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neuralset.extractors.meta.ExtractorPCA

pydantic model neuralset.extractors.meta.ExtractorPCA

Applies a PCA to another extractor’s data The underlying extractor is first computed through the prepare method, and then the current extractor applies the PCA on it. Both caches are stored.

Parameters:

• extractor (Extractor) – the underlying extractor on which the PCA must be applied

• n_components (int) – the number of components of the PCA

• whiten (bool) – whether the whiten post PCA

Fields:

• event_types (str | tuple[str, ...])

• extractor (neuralset.extractors.base.BaseExtractor)

• infra (exca.map.MapInfra)

• n_components (int)

• whiten (bool)

field extractor: BaseExtractor [Required]

field n_components: int [Required]

field whiten: bool = True

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

field infra: MapInfra = MapInfra()

prepare(obj: Any) → 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.

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

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