pm4py.algo.discovery.batches.variants.pandas module#

class pm4py.algo.discovery.batches.variants.pandas.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
RESOURCE_KEY = 'pm4py:param:resource_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
EVENT_ID_KEY = 'event_id_key'#
MERGE_DISTANCE = 'merge_distance'#
MIN_BATCH_SIZE = 'min_batch_size'#
pm4py.algo.discovery.batches.variants.pandas.apply(log: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → List[Tuple[Tuple[str, str], int, Dict[str, Any]]][source]#

Provided a Pandas 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 – Dataframe

  • 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:

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