pm4py.llm.abstract_dfg#

pm4py.llm.abstract_dfg(log_obj: DataFrame | EventLog | EventStream, max_len: int = 10000, include_performance: bool = True, relative_frequency: bool = False, response_header: bool = True, primary_performance_aggregation: str = 'mean', secondary_performance_aggregation: str | None = None, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') str[source]#

Obtains the DFG (Directly-Follows Graph) abstraction of a traditional event log.

Return type:

str

Parameters:
  • log_obj – The log object to abstract.

  • max_len (int) – Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).

  • include_performance (bool) – Whether to include the performance of the paths in the abstraction.

  • relative_frequency (bool) – Whether to use relative instead of absolute frequency of the paths.

  • response_header (bool) – Whether to include a short header before the paths, describing the abstraction.

  • primary_performance_aggregation (str) – Primary aggregation method for the arc’s performance (default: “mean”). Other options: “median”, “min”, “max”, “sum”, “stdev”.

  • secondary_performance_aggregation – (Optional) Secondary aggregation method for the arc’s performance (default: None). Other options: “mean”, “median”, “min”, “max”, “sum”, “stdev”.

  • activity_key (str) – The column name to be used as activity.

  • timestamp_key (str) – The column name to be used as timestamp.

  • case_id_key (str) – The column name to be used as case identifier.

Returns:

The DFG abstraction as a string.

import pm4py

log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_dfg(log))