.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_custom_parser.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.py: .. _l-custom-parser: When a custom model is neither a classifier nor a regressor =========================================================== *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*. .. contents:: :local: Iris and scoring ++++++++++++++++ A new class is created, it trains any classifier and implements the method *validate* mentioned above. .. GENERATED FROM PYTHON SOURCE LINES 26-87 .. code-block:: default 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 onnxconverter_common.onnx_ops import ( apply_identity, apply_cast, apply_greater ) from skl2onnx import to_onnx, get_model_alias from skl2onnx.proto import onnx_proto from skl2onnx.common._registration import get_shape_calculator from skl2onnx.common.data_types import FloatTensorType, Int64TensorType 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 88-91 Let's now measure the indicator which tells if the probability of a prediction is above a threshold. .. GENERATED FROM PYTHON SOURCE LINES 91-94 .. code-block:: default print(model.validate(X_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none [1 1 1 0 0 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 1 0 1] .. GENERATED FROM PYTHON SOURCE LINES 95-101 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 101-108 .. code-block:: default 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 109-115 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 115-132 .. code-block:: default def validator_classifier_shape_calculator(operator): input0 = operator.inputs[0] # inputs 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 133-134 Then the converter. .. GENERATED FROM PYTHON SOURCE LINES 134-189 .. code-block:: default def validator_classifier_converter(scope, operator, container): outputs = operator.outputs # outputs in ONNX graph op = operator.raw_operator # scikit-learn model (mmust be fitted) # We reuse existing converter and declare it # as a local operator. model = op.estimator_ alias = get_model_alias(type(model)) val_op = scope.declare_local_operator(alias, model) val_op.inputs = operator.inputs # We add an intermediate outputs. val_label = scope.declare_local_variable('val_label', Int64TensorType()) val_prob = scope.declare_local_variable('val_prob', FloatTensorType()) val_op.outputs.append(val_label) val_op.outputs.append(val_prob) # We adjust the output of the submodel. shape_calc = get_shape_calculator(alias) shape_calc(val_op) # We now handle the validation. val_max = scope.get_unique_variable_name('val_max') if container.target_opset >= 18: axis_name = scope.get_unique_variable_name('axis') container.add_initializer( axis_name, onnx_proto.TensorProto.INT64, [1], [1]) container.add_node( 'ReduceMax', [val_prob.full_name, axis_name], val_max, name=scope.get_unique_operator_name('ReduceMax'), keepdims=0) else: container.add_node( 'ReduceMax', val_prob.full_name, val_max, name=scope.get_unique_operator_name('ReduceMax'), axes=[1], keepdims=0) th_name = scope.get_unique_variable_name('threshold') container.add_initializer( th_name, onnx_proto.TensorProto.FLOAT, [1], [op.threshold]) val_bin = scope.get_unique_variable_name('val_bin') apply_greater(scope, [val_max, th_name], val_bin, container) val_val = scope.get_unique_variable_name('validate') apply_cast(scope, val_bin, val_val, container, to=onnx_proto.TensorProto.INT64) # We finally link the intermediate output to the shared converter. apply_identity(scope, val_label.full_name, outputs[0].full_name, container) apply_identity(scope, val_prob.full_name, outputs[1].full_name, container) apply_identity(scope, val_val, outputs[2].full_name, container) .. GENERATED FROM PYTHON SOURCE LINES 190-191 Then the registration. .. GENERATED FROM PYTHON SOURCE LINES 191-197 .. code-block:: default update_registered_converter(ValidatorClassifier, 'CustomValidatorClassifier', validator_classifier_shape_calculator, validator_classifier_converter) .. GENERATED FROM PYTHON SOURCE LINES 198-199 And conversion... .. GENERATED FROM PYTHON SOURCE LINES 199-206 .. code-block:: default 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 207-214 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 214-234 .. code-block:: default 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) # end return this_operator.outputs .. GENERATED FROM PYTHON SOURCE LINES 235-236 Registration. .. GENERATED FROM PYTHON SOURCE LINES 236-243 .. code-block:: default update_registered_converter(ValidatorClassifier, 'CustomValidatorClassifier', validator_classifier_shape_calculator, validator_classifier_converter, parser=validator_classifier_parser) .. GENERATED FROM PYTHON SOURCE LINES 244-245 And conversion again. .. GENERATED FROM PYTHON SOURCE LINES 245-249 .. code-block:: default model_onnx = to_onnx(model, X_test[:1].astype(np.float32), target_opset=12) .. GENERATED FROM PYTHON SOURCE LINES 250-254 Final test ++++++++++ We need now to check the results are the same with ONNX. .. GENERATED FROM PYTHON SOURCE LINES 254-270 .. code-block:: default X32 = X_test[:5].astype(np.float32) sess = rt.InferenceSession(model_onnx.SerializeToString()) 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 0 1 1 1] onnx [2 0 1 1 1] --probabilities-- sklearn [[7.07999425e-04 2.48572306e-01 7.50719694e-01] [8.21007140e-01 1.78895436e-01 9.74239685e-05] [2.10535503e-02 7.60262548e-01 2.18683902e-01] [1.07188246e-02 6.36087208e-01 3.53193967e-01] [8.79788000e-03 6.77701629e-01 3.13500491e-01]] onnx [[7.07984611e-04 2.48572275e-01 7.50719726e-01] [8.21007073e-01 1.78895399e-01 9.74424402e-05] [2.10535377e-02 7.60262549e-01 2.18683958e-01] [1.07187675e-02 6.36087179e-01 3.53194058e-01] [8.79786070e-03 6.77701533e-01 3.13500643e-01]] --validation-- sklearn [1 1 1 0 0] onnx [1 1 1 0 0] .. GENERATED FROM PYTHON SOURCE LINES 271-275 It looks good. Display the ONNX graph ++++++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 275-289 .. code-block:: default 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_001.png :alt: plot custom parser :srcset: /auto_examples/images/sphx_glr_plot_custom_parser_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-0.5, 2495.5, 4934.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 290-291 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 291-297 .. code-block:: default 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: 1.23.5 scikit-learn: 1.2.2 onnx: 1.13.1 onnxruntime: 1.14.1 skl2onnx: 1.14.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 9.047 seconds) .. _sphx_glr_download_auto_examples_plot_custom_parser.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_custom_parser.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_custom_parser.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_