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Update GPUTreeshap (#6163)

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* Reduce shap test duration

* Test interoperability with shap package

* Add feature interactions

* Update GPUTreeShap
This commit is contained in:
Rory Mitchell
2020-09-28 09:43:47 +13:00
committed by GitHub
parent 434a3f35a3
commit dda9e1e487
14 changed files with 176 additions and 87 deletions
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35
tests/python-gpu/test_gpu_prediction.py
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@@ -16,7 +16,7 @@ shap_parameter_strategy = strategies.fixed_dictionaries({
'max_depth': strategies.integers(0, 11),
'max_leaves': strategies.integers(0, 256),
'num_parallel_tree': strategies.sampled_from([1, 10]),
})
}).filter(lambda x: x['max_depth'] > 0 or x['max_leaves'] > 0)
class TestGPUPredict(unittest.TestCase):
@@ -194,26 +194,31 @@ class TestGPUPredict(unittest.TestCase):
for i in range(10):
run_threaded_predict(X, rows, predict_df)
@given(strategies.integers(1, 200),
tm.dataset_strategy, shap_parameter_strategy, strategies.booleans())
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None)
def test_shap(self, num_rounds, dataset, param, all_rows):
if param['max_depth'] == 0 and param['max_leaves'] == 0:
return
def test_shap(self, num_rounds, dataset, param):
param.update({"predictor": "gpu_predictor", "gpu_id": 0})
param = dataset.set_params(param)
dmat = dataset.get_dmat()
bst = xgb.train(param, dmat, num_rounds)
if all_rows:
test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
else:
test_dmat = xgb.DMatrix(dataset.X[0:1, :])
test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
shap = bst.predict(test_dmat, pred_contribs=True)
bst.set_param({"predictor": "cpu_predictor"})
cpu_shap = bst.predict(test_dmat, pred_contribs=True)
margin = bst.predict(test_dmat, output_margin=True)
assert np.allclose(shap, cpu_shap, 1e-3, 1e-3)
# feature contributions should add up to predictions
assume(len(dataset.y) > 0)
assert np.allclose(np.sum(shap, axis=len(shap.shape) - 1), margin, 1e-3, 1e-3)
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, max_examples=20)
def test_shap_interactions(self, num_rounds, dataset, param):
param.update({"predictor": "gpu_predictor", "gpu_id": 0})
param = dataset.set_params(param)
dmat = dataset.get_dmat()
bst = xgb.train(param, dmat, num_rounds)
test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
shap = bst.predict(test_dmat, pred_interactions=True)
margin = bst.predict(test_dmat, output_margin=True)
assume(len(dataset.y) > 0)
assert np.allclose(np.sum(shap, axis=(len(shap.shape) - 1, len(shap.shape) - 2)), margin,
1e-3, 1e-3)