def test_multiple_transform(self): x = pandas.DataFrame(data=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) x.columns = "X1 X2".split() name = self.get_name("mul_1.pb") with open(name, "rb") as f: content = f.read() res = list(OnnxTransformer.enumerate_create(content)) assert len(res) > 0 for k, tr in res: tr.fit() try: tr.transform(x) except RuntimeError: pass
def test_pipeline_iris(self): iris = load_iris() X, y = iris.data, iris.target pipe = make_pipeline(PCA(n_components=2), LogisticRegression()) pipe.fit(X, y) onx = convert_sklearn(pipe, initial_types=[('input', FloatTensorType( (1, X.shape[1])))]) onx_bytes = onx.SerializeToString() res = list(OnnxTransformer.enumerate_create(onx_bytes)) outputs = [] shapes = [] for k, tr in res: outputs.append(k) tr.fit() y = tr.transform(X) self.assertEqual(y.shape[0], X.shape[0]) shapes.append(y.shape) self.assertEqual(len(set(outputs)), len(outputs)) shapes = set(shapes) self.assertEqual(shapes, {(150, 3), (150, 4), (150, 2), (150, )})
fig, ax = plt.subplots(figsize=(40, 20)) ax.imshow(image) ax.axis('off') ########################### # Visualize intermediate outputs # ++++++++++++++++++++++++++++++ from skonnxrt.sklapi import OnnxTransformer # noqa with open("TfidfVectorizer.onnx", "rb") as f: content = f.read() input = corpus[2] print("with input:", [input]) for step in OnnxTransformer.enumerate_create(content): print("-> node '{}'".format(step[0])) step[1].fit() print(step[1].transform(input)) ################################# # **Versions used for this example** import numpy, sklearn # noqa print("numpy:", numpy.__version__) print("scikit-learn:", sklearn.__version__) import onnx, onnxruntime, skl2onnx, skonnxrt # noqa print("onnx: ", onnx.__version__) print("onnxruntime: ", onnxruntime.__version__) print("scikit-onnxruntime: ", skonnxrt.__version__) print("skl2onnx: ", skl2onnx.__version__)