pm4py.llm.abstract_variants#

pm4py.llm.abstract_variants(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 variants abstraction of a traditional event log

Parameters:

  • log_obj – log object

  • max_len (int) – maximum length of the (string) abstraction

  • include_performance (bool) – (boolean) includes the performance of the variants in the abstraction

  • relative_frequency (bool) – (boolean) uses the relative instead of the absolute frequency of the variants

  • response_header (bool) – includes a short header before the variants, pointing to the description of the abstraction

  • primary_performance_aggregation (str) – primary aggregation to be used for the arc’s performance (default: mean, other options: median, min, max, sum, stdev)

  • secondary_performance_aggregation – (optional) secondary aggregation to be used for the arc’s performance (default None, other options: mean, median, min, max, sum, stdev)

  • activity_key (str) – the column to be used as activity

  • timestamp_key (str) – the column to be used as timestamp

  • case_id_key (str) – the column to be used as case identifier

Return type:

str

import pm4py

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