nirs4all.pipeline.explainer module

Pipeline explainer - Handles SHAP explanation generation.

This module provides the Explainer class for generating model explanations using SHAP (SHapley Additive exPlanations) on trained pipelines.

class nirs4all.pipeline.explainer.Explainer(runner: PipelineRunner)[source]

Bases: object

Handles SHAP explanation generation for trained models.

This class manages the explanation workflow: loading saved models, replaying pipelines to capture the trained model, and generating SHAP explanations with visualizations.

runner

Parent PipelineRunner instance

saver

File saver for managing outputs

manifest_manager

Manager for pipeline manifests

pipeline_uid

Unique identifier for the pipeline

artifact_loader

Loader for trained model artifacts

config_path

Path to the pipeline configuration

target_model

Metadata for the target model

captured_model

Tuple of (model, controller) captured during replay

capture_model(model: Any, controller: Any)[source]

Capture a model during pipeline execution for SHAP analysis.

This method is called by the model controller during explain mode to capture the trained model instance.

Parameters:

  • model – Trained model instance

  • controller – Controller that trained the model

explain(prediction_obj: Dict[str, Any] | str, dataset: DatasetConfigs | SpectroDataset | ndarray | Tuple[ndarray, ...] | Dict | List[Dict] | str | List[str], dataset_name: str = 'explain_dataset', shap_params: Dict[str, Any] | None = None, verbose: int = 0, plots_visible: bool = True) → Tuple[Dict[str, Any], str][source]

Generate SHAP explanations for a saved model.

Parameters:

  • prediction_obj – Model identifier (dict with config_path or prediction ID)

  • dataset – Dataset to explain on

  • dataset_name – Name for the dataset

  • shap_params – SHAP configuration parameters

  • verbose – Verbosity level

  • plots_visible – Whether to display plots interactively

Returns:

Tuple of (shap_results_dict, output_directory_path)

Example

>>> explainer = Explainer(runner)
>>> shap_results, out_dir = explainer.explain(
...     {"config_path": "0001_abc123"},
...     X_test,
...     shap_params={"n_samples": 200, "visualizations": ["spectral", "summary"]}
... )