Source code for pm4py.algo.discovery.performance_spectrum.variants.log_disconnected

'''
    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 enum import Enum

from pm4py.objects.log.util import sorting
from pm4py.util import constants, exec_utils
from pm4py.util import points_subset
from pm4py.util import xes_constants as xes
from pm4py.objects.log.util import basic_filter
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream


[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY PARAMETER_SAMPLE_SIZE = "sample_size" SORT_LOG_REQUIRED = "sort_log_required"
[docs]def apply(log: EventLog, list_activities: List[str], sample_size: int, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]: """ Finds the disconnected performance spectrum provided a log and a list of activities Parameters ------------- log Log list_activities List of activities interesting for the performance spectrum (at least two) sample_size Size of the sample parameters Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.TIMESTAMP_KEY Returns ------------- points Points of the performance spectrum """ if parameters is None: parameters = {} sort_log_required = exec_utils.get_param_value(Parameters.SORT_LOG_REQUIRED, parameters, True) all_acti_combs = set(tuple(list_activities[j:j + i]) for i in range(2, len(list_activities) + 1) for j in range(0, len(list_activities) - i + 1)) two_acti_combs = set((list_activities[i], list_activities[i + 1]) for i in range(len(list_activities) - 1)) activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY) timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY) case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, xes.DEFAULT_TRACEID_KEY) parameters[Parameters.ATTRIBUTE_KEY] = activity_key log = basic_filter.filter_log_events_attr(log, list_activities, parameters=parameters) if sort_log_required: log = sorting.sort_timestamp_log(log, timestamp_key=timestamp_key) points = [] for trace in log: matches = [(i, i + 1) for i in range(len(trace) - 1) if (trace[i][activity_key], trace[i + 1][activity_key]) in two_acti_combs] i = 0 while i < len(matches) - 1: matchAct = (trace[mi][activity_key] for mi in (matches[i] + matches[i + 1][1:])) if matches[i][-1] == matches[i + 1][0] and matchAct in all_acti_combs: matches[i] = matches[i] + matches[i + 1][1:] del matches[i + 1] i = 0 else: i += 1 if matches: matches = set(matches) timest_comb = [{'points': [(trace[i][activity_key], trace[i][timestamp_key].timestamp()) for i in match]} for match in matches] for p in timest_comb: p['case_id'] = trace.attributes[case_id_key] points += timest_comb points = sorted(points, key=lambda x: min(x['points'], key=lambda x: x[1])[1]) if len(points) > sample_size: points = points_subset.pick_chosen_points_list(sample_size, points) return points