
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_convert_pipeline_vectorizer.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_convert_pipeline_vectorizer.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_convert_pipeline_vectorizer.py:


Train, convert and predict with ONNX Runtime
============================================

This example demonstrates an end to end scenario
starting with the training of a scikit-learn pipeline
which takes as inputs not a regular vector but a
dictionary ``{ int: float }`` as its first step is a
`DictVectorizer <http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html>`_.

.. contents::
    :local:

Train a pipeline
++++++++++++++++

The first step consists in retrieving the boston datset.

.. GENERATED FROM PYTHON SOURCE LINES 22-34

.. code-block:: default

    import pandas
    from sklearn.datasets import load_boston

    boston = load_boston()
    X, y = boston.data, boston.target

    from sklearn.model_selection import train_test_split

    X_train, X_test, y_train, y_test = train_test_split(X, y)
    X_train_dict = pandas.DataFrame(X_train[:, 1:]).T.to_dict().values()
    X_test_dict = pandas.DataFrame(X_test[:, 1:]).T.to_dict().values()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.

        The Boston housing prices dataset has an ethical problem. You can refer to
        the documentation of this function for further details.

        The scikit-learn maintainers therefore strongly discourage the use of this
        dataset unless the purpose of the code is to study and educate about
        ethical issues in data science and machine learning.

        In this special case, you can fetch the dataset from the original
        source::

            import pandas as pd
            import numpy as np

            data_url = "http://lib.stat.cmu.edu/datasets/boston"
            raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
            data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
            target = raw_df.values[1::2, 2]

        Alternative datasets include the California housing dataset (i.e.
        :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
        dataset. You can load the datasets as follows::

            from sklearn.datasets import fetch_california_housing
            housing = fetch_california_housing()

        for the California housing dataset and::

            from sklearn.datasets import fetch_openml
            housing = fetch_openml(name="house_prices", as_frame=True)

        for the Ames housing dataset.
      warnings.warn(msg, category=FutureWarning)




.. GENERATED FROM PYTHON SOURCE LINES 35-36

We create a pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 36-45

.. code-block:: default


    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.feature_extraction import DictVectorizer
    from sklearn.pipeline import make_pipeline

    pipe = make_pipeline(DictVectorizer(sparse=False), GradientBoostingRegressor())

    pipe.fit(X_train_dict, y_train)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer(sparse=False)),
                    (&#x27;gradientboostingregressor&#x27;, GradientBoostingRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer(sparse=False)),
                    (&#x27;gradientboostingregressor&#x27;, GradientBoostingRegressor())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">DictVectorizer</label><div class="sk-toggleable__content"><pre>DictVectorizer(sparse=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 46-48

We compute the prediction on the test set
and we show the confusion matrix.

.. GENERATED FROM PYTHON SOURCE LINES 48-53

.. code-block:: default

    from sklearn.metrics import r2_score

    pred = pipe.predict(X_test_dict)
    print(r2_score(y_test, pred))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    0.8778842817557302




.. GENERATED FROM PYTHON SOURCE LINES 54-60

Conversion to ONNX format
+++++++++++++++++++++++++

We use module
`sklearn-onnx <https://github.com/onnx/sklearn-onnx>`_
to convert the model into ONNX format.

.. GENERATED FROM PYTHON SOURCE LINES 60-70

.. code-block:: default


    from skl2onnx import convert_sklearn
    from skl2onnx.common.data_types import DictionaryType, FloatTensorType, Int64TensorType, SequenceType

    # initial_type = [('float_input', DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
    initial_type = [("float_input", DictionaryType(Int64TensorType([1]), FloatTensorType([])))]
    onx = convert_sklearn(pipe, initial_types=initial_type)
    with open("pipeline_vectorize.onnx", "wb") as f:
        f.write(onx.SerializeToString())








.. GENERATED FROM PYTHON SOURCE LINES 71-73

We load the model with ONNX Runtime and look at
its input and output.

.. GENERATED FROM PYTHON SOURCE LINES 73-84

.. code-block:: default

    import onnxruntime as rt
    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument

    sess = rt.InferenceSession("pipeline_vectorize.onnx", providers=rt.get_available_providers())

    import numpy

    inp, out = sess.get_inputs()[0], sess.get_outputs()[0]
    print("input name='{}' and shape={} and type={}".format(inp.name, inp.shape, inp.type))
    print("output name='{}' and shape={} and type={}".format(out.name, out.shape, out.type))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    input name='float_input' and shape=[] and type=map(int64,tensor(float))
    output name='variable' and shape=[None, 1] and type=tensor(float)




.. GENERATED FROM PYTHON SOURCE LINES 85-87

We compute the predictions.
We could do that in one call:

.. GENERATED FROM PYTHON SOURCE LINES 87-93

.. code-block:: default


    try:
        pred_onx = sess.run([out.name], {inp.name: X_test_dict})[0]
    except (RuntimeError, InvalidArgument) as e:
        print(e)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: ((seq(map(int64,tensor(float))))) , expected: ((map(int64,tensor(float))))




.. GENERATED FROM PYTHON SOURCE LINES 94-96

But it fails because, in case of a DictVectorizer,
ONNX Runtime expects one observation at a time.

.. GENERATED FROM PYTHON SOURCE LINES 96-98

.. code-block:: default

    pred_onx = [sess.run([out.name], {inp.name: row})[0][0, 0] for row in X_test_dict]








.. GENERATED FROM PYTHON SOURCE LINES 99-100

We compare them to the model's ones.

.. GENERATED FROM PYTHON SOURCE LINES 100-102

.. code-block:: default

    print(r2_score(pred, pred_onx))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    0.999999999999964




.. GENERATED FROM PYTHON SOURCE LINES 103-105

Very similar. *ONNX Runtime* uses floats instead of doubles,
that explains the small discrepencies.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  1.044 seconds)


.. _sphx_glr_download_auto_examples_plot_convert_pipeline_vectorizer.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_convert_pipeline_vectorizer.py <plot_convert_pipeline_vectorizer.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_convert_pipeline_vectorizer.ipynb <plot_convert_pipeline_vectorizer.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
