概率作为向量或 ZipMap

分类器通常返回一个概率矩阵。默认情况下，sklearn-onnx 将该矩阵转换为一个字典列表，其中每个概率都映射到其类 ID 或名称。这种机制保留了类名。这种转换会增加预测时间，并且并非总是需要。让我们看看如何在 Iris 示例上停用此行为。

训练模型并转换它

from timeit import repeat
import numpy
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import onnxruntime as rt
import onnx
import skl2onnx
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import convert_sklearn
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = LogisticRegression(max_iter=500)
clr.fit(X_train, y_train)
print(clr)

initial_type = [("float_input", FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type, target_opset=12)

LogisticRegression(max_iter=500)

输出类型

让我们使用 onnxruntime 确认概率的输出类型是字典列表。

sess = rt.InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
res = sess.run(None, {"float_input": X_test.astype(numpy.float32)})
print(res[1][:2])
print("probabilities type:", type(res[1]))
print("type for the first observations:", type(res[1][0]))

[{0: 9.76420778897591e-05, 1: 0.046403080224990845, 2: 0.9534992575645447}, {0: 0.018943823873996735, 1: 0.8978846669197083, 2: 0.083171546459198}]
probabilities type: <class 'list'>
type for the first observations: <class 'dict'>

无 ZipMap

让我们移除 ZipMap 操作符。

initial_type = [("float_input", FloatTensorType([None, 4]))]
options = {id(clr): {"zipmap": False}}
onx2 = convert_sklearn(
    clr, initial_types=initial_type, options=options, target_opset=12
)

sess2 = rt.InferenceSession(
    onx2.SerializeToString(), providers=["CPUExecutionProvider"]
)
res2 = sess2.run(None, {"float_input": X_test.astype(numpy.float32)})
print(res2[1][:2])
print("probabilities type:", type(res2[1]))
print("type for the first observations:", type(res2[1][0]))

[[9.7642078e-05 4.6403080e-02 9.5349926e-01]
 [1.8943824e-02 8.9788467e-01 8.3171546e-02]]
probabilities type: <class 'numpy.ndarray'>
type for the first observations: <class 'numpy.ndarray'>

每个类一个输出

此选项会移除最终的 ZipMap 操作符并将概率拆分为列。最终模型为标签生成一个输出，并为每个类生成一个输出。

options = {id(clr): {"zipmap": "columns"}}
onx3 = convert_sklearn(
    clr, initial_types=initial_type, options=options, target_opset=12
)

sess3 = rt.InferenceSession(
    onx3.SerializeToString(), providers=["CPUExecutionProvider"]
)
res3 = sess3.run(None, {"float_input": X_test.astype(numpy.float32)})
for i, out in enumerate(sess3.get_outputs()):
    print(
        "output: '{}' shape={} values={}...".format(
            out.name, res3[i].shape, res3[i][:2]
        )
    )

output: 'output_label' shape=(38,) values=[2 1]...
output: 'i0' shape=(38,) values=[9.7642078e-05 1.8943824e-02]...
output: 'i1' shape=(38,) values=[0.04640308 0.89788467]...
output: 'i2' shape=(38,) values=[0.95349926 0.08317155]...

让我们比较预测时间

X32 = X_test.astype(numpy.float32)

print("Time with ZipMap:")
print(repeat(lambda: sess.run(None, {"float_input": X32}), number=100, repeat=10))

print("Time without ZipMap:")
print(repeat(lambda: sess2.run(None, {"float_input": X32}), number=100, repeat=10))

print("Time without ZipMap but with columns:")
print(repeat(lambda: sess3.run(None, {"float_input": X32}), number=100, repeat=10))

# The prediction is much faster without ZipMap
# on this example.
# The optimisation is even faster when the classes
# are described with strings and not integers
# as the final result (list of dictionaries) may copy
# many times the same information with onnxruntime.

Time with ZipMap:
[0.004339300000083313, 0.003741600000012113, 0.0030674999998154817, 0.002657600000020466, 0.002626199999667733, 0.0029488000000128523, 0.0027304000000185624, 0.0026580999997349863, 0.003430199999911565, 0.0028902999997626466]
Time without ZipMap:
[0.001563100000112172, 0.002031699999861303, 0.0012540000002445595, 0.0012501999999585678, 0.0015628999999535154, 0.0018725999998423504, 0.0012913999999000225, 0.0015812999999980093, 0.0016777000000729458, 0.0020423000000846514]
Time without ZipMap but with columns:
[0.0032931999999163963, 0.002602000000024418, 0.002499299999726645, 0.002414399999906891, 0.002675799999906303, 0.004039400000237947, 0.0024936999998317333, 0.0022391000002244255, 0.00329169999986334, 0.003487199999653967]

此示例使用的版本

print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)

numpy: 1.23.5
scikit-learn: 1.4.dev0
onnx:  1.15.0
onnxruntime:  1.16.0+cu118
skl2onnx:  1.16.0