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Implement contribution prediction with QuantileDMatrix (#10043)

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Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Louis Desreumaux
2024-02-19 14:03:29 +01:00
committed by GitHub
parent 057f03cacc
commit edf501d227
6 changed files with 137 additions and 55 deletions
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28
tests/python/test_shap.py
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@@ -2,7 +2,6 @@ import itertools
import re
import numpy as np
import scipy
import scipy.special
import xgboost as xgb
@@ -256,3 +255,30 @@ class TestSHAP:
brute_force[-1, -1] += base_score
fast_method = bst.predict(xgb.DMatrix(X[0:1, :]), pred_interactions=True)
assert np.linalg.norm(brute_force - fast_method[0, :, :]) < 1e-4
def test_shap_values(self) -> None:
from sklearn.datasets import make_classification, make_regression
def assert_same(X: np.ndarray, y: np.ndarray) -> None:
Xy = xgb.DMatrix(X, y)
booster = xgb.train({}, Xy, num_boost_round=4)
shap_dm = booster.predict(Xy, pred_contribs=True)
Xy = xgb.QuantileDMatrix(X, y)
shap_qdm = booster.predict(Xy, pred_contribs=True)
np.testing.assert_allclose(shap_dm, shap_qdm)
margin = booster.predict(Xy, output_margin=True)
np.testing.assert_allclose(
np.sum(shap_qdm, axis=len(shap_qdm.shape) - 1), margin, 1e-3, 1e-3
)
shap_dm = booster.predict(Xy, pred_interactions=True)
Xy = xgb.QuantileDMatrix(X, y)
shap_qdm = booster.predict(Xy, pred_interactions=True)
np.testing.assert_allclose(shap_dm, shap_qdm)
X, y = make_regression()
assert_same(X, y)
X, y = make_classification()
assert_same(X, y)