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| ''' | |
| 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 pm4py.algo.conformance.tokenreplay.variants import token_replay | |
| from pm4py.statistics.variants.log import get as variants_module | |
| from pm4py.objects.petri_net.obj import PetriNet | |
| from pm4py.objects.random_variables.random_variable import RandomVariable | |
| from pm4py.objects.petri_net.utils import performance_map | |
| from pm4py.util import exec_utils, xes_constants | |
| from pm4py.algo.conformance.tokenreplay import algorithm as executor | |
| from enum import Enum | |
| from pm4py.util import constants | |
| from copy import copy | |
| class Parameters(Enum): | |
| ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY | |
| TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY | |
| TOKEN_REPLAY_VARIANT = "token_replay_variant" | |
| PARAM_NUM_SIMULATIONS = "num_simulations" | |
| PARAM_FORCE_DISTRIBUTION = "force_distribution" | |
| PARAM_ENABLE_DIAGNOSTICS = "enable_diagnostics" | |
| PARAM_DIAGN_INTERVAL = "diagn_interval" | |
| PARAM_CASE_ARRIVAL_RATIO = "case_arrival_ratio" | |
| PARAM_PROVIDED_SMAP = "provided_stochastic_map" | |
| PARAM_MAP_RESOURCES_PER_PLACE = "map_resources_per_place" | |
| PARAM_DEFAULT_NUM_RESOURCES_PER_PLACE = "default_num_resources_per_place" | |
| PARAM_SMALL_SCALE_FACTOR = "small_scale_factor" | |
| PARAM_MAX_THREAD_EXECUTION_TIME = "max_thread_exec_time" | |
| def get_map_from_log_and_net(log, net, initial_marking, final_marking, force_distribution=None, parameters=None): | |
| """ | |
| Get transition stochastic distribution map given the log and the Petri net | |
| Parameters | |
| ----------- | |
| log | |
| Event log | |
| net | |
| Petri net | |
| initial_marking | |
| Initial marking of the Petri net | |
| final_marking | |
| Final marking of the Petri net | |
| force_distribution | |
| If provided, distribution to force usage (e.g. EXPONENTIAL) | |
| parameters | |
| Parameters of the algorithm, including: | |
| Parameters.ACTIVITY_KEY -> activity name | |
| Parameters.TIMESTAMP_KEY -> timestamp key | |
| Returns | |
| ----------- | |
| stochastic_map | |
| Map that to each transition associates a random variable | |
| """ | |
| stochastic_map = {} | |
| if parameters is None: | |
| parameters = {} | |
| token_replay_variant = exec_utils.get_param_value(Parameters.TOKEN_REPLAY_VARIANT, parameters, | |
| executor.Variants.TOKEN_REPLAY) | |
| activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) | |
| timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, | |
| xes_constants.DEFAULT_TIMESTAMP_KEY) | |
| parameters_variants = {constants.PARAMETER_CONSTANT_ACTIVITY_KEY: activity_key} | |
| variants_idx = variants_module.get_variants_from_log_trace_idx(log, parameters=parameters_variants) | |
| variants = variants_module.convert_variants_trace_idx_to_trace_obj(log, variants_idx) | |
| parameters_tr = copy(parameters) | |
| parameters_tr[token_replay.Parameters.ACTIVITY_KEY] = activity_key | |
| parameters_tr[token_replay.Parameters.VARIANTS] = variants | |
| parameters_ses = copy(parameters) | |
| # do the replay | |
| aligned_traces = executor.apply(log, net, initial_marking, final_marking, variant=token_replay_variant, | |
| parameters=parameters_tr) | |
| element_statistics = performance_map.single_element_statistics(log, net, initial_marking, | |
| aligned_traces, variants_idx, | |
| activity_key=activity_key, | |
| timestamp_key=timestamp_key, | |
| parameters=parameters_ses) | |
| for el in element_statistics: | |
| if type(el) is PetriNet.Transition and "performance" in element_statistics[el]: | |
| values = element_statistics[el]["performance"] | |
| rand = RandomVariable() | |
| rand.calculate_parameters(values, force_distribution=force_distribution) | |
| no_of_times_enabled = element_statistics[el]['no_of_times_enabled'] | |
| no_of_times_activated = element_statistics[el]['no_of_times_activated'] | |
| if no_of_times_enabled > 0: | |
| rand.set_weight(float(no_of_times_activated) / float(no_of_times_enabled)) | |
| else: | |
| rand.set_weight(0.0) | |
| stochastic_map[el] = rand | |
| return stochastic_map | |