pm4py.algo.discovery.correlation_mining.variants package¶

Submodules¶

pm4py.algo.discovery.correlation_mining.variants.classic 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.variants.classic.Parameters[source]¶

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'¶

EXACT_TIME_MATCHING = 'exact_time_matching'¶

INDEX_KEY = 'index_key'¶

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'¶

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'¶

pm4py.algo.discovery.correlation_mining.variants.classic.apply(log: Union[pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream, pandas.core.frame.DataFrame], parameters: Optional[Dict[Union[str, pm4py.algo.discovery.correlation_mining.variants.classic.Parameters], Any]] = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]¶

Apply the correlation miner to an event stream (other types of logs are converted to that)

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
parameters – Parameters of the algorithm

Returns:

dfg – DFG
performance_dfg – Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.classic.get_PS_dur_matrix(activities_grouped, activities, parameters=None)[source]¶

Combined methods to get the two matrixes

Parameters:

activities_grouped – Grouped activities
activities – List of activities of the log
parameters – Parameters of the algorithm

Returns:

PS_matrix – Precede-succeed matrix
duration_matrix – Duration matrix

pm4py.algo.discovery.correlation_mining.variants.classic.get_duration_matrix(activities, activities_grouped, timestamp_key, start_timestamp_key, exact=False)[source]¶

Calculates the duration matrix

Parameters:	activities – Ordered list of activities of the log activities_grouped – Grouped list of activities timestamp_key – Timestamp key start_timestamp_key – Start timestamp key (events start) exact – Performs an exact matching of the times (True/False)
Returns:	Duration matrix
Return type:	duration_matrix

pm4py.algo.discovery.correlation_mining.variants.classic.get_precede_succeed_matrix(activities, activities_grouped, timestamp_key, start_timestamp_key)[source]¶

Calculates the precede succeed matrix

Parameters:	activities – Ordered list of activities of the log activities_grouped – Grouped list of activities timestamp_key – Timestamp key start_timestamp_key – Start timestamp key (events start)
Returns:	Precede succeed matrix
Return type:	precede_succeed_matrix

pm4py.algo.discovery.correlation_mining.variants.classic.preprocess_log(log, activities=None, parameters=None)[source]¶

Preprocess a log to enable correlation mining

Parameters:

log – Log object
activities – (if provided) list of activities of the log
parameters – Parameters of the algorithm

Returns:

transf_stream – Transformed stream
activities_grouped – Grouped activities
activities – List of activities of the log

pm4py.algo.discovery.correlation_mining.variants.classic.resolve_lp_get_dfg(PS_matrix, duration_matrix, activities, activities_counter)[source]¶

Resolves a LP problem to get a DFG

Parameters:

PS_matrix – Precede-succeed matrix
duration_matrix – Duration matrix
activities – List of activities of the log
activities_counter – Counter of the activities

Returns:

dfg – DFG
performance_dfg – Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.classic_split 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.variants.classic_split.Parameters[source]¶

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'¶

SAMPLE_SIZE = 'sample_size'¶

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'¶

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'¶

pm4py.algo.discovery.correlation_mining.variants.classic_split.apply(log: Union[pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream, pandas.core.frame.DataFrame], parameters: Optional[Dict[Union[str, pm4py.algo.discovery.correlation_mining.variants.classic_split.Parameters], Any]] = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]¶

Applies the correlation miner (splits the log in smaller chunks)

Parameters:

log – Log object
parameters – Parameters of the algorithm

Returns:

dfg – Frequency DFG
performance_dfg – Performance DFG

pm4py.algo.discovery.correlation_mining.variants.trace_based 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.variants.trace_based.Parameters[source]¶

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'¶

CASE_ID_KEY = 'pm4py:param:case_id_key'¶

INDEX_KEY = 'index_key'¶

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'¶

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'¶

pm4py.algo.discovery.correlation_mining.variants.trace_based.apply(log: Union[pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream, pandas.core.frame.DataFrame], parameters: Optional[Dict[Union[str, pm4py.algo.discovery.correlation_mining.variants.trace_based.Parameters], Any]] = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]¶

Novel approach of correlation mining, that creates the PS-matrix and the duration matrix using the order list of events of each trace of the log

Parameters:

log – Event log
parameters – Parameters

Returns:

dfg – DFG
performance_dfg – Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_PS_duration_matrix(activities, trace_grouped_list, parameters=None)[source]¶

Gets the precede-succeed matrix

Parameters:

activities – Activities
trace_grouped_list – Grouped list of simplified traces (per activity)
parameters – Parameters of the algorithm

Returns:

PS_matrix – precede-succeed matrix
duration_matrix – Duration matrix

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_duration_matrix(activities, trace_grouped_list, timestamp_key, start_timestamp_key)[source]¶

Calculates the duration matrix

Parameters:	activities – Sorted list of activities of the log trace_grouped_list – A list of lists of lists, containing for each trace and each activity the events having such activity timestamp_key – The key to be used as timestamp start_timestamp_key – The key to be used as start timestamp
Returns:	The duration matrix
Return type:	mat

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_precede_succeed_matrix(activities, trace_grouped_list, timestamp_key, start_timestamp_key)[source]¶

Calculates the precede succeed matrix

Parameters:	activities – Sorted list of activities of the log trace_grouped_list – A list of lists of lists, containing for each trace and each activity the events having such activity timestamp_key – The key to be used as timestamp start_timestamp_key – The key to be used as start timestamp
Returns:	The precede succeed matrix
Return type:	mat

pm4py.algo.discovery.correlation_mining.variants.trace_based.preprocess_log(log, activities=None, activities_counter=None, parameters=None)[source]¶

Preprocess the log to get a grouped list of simplified traces (per activity)

Parameters:

log – Log object
activities – (if provided) activities of the log
activities_counter – (if provided) counter of the activities of the log
parameters – Parameters of the algorithm

Returns:

traces_list – List of simplified traces of the log
trace_grouped_list – Grouped list of simplified traces (per activity)
activities – Activities of the log
activities_counter – Activities counter

pm4py.algo.discovery.correlation_mining.variants.trace_based.resolve_lp_get_dfg(PS_matrix, duration_matrix, activities, activities_counter)[source]¶

Resolves a LP problem to get a DFG

Parameters:

PS_matrix – Precede-succeed matrix
duration_matrix – Duration matrix
activities – List of activities of the log
activities_counter – Counter for the activities of the log

Returns:

dfg – Frequency DFG
performance_dfg – Performance DFG

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/>.