pm4py.algo.discovery.correlation_mining package
Subpackages
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(value)[source]
Bases:
enum.Enum
An enumeration.
- CLASSIC = <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic.py'>
- CLASSIC_SPLIT = <module 'pm4py.algo.discovery.correlation_mining.variants.classic_split' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic_split.py'>
- TRACE_BASED = <module 'pm4py.algo.discovery.correlation_mining.variants.trace_based' from 'C:\\Users\\berti\\pm4py-core\\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, 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/>.