utils
from pm4py.util import xes_constants as xes_util
from pm4py.util import constants, pandas_utils
from enum import Enum
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 Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
[docs]
class Variants(Enum):
ALPHA_VERSION_CLASSIC = variants.classic
ALPHA_VERSION_PLUS = variants.plus
ALPHA_VERSION_CLASSIC = Variants.ALPHA_VERSION_CLASSIC
ALPHA_VERSION_PLUS = Variants.ALPHA_VERSION_PLUS
DEFAULT_VARIANT = ALPHA_VERSION_CLASSIC
VERSIONS = {Variants.ALPHA_VERSION_CLASSIC, Variants.ALPHA_VERSION_PLUS}
[docs]
def apply(
log: Union[EventLog, EventStream, pd.DataFrame],
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
variant=DEFAULT_VARIANT,
) -> Tuple[PetriNet, Marking, Marking]:
"""
Apply the Alpha Miner on top of a log
Parameters
-----------
log
Log
variant
Variant of the algorithm to use:
- Variants.ALPHA_VERSION_CLASSIC
- Variants.ALPHA_VERSION_PLUS
parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Name of the attribute that contains the activity
Returns
-----------
net
Petri net
marking
Initial marking
final_marking
Final marking
"""
if parameters is None:
parameters = {}
case_id_glue = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, pmutil.constants.CASE_CONCEPT_NAME
)
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_util.DEFAULT_NAME_KEY
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY, parameters, None
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY, parameters, xes_util.DEFAULT_TIMESTAMP_KEY
)
if (
pandas_utils.check_is_pandas_dataframe(log)
and variant == ALPHA_VERSION_CLASSIC
):
dfg = df_statistics.get_dfg_graph(
log,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
start_timestamp_key=start_timestamp_key,
)
start_activities = start_activities_get.get_start_activities(
log, parameters=parameters
)
end_activities = end_activities_get.get_end_activities(
log, parameters=parameters
)
return exec_utils.get_variant(variant).apply_dfg_sa_ea(
dfg, start_activities, end_activities, parameters=parameters
)
return exec_utils.get_variant(variant).apply(
log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG),
parameters,
)
[docs]
def apply_dfg(
dfg: Dict[Tuple[str, str], int],
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
variant=ALPHA_VERSION_CLASSIC,
) -> Tuple[PetriNet, Marking, Marking]:
"""
Apply Alpha Miner directly on top of a DFG graph
Parameters
-----------
dfg
Directly-Follows graph
variant
Variant of the algorithm to use (classic)
parameters
Possible parameters of the algorithm, including:
activity key -> Name of the attribute that contains the activity
Returns
-----------
net
Petri net
marking
Initial marking
final_marking
Final marking
"""
if parameters is None:
parameters = {}
return exec_utils.get_variant(variant).apply_dfg(dfg, parameters)
