Source code for pm4py.algo.conformance.log_skeleton.algorithm

'''
    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.conformance.log_skeleton.variants import classic
from pm4py.objects.log.obj import Trace
from pm4py.objects.conversion.log import converter as log_conversion
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, Tuple, List, Set
from pm4py.objects.log.obj import EventLog, Trace
import pandas as pd


[docs]class Variants(Enum): CLASSIC = classic
CLASSIC = Variants.CLASSIC DEFAULT_VARIANT = Variants.CLASSIC
[docs]def apply(obj: Union[EventLog, Trace, pd.DataFrame], model: Dict[str, Any], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> List[Set[Any]]: """ Apply log-skeleton based conformance checking given an event log/trace and a log-skeleton model Parameters -------------- obj Object (event log/trace) model Log-skeleton model variant Variant of the algorithm, possible values: Variants.CLASSIC parameters Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.CONSIDERED_CONSTRAINTS, among: equivalence, always_after, always_before, never_together, directly_follows, activ_freq Returns -------------- aligned_traces Conformance checking results for each trace: - Outputs.IS_FIT => boolean that tells if the trace is perfectly fit according to the model - Outputs.DEV_FITNESS => deviation based fitness (between 0 and 1; the more the trace is near to 1 the more fit is) - Outputs.DEVIATIONS => list of deviations in the model """ if parameters is None: parameters = {} if type(obj) is Trace: return exec_utils.get_variant(variant).apply_trace(log_conversion.apply(obj, variant=log_conversion.Variants.TO_EVENT_LOG, parameters=parameters), model, parameters=parameters) else: return exec_utils.get_variant(variant).apply_log(log_conversion.apply(obj, variant=log_conversion.Variants.TO_EVENT_LOG, parameters=parameters), model, parameters=parameters)
[docs]def apply_from_variants_list(var_list: List[List[str]], model: Dict[str, Any], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> List[Set[Any]]: """ Performs conformance checking using the log skeleton, applying it from a list of variants Parameters -------------- var_list List of variants model Log skeleton model variant Variant of the algorithm, possible values: Variants.CLASSIC parameters Parameters Returns -------------- conformance_dictio Dictionary containing, for each variant, the result of log skeleton checking """ if parameters is None: parameters = {} return exec_utils.get_variant(variant).apply_from_variants_list(var_list, model, parameters=parameters)
[docs]def get_diagnostics_dataframe(log: EventLog, conf_result: List[Set[Any]], variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> pd.DataFrame: """ Gets the diagnostics dataframe from a log and the results of log skeleton-based conformance checking Parameters -------------- log Event log conf_result Results of conformance checking Returns -------------- diagn_dataframe Diagnostics dataframe """ if parameters is None: parameters = {} return exec_utils.get_variant(variant).get_diagnostics_dataframe(log, conf_result, parameters=parameters)