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(value)[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(value)[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(value)[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/>.