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

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
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, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd


[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY METRIC_NORMALIZATION = "metric_normalization"
[docs]def apply(log: EventLog, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> List[Any]: """ 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] metric_matrix = np.zeros((len(resources_keys), len(resources_keys))) 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) metric_matrix[i, j] = r return [metric_matrix, resources_keys, False]