nicetoolbox.detectors.base_detector.BaseDetector¶

class nicetoolbox.detectors.base_detector.BaseDetector(io: SequenceIO, data: SequenceData, sequence_context: SequenceRuntimeConfig, algorithm_instance: str)[source]¶

Bases: ABC

Abstract base class for ALL detectors.

Defines the common interface that both method and feature detectors implement. This enables a unified detector loop in main.py.

Initialize base detector with references.

Subclasses should call super().__init__() and set inference_config.

Methods

`compute_output_folders`	Compute extra output folders for all components.
`compute_result_folders`	Compute result folders for all components.
`compute_viz_folders`	Compute visualization folders for all components.
`run`	Execute the detector's main computation.
`visualization`	Visualize detector output.

Attributes

`predictions_mapping`	Access predictions mapping from runtime config.
`algorithm_type`
`data`
`io`
`sequence_context`
`detector_config`
`algorithm_instance`
`inference_config`
`components`
`visualize`

compute_output_folders(requires_out_folder: bool) → Dict[str, str][source]¶: Compute extra output folders for all components.

compute_result_folders() → Dict[str, str][source]¶: Compute result folders for all components.

compute_viz_folders(visualize: bool) → Dict[str, str][source]¶: Compute visualization folders for all components.

property predictions_mapping¶: Access predictions mapping from runtime config.

abstract run() → Any | None[source]¶

Execute the detector’s main computation.

For method detectors: runs subprocess inference + post_inference() For feature detectors: runs compute()

Returns:: Optional data for visualization (feature detectors currently return computed data)

abstract visualization(data: Any) → None[source]¶

Visualize detector output.