pm4py.algo.discovery.correlation_mining 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

Subpackages#

pm4py.algo.discovery.correlation_mining.variants package

Submodules#

pm4py.algo.discovery.correlation_mining.algorithm 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.discovery.correlation_mining.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

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'>#

CLASSIC = <module 'pm4py.algo.discovery.correlation_mining.variants.classic' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\correlation_mining\\variants\\classic.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: EventLog | EventStream | DataFrame, variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = 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#

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

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: C matrix

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)

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#

times_mean: Mean of times

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#

times_mean: Mean of times

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#

times0: Correspondence between execution times

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#

times0: Correspondence between execution times