Source code for pm4py.algo.filtering.pandas.rework.rework_filter

'''
    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 enum import Enum
from pm4py.util import constants, xes_constants, exec_utils, pandas_utils
from typing import Optional, Dict, Any
import pandas as pd
from copy import copy




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    MIN_OCCURRENCES = "min_occurrences"
    POSITIVE = "positive"



INT_CASE_ACT_SIZE = "@@int_case_act_size"




def apply(
    df0: pd.DataFrame,
    activity: str,
    parameters: Optional[Dict[Any, Any]] = None,
) -> pd.DataFrame:
    """
    Applies the rework filter on the provided dataframe and activity.
    This filter the cases of the log having at least Parameters.MIN_OCCURRENCES (default: 2) occurrences
    of the given activity.

    It is also possible (setting Parameters.POSITIVE to False) to retrieve the cases of the log not having the
    given activity or having the activity occurred less than Parameters.MIN_OCCURRENCES times.

    Parameters
    -------------------
    df0
        Dataframe
    activity
        Activity of which the rework shall be filtered
    parameters
        Parameters of the filter, including:
        - Parameters.ACTIVITY_KEY => the attribute to use as activity
        - Parameters.CASE_ID_KEY => the attribute to use as case ID
        - Parameters.MIN_OCCURRENCES => the minimum number of occurrences for the activity
        - Parameters.POSITIVE => if True, filters the cases of the log having at least MIN_OCCURRENCES occurrences.
            if False, filters the cases of the log where such behavior does not occur.

    Returns
    -----------------
    filtered_df
        Filtered dataframe
    """
    if parameters is None:
        parameters = {}

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
    )
    case_id_key = exec_utils.get_param_value(
        Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
    )
    min_occurrences = exec_utils.get_param_value(
        Parameters.MIN_OCCURRENCES, parameters, 2
    )
    positive = exec_utils.get_param_value(
        Parameters.POSITIVE, parameters, True
    )

    df = df0.copy()
    df = df[list({activity_key, case_id_key})]
    df = df[df[activity_key] == activity]
    df[INT_CASE_ACT_SIZE] = df.groupby([activity_key, case_id_key]).cumcount()
    cases = pandas_utils.format_unique(
        df[df[INT_CASE_ACT_SIZE] >= (min_occurrences - 1)][
            case_id_key
        ].unique()
    )

    if positive:
        ret = df0[df0[case_id_key].isin(cases)]
    else:
        ret = df0[~df0[case_id_key].isin(cases)]

    ret.attrs = copy(df0.attrs) if hasattr(df0, "attrs") else {}
    return ret