.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_tutorial/plot_jfunction_transformer.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_tutorial_plot_jfunction_transformer.py: Issues with FunctionTransformer =============================== A pipeline including a `FunctionTransformer `_ cannot be automatically converted into onnx because there is no converter able to convert custom python code into ONNX. A custom converter needs to be written specifically for it. Initial try +++++++++++ A very simple pipeline and the first attempt to convert it into ONNX. .. GENERATED FROM PYTHON SOURCE LINES 16-73 .. code-block:: Python import numpy as np from numpy.testing import assert_allclose from onnx.version_converter import convert_version from pandas import DataFrame from sklearn.base import TransformerMixin, BaseEstimator from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import FunctionTransformer from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from skl2onnx import to_onnx # For the custom converter from skl2onnx import update_registered_converter from skl2onnx.common.utils import check_input_and_output_numbers from skl2onnx.algebra.onnx_ops import OnnxSlice, OnnxSub, OnnxDiv, OnnxMul, OnnxCastLike from skl2onnx.helpers import add_onnx_graph import onnxscript from onnxscript import opset18 as op # To check discrepancies from onnx.reference import ReferenceEvaluator from onnxruntime import InferenceSession def calculate_growth(df): df["c"] = 100 * (df["a"] - df["b"]) / df["b"] return df mapper = ColumnTransformer( transformers=[ ("c", FunctionTransformer(calculate_growth), ["a", "b"]), ], remainder="passthrough", verbose_feature_names_out=False, ) mapper.set_output(transform="pandas") pipe = Pipeline([("mapper", mapper), ("classifier", DecisionTreeClassifier())]) data = DataFrame( [ dict(a=2, b=1, f=5), dict(a=50, b=4, f=10), dict(a=5, b=2, f=4), dict(a=100, b=6, f=20), ] ) y = np.array([0, 1, 0, 1], dtype=np.int64) pipe.fit(data, y) try: to_onnx(pipe, data[:1], options={"zipmap": False}) except Exception as e: print("It does not work:", e) .. rst-class:: sphx-glr-script-out .. code-block:: none It does not work: FunctionTransformer is not supported unless the transform function is None (= identity). You may raise an issue at https://github.com/onnx/sklearn-onnx/issues. .. GENERATED FROM PYTHON SOURCE LINES 74-79 Use of custom transformer +++++++++++++++++++++++++ It is easier to write a custom converter if the FunctionTransformer is implemented as a custom transformer. .. GENERATED FROM PYTHON SOURCE LINES 79-108 .. code-block:: Python class GrowthCalculator(BaseEstimator, TransformerMixin): def __init__(self): pass def calculate_growth(self, x, y): return 100 * (x - y) / y def fit(self, X, y=None): return self def transform(self, X, y=None): x = X.apply(lambda x: self.calculate_growth(x.a, x.b), axis=1) return x.values.reshape((-1, 1)) mapper = ColumnTransformer( transformers=[ ("ab", FunctionTransformer(), ["a", "b"]), # We keep the first column. ("c", GrowthCalculator(), ["a", "b"]), # We add a new one. ], remainder="passthrough", verbose_feature_names_out=False, ) pipe_tr = Pipeline([("mapper", mapper), ("classifier", DecisionTreeClassifier())]) pipe_tr.fit(data, y) .. raw:: html 
Pipeline(steps=[('mapper',
                     ColumnTransformer(remainder='passthrough',
                                       transformers=[('ab', FunctionTransformer(),
                                                      ['a', 'b']),
                                                     ('c', GrowthCalculator(),
                                                      ['a', 'b'])],
                                       verbose_feature_names_out=False)),
                    ('classifier', DecisionTreeClassifier())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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.. GENERATED FROM PYTHON SOURCE LINES 109-110 Both pipelines return the same output. .. GENERATED FROM PYTHON SOURCE LINES 110-112 .. code-block:: Python assert_allclose(pipe.predict_proba(data), pipe_tr.predict_proba(data)) .. GENERATED FROM PYTHON SOURCE LINES 113-114 Let's check it produces the same number of features. .. GENERATED FROM PYTHON SOURCE LINES 114-116 .. code-block:: Python assert_allclose(pipe.steps[0][-1].transform(data), pipe_tr.steps[0][-1].transform(data)) .. GENERATED FROM PYTHON SOURCE LINES 117-118 But the conversion still fails with a different error message. .. GENERATED FROM PYTHON SOURCE LINES 118-125 .. code-block:: Python try: to_onnx(pipe_tr, data[:1], options={"zipmap": False}) except Exception as e: print("It does not work:", e) .. rst-class:: sphx-glr-script-out .. code-block:: none It does not work: 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 126-131 Custom converter ++++++++++++++++ We need to implement the method `calculate_growth` in ONNX. The first function returns the expected type and shape. .. GENERATED FROM PYTHON SOURCE LINES 131-177 .. code-block:: Python def growth_shape_calculator(operator): check_input_and_output_numbers(operator, input_count_range=1, output_count_range=1) # Gets the input type, the transformer works on any numerical type. input_type = operator.inputs[0].type.__class__ # The first dimension is usually dynamic (batch dimension). input_dim = operator.inputs[0].get_first_dimension() operator.outputs[0].type = input_type([input_dim, 1]) def growth_converter(scope, operator, container): # No need to retrieve the fitted estimator, it is not trained. # op = operator.raw_operator opv = container.target_opset X = operator.inputs[0] # 100 * (x-y)/y --> 100 * (X[0] - X[1]) / X[1] zero = np.array([0], dtype=np.int64) one = np.array([1], dtype=np.int64) two = np.array([2], dtype=np.int64) hundred = np.array([100], dtype=np.float32) # Slice(data, starts, ends, axes) x0 = OnnxSlice(X, zero, one, one, op_version=opv) x1 = OnnxSlice(X, one, two, one, op_version=opv) z = OnnxMul( OnnxCastLike(hundred, X, op_version=opv), OnnxDiv(OnnxSub(x0, x1, op_version=opv), x1, op_version=opv), op_version=opv, output_names=operator.outputs[0], ) z.add_to(scope, container) update_registered_converter( GrowthCalculator, "AliasGrowthCalculator", growth_shape_calculator, growth_converter, ) onx = to_onnx(pipe_tr, data[:1], target_opset=18, options={"zipmap": False}) .. GENERATED FROM PYTHON SOURCE LINES 178-182 Let's check there is no discrepancies +++++++++++++++++++++++++++++++++++++ First the expected values .. GENERATED FROM PYTHON SOURCE LINES 182-186 .. code-block:: Python expected = (pipe_tr.predict(data), pipe_tr.predict_proba(data)) print(expected) .. rst-class:: sphx-glr-script-out .. code-block:: none (array([0, 1, 0, 1]), array([[1., 0.], [0., 1.], [1., 0.], [0., 1.]])) .. GENERATED FROM PYTHON SOURCE LINES 187-188 Then let's check with :class:`onnx.reference.ReferenceEvaluator`. .. GENERATED FROM PYTHON SOURCE LINES 188-202 .. code-block:: Python feeds = { "a": data["a"].values.reshape((-1, 1)), "b": data["b"].values.reshape((-1, 1)), "f": data["f"].values.reshape((-1, 1)), } # verbose=10 to show intermediate results ref = ReferenceEvaluator(onx, verbose=0) got = ref.run(None, feeds) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1]) .. GENERATED FROM PYTHON SOURCE LINES 203-204 Then with the runtime used to deploy, onnxruntime for example. .. GENERATED FROM PYTHON SOURCE LINES 204-211 .. code-block:: Python ref = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"]) got = ref.run(None, feeds) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1]) .. GENERATED FROM PYTHON SOURCE LINES 212-218 Custom converter with onnxscript ++++++++++++++++++++++++++++++++ `onnxscript `_ offers a less verbose API than what onnx package implements. Let's see how to use it to write the converters. .. GENERATED FROM PYTHON SOURCE LINES 218-229 .. code-block:: Python @onnxscript.script() def calculate_onnxscript_verbose(X): # onnxscript must define an opset. We use an identity node # from a specific opset to set it (otherwise it fails). x0 = op.Slice(X, [0], [1], [1]) x1 = op.Slice(X, [1], [2], [1]) return op.Mul(op.Div(op.Sub(x0, x1), x1), 100) .. GENERATED FROM PYTHON SOURCE LINES 230-234 This version uses the strict definition of ONNX operators. The code can be more simple if regular python operators are used. They may not be converted into ONNX but an error message is raised in that case. .. GENERATED FROM PYTHON SOURCE LINES 234-246 .. code-block:: Python @onnxscript.script() def calculate_onnxscript(X): # onnxscript must define an opset. We use an identity node # from a specific opset to set it (otherwise it fails). xi = op.Identity(X) x0 = xi[:, :1] x1 = xi[:, 1:] return (x0 - x1) / x1 * 100 .. GENERATED FROM PYTHON SOURCE LINES 247-248 We can also check that it is equivalent to the python implementation. .. GENERATED FROM PYTHON SOURCE LINES 248-252 .. code-block:: Python f_expected = calculate_growth(data)["c"].values f_got = calculate_onnxscript(data[["a", "b"]].values.astype(np.float32)) assert_allclose(f_expected.ravel(), f_got.ravel(), atol=1e-6) .. GENERATED FROM PYTHON SOURCE LINES 253-254 Let's use it in the converter. .. GENERATED FROM PYTHON SOURCE LINES 254-276 .. code-block:: Python def growth_converter_onnxscript(scope, operator, container): # No need to retrieve the fitted estimator, it is not trained. # op = operator.raw_operator opv = container.target_opset # 100 * (x-y)/y --> 100 * (X[0] - X[1]) / X[1] proto = calculate_onnxscript.to_model_proto() # The function is written with opset 18, it needs to be converted # to the opset required by the user when the conversion starts. proto_version = convert_version(proto, opv) add_onnx_graph(scope, operator, container, proto_version) update_registered_converter( GrowthCalculator, "AliasGrowthCalculator", growth_shape_calculator, growth_converter_onnxscript, ) .. GENERATED FROM PYTHON SOURCE LINES 277-278 Let's check it works. .. GENERATED FROM PYTHON SOURCE LINES 278-282 .. code-block:: Python onx = to_onnx(pipe_tr, data[:1], target_opset=18, options={"zipmap": False}) .. GENERATED FROM PYTHON SOURCE LINES 283-284 And again the discrepancies. .. GENERATED FROM PYTHON SOURCE LINES 284-291 .. code-block:: Python ref = ReferenceEvaluator(onx, verbose=0) got = ref.run(None, feeds) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1]) .. GENERATED FROM PYTHON SOURCE LINES 292-293 Finally. .. GENERATED FROM PYTHON SOURCE LINES 293-294 .. code-block:: Python print("done.") .. rst-class:: sphx-glr-script-out .. code-block:: none done. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.222 seconds) .. _sphx_glr_download_auto_tutorial_plot_jfunction_transformer.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_jfunction_transformer.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_jfunction_transformer.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_