'''
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 pm4py.objects.conversion.log import converter as log_conversion
from pm4py.algo.discovery.log_skeleton.variants import classic
from enum import Enum
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
[docs]class Variants(Enum):
CLASSIC = classic
CLASSIC = Variants.CLASSIC
DEFAULT_VARIANT = CLASSIC
VERSIONS = {CLASSIC}
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
"""
Discover a log skeleton from an event log
Parameters
-------------
log
Event log
variant
Variant of the algorithm, possible values:
- Variants.CLASSIC
parameters
Parameters of the algorithm, including:
- the activity key (Parameters.ACTIVITY_KEY)
- the noise threshold (Parameters.NOISE_THRESHOLD)
Returns
-------------
model
Log skeleton model
"""
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_from_variants_list(var_list: List[Tuple[str, int]], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
"""
Discovers the log skeleton from the variants list
Parameters
---------------
var_list
Variants list
variant
Variant of the algorithm, possible values:
- Variants.CLASSIC
parameters
Parameters
Returns
-------------
model
Log skeleton model
"""
return exec_utils.get_variant(variant).apply_from_variants_list(var_list, parameters=parameters)