pm4py.filtering.filter_log_relative_occurrence_event_attribute#
- pm4py.filtering.filter_log_relative_occurrence_event_attribute(log: EventLog | DataFrame, min_relative_stake: float, attribute_key: str = 'concept:name', level: str = 'cases', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') EventLog | DataFrame [source]#
Filters the event log, keeping only the events that have an attribute value which occurs: - in at least the specified (min_relative_stake) percentage of events when level=”events”, - in at least the specified (min_relative_stake) percentage of cases when level=”cases”.
- Parameters:
log – Event log or Pandas DataFrame.
min_relative_stake (
float
) – Minimum percentage of cases (expressed as a number between 0 and 1) in which the attribute should occur.attribute_key (
str
) – The attribute to filter.level (
str
) – The level of the filter (if level=”events”, then events; if level=”cases”, then cases).timestamp_key (
str
) – Attribute to be used for the timestamp.case_id_key (
str
) – Attribute to be used as case identifier.
- Returns:
Filtered event log or Pandas DataFrame.
import pm4py filtered_dataframe = pm4py.filter_log_relative_occurrence_event_attribute( dataframe, 0.5, attribute_key='concept:name', level='cases', case_id_key='case:concept:name', timestamp_key='time:timestamp' )