pm4py.algo.discovery.correlation_mining package¶

Subpackages¶

pm4py.algo.discovery.correlation_mining.variants package

Submodules¶

pm4py.algo.discovery.correlation_mining.algorithm module¶

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/>.

class pm4py.algo.discovery.correlation_mining.algorithm.Variants[source]¶

Bases: enum.Enum

An enumeration.

CLASSIC = <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from '/home/docs/checkouts/readthedocs.org/user_builds/pm4py-source/checkouts/latest/pm4py/algo/discovery/correlation_mining/variants/classic.py'>¶

CLASSIC_SPLIT = <module 'pm4py.algo.discovery.correlation_mining.variants.classic_split' from '/home/docs/checkouts/readthedocs.org/user_builds/pm4py-source/checkouts/latest/pm4py/algo/discovery/correlation_mining/variants/classic_split.py'>¶

TRACE_BASED = <module 'pm4py.algo.discovery.correlation_mining.variants.trace_based' from '/home/docs/checkouts/readthedocs.org/user_builds/pm4py-source/checkouts/latest/pm4py/algo/discovery/correlation_mining/variants/trace_based.py'>¶

pm4py.algo.discovery.correlation_mining.algorithm.apply(log: Union[pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream, pandas.core.frame.DataFrame], variant=<Variants.CLASSIC: <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from '/home/docs/checkouts/readthedocs.org/user_builds/pm4py-source/checkouts/latest/pm4py/algo/discovery/correlation_mining/variants/classic.py'>>, parameters: Optional[Dict[Any, Any]] = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]¶

Applies the Correlation Miner to the event stream (a log is converted to a stream)

The approach is described in: Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. “Correlation miner: mining business process models and event correlations without case identifiers.” International Journal of Cooperative Information Systems 26.02 (2017): 1742002.

Parameters:

log – Log object
variant – Variant of the algorithm to use
parameters – Parameters of the algorithm

Returns:

dfg – Directly-follows graph
performance_dfg – Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.util module¶

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/>.

pm4py.algo.discovery.correlation_mining.util.calculate_time_match_fifo(ai, aj, times0=None)[source]¶

Associate the times between two lists of timestamps using FIFO

Parameters:	ai – First list of timestamps aj – Second list of timestamps times0 – Correspondence between execution times
Returns:	Correspondence between execution times
Return type:	times0

pm4py.algo.discovery.correlation_mining.util.calculate_time_match_rlifo(ai, aj, times1=None)[source]¶

Associate the times between two lists of timestamps using LIFO (start from end)

Parameters:	ai – First list of timestamps aj – Second list of timestamps times0 – Correspondence between execution times
Returns:	Correspondence between execution times
Return type:	times0

pm4py.algo.discovery.correlation_mining.util.get_c_matrix(PS_matrix, duration_matrix, activities, activities_counter)[source]¶

Calculates the C-matrix out of the PS matrix and the duration matrix

Parameters:	PS_matrix – PS matrix duration_matrix – Duration matrix activities – Ordered list of activities of the log activities_counter – Counter of activities
Returns:	C matrix
Return type:	c_matrix

pm4py.algo.discovery.correlation_mining.util.greedy_match_return_avg_time(ai, aj)[source]¶

Matches two list of times with a greedy method and returns the average.

Parameters:	ai – First list aj – Second list parameters – Parameters of the algorithm
Returns:	Mean of times
Return type:	times_mean

pm4py.algo.discovery.correlation_mining.util.match_return_avg_time(ai, aj, exact=False)[source]¶

Matches two list of times (exact or greedy) and returns the average.

Parameters:	ai – First list aj – Second list
Returns:	Mean of times
Return type:	times_mean

pm4py.algo.discovery.correlation_mining.util.resolve_LP(C_matrix, duration_matrix, activities, activities_counter)[source]¶

Formulates and solve the LP problem

Parameters:

C_matrix – C_matrix
duration_matrix – Duration matrix
activities – Ordered list of activities of the log
activities_counter – Counter of activities

Returns:

dfg – Directly-Follows Graph
performance_dfg – Performance DFG (containing the estimated performance for the arcs)

Module contents¶

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/>.