pm4py.ml.extract_ocel_features#

pm4py.ml.extract_ocel_features(ocel: OCEL, obj_type: str, enable_object_lifecycle_paths: bool = True, enable_object_work_in_progress: bool = False, object_str_attributes: Collection[str] | None = None, object_num_attributes: Collection[str] | None = None, include_obj_id: bool = False, debug: bool = False) → DataFrame[source]#

Extracts from an object-centric event log a set of features (returned as dataframe) computed on the OCEL for the objects of a given object type.

Implements the approach described in: Berti, A., Herforth, J., Qafari, M.S. et al. Graph-based feature extraction on object-centric event logs. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00428-2

Parameters:

  • ocel (OCEL) – object-centric event log

  • obj_type (str) – object type that should be considered

  • enable_object_lifecycle_paths (bool) – enables the “lifecycle paths” feature

  • enable_object_work_in_progress (bool) – enables the “work in progress” feature (which has an high computational cost)

  • object_str_attributes – string attributes at the object level to one-hot encode during the feature extraction

  • object_num_attributes – numeric attributes at the object level to one-hot encode during the feature extraction

  • include_obj_id (bool) – includes the object identifier as column of the “features” dataframe

  • debug (bool) – enables debugging mode (telling at which point of the feature extraction you are)

Return type:

pd.DataFrame

import pm4py

ocel = pm4py.read_ocel('log.jsonocel')
fea_df = pm4py.extract_ocel_features(ocel, "item")
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