pm4py.algo.discovery.batches.utils package

Submodules

pm4py.algo.discovery.batches.utils.detection 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.batches.utils.detection.BatchType

Bases: enum.Enum

An enumeration.

BATCHING_END = 'Batching on End'

BATCHING_START = 'Batching on Start'

CONC_BATCHING = 'Concurrent batching'

SEQ_BATCHING = 'Sequential batching'

SIMULTANEOUS = 'Simultaneous'

class pm4py.algo.discovery.batches.utils.detection.Parameters

Bases: enum.Enum

An enumeration.

MERGE_DISTANCE = 'merge_distance'

MIN_BATCH_SIZE = 'min_batch_size'

pm4py.algo.discovery.batches.utils.detection.detect(actres_grouping: Dict[Tuple[str, str], List[Tuple[float, float, str]]], parameters: Optional[Dict[Union[str, pm4py.algo.discovery.batches.utils.detection.Parameters], Any]] = None) → List[Tuple[Tuple[str, str], int, Dict[str, Any]]]

Provided an activity-resource grouping of the events of the event log, returns a list having as elements the activity-resources with the batches that are detected, divided in: - Simultaneous (all the events in the batch have identical start and end timestamps) - Batching at start (all the events in the batch have identical start timestamp) - Batching at end (all the events in the batch have identical end timestamp) - Sequential batching (for all the consecutive events, the end of the first is equal to the start of the second) - Concurrent batching (for all the consecutive events that are not sequentially matched)

The approach has been described in the following paper: Martin, N., Swennen, M., Depaire, B., Jans, M., Caris, A., & Vanhoof, K. (2015, December). Batch Processing: Definition and Event Log Identification. In SIMPDA (pp. 137-140).

Parameters:

• actres_grouping – Activity-resource grouping of events
• parameters – Parameters of the algorithm

Returns:

A (sorted) list containing tuples. Each tuple contain: - Index 0: the activity-resource for which at least one batch has been detected - Index 1: the number of batches for the given activity-resource - Index 2: a list containing all the batches. Each batch is described by:

# The start timestamp of the batch # The complete timestamp of the batch # The list of events that are executed in the batch

Return type:

list_batches

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/>.