pm4py.objects.conversion.log.variants.to_nx module#

PM4Py – A Process Mining Library for Python

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class pm4py.objects.conversion.log.variants.to_nx.Parameters(*values)[source]#

Bases: Enum

INCLUDE_DF = 'include_df'#
CASE_ID_ATTRIBUTE = 'case_id_attribute'#
OTHER_CASE_ATTRIBUTES_AS_NODES = 'other_case_attributes_as_nodes'#
EVENT_ATTRIBUTES_AS_NODES = 'event_attributes_as_nodes'#
pm4py.objects.conversion.log.variants.to_nx.apply(log_obj: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None)[source]#

Converts an event log object to a NetworkX DiGraph object. The nodes of the graph are the events, the cases (and possibly the attributes of the log). The edges are: - Connecting each event to the corresponding case (BELONGS_TO type) - Connecting every event to the directly-following one (DF type, if enabled) - Connecting every case/event to the given attribute values (ATTRIBUTE_EDGE type)

Parameters:

  • log_obj – Log object (EventLog, EventStream, Pandas dataframe)

  • parameters – Parameters of the conversion, including: - Parameters.INCLUDE_DF => include the directly-follows graph relation in the graph - Parameters.CASE_ID_ATTRIBUTE => specify which attribute at the case level should be considered the case ID - Parameters.OTHER_CASE_ATTRIBUTES_AS_NODES => specify which attributes at the case level should be inserted in the graph as nodes (other than the caseID) (list, default empty) - Parameters.EVENT_ATTRIBUTES_AS_NODES => specify which attributes at the event level should be inserted in the graph as nodes (list, default empty)

Returns:

NetworkX DiGraph object

Return type:

nx_digraph