pm4py.algo.discovery.minimum_self_distance.variants package

Submodules

pm4py.algo.discovery.minimum_self_distance.variants.log module

class pm4py.algo.discovery.minimum_self_distance.variants.log.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
pm4py.algo.discovery.minimum_self_distance.variants.log.apply(log: Union[pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventLog, pm4py.objects.log.obj.EventStream], parameters: Optional[Dict[str, Any]] = None) → Dict[str, int][source]

This algorithm computes the minimum self-distance for each activity observed in an event log. The self distance of a in <a> is infinity, of a in <a,a> is 0, in <a,b,a> is 1, etc. The minimum self distance is the minimal observed self distance value in the event log. The activity key needs to be specified in the parameters input object (if None, default value ‘concept:name’ is used).

Parameters
  • log – event log (either EventLog or EventStream)

  • parameters – parameters object;

Returns

Return type

dict mapping an activity to its self-distance, if it exists, otherwise it is not part of the dict.

pm4py.algo.discovery.minimum_self_distance.variants.pandas module

class pm4py.algo.discovery.minimum_self_distance.variants.pandas.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
CASE_ID_KEY = 'case_id_glue'
pm4py.algo.discovery.minimum_self_distance.variants.pandas.apply(df, parameters=None)[source]

This algorithm computes the minimum self-distance for each activity observed in an event log. The self distance of a in <a> is infinity, of a in <a,a> is 0, in <a,b,a> is 1, etc. The minimum self distance is the minimal observed self distance value in the event log. The activity key needs to be specified in the parameters input object (if None, default value ‘concept:name’ is used).

Parameters
  • df – Pandas dataframe

  • parameters – parameters object;

Returns

Return type

dict mapping an activity to its self-distance, if it exists, otherwise it is not part of the dict.

Module contents