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Fix trying to get pointer to None in svm/linear.pyx #5615

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merged 4 commits into from
Oct 31, 2023
Merged

Fix trying to get pointer to None in svm/linear.pyx #5615

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5b43dad
Fix trying to get pointer to None
yosider Oct 10, 2023
2cde95e
add test for LinearSVC
yosider Oct 12, 2023
9b7407e
Merge branch 'branch-23.12' into bug-linearsvc-none-ptr
csadorf Oct 27, 2023
dc927c9
Merge branch 'branch-23.12' into bug-linearsvc-none-ptr
dantegd Oct 31, 2023
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4 changes: 2 additions & 2 deletions python/cuml/svm/linear.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -300,8 +300,8 @@ cdef class LinearSVMWrapper:
if self.dtype != np.float32 and self.dtype != np.float64:
raise TypeError('Input data type must be float32 or float64')

cdef uintptr_t Xptr = <uintptr_t>X.ptr
cdef uintptr_t yptr = <uintptr_t>y.ptr
cdef uintptr_t Xptr = <uintptr_t>X.ptr if X is not None else 0
cdef uintptr_t yptr = <uintptr_t>y.ptr if y is not None else 0
cdef uintptr_t swptr = <uintptr_t>sampleWeight.ptr \
if sampleWeight is not None else 0
cdef size_t nCols = 0
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37 changes: 37 additions & 0 deletions python/cuml/tests/test_pickle.py
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Expand Up @@ -740,6 +740,43 @@ def assert_model(pickled_model, data):
pickle_save_load(tmpdir, create_mod, assert_model)


@pytest.mark.parametrize("datatype", [np.float32, np.float64])
@pytest.mark.parametrize(
"params", [{"probability": True}, {"probability": False}]
)
@pytest.mark.parametrize("multiclass", [True, False])
def test_linear_svc_pickle(tmpdir, datatype, params, multiclass):
result = {}

def create_mod():
model = cuml.svm.LinearSVC(**params)
iris = load_iris()
iris_selection = np.random.RandomState(42).choice(
[True, False], 150, replace=True, p=[0.75, 0.25]
)
X_train = iris.data[iris_selection]
y_train = iris.target[iris_selection]
if not multiclass:
y_train = (y_train > 0).astype(datatype)
data = [X_train, y_train]
result["model"] = model.fit(X_train, y_train)
return model, data

def assert_model(pickled_model, data):
if result["model"].probability:
print("Comparing probabilistic LinearSVC")
compare_probabilistic_svm(
result["model"], pickled_model, data[0], data[1], 0, 0
)
else:
print("comparing base LinearSVC")
pred_before = result["model"].predict(data[0])
pred_after = pickled_model.predict(data[0])
assert array_equal(pred_before, pred_after)

pickle_save_load(tmpdir, create_mod, assert_model)


@pytest.mark.parametrize("datatype", [np.float32, np.float64])
@pytest.mark.parametrize("nrows", [unit_param(500)])
@pytest.mark.parametrize("ncols", [unit_param(16)])
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