Source code for pm4py.algo.organizational_mining.sna.variants.log.jointactivities

'''
    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 collections import Counter

import numpy as np

from pm4py.objects.conversion.log import converter as log_converter
from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util import constants

from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.org.sna.obj import SNA




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY
    METRIC_NORMALIZATION = "metric_normalization"





def apply(
    log: EventLog,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> SNA:
    """
    Calculates the Joint Activities / Similar Task metric

    Parameters
    ------------
    log
        Log
    parameters
        Possible parameters of the algorithm

    Returns
    -----------
    tuple
        Tuple containing the metric matrix and the resources list. Moreover, last boolean indicates that the metric is
        directed.
    """
    from scipy.stats import pearsonr

    if parameters is None:
        parameters = {}

    resource_key = exec_utils.get_param_value(
        Parameters.RESOURCE_KEY, parameters, xes.DEFAULT_RESOURCE_KEY
    )
    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY
    )
    stream = log_converter.apply(
        log,
        variant=log_converter.TO_EVENT_STREAM,
        parameters={"deepcopy": False, "include_case_attributes": False},
    )
    activities = Counter(event[activity_key] for event in stream)
    resources = Counter(event[resource_key] for event in stream)
    activity_resource_couples = Counter(
        (event[resource_key], event[activity_key]) for event in stream
    )
    activities_keys = sorted(list(activities.keys()))
    resources_keys = sorted(list(resources.keys()))
    rsc_act_matrix = np.zeros((len(resources_keys), len(activities_keys)))
    for arc in activity_resource_couples.keys():
        i = resources_keys.index(arc[0])
        j = activities_keys.index(arc[1])
        rsc_act_matrix[i, j] += activity_resource_couples[arc]
    connections = {}
    for i in range(rsc_act_matrix.shape[0]):
        vect_i = rsc_act_matrix[i, :]
        for j in range(rsc_act_matrix.shape[0]):
            if not i == j:
                vect_j = rsc_act_matrix[j, :]
                r, p = pearsonr(vect_i, vect_j)
                connections[(resources_keys[i], resources_keys[j])] = r

    return SNA(connections, False)