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__)