Source code for pm4py.algo.discovery.ocel.interleavings.algorithm

'''
    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.discovery.ocel.interleavings.variants import (
    timestamp_interleavings,
)
from enum import Enum
from pm4py.util import exec_utils
import pandas as pd
from typing import Optional, Dict, Any


[docs] class Variants(Enum): TIMESTAMP_INTERLEAVINGS = timestamp_interleavings
[docs] def apply( left_df: pd.DataFrame, right_df: pd.DataFrame, case_relations: pd.DataFrame, variant=Variants.TIMESTAMP_INTERLEAVINGS, parameters: Optional[Dict[Any, Any]] = None, ): """ Discover the interleavings between two dataframes, given also a dataframe about the relations of the cases. Parameters ----------------- left_df Left dataframe right_df Right dataframe case_relations Dictionary associating the cases of the first dataframe (column: case:concept:name_LEFT) to the cases of the second dataframe (column: case:concept:name_RIGHT) variant Variant of the algorithm to be used, possible values: - Variants.TIMESTAMP_INTERLEAVINGS parameters Variant-specific parameters Returns ----------------- interleavings Interleavings dataframe """ return exec_utils.get_variant(variant).apply( left_df, right_df, case_relations, parameters )