pm4py.statistics.eventually_follows.polars.get module#

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class pm4py.statistics.eventually_follows.polars.get.Parameters(*values)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
KEEP_FIRST_FOLLOWING = 'keep_first_following'#
pm4py.statistics.eventually_follows.polars.get.get_partial_order_dataframe(lf: polars.LazyFrame, start_timestamp_key=None, timestamp_key='time:timestamp', case_id_glue='case:concept:name', activity_key='concept:name', keep_first_following=True, business_hours=False, business_hours_slot=None, workcalendar=None) → polars.LazyFrame[source]#

Gets the partial order between events (of the same case) in a Polars LazyFrame

Parameters:

  • lf – LazyFrame

  • start_timestamp_key – Start timestamp key (if not provided, defaulted to the timestamp_key)

  • timestamp_key – Complete timestamp

  • case_id_glue – Column of the dataframe to use as case ID

  • activity_key – Activity key

  • keep_first_following – Keep only the first event following the given event

  • business_hours – Whether to compute business hours

  • business_hours_slot – Business hours slot configuration

  • workcalendar – Work calendar configuration

Returns:

Partial order LazyFrame (with flow time between events)

Return type:

part_ord_dataframe

pm4py.statistics.eventually_follows.polars.get.apply(lf: polars.LazyFrame, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Tuple[str, str], int][source]#

Computes the eventually-follows relationships between activities

Parameters:

  • lf – Polars LazyFrame

  • parameters – Parameters of the algorithm

Returns:

Dictionary with eventually-follows relationships and their counts

Return type:

ret_dict