.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_custom_parser_alternative.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_plot_custom_parser_alternative.py: .. _l-custom-parser-alternative: When a custom model is neither a classifier nor a regressor (alternative) ========================================================================= .. note:: This example rewrites :ref:`l-custom-parser` by using the syntax proposed in example :ref:`l-onnx-operators` to write the custom converter, shape calculator and parser. *scikit-learn*'s API specifies that a regressor produces one outputs and a classifier produces two outputs, predicted labels and probabilities. The goal here is to add a third result which tells if the probability is above a given threshold. That's implemented in method *validate*. Iris and scoring ++++++++++++++++ A new class is created, it trains any classifier and implements the method *validate* mentioned above. .. GENERATED FROM PYTHON SOURCE LINES 28-93 .. code-block:: Python import inspect import numpy as np import skl2onnx import onnx import sklearn from sklearn.base import ClassifierMixin, BaseEstimator, clone from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from skl2onnx import update_registered_converter import os from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer import onnxruntime as rt from skl2onnx import to_onnx, get_model_alias from skl2onnx.proto import onnx_proto from skl2onnx.common.data_types import FloatTensorType, Int64TensorType from skl2onnx.algebra.onnx_ops import ( OnnxGreater, OnnxCast, OnnxReduceMaxApi18, OnnxIdentity, ) from skl2onnx.algebra.onnx_operator import OnnxSubEstimator import matplotlib.pyplot as plt class ValidatorClassifier(BaseEstimator, ClassifierMixin): def __init__(self, estimator=None, threshold=0.75): ClassifierMixin.__init__(self) BaseEstimator.__init__(self) if estimator is None: estimator = LogisticRegression(solver="liblinear") self.estimator = estimator self.threshold = threshold def fit(self, X, y, sample_weight=None): sig = inspect.signature(self.estimator.fit) if "sample_weight" in sig.parameters: self.estimator_ = clone(self.estimator).fit( X, y, sample_weight=sample_weight ) else: self.estimator_ = clone(self.estimator).fit(X, y) return self def predict(self, X): return self.estimator_.predict(X) def predict_proba(self, X): return self.estimator_.predict_proba(X) def validate(self, X): pred = self.predict_proba(X) mx = pred.max(axis=1) return (mx >= self.threshold) * 1 data = load_iris() X, y = data.data, data.target X_train, X_test, y_train, y_test = train_test_split(X, y) model = ValidatorClassifier() model.fit(X_train, y_train) .. raw:: html

ValidatorClassifier(estimator=LogisticRegression(solver='liblinear'))

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.. GENERATED FROM PYTHON SOURCE LINES 94-97 Let's now measure the indicator which tells if the probability of a prediction is above a threshold. .. GENERATED FROM PYTHON SOURCE LINES 97-100 .. code-block:: Python print(model.validate(X_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none [0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1 1 0 1 1 1 0 0 0] .. GENERATED FROM PYTHON SOURCE LINES 101-107 Conversion to ONNX +++++++++++++++++++ The conversion fails for a new model because the library does not know any converter associated to this new model. .. GENERATED FROM PYTHON SOURCE LINES 107-113 .. code-block:: Python try: to_onnx(model, X_train[:1].astype(np.float32), target_opset=12) except RuntimeError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none Unable to find a shape calculator for type ''. It usually means the pipeline being converted contains a transformer or a predictor with no corresponding converter implemented in sklearn-onnx. If the converted is implemented in another library, you need to register the converted so that it can be used by sklearn-onnx (function update_registered_converter). If the model is not yet covered by sklearn-onnx, you may raise an issue to https://github.com/onnx/sklearn-onnx/issues to get the converter implemented or even contribute to the project. If the model is a custom model, a new converter must be implemented. Examples can be found in the gallery. .. GENERATED FROM PYTHON SOURCE LINES 114-120 Custom converter ++++++++++++++++ We reuse some pieces of code from :ref:`l-custom-model`. The shape calculator defines the shape of every output of the converted model. .. GENERATED FROM PYTHON SOURCE LINES 120-137 .. code-block:: Python def validator_classifier_shape_calculator(operator): input0 = operator.inputs[0] # first input in ONNX graph outputs = operator.outputs # outputs in ONNX graph op = operator.raw_operator # scikit-learn model (mmust be fitted) if len(outputs) != 3: raise RuntimeError("3 outputs expected not {}.".format(len(outputs))) N = input0.type.shape[0] # number of observations C = op.estimator_.classes_.shape[0] # dimension of outputs outputs[0].type = Int64TensorType([N]) # label outputs[1].type = FloatTensorType([N, C]) # probabilities outputs[2].type = Int64TensorType([C]) # validation .. GENERATED FROM PYTHON SOURCE LINES 138-139 Then the converter. .. GENERATED FROM PYTHON SOURCE LINES 139-167 .. code-block:: Python def validator_classifier_converter(scope, operator, container): input0 = operator.inputs[0] # first input in ONNX graph outputs = operator.outputs # outputs in ONNX graph op = operator.raw_operator # scikit-learn model (mmust be fitted) opv = container.target_opset # The model calls another one. The class `OnnxSubEstimator` # calls the converter for this operator. model = op.estimator_ onnx_op = OnnxSubEstimator(model, input0, op_version=opv, options={"zipmap": False}) rmax = OnnxReduceMaxApi18(onnx_op[1], axes=[1], keepdims=0, op_version=opv) great = OnnxGreater( rmax, np.array([op.threshold], dtype=np.float32), op_version=opv ) valid = OnnxCast(great, to=onnx_proto.TensorProto.INT64, op_version=opv) r1 = OnnxIdentity(onnx_op[0], output_names=[outputs[0].full_name], op_version=opv) r2 = OnnxIdentity(onnx_op[1], output_names=[outputs[1].full_name], op_version=opv) r3 = OnnxIdentity(valid, output_names=[outputs[2].full_name], op_version=opv) r1.add_to(scope, container) r2.add_to(scope, container) r3.add_to(scope, container) .. GENERATED FROM PYTHON SOURCE LINES 168-169 Then the registration. .. GENERATED FROM PYTHON SOURCE LINES 169-178 .. code-block:: Python update_registered_converter( ValidatorClassifier, "CustomValidatorClassifier", validator_classifier_shape_calculator, validator_classifier_converter, ) .. GENERATED FROM PYTHON SOURCE LINES 179-180 And conversion... .. GENERATED FROM PYTHON SOURCE LINES 180-186 .. code-block:: Python try: to_onnx(model, X_test[:1].astype(np.float32), target_opset=12) except RuntimeError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none 3 outputs expected not 2. .. GENERATED FROM PYTHON SOURCE LINES 187-194 It fails because the library expected the model to behave like a classifier which produces two outputs. We need to add a custom parser to tell the library this model produces three outputs. Custom parser +++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 194-215 .. code-block:: Python def validator_classifier_parser(scope, model, inputs, custom_parsers=None): alias = get_model_alias(type(model)) this_operator = scope.declare_local_operator(alias, model) # inputs this_operator.inputs.append(inputs[0]) # outputs val_label = scope.declare_local_variable("val_label", Int64TensorType()) val_prob = scope.declare_local_variable("val_prob", FloatTensorType()) val_val = scope.declare_local_variable("val_val", Int64TensorType()) this_operator.outputs.append(val_label) this_operator.outputs.append(val_prob) this_operator.outputs.append(val_val) # ends return this_operator.outputs .. GENERATED FROM PYTHON SOURCE LINES 216-217 Registration. .. GENERATED FROM PYTHON SOURCE LINES 217-227 .. code-block:: Python update_registered_converter( ValidatorClassifier, "CustomValidatorClassifier", validator_classifier_shape_calculator, validator_classifier_converter, parser=validator_classifier_parser, ) .. GENERATED FROM PYTHON SOURCE LINES 228-229 And conversion again. .. GENERATED FROM PYTHON SOURCE LINES 229-232 .. code-block:: Python model_onnx = to_onnx(model, X_test[:1].astype(np.float32), target_opset=12) .. GENERATED FROM PYTHON SOURCE LINES 233-237 Final test ++++++++++ We need now to check the results are the same with ONNX. .. GENERATED FROM PYTHON SOURCE LINES 237-255 .. code-block:: Python X32 = X_test[:5].astype(np.float32) sess = rt.InferenceSession( model_onnx.SerializeToString(), providers=["CPUExecutionProvider"] ) results = sess.run(None, {"X": X32}) print("--labels--") print("sklearn", model.predict(X32)) print("onnx", results[0]) print("--probabilities--") print("sklearn", model.predict_proba(X32)) print("onnx", results[1]) print("--validation--") print("sklearn", model.validate(X32)) print("onnx", results[2]) .. rst-class:: sphx-glr-script-out .. code-block:: none --labels-- sklearn [2 1 0 2 2] onnx [2 1 0 2 2] --probabilities-- sklearn [[4.35572853e-04 2.50370783e-01 7.49193644e-01] [6.40021595e-02 7.74525152e-01 1.61472688e-01] [8.72966069e-01 1.27016600e-01 1.73305757e-05] [2.88656526e-03 4.13689781e-01 5.83423654e-01] [6.84848807e-04 4.18572039e-01 5.80743112e-01]] onnx [[4.3557768e-04 2.5037074e-01 7.4919367e-01] [6.4002089e-02 7.7452540e-01 1.6147259e-01] [8.7296611e-01 1.2701656e-01 1.7331236e-05] [2.8865638e-03 4.1368976e-01 5.8342361e-01] [6.8487308e-04 4.1857198e-01 5.8074319e-01]] --validation-- sklearn [0 1 1 0 0] onnx [0 1 1 0 0] .. GENERATED FROM PYTHON SOURCE LINES 256-260 It looks good. Display the ONNX graph ++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 260-278 .. code-block:: Python pydot_graph = GetPydotGraph( model_onnx.graph, name=model_onnx.graph.name, rankdir="TB", node_producer=GetOpNodeProducer( "docstring", color="yellow", fillcolor="yellow", style="filled" ), ) pydot_graph.write_dot("validator_classifier.dot") os.system("dot -O -Gdpi=300 -Tpng validator_classifier.dot") image = plt.imread("validator_classifier.dot.png") fig, ax = plt.subplots(figsize=(40, 20)) ax.imshow(image) ax.axis("off") .. image-sg:: /auto_examples/images/sphx_glr_plot_custom_parser_alternative_001.png :alt: plot custom parser alternative :srcset: /auto_examples/images/sphx_glr_plot_custom_parser_alternative_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (np.float64(-0.5), np.float64(3557.5), np.float64(4934.5), np.float64(-0.5)) .. GENERATED FROM PYTHON SOURCE LINES 279-280 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 280-286 .. code-block:: Python print("numpy:", np.__version__) print("scikit-learn:", sklearn.__version__) print("onnx: ", onnx.__version__) print("onnxruntime: ", rt.__version__) print("skl2onnx: ", skl2onnx.__version__) .. rst-class:: sphx-glr-script-out .. code-block:: none numpy: 2.2.0 scikit-learn: 1.6.0 onnx: 1.18.0 onnxruntime: 1.21.0+cu126 skl2onnx: 1.18.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.343 seconds) .. _sphx_glr_download_auto_examples_plot_custom_parser_alternative.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_custom_parser_alternative.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_custom_parser_alternative.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_custom_parser_alternative.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_