Probabilities or raw scores

A classifier usually returns a matrix of probabilities. By default, sklearn-onnx creates an ONNX graph which returns probabilities but it may skip that step and return raw scores if the model implements the method decision_function. Option 'raw_scores' is used to change the default behaviour. Let’s see that on a simple example.

Train a model and convert it

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)

Output type

Let’s confirm the output type of the probabilities is a list of dictionaries with onnxruntime.

sess = rt.InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
res = sess.run(None, {"float_input": X_test.astype(numpy.float32)})
print("skl", clr.predict_proba(X_test[:1]))
print("onnx", res[1][:2])

skl [[1.04043506e-04 1.42225538e-01 8.57670419e-01]]
onnx [{0: 0.00010404348722659051, 1: 0.14222556352615356, 2: 0.8576703667640686}, {0: 1.6923347629926866e-06, 1: 0.026233060285449028, 2: 0.9737652540206909}]

Raw scores and decision_function

initial_type = [("float_input", FloatTensorType([None, 4]))]
options = {id(clr): {"raw_scores": True}}
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("skl", clr.decision_function(X_test[:1]))
print("onnx", res2[1][:2])

skl [[-5.41250875  1.80785147  3.60465728]]
onnx [{0: -5.412508964538574, 1: 1.8078515529632568, 2: 3.6046571731567383}, {0: -7.6371612548828125, 1: 2.011505365371704, 2: 5.625655174255371}]

Versions used for this example

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

numpy: 2.4.1
scikit-learn: 1.8.0
onnx:  1.21.0
onnxruntime:  1.24.0
skl2onnx:  1.20.0