wehub-resource-sync
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Literal
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import numpy as np
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from shap import links
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from shap.models import Model
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from shap.utils import MaskedModel
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from .._explainer import Explainer
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if TYPE_CHECKING:
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from collections.abc import Callable
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from numpy.typing import NDArray
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class Random(Explainer):
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"""Simply returns random (normally distributed) feature attributions.
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This is only for benchmark comparisons. It supports both fully random attributions and random
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attributions that are constant across all explanations.
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"""
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def __init__(
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self,
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model: Any,
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masker: Any,
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link: Callable[..., Any] = links.identity,
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feature_names: list[str] | list[list[str]] | None = None,
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linearize_link: bool = True,
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constant: bool = False,
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**call_args: Any,
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) -> None:
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super().__init__(model, masker, link=link, linearize_link=linearize_link, feature_names=feature_names)
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if not isinstance(model, Model):
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self.model = Model(model)
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for arg in call_args:
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self.__call__.__kwdefaults__[arg] = call_args[arg] # type: ignore[index]
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self.constant: bool = constant
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self.constant_attributions: NDArray[np.floating[Any]] | None = None
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def explain_row(
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self,
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*row_args: Any,
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max_evals: int | Literal["auto"],
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main_effects: bool,
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error_bounds: bool,
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outputs: Any,
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silent: bool,
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**kwargs: Any,
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) -> dict[str, Any]:
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"""Explain a single row and return feature attributions."""
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# build a masked version of the model for the current input sample
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fm = MaskedModel(self.model, self.masker, self.link, self.linearize_link, *row_args)
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# compute any custom clustering for this row
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row_clustering: NDArray[np.floating[Any]] | None = None
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if getattr(self.masker, "clustering", None) is not None:
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if isinstance(self.masker.clustering, np.ndarray):
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row_clustering = self.masker.clustering
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elif callable(self.masker.clustering):
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row_clustering = self.masker.clustering(*row_args)
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else:
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raise NotImplementedError(
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"The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!"
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)
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# compute the correct expected values
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masks = np.zeros(1, dtype=int)
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outputs = fm(masks, zero_index=0, batch_size=1)
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expected_value = outputs[0]
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# generate random feature attributions
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# we produce small values so our explanation errors are similar to a constant function
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row_values: NDArray[np.floating[Any]] = np.random.randn(*((len(fm),) + outputs.shape[1:])) * 0.001
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return {
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"values": row_values,
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"expected_values": expected_value,
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"mask_shapes": fm.mask_shapes,
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"main_effects": None,
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"clustering": row_clustering,
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"error_std": None,
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"output_names": self.model.output_names if hasattr(self.model, "output_names") else None,
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}
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