def assert_model(pickled_model, data):
compare_svm(result["model"],
pickled_model,
data[0],
data[1],
cmp_sv=0,
dcoef_tol=0)
def assert_model(pickled_model, data):
if result["model"].probability:
print("Comparing probabilistic svc")
compare_probabilistic_svm(result["model"], pickled_model, data[0],
data[1], 0, 0)
else:
print("comparing base svc")
compare_svm(result["model"], pickled_model, data[0], data[1])
def test_svm_pickle(tmpdir, datatype, nrows, ncols):
model = cuml.svm.SVC()
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]
y_train = (y_train > 0).astype(datatype)
model.fit(X_train, y_train)
model_pickle = pickle_save_load(tmpdir, model)
compare_svm(model, model_pickle, X_train, y_train, cmp_sv=0, dcoef_tol=0)