pm4py.algo.transformation.log_to_features package

Submodules

pm4py.algo.transformation.log_to_features.algorithm module

class pm4py.algo.transformation.log_to_features.algorithm.Variants(value)[source]

Bases: enum.Enum

An enumeration.

EVENT_BASED = <module 'pm4py.algo.transformation.log_to_features.variants.event_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\transformation\\log_to_features\\variants\\event_based.py'>
TRACE_BASED = <module 'pm4py.algo.transformation.log_to_features.variants.trace_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\transformation\\log_to_features\\variants\\trace_based.py'>
pm4py.algo.transformation.log_to_features.algorithm.apply(log: Union[pm4py.objects.log.obj.EventLog, pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventStream], variant: Any = <Variants.TRACE_BASED: <module 'pm4py.algo.transformation.log_to_features.variants.trace_based' from 'C:\\Users\\berti\\FRAUNHOFER\\pm4py-core\\pm4py\\algo\\transformation\\log_to_features\\variants\\trace_based.py'>>, parameters: Optional[Dict[str, Any]] = None)[source]

Extracts the features from a log object

Parameters
  • log – Event log

  • variant – Variant of the feature extraction to use: - Variants.EVENT_BASED => (default) extracts, for each trace, a list of numerical vectors containing for each

    event the corresponding features

    • Variants.TRACE_BASED => extracts for each trace a single numerical vector containing the features

      of the trace

Returns

  • data – Data to provide for decision tree learning

  • feature_names – Names of the features, in order

Module contents