Source code for pm4py.algo.discovery.batches.algorithm

'''
    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)