pm4py.algo.simulation.montecarlo.utils package#

PM4Py – A Process Mining Library for Python

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

Submodules#

pm4py.algo.simulation.montecarlo.utils.replay module#

PM4Py – A Process Mining Library for Python

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

class pm4py.algo.simulation.montecarlo.utils.replay.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#

TIMESTAMP_KEY = 'pm4py:param: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'#

pm4py.algo.simulation.montecarlo.utils.replay.get_map_from_log_and_net(log, net, initial_marking, final_marking, force_distribution=None, parameters=None)[source]#

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