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WIP: [python-package] ensure predict() always returns an array #6348

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WIP: [python-package] ensure predict() always returns an array #6348

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d3782da
[python-package] ensure predict() always returns an array
jameslamb Mar 1, 2024
7605296
update tests
jameslamb Mar 1, 2024
5a45947
remove special case from dask test
jameslamb Mar 1, 2024
92d3171
add scikit-learn type hints
jameslamb Mar 1, 2024
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update tests
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@jameslamb
jameslamb committed Mar 1, 2024
commit 7605296b62339d3d372a74ee5426f21e0d8434f2
34 changes: 14 additions & 20 deletions tests/python_package_test/test_engine.py
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Original file line number Diff line number Diff line change
@@ -1834,37 +1834,31 @@ def test_contribs_sparse_multiclass():
lgb_train = lgb.Dataset(X_train, y_train)
gbm = lgb.train(params, lgb_train, num_boost_round=20)
contribs_csr = gbm.predict(X_test, pred_contrib=True)
assert isinstance(contribs_csr, list)
for perclass_contribs_csr in contribs_csr:
assert isspmatrix_csr(perclass_contribs_csr)
isspmatrix_csr(contribs_csr)
# convert data to dense and get back same contribs
contribs_dense = gbm.predict(X_test.toarray(), pred_contrib=True)
# validate the values are the same
contribs_csr_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csr]), 0, 1)
contribs_csr_arr_re = contribs_csr_array.reshape(
(contribs_csr_array.shape[0], contribs_csr_array.shape[1] * contribs_csr_array.shape[2])
)
if platform.machine() == "aarch64":
np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense, rtol=1, atol=1e-12)
np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense, rtol=1, atol=1e-12)
else:
np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense)
contribs_dense_re = contribs_dense.reshape(contribs_csr_array.shape)
assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(contribs_dense_re, axis=2)) < 1e-4
np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense)
# values should sum to predictions
preds_by_class = np.hstack(
[
np.sum(contribs_dense[:, i * (n_features + 1) : (i + 1) * (n_features + 1)], axis=1).reshape(-1, 1)
for i in range(n_labels)
]
)
assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - preds_by_class) < 1e-4
# validate using CSC matrix
X_test_csc = X_test.tocsc()
contribs_csc = gbm.predict(X_test_csc, pred_contrib=True)
assert isinstance(contribs_csc, list)
for perclass_contribs_csc in contribs_csc:
assert isspmatrix_csc(perclass_contribs_csc)
isspmatrix_csc(contribs_csc)
# validate the values are the same
contribs_csc_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csc]), 0, 1)
contribs_csc_array = contribs_csc_array.reshape(
(contribs_csc_array.shape[0], contribs_csc_array.shape[1] * contribs_csc_array.shape[2])
)
if platform.machine() == "aarch64":
np.testing.assert_allclose(contribs_csc_array, contribs_dense, rtol=1, atol=1e-12)
np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense, rtol=1, atol=1e-12)
else:
np.testing.assert_allclose(contribs_csc_array, contribs_dense)
np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense)


@pytest.mark.skipif(psutil.virtual_memory().available / 1024 / 1024 / 1024 < 3, reason="not enough RAM")