def test_truncated_svd(self):
N, C, K = 2, 3, 2
x = create_tensor(N, C)
svd = TruncatedSVD(n_components=K)
svd.fit(x)
model_onnx = convert_sklearn(svd, initial_types=[('input', FloatTensorType(shape=[1, C]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(x, svd, model_onnx, basename="SklearnTruncatedSVD")
def test_truncated_svd_arpack(self):
X = create_tensor(10, 10)
svd = TruncatedSVD(n_components=5, algorithm='arpack', n_iter=10,
tol=0.1, random_state=42).fit(X)
model_onnx = convert_sklearn(svd,
initial_types=[
("input",
FloatTensorType(shape=X.shape))
])
self.assertTrue(model_onnx is not None)
dump_data_and_model(X, svd, model_onnx,
basename="SklearnTruncatedSVDArpack")
def test_truncated_svd_int(self):
X = create_tensor(5, 5).astype(np.int64)
svd = TruncatedSVD(n_iter=20, random_state=42).fit(X)
model_onnx = convert_sklearn(svd,
initial_types=[
("input",
Int64TensorType([None, X.shape[1]]))
],
target_opset=TARGET_OPSET)
self.assertTrue(model_onnx is not None)
dump_data_and_model(X,
svd,
model_onnx,
basename="SklearnTruncatedSVDInt")
def test_truncated_svd_int(self):
X = create_tensor(5, 5).astype(np.int64)
svd = TruncatedSVD(n_iter=20, random_state=42).fit(X)
model_onnx = convert_sklearn(svd,
initial_types=[
("input",
Int64TensorType([None, X.shape[1]]))
])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X, svd, model_onnx,
basename="SklearnTruncatedSVDInt",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)