'''
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.algo.conformance.log_skeleton.variants import classic
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, 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(
obj, model, parameters=parameters
)
else:
return exec_utils.get_variant(variant).apply_log(
obj, 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
)
