pm4py.ml.extract_temporal_features_dataframe#
- pm4py.ml.extract_temporal_features_dataframe(log: EventLog | DataFrame, grouper_freq='W', activity_key='concept:name', timestamp_key='time:timestamp', case_id_key=None, start_timestamp_key='time:timestamp', resource_key='org:resource') DataFrame[source]#
Extracts a dataframe containing the temporal features of the provided log object
Implements the approach described in the paper: Pourbafrani, Mahsa, Sebastiaan J. van Zelst, and Wil MP van der Aalst. “Supporting automatic system dynamics model generation for simulation in the context of process mining.” International Conference on Business Information Systems. Springer, Cham, 2020.
- Parameters:
log – log object (event log / Pandas dataframe)
grouper_freq (
str) – the grouping frequency (D, W, M, Y) to useactivity_key (
str) – the attribute to be used as activitytimestamp_key (
str) – the attribute to be used as timestampcase_id_key – (if provided, otherwise default) the attribute to be used as case identifier
resource_key (
str) – the attribute to be used as resourcestart_timestamp_key (
str) – the attribute to be used as start timestamp
- Return type:
pd.DataFrame
import pm4py temporal_features_df = pm4py.extract_temporal_features_dataframe(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')