'''
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.algo.discovery.correlation_mining.variants import classic_split, classic, trace_based
from pm4py.util import exec_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
[docs]class Variants(Enum):
CLASSIC_SPLIT = classic_split
CLASSIC = classic
TRACE_BASED = trace_based
DEFAULT_VARIANT = Variants.CLASSIC
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]]:
"""
Applies the Correlation Miner to the event stream (a log is converted to a stream)
The approach is described in:
Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. "Correlation miner: mining business process models and event
correlations without case identifiers." International Journal of Cooperative Information Systems 26.02 (2017):
1742002.
Parameters
-------------
log
Log object
variant
Variant of the algorithm to use
parameters
Parameters of the algorithm
Returns
--------------
dfg
Directly-follows graph
performance_dfg
Performance DFG (containing the estimated performance for the arcs)
"""
if parameters is None:
parameters = {}
return exec_utils.get_variant(variant).apply(log, parameters=parameters)