Explainer Base

class xaicompare.adapters.explainers.explainer_base.ExplainerAdapter(model_adapter: Any, config: Dict[str, Any] | None = None)

Bases: ABC

Base class that standardizes how different explainers are called and what they return.

Concrete implementations (e.g., SHAP Tree for XGBoost/LightGBM, SHAP Kernel for generic models, LIME-Text, etc.) should subclass this.

Contract:

• global_importance(X, rows_limit) -> (mean_abs, feature_names)

  • mean_abs: 1D np.ndarray of length n_features (mean absolute importance)

  • feature_names: List[str] of length n_features (ordering aligns with mean_abs)

• local_explanations(X_row) -> 1D np.ndarray of length n_features (signed values)

  • X_row represents a single sample (shape (1, …)); caller may pass a slice X[i:i+1]

_abc_impl = <_abc._abc_data object>

abstractmethod global_importance(x, rows_limit: int = 200) → Tuple[ndarray, List[str]]

Compute a global importance estimate (typically mean |contribution|) in a memory-safe way across up to rows_limit rows of X.

Returns:

• mean_abs (np.ndarray) – 1D array of length n_features with mean absolute importance per feature.

• feature_names (List[str]) – The feature names in the same order as ‘mean_abs’.

abstractmethod limitation_text() → str

abstractmethod local_explanations(x_row) → ndarray

Explain a single row. Should return a vector of signed contributions whose length matches the feature space (n_features).

Notes

• Implementations may aggregate across classes if multi-class (e.g. sum over classes).

• If there is a bias term, do NOT return it here; only per-feature values.

name() → str

Human-readable name of the explainer.