def test_change_onnx_domain(self):
model = make_pipeline(StandardScaler())
X = numpy.array([[0.1, 1.1], [0.2, 2.2], [0.4, 2.2], [0.2, 2.4]])
model.fit(X)
model_onnx = convert_sklearn(model, "pipe3",
[("input", FloatTensorType([None, 2]))])
model_onnx = change_onnx_domain(model_onnx,
{'Scaler': ('ScalerNew', 'ML2')})
self.assertIn('domain: "ML2"', str(model_onnx))
self.assertIn('op_type: "ScalerNew"', str(model_onnx))
def common_test_gpc(self, dtype=np.float32, n_classes=2):
gp = GaussianProcessClassifier()
gp, X = self.fit_classification_model(gp, n_classes=n_classes)
# return_cov=False, return_std=False
if dtype == np.float32:
cls = FloatTensorType
else:
cls = DoubleTensorType
model_onnx = to_onnx(gp,
initial_types=[('X', cls([None, None]))],
target_opset=TARGET_OPSET,
options={
GaussianProcessClassifier: {
'zipmap': False,
'optim': 'cdist'
}
})
self.assertTrue(model_onnx is not None)
try:
sess = InferenceSession(model_onnx.SerializeToString())
except OrtFail:
if not hasattr(self, 'path'):
return
suffix = 'Double' if dtype == np.float64 else 'Float'
# Operator Solve is missing
model_onnx = change_onnx_domain(
model_onnx, {'Solve': ('Solve%s' % suffix, 'ai.onnx.contrib')})
so = SessionOptions()
so.register_custom_ops_library(self.path)
sess = InferenceSession(model_onnx.SerializeToString(), so)
res = sess.run(None, {'X': X.astype(dtype)})
assert_almost_equal(res[0].ravel(), gp.predict(X).ravel())
assert_almost_equal(res[1], gp.predict_proba(X), decimal=3)
return
dt = 32 if dtype == np.float32 else 64
dump_data_and_model(X.astype(dtype),
gp,
model_onnx,
verbose=False,
basename="SklearnGaussianProcessRBFT%d%d" %
(n_classes, dt))