.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples_sklearn/plot_sklearn_knn.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_sklearn_plot_sklearn_knn.py: .. _l-plot-sklearn-knn: KNeighbors: choosing between CDist and standard ONNX ===================================================== :func:`yobx.sklearn.to_onnx` converts a fitted :class:`sklearn.neighbors.KNeighborsClassifier` or :class:`sklearn.neighbors.KNeighborsRegressor` into an :class:`onnx.ModelProto`. Two implementations are available, selected automatically at conversion time based on the ``target_opset`` argument: * **Standard ONNX** (default) — uses built-in ONNX operators only (``Mul``, ``ReduceSum``, ``MatMul``, ``TopK``, …). Runs on *any* ONNX-compatible runtime. Requires at least opset 13 for the classifier (``ReduceSum`` with axes-as-input) and opset 18 for the regressor (``ReduceMean`` with axes-as-input). * **CDist path** (opt-in) — delegates pairwise distance computation to the ``com.microsoft.CDist`` custom operator, which is natively accelerated by `ONNX Runtime `_. Enable it by passing ``target_opset={"": , "com.microsoft": 1}``. When to prefer CDist -------------------- Use the CDist path when all of the following are true: 1. You are deploying to **ONNX Runtime** (which ships with CDist). 2. The training set is large (many rows *M* in ``_fit_X``). CDist is fused at the C++ level and avoids materialising the full ``(N, M)`` intermediate matrix in Python. 3. You are comfortable with a model that cannot run on runtimes that do not implement the ``com.microsoft`` custom domain. Use the standard ONNX path when you need maximum portability. .. GENERATED FROM PYTHON SOURCE LINES 40-48 .. code-block:: Python import numpy as np import onnxruntime from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from yobx.sklearn import to_onnx .. GENERATED FROM PYTHON SOURCE LINES 49-51 1. Train a KNeighborsClassifier -------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 51-59 .. code-block:: Python rng = np.random.default_rng(0) X = rng.standard_normal((80, 4)).astype(np.float32) y = (X[:, 0] > 0).astype(np.int64) clf = KNeighborsClassifier(n_neighbors=5) clf.fit(X, y) .. raw:: html

KNeighborsClassifier()

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.. GENERATED FROM PYTHON SOURCE LINES 60-66 Standard ONNX path (default) ---------------------------- Pass a plain integer opset (or omit ``target_opset`` entirely). The converter uses built-in ONNX operators only and the resulting model is portable across any ONNX runtime. .. GENERATED FROM PYTHON SOURCE LINES 66-80 .. code-block:: Python onx_std = to_onnx(clf, (X,)) op_types_std = {(n.op_type, n.domain or "") for n in onx_std.graph.node} print("Standard path nodes:", sorted({t for t, _ in op_types_std})) assert ("CDist", "com.microsoft") not in op_types_std assert ("TopK", "") in op_types_std ref_std = onnxruntime.InferenceSession( onx_std.SerializeToString(), providers=["CPUExecutionProvider"] ) labels_std = ref_std.run(None, {"X": X})[0] assert (labels_std == clf.predict(X).astype(np.int64)).all() print("Standard path labels match sklearn ✓") .. rst-class:: sphx-glr-script-out .. code-block:: none Standard path nodes: ['Add', 'ArgMax', 'Div', 'Gather', 'MatMul', 'Max', 'Mul', 'OneHot', 'ReduceSum', 'Reshape', 'Sqrt', 'Sub', 'TopK'] Standard path labels match sklearn ✓ .. GENERATED FROM PYTHON SOURCE LINES 81-87 CDist path (com.microsoft) --------------------------- Pass a ``dict`` target opset that includes ``"com.microsoft": 1``. The converter inserts a single ``com.microsoft.CDist`` node for distance computation, which ONNX Runtime executes via a fused C++ kernel. .. GENERATED FROM PYTHON SOURCE LINES 87-100 .. code-block:: Python onx_cd = to_onnx(clf, (X,), target_opset={"": 18, "com.microsoft": 1}) op_types_cd = {(n.op_type, n.domain or "") for n in onx_cd.graph.node} print("CDist path nodes:", sorted({t for t, _ in op_types_cd})) assert ("CDist", "com.microsoft") in op_types_cd ref_cd = onnxruntime.InferenceSession( onx_cd.SerializeToString(), providers=["CPUExecutionProvider"] ) labels_cd = ref_cd.run(None, {"X": X})[0] assert (labels_cd == clf.predict(X).astype(np.int64)).all() print("CDist path labels match sklearn ✓") .. rst-class:: sphx-glr-script-out .. code-block:: none CDist path nodes: ['ArgMax', 'CDist', 'Div', 'Gather', 'Max', 'OneHot', 'ReduceSum', 'Reshape', 'TopK'] CDist path labels match sklearn ✓ .. GENERATED FROM PYTHON SOURCE LINES 101-103 Both paths produce identical predictions ---------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 103-107 .. code-block:: Python assert (labels_std == labels_cd).all(), "Paths produce different labels!" print("Both paths agree ✓") .. rst-class:: sphx-glr-script-out .. code-block:: none Both paths agree ✓ .. GENERATED FROM PYTHON SOURCE LINES 108-113 2. KNeighborsRegressor — standard ONNX path -------------------------------------------- The regressor uses ``ReduceMean`` with axes as an input, which requires at least opset 18. .. GENERATED FROM PYTHON SOURCE LINES 113-130 .. code-block:: Python X_r = rng.standard_normal((60, 3)).astype(np.float32) y_r = (X_r[:, 0] * 3 + X_r[:, 1]).astype(np.float32) reg = KNeighborsRegressor(n_neighbors=5) reg.fit(X_r, y_r) onx_reg = to_onnx(reg, (X_r,)) # uses opset 18 by default ref_reg = onnxruntime.InferenceSession( onx_reg.SerializeToString(), providers=["CPUExecutionProvider"] ) preds_onnx = ref_reg.run(None, {"X": X_r})[0] preds_sk = reg.predict(X_r).astype(np.float32) assert np.allclose(preds_onnx, preds_sk, atol=1e-5), "Regressor predictions differ!" print("Regressor predictions match sklearn ✓") .. rst-class:: sphx-glr-script-out .. code-block:: none Regressor predictions match sklearn ✓ .. GENERATED FROM PYTHON SOURCE LINES 131-136 3. Inside a Pipeline -------------------- Both implementations work transparently inside a scikit-learn :class:`~sklearn.pipeline.Pipeline`. .. GENERATED FROM PYTHON SOURCE LINES 136-147 .. code-block:: Python pipe = Pipeline([("scaler", StandardScaler()), ("clf", KNeighborsClassifier(n_neighbors=5))]) pipe.fit(X, y) onx_pipe = to_onnx(pipe, (X,)) ref_pipe = onnxruntime.InferenceSession( onx_pipe.SerializeToString(), providers=["CPUExecutionProvider"] ) labels_pipe = ref_pipe.run(None, {"X": X})[0] assert (labels_pipe == pipe.predict(X).astype(np.int64)).all() print("Pipeline labels match sklearn ✓") .. rst-class:: sphx-glr-script-out .. code-block:: none Pipeline labels match sklearn ✓ .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.274 seconds) .. _sphx_glr_download_auto_examples_sklearn_plot_sklearn_knn.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sklearn_knn.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_sklearn_knn.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_sklearn_knn.zip ` .. include:: plot_sklearn_knn.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_