Zhihao Dai
846f28c79a
* Init asv Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> * Add exact explainer suite Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> * Add partition explainer suite Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> * Add permutation explainer suite Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> * Clear out init py Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> * Remove basic suite Co-authored-by: Zhihao Dai <zhihao.dai@eng.ox.ac.uk> --------- Co-authored-by: Tobias Pitters <31857876+CloseChoice@users.noreply.github.com>
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
from xgboost import XGBClassifier
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from shap.datasets import adult
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from shap.explainers import ExactExplainer
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from shap.maskers import Partition
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class ExactSuite:
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# Adapted from tests/explainers/test_exact.py
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max_samples = 100
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def setup(self):
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self.model = XGBClassifier(tree_method="exact", base_score=0.5)
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# get a dataset on income prediction
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self.X, self.y = adult()
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if self.max_samples is not None:
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self.X = self.X.iloc[: self.max_samples]
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self.y = self.y[: self.max_samples]
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self.X = self.X.values
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# fit the model on the data
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self.model.fit(self.X, self.y)
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def time_single_output(self):
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ex = ExactExplainer(self.model.predict, self.X)
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_ = ex(self.X)
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def time_multi_output(self):
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ex = ExactExplainer(self.model.predict_proba, self.X)
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_ = ex(self.X)
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def time_interactions(self):
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ex = ExactExplainer(self.model.predict, self.X)
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_ = ex(self.X, interactions=True)
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def time_single_output_partition_masker(self):
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ex = ExactExplainer(self.model.predict, masker=Partition(self.X))
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_ = ex(self.X)
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def time_multi_output_partition_masker(self):
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ex = ExactExplainer(self.model.predict_proba, masker=Partition(self.X))
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_ = ex(self.X)
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