pm4py.algo.discovery.batches.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.discovery.batches.variants.log.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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'#

MERGE_DISTANCE = 'merge_distance'#

MIN_BATCH_SIZE = 'min_batch_size'#

pm4py.algo.discovery.batches.variants.log.apply(log: EventLog, parameters: Dict[str | Parameters, Any] | None = None) → List[Tuple[Tuple[str, str], int, Dict[str, Any]]][source]#

Provided an 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#

log

Event log 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

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.discovery.batches.variants.pandas.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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)

Parameters#

log

Dataframe

parameters

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