From scikit-learn to ONNX

Function skl2onnx.to_onnx is the main entrypoint to convert a scikit-learn pipeline into ONNX. The same function was extended in this package into to_onnx to handle dataframes, an extended list of supported converters, scorers. It works exactly the same:

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import numpy
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
from mlprodict.onnx_conv import to_onnx
from mlprodict.onnxrt import OnnxInference

iris = load_iris()
X = iris.data.astype(numpy.float32)
X_train, X_test = train_test_split(X)
clr = KMeans(n_clusters=3)
clr.fit(X_train)

model_def = to_onnx(clr, X_train.astype(numpy.float32),
                    target_opset=12)

oinf = OnnxInference(model_def, runtime='python')
print(oinf.run({'X': X_test[:5]}))

>>>

    somewhere/.local/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
      warnings.warn(
    {'label': array([1, 1, 0, 0, 1]), 'scores': array([[3.328, 0.305, 4.967],
           [3.535, 0.469, 5.182],
           [1.02 , 2.626, 2.716],
           [0.725, 4.064, 1.177],
           [3.346, 0.342, 4.904]], dtype=float32)}

This new version extends the conversion to scorers through convert_scorer.