'''
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/>.
'''
from enum import Enum
from typing import Union, Optional, Dict, Any, List, Tuple
import pandas as pd
from pm4py.algo.discovery.batches.variants import pandas, log
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils
[docs]class Variants(Enum):
LOG = log
PANDAS = pandas
[docs]def apply(log: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None) -> List[
Tuple[Tuple[str, str], int, Dict[str, Any]]]:
"""
Provided an event log / dataframe, 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
-------------------
log
Event log / dataframe object
parameters
Parameters of the algorithm:
- ACTIVITY_KEY => the attribute that should be used as activity
- RESOURCE_KEY => the attribute that should be used as resource
- START_TIMESTAMP_KEY => the attribute that should be used as start timestamp
- TIMESTAMP_KEY => the attribute that should be used as timestamp
- CASE_ID_KEY => the attribute that should be used as case identifier
- MERGE_DISTANCE => the maximum time distance between non-overlapping intervals in order for them to be
considered belonging to the same batch (default: 15*60 15 minutes)
- MIN_BATCH_SIZE => the minimum number of events for a batch to be considered (default: 2)
Returns
------------------
list_batches
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
"""
if parameters is None:
parameters = {}
if type(log) is pd.DataFrame:
return exec_utils.get_variant(Variants.PANDAS).apply(log, parameters=parameters)
else:
return exec_utils.get_variant(Variants.LOG).apply(log, parameters=parameters)