Source code for pm4py.algo.discovery.correlation_mining.algorithm

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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
[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)