chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""GPU accelerated tree explanations"""
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import numpy as np
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from ..utils import assert_import, record_import_error
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from ._tree import (
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TreeExplainer,
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_xgboost_cat_unsupported,
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feature_perturbation_codes,
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output_transform_codes,
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)
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try:
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from .. import _cext_gpu # type: ignore
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except ImportError as e:
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record_import_error("cext_gpu", "cuda extension was not built during install!", e)
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class GPUTreeExplainer(TreeExplainer):
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"""Experimental GPU accelerated version of TreeExplainer. Currently requires source build with
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cuda available and 'CUDA_PATH' environment variable defined.
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Examples
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--------
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See `GPUTree explainer examples <https://shap.readthedocs.io/en/latest/example_notebooks/api_examples/explainers/GPUTree.html>`_
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"""
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def shap_values(self, X, y=None, tree_limit=None, approximate=False, check_additivity=True, from_call=False):
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"""Estimate the SHAP values for a set of samples.
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Parameters
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----------
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X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
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A matrix of samples (# samples x # features) on which to explain the model's output.
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y : numpy.array
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An array of label values for each sample. Used when explaining loss functions.
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tree_limit : None (default) or int
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Limit the number of trees used by the model. By default None means no use the limit
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of the
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original model, and -1 means no limit.
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approximate : bool
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Not supported.
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check_additivity : bool
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Run a validation check that the sum of the SHAP values equals the output of the
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model. This
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check takes only a small amount of time, and will catch potential unforeseen errors.
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Note that this check only runs right now when explaining the margin of the model.
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Returns
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-------
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array or list
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For models with a single output this returns a matrix of SHAP values
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(# samples x # features). Each row sums to the difference between the model output
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for that
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sample and the expected value of the model output (which is stored in the expected_value
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attribute of the explainer when it is constant). For models with vector outputs this
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returns
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a list of such matrices, one for each output.
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"""
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assert not approximate, "approximate not supported"
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X, y, X_missing, flat_output, tree_limit, check_additivity = self._validate_inputs(
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X, y, tree_limit, check_additivity
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)
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model = self.model
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_xgboost_cat_unsupported(model)
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transform = model.get_transform()
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# run the core algorithm using the C extension
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assert_import("cext_gpu")
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phi = np.zeros((X.shape[0], X.shape[1] + 1, self.model.num_outputs))
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_cext_gpu.dense_tree_shap(
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self.model.children_left,
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self.model.children_right,
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self.model.children_default,
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self.model.features,
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self.model.thresholds,
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self.model.threshold_types,
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self.model.values,
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self.model.node_sample_weight,
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self.model.max_depth,
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X,
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X_missing,
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y,
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self.data,
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self.data_missing,
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tree_limit,
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self.model.base_offset,
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phi,
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feature_perturbation_codes[self.feature_perturbation],
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output_transform_codes[transform],
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False,
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)
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out = self._get_shap_output(phi, flat_output)
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if check_additivity and self.model.model_output == "raw":
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self.assert_additivity(out, self.model.predict(X)) # type: ignore[arg-type]
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return out
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def shap_interaction_values(self, X, y=None, tree_limit=None):
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"""Estimate the SHAP interaction values for a set of samples.
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Parameters
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----------
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X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
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A matrix of samples (# samples x # features) on which to explain the model's output.
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y : numpy.array
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An array of label values for each sample. Used when explaining loss functions (not
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yet supported).
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tree_limit : None (default) or int
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Limit the number of trees used by the model. By default None means no use the limit
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of the
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original model, and -1 means no limit.
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Returns
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-------
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array or list
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For models with a single output this returns a tensor of SHAP values
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(# samples x # features x # features). The matrix (# features x # features) for each
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sample sums
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to the difference between the model output for that sample and the expected value of
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the model output
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(which is stored in the expected_value attribute of the explainer). Each row of this
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matrix sums to the
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SHAP value for that feature for that sample. The diagonal entries of the matrix
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represent the
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"main effect" of that feature on the prediction and the symmetric off-diagonal
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entries represent the
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interaction effects between all pairs of features for that sample. For models with
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vector outputs
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this returns a list of tensors, one for each output.
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"""
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if self.model.model_output != "raw":
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raise ValueError('Only model_output = "raw" is supported for SHAP interaction values right now!')
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if self.feature_perturbation == "interventional":
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raise ValueError(
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'feature_perturbation="interventional" is not yet supported for interaction values. '
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'Use feature_perturbation="tree_path_dependent" instead.'
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)
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transform = "identity"
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X, y, X_missing, flat_output, tree_limit, _ = self._validate_inputs(X, y, tree_limit, False)
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# run the core algorithm using the C extension
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assert_import("cext_gpu")
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phi = np.zeros((X.shape[0], X.shape[1] + 1, X.shape[1] + 1, self.model.num_outputs))
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_cext_gpu.dense_tree_shap(
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self.model.children_left,
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self.model.children_right,
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self.model.children_default,
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self.model.features,
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self.model.thresholds,
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self.model.threshold_types,
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self.model.values,
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self.model.node_sample_weight,
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self.model.max_depth,
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X,
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X_missing,
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y,
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self.data,
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self.data_missing,
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tree_limit,
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self.model.base_offset,
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phi,
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feature_perturbation_codes[self.feature_perturbation],
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output_transform_codes[transform],
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True,
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)
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return self._get_shap_interactions_output(phi, flat_output)
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