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neuralset.extractors.base.EventField

pydantic model neuralset.extractors.base.EventField

Extractor which extracts an int or float attribute from an event.

event_field can be either an attribute of the event or a key in the event.extra dictionary.

Parameters:

• event_types (str or tuple of str) – Type of event(s) to apply this extractor to.

• event_field (str) – Field to extract from the event.

Fields:

• event_field (str)

• event_types (str | tuple[str, ...])

field event_types: str | tuple[str, ...] = 'Event'

field event_field: str [Required]

prepare(obj: DataFrame | Sequence[Event] | Sequence[Segment]) → None

Pre-compute and cache extractor data for a collection of events.

This method triggers _get_data on every matching event so that expensive computation (e.g. model inference) is done once and cached. It then calls the extractor on a single event to populate the output shape, which is needed when allow_missing=True.

Call prepare before using the extractor in a dataloader.

Parameters:

obj (DataFrame or sequence of Event or sequence of Segment) – The structure containing the events. When calling prepare on several objects, prefer passing a list of events or segments over a DataFrame to avoid redundant conversion overhead.

get_static(event: Event) → Tensor

Return a single feature vector for the given event.

Override this method in subclasses to produce a static (non-temporal) embedding for one event. The returned tensor should have no time dimension — temporal wrapping is handled by BaseStatic automatically.

Parameters:

event (Event) – The event to extract a feature from.

Returns:

A tensor of shape (*feature_shape,) (no time axis).

Return type:

torch.Tensor

requirements: tp.ClassVar[tuple[str, ...]] = ()

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