def test_onnxt_add(self):
idi = numpy.identity(2)
onx = OnnxAdd('X', idi, output_names=['Y'])
model_def = onx.to_onnx({'X': idi.astype(numpy.float32)})
oinf = OnnxInference(model_def, runtime="python")
res = oinf.switch_initializers_dtype()
self.assertEqual(len(res), 1)
self.assertEqual(res[0][:4], ('pass1', '+', 'init', 'Ad_Addcst'))
def test_onnxt_iris_gaussian_process_dot_product(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = GaussianProcessRegressor(
kernel=DotProduct(), alpha=100)
clr.fit(X_train, y_train)
ym, std = clr.predict(X_test, return_std=True)
model_def = to_onnx(
clr, X_train.astype(numpy.float32),
options={GaussianProcessRegressor: {'return_std': True}})
oinf = OnnxInference(model_def, runtime='python')
res = oinf.run({'X': X_test.astype(numpy.float32)})
ym2, std2 = res['GPmean'], res['GPcovstd']
self.assertEqualArray(numpy.squeeze(ym), numpy.squeeze(ym2),
decimal=5)
self.assertEqualArray(std, std2, decimal=4)
res = oinf.switch_initializers_dtype(clr)
last = res[-1]
self.assertEqual(last[0], 'pass2')
_linv = 0
for a in enumerate_fitted_arrays(clr):
if "_K_inv" in a[-2]:
_linv += 1
self.assertEqual(_linv, 1)
res = oinf.run({'X': X_test})
ym3, std3 = res['GPmean'], res['GPcovstd']
self.assertEqualArray(ym3, ym2, decimal=5)
self.assertEqualArray(std3, std2, decimal=4)
d1 = numpy.sum(numpy.abs(ym.ravel() - ym2.ravel()))
d2 = numpy.sum(numpy.abs(ym.ravel() - ym3.ravel()))
d3 = numpy.sum(numpy.abs(ym2.ravel() - ym3.ravel()))
self.assertLess(d2, min(d1, d3) / 2)
d1 = numpy.sum(numpy.abs(std.ravel() - std2.ravel()))
d2 = numpy.sum(numpy.abs(std.ravel() - std3.ravel()))
d3 = numpy.sum(numpy.abs(std2.ravel() - std3.ravel()))
self.assertLess(d2, min(d1, d3) / 2)
def test_onnxt_iris_gaussian_process_exp_sine_squared_13(self):
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = GaussianProcessRegressor(kernel=ExpSineSquared(), alpha=100)
clr.fit(X_train, y_train)
ym, std = clr.predict(X_test, return_std=True)
model_def = to_onnx(
clr,
X_train.astype(numpy.float32),
options={GaussianProcessRegressor: {
'return_std': True
}},
target_opset=15) # opset=13, 14, ...
oinf = OnnxInference(model_def, runtime='python')
res = oinf.run({'X': X_test.astype(numpy.float32)})
ym2, std2 = res['GPmean'], res['GPcovstd']
self.assertEqualArray(numpy.squeeze(ym), numpy.squeeze(ym2), decimal=5)
self.assertEqualArray(std, std2, decimal=5)
res = oinf.switch_initializers_dtype(clr)
last = res[-1]
self.assertEqual(last[0], 'pass2')
res = oinf.run({'X': X_test.astype(numpy.float64)})
ym3, std3 = res['GPmean'], res['GPcovstd']
self.assertEqualArray(ym3, ym2)
self.assertEqualArray(std3, std2, decimal=5)
d1 = numpy.sum(numpy.abs(ym.ravel() - ym2.ravel()))
d2 = numpy.sum(numpy.abs(ym.ravel() - ym3.ravel()))
d3 = numpy.sum(numpy.abs(ym2.ravel() - ym3.ravel()))
self.assertLess(d2, min(d1, d3) / 2)
d1 = numpy.sum(numpy.abs(std.ravel() - std2.ravel()))
d2 = numpy.sum(numpy.abs(std.ravel() - std3.ravel()))
d3 = numpy.sum(numpy.abs(std2.ravel() - std3.ravel()))
self.assertLess(d2, min(d1, d3) / 2)