pm4py.filtering.filter_four_eyes_principle#

pm4py.filtering.filter_four_eyes_principle(log: EventLog | DataFrame, activity1: str, activity2: str, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', resource_key: str = 'org:resource', keep_violations: bool = False) → EventLog | DataFrame[source]#

Filters out the cases of the log that violate the four-eyes principle on the provided activities.

Parameters:

log – Event log or Pandas DataFrame.
activity1 (str) – First activity.
activity2 (str) – Second activity.
activity_key (str) – Attribute to be used for the activity.
timestamp_key (str) – Attribute to be used for the timestamp.
case_id_key (str) – Attribute to be used as case identifier.
resource_key (str) – Attribute to be used as resource.
keep_violations (bool) – Boolean indicating whether to discard (if False) or retain (if True) the violations.

Returns:

Filtered event log or Pandas DataFrame.

import pm4py

filtered_dataframe = pm4py.filter_four_eyes_principle(
    dataframe,
    'Act. A',
    'Act. B',
    activity_key='concept:name',
    resource_key='org:resource',
    timestamp_key='time:timestamp',
    case_id_key='case:concept:name'
)