pm4py.algo.simulation.playout.dfg.variants 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.playout.dfg.variants.classic 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.playout.dfg.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

MAX_NO_VARIANTS = 'max_no_variants'#

MIN_WEIGHTED_PROBABILITY = 'min_weighted_probability'#

MAX_NO_OCC_PER_ACTIVITY = 'max_no_occ_per_activitiy'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

INTERRUPT_SIMULATION_WHEN_DFG_COMPLETE = 'interrupt_simulation_when_dfg_complete'#

ADD_TRACE_IF_TAKES_NEW_ELS_TO_DFG = 'add_trace_if_takes_new_els_to_dfg'#

RETURN_VARIANTS = 'return_variants'#

MAX_EXECUTION_TIME = 'max_execution_time'#

RETURN_ONLY_IF_COMPLETE = 'return_only_if_complete'#

MIN_VARIANT_OCC = 'min_variant_occ'#

pm4py.algo.simulation.playout.dfg.variants.classic.get_node_tr_probabilities(dfg, start_activities, end_activities)[source]#

Gets the transition probabilities between the nodes of a DFG

Parameters#

dfg: DFG
start_activities: Start activities
end_activities: End activities

Returns#

weighted_start_activities: Start activities, with a relative weight going from 0 to 1
node_transition_probabilities: The transition probabilities between the nodes of the DFG (the end node is None)

pm4py.algo.simulation.playout.dfg.variants.classic.get_traces(dfg, start_activities, end_activities, parameters=None)[source]#

Gets the most probable traces from the DFG, one-by-one (iterator), until the least probable

Parameters#

dfg: Complete DFG
start_activities: Start activities
end_activities: End activities
parameters: Parameters of the algorithm, including: - Parameters.MAX_NO_OCC_PER_ACTIVITY => the maximum number of occurrences per activity in the traces of the log

(default: 2)

Returns#

yielded_trace: Trace of the simulation

pm4py.algo.simulation.playout.dfg.variants.classic.apply(dfg: Dict[Tuple[str, str], int], start_activities: Dict[str, int], end_activities: Dict[str, int], parameters: Dict[str | Parameters, Any] | None = None) → EventLog | Dict[Tuple[str, str], int][source]#

Applies the playout algorithm on a DFG, extracting the most likely traces according to the DFG

Parameters#

dfg

Complete DFG

start_activities

Start activities

end_activities

End activities

parameters

Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY => the activity key of the simulated log - Parameters.TIMESTAMP_KEY => the timestamp key of the simulated log - Parameters.MAX_NO_VARIANTS => the maximum number of variants generated by the method (default: 3000) - Parameters.MIN_WEIGHTED_PROBABILITY => the minimum overall weighted probability that makes the method stop

(default: 1)

Parameters.MAX_NO_OCC_PER_ACTIVITY => the maximum number of occurrences per activity in the traces of the log
(default: 2)
Parameters.INTERRUPT_SIMULATION_WHEN_DFG_COMPLETE => interrupts the simulation when the DFG of the simulated
log has the same keys to the DFG of the original log (all behavior is contained) (default: False)
Parameters.ADD_TRACE_IF_TAKES_NEW_ELS_TO_DFG => adds a simulated trace to the simulated log only if it adds
elements to the simulated DFG, e.g., it adds behavior; skip insertion otherwise (default: False)
Parameters.RETURN_VARIANTS => returns the traces as variants with a likely number of occurrences

Returns#

simulated_log: Simulated log

pm4py.algo.simulation.playout.dfg.variants.classic.get_trace_probability(trace, dfg, start_activities, end_activities, parameters=None)[source]#

Given a trace of a log, gets its probability given the complete DFG

Parameters#

trace: Trace of a log
dfg: Complete DFG
start_activities: Start activities of the model
end_activities: End activities of the model
parameters: Parameters of the algorithm: - Parameters.ACTIVITY_KEY => activity key

Returns#

prob: The probability of the trace according to the DFG

pm4py.algo.simulation.playout.dfg.variants.performance 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.playout.dfg.variants.performance.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

NUM_TRACES = 'num_traces'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

CASE_ID_KEY = 'pm4py:param:case_id_key'#

CASE_ARRIVAL_RATE = 'case_arrival_rate'#

PERFORMANCE_DFG = 'performance_dfg'#

PARAM_ARTIFICIAL_START_ACTIVITY = 'pm4py:param:art_start_act'#

PARAM_ARTIFICIAL_END_ACTIVITY = 'pm4py:param:art_end_act'#

pm4py.algo.simulation.playout.dfg.variants.performance.dict_based_choice(dct: Dict[str, float]) → str[source]#

Performs a weighted choice, given a dictionary associating a weight to each possible choice

Parameters#

dct: Dictionary associating a weight to each choice

Returns#

choice: Choice

pm4py.algo.simulation.playout.dfg.variants.performance.apply(frequency_dfg: Dict[Tuple[str, str], int], start_activities: Dict[str, int], end_activities: Dict[str, int], parameters: Dict[Any, Any] | None = None) → EventLog[source]#

Simulates a log out with the transition probabilities provided by the frequency DFG, and the time deltas provided by the performance DFG

Parameters#

frequency_dfg: Frequency DFG
start_activities: Start activities
end_activities: End activities
parameters: Parameters of the algorithm, including: - Parameters.NUM_TRACES: the number of traces of the simulated log - Parameters.ACTIVITY_KEY: the activity key to be used in the simulated log - Parameters.TIMESTAMP_KEY: the timestamp key to be used in the simulated log - Parameters.CASE_ID_KEY: the case identifier key to be used in the simulated log - Parameters.CASE_ARRIVAL_RATE: the average distance (in seconds) between the start of two cases (default: 1) - Parameters.PERFORMANCE_DFG: (mandatory) the performance DFG that is used for the time deltas.

Returns#

simulated_log: Simulated log