pm4py.algo.transformation.log_to_features.variants.event_based module#

class pm4py.algo.transformation.log_to_features.variants.event_based.Parameters(*values)[source]#

Bases: Enum

STR_EVENT_ATTRIBUTES = 'str_ev_attr'#
NUM_EVENT_ATTRIBUTES = 'num_ev_attr'#
FEATURE_NAMES = 'feature_names'#
MIN_NUM_DIFF_STR_VALUES = 'min_num_diff_str_values'#
MAX_NUM_DIFF_STR_VALUES = 'max_num_diff_str_values'#
pm4py.algo.transformation.log_to_features.variants.event_based.extract_all_ev_features_names_from_log(log: EventLog, str_ev_attr: List[str], num_ev_attr: List[str], parameters: Dict[str | Parameters, Any] | None = None) → List[str][source]#

Extracts the feature names from an event log.

Parameters:

  • log – Event log

  • str_ev_attr – (if provided) list of string event attributes to consider in extracting the feature names

  • num_ev_attr – (if provided) list of integer event attributes to consider in extracting the feature names

  • parameters –

    Parameters, including:

    • MIN_NUM_DIFF_STR_VALUES => minimum number of distinct values to include an attribute as feature(s)

    • MAX_NUM_DIFF_STR_VALUES => maximum number of distinct values to include an attribute as feature(s)

Returns:

List of feature names

Return type:

feature_names

pm4py.algo.transformation.log_to_features.variants.event_based.extract_features(log: EventLog, feature_names: List[str], parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Any, List[str]][source]#

Extracts the matrix of the features from an event log

Parameters:

  • log – Event log

  • feature_names – Features to consider (in the given order)

Returns:

  • data – Data to provide for decision tree learning

  • feature_names – Names of the features, in order

pm4py.algo.transformation.log_to_features.variants.event_based.apply(log: EventLog, parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Any, List[str]][source]#

Extracts all the features for the traces of an event log (each trace becomes a vector of vectors, where each event has its own vector)

Parameters:

  • log – Event log

  • parameters –

    Parameters of the algorithm, including:

    • STR_EVENT_ATTRIBUTES => string event attributes to consider in the features extraction

    • NUM_EVENT_ATTRIBUTES => numeric event attributes to consider in the features extraction

    • FEATURE_NAMES => features to consider (in the given order)

Returns:

  • data – Data to provide for decision tree learning

  • feature_names – Names of the features, in order