Source code for pm4py.algo.discovery.minimum_self_distance.variants.pandas

'''
    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 exec_utils, constants, xes_constants, pandas_utils




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY



CONCAT_ACT_CASE = "@@concat_act_case"
INT_CASE_ACT_SIZE = "@@int_case_act_size"
DIFF_INDEX = "@@diff_index"




def apply(df, parameters=None):
    """
    This algorithm computes the minimum self-distance for each activity observed in an event log.
    The self distance of a in <a> is infinity, of a in <a,a> is 0, in <a,b,a> is 1, etc.
    The minimum self distance is the minimal observed self distance value in the event log.
    The activity key needs to be specified in the parameters input object (if None, default value 'concept:name' is used).


    Parameters
    ----------
    df
        Pandas dataframe
    parameters
        parameters object;

    Returns
    -------
        dict mapping an activity to its self-distance, if it exists, otherwise it is not part of the dict.
    """
    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
    )

    df = df.copy()
    df = df[list({activity_key, case_id_key})]
    df = pandas_utils.insert_ev_in_tr_index(
        df,
        case_id=case_id_key,
        column_name=constants.DEFAULT_INDEX_IN_TRACE_KEY,
    )
    df[CONCAT_ACT_CASE] = df[case_id_key] + df[activity_key]
    df[INT_CASE_ACT_SIZE] = df.groupby(CONCAT_ACT_CASE).cumcount()
    df_last = df.groupby(CONCAT_ACT_CASE).last().reset_index()
    df_last = df_last[df_last[INT_CASE_ACT_SIZE] > 0]
    df = df[df[CONCAT_ACT_CASE].isin(df_last[CONCAT_ACT_CASE])]
    df = df.merge(df, on=[activity_key, case_id_key], suffixes=("", "_2"))
    df[DIFF_INDEX] = (
        df[constants.DEFAULT_INDEX_IN_TRACE_KEY + "_2"]
        - df[constants.DEFAULT_INDEX_IN_TRACE_KEY]
        - 1
    )
    df = df[df[DIFF_INDEX] >= 0]
    ret = df.groupby(activity_key)[DIFF_INDEX].agg("min").to_dict()
    return ret