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')", )