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

.. only:: html

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

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

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

.. _sphx_glr_gyexamples_plot_speedup_pca.py:


.. _l-Speedup-pca:

Speed up scikit-learn inference with ONNX
=========================================

Is it possible to make :epkg:`scikit-learn` faster with ONNX?
That's question this example tries to answer. The scenario is
is the following:

* a model is trained
* it is converted into ONNX for inference
* it selects a runtime to compute the prediction

The following runtime are tested:

* `python`: python runtime for ONNX
* `onnxruntime1`: :epkg:`onnxruntime`
* `numpy`: the ONNX graph is converted into numpy code
* `numba`: the numpy code is accelerated with :epkg:`numba`.

.. contents::
    :local:

PCA
+++

Let's look at a very simple model, a PCA.

.. GENERATED FROM PYTHON SOURCE LINES 30-41

.. code-block:: default


    import numpy
    from pandas import DataFrame
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_regression
    from sklearn.decomposition import PCA
    from pyquickhelper.pycode.profiling import profile
    from mlprodict.sklapi import OnnxSpeedupTransformer
    from cpyquickhelper.numbers.speed_measure import measure_time
    from tqdm import tqdm








.. GENERATED FROM PYTHON SOURCE LINES 42-43

Data and models to test.

.. GENERATED FROM PYTHON SOURCE LINES 43-57

.. code-block:: default


    data, _ = make_regression(1000, n_features=20)
    data = data.astype(numpy.float32)
    models = [
        ('sklearn', PCA(n_components=10)),
        ('python', OnnxSpeedupTransformer(
            PCA(n_components=10), runtime='python')),
        ('onnxruntime1', OnnxSpeedupTransformer(
            PCA(n_components=10), runtime='onnxruntime1')),
        ('numpy', OnnxSpeedupTransformer(
            PCA(n_components=10), runtime='numpy')),
        ('numba', OnnxSpeedupTransformer(
            PCA(n_components=10), runtime='numba'))]








.. GENERATED FROM PYTHON SOURCE LINES 58-59

Training.

.. GENERATED FROM PYTHON SOURCE LINES 59-63

.. code-block:: default


    for name, model in tqdm(models):
        model.fit(data)





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

 .. code-block:: none


      0%|          | 0/5 [00:00<?, ?it/s]
     40%|####      | 2/5 [00:00<00:00, 11.53it/s]
     80%|########  | 4/5 [00:01<00:00,  2.57it/s]
    100%|##########| 5/5 [00:04<00:00,  1.33s/it]
    100%|##########| 5/5 [00:04<00:00,  1.01it/s]




.. GENERATED FROM PYTHON SOURCE LINES 64-65

Profiling of runtime `onnxruntime1`.

.. GENERATED FROM PYTHON SOURCE LINES 65-76

.. code-block:: default



    def fct():
        for i in range(1000):
            models[2][1].transform(data)


    res = profile(fct, pyinst_format="text")
    print(res[1])






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

 .. code-block:: none


      _     ._   __/__   _ _  _  _ _/_   Recorded: 03:25:57 AM Samples:  523
     /_//_/// /_\ / //_// / //_'/ //     Duration: 0.667     CPU time: 2.641
    /   _/                      v4.4.0

    Program: somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_speedup_pca.py

    0.666 profile  ../pycode/profiling.py:455
    `- 0.666 fct  plot_speedup_pca.py:67
          [28 frames hidden]  plot_speedup_pca, sklearn, mlprodict,...
             0.632 OnnxWholeSession.run  mlprodict/onnxrt/ops_whole/session.py:97






.. GENERATED FROM PYTHON SOURCE LINES 77-78

Profiling of runtime `numpy`.

.. GENERATED FROM PYTHON SOURCE LINES 78-87

.. code-block:: default


    def fct():
        for i in range(1000):
            models[3][1].transform(data)


    res = profile(fct, pyinst_format="text")
    print(res[1])





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

 .. code-block:: none


      _     ._   __/__   _ _  _  _ _/_   Recorded: 03:25:58 AM Samples:  284
     /_//_/// /_\ / //_// / //_'/ //     Duration: 0.304     CPU time: 0.303
    /   _/                      v4.4.0

    Program: somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_speedup_pca.py

    0.303 profile  ../pycode/profiling.py:455
    `- 0.303 fct  plot_speedup_pca.py:79
          [34 frames hidden]  plot_speedup_pca, sklearn, mlprodict,...
             0.269 numpy_mlprodict_ONNX_PCA  <string>:11






.. GENERATED FROM PYTHON SOURCE LINES 88-91

The class *OnnxSpeedupTransformer* converts the PCA
into ONNX and then converts it into a python code using
*numpy*. The code is the following.

.. GENERATED FROM PYTHON SOURCE LINES 91-94

.. code-block:: default


    print(models[3][1].numpy_code_)





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

 .. code-block:: none

    import numpy
    import scipy.special as scipy_special
    import scipy.spatial.distance as scipy_distance
    from mlprodict.onnx_tools.exports.numpy_helper import (
        argmax_use_numpy_select_last_index,
        argmin_use_numpy_select_last_index,
        array_feature_extrator,
        make_slice)


    def numpy_mlprodict_ONNX_PCA(X):
        '''
        Numpy function for ``mlprodict_ONNX_PCA``.

        * producer: skl2onnx
        * version: 0
        * description: 
        '''
        # initializers

        list_value = [-0.5067367553710938, -0.32151809334754944, -0.05145944654941559, 0.07127761840820312, -0.04267493635416031, -0.11673732101917267, -0.1473727971315384, -0.15029238164424896, -0.23439958691596985, 0.39646437764167786, 0.30630427598953247, -0.37292757630348206, -0.07508348673582077, 0.004433620721101761, 0.04703060910105705, 0.29840517044067383, -0.06853567808866501, 0.24013546109199524, 0.4505208730697632, -0.015199273824691772, 0.11567263305187225, -0.11265850067138672, -0.3801497519016266, -0.13729508221149445, 0.08391162008047104, -0.03998395800590515, -0.3874768614768982, 0.12708556652069092, -0.07222757488489151, 0.10994700342416763, 0.23622679710388184, -0.20161578059196472, -0.17666909098625183, 0.3874513506889343, 0.1987515389919281, 0.17488011717796326, 0.21308909356594086, -0.14792722463607788, -0.07723761349916458, -0.3194519877433777, 0.005792118608951569, 0.18009474873542786, 0.3363724648952484, 0.25799739360809326, 0.27424877882003784, -0.29587453603744507, 0.1344870626926422, -0.14384956657886505, 0.4493676722049713, 0.2463940978050232, 0.03778448700904846, -0.33750906586647034, -0.2391706109046936, -0.03157924860715866, 0.0777701586484909, -0.19794175028800964, 0.05461816489696503, -0.18469876050949097, -0.11378421634435654, -0.013973366469144821, 0.12114843726158142, 0.21663418412208557, 0.12665724754333496, -0.19134721159934998, -0.2386147528886795, 0.14158907532691956, 0.1323249787092209, 0.005949962884187698, -0.21775154769420624, -0.1683511883020401, 0.20672805607318878, -0.10196325182914734, -0.01040369551628828, -0.23020119965076447, 0.08534082025289536, -0.0859760120511055, 0.058721136301755905, 0.23717492818832397, 0.19332104921340942, 0.1748892217874527, 0.14939863979816437, 0.3381364643573761, -0.3807629644870758, 0.010121817700564861, -0.13125498592853546, 0.15186849236488342, 0.01093701459467411, 0.037533052265644073, -0.06356837600469589, 0.29494503140449524, 0.12641650438308716, -0.01686842553317547, -0.030905520543456078, 0.22283193469047546, -0.16051693260669708, -0.10563787072896957, -0.24627335369586945, -0.3385445177555084, 0.23178917169570923,
                      0.2573137581348419, 0.45094603300094604, -0.06837690621614456, 0.13998538255691528, 0.2980887293815613, -0.5009583830833435, -0.1906174123287201, -0.039430104196071625, 0.20523692667484283, -0.1839393973350525, 0.3247809112071991, -0.26199373602867126, 0.011608600616455078, 0.006415803916752338, -0.06258974224328995, 0.20644383132457733, 0.05381537601351738, -0.20593087375164032, 0.44394606351852417, 0.21169179677963257, 0.09889845550060272, -0.18559519946575165, 0.26332205533981323, -0.22970151901245117, 0.6082763671875, 0.10286825150251389, 0.0329391285777092, 0.06118611618876457, 0.16409413516521454, -0.04447919875383377, -0.09132901579141617, -0.05354955792427063, 0.33986032009124756, -0.21200034022331238, -0.3175372779369354, -0.19614379107952118, 0.07239825278520584, 0.09320909529924393, -0.3494255840778351, 0.31601786613464355, 0.056582849472761154, 0.04646087810397148, 0.042027346789836884, 0.4445549547672272, 0.04736742004752159, -0.10475647449493408, 0.009311294183135033, -0.5570416450500488, -0.1053469181060791, 0.016088340431451797, -0.3296317458152771, -0.17308159172534943, 0.010242731310427189, -0.014094172976911068, 0.2028052657842636, -0.20289117097854614, 0.5946755409240723, -0.06317837536334991, -0.07546507567167282, 0.20626795291900635, 0.15703515708446503, 0.17143671214580536, 0.07143831998109818, -0.3368282616138458, 0.00934063270688057, 0.07573540508747101, -0.37361574172973633, -0.15489810705184937, -0.21196970343589783, 0.18515163660049438, -0.2139422446489334, 0.0381578654050827, -0.00867514032870531, -0.08442331105470657, 0.04178505390882492, 0.06234220042824745, 0.21655569970607758, -0.43873488903045654, -0.25793230533599854, -0.05557382106781006, -0.080104760825634, 0.3089466691017151, 0.01674206741154194, 0.21306872367858887, -0.09531918168067932, 0.5193166732788086, 0.29290080070495605, 0.045323487371206284, -0.2897208333015442, -0.2799742817878723, 0.3735027015209198, -0.1237722784280777, -0.4346633851528168, 0.0490194708108902, -0.03333911672234535, -0.29473116993904114, 0.07211415469646454, 0.29329872131347656, -0.23274967074394226, 0.18552133440971375, -0.059958960860967636]
        B = numpy.array(list_value, dtype=numpy.float32).reshape((20, 10))

        list_value = [-0.016928449273109436, 0.004371359944343567, -0.04217253997921944, -0.014449816197156906, -0.03853035345673561, 0.049470629543066025, -0.039256710559129715, 0.007685807067900896, 0.013661731965839863, -0.013782888650894165,
                      0.011968047358095646, 0.010710232891142368, 0.005895240232348442, 0.005286639556288719, -0.024296876043081284, 0.0047693196684122086, -0.007472604978829622, -0.0024257597979158163, -0.02468179538846016, -0.07006240636110306]
        C = numpy.array(list_value, dtype=numpy.float32)

        # nodes

        D = X - C
        variable = D @ B

        return variable





.. GENERATED FROM PYTHON SOURCE LINES 95-96

Benchmark.

.. GENERATED FROM PYTHON SOURCE LINES 96-117

.. code-block:: default


    bench = []
    for name, model in tqdm(models):
        for size in (1, 10, 100, 1000, 10000, 100000, 200000):
            data, _ = make_regression(size, n_features=20)
            data = data.astype(numpy.float32)

            # We run it a first time (numba compiles
            # the function during the first execution).
            model.transform(data)
            res = measure_time(
                lambda: model.transform(data), div_by_number=True,
                context={'data': data, 'model': model})
            res['name'] = name
            res['size'] = size
            bench.append(res)

    df = DataFrame(bench)
    piv = df.pivot("size", "name", "average")
    piv





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

 .. code-block:: none


      0%|          | 0/5 [00:00<?, ?it/s]
     20%|##        | 1/5 [00:31<02:05, 31.49s/it]
     40%|####      | 2/5 [00:56<01:23, 27.74s/it]
     60%|######    | 3/5 [01:08<00:40, 20.31s/it]
     80%|########  | 4/5 [01:31<00:21, 21.55s/it]
    100%|##########| 5/5 [02:00<00:00, 24.28s/it]
    100%|##########| 5/5 [02:00<00:00, 24.13s/it]
    somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_speedup_pca.py:114: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
      piv = df.pivot("size", "name", "average")


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th>name</th>
          <th>numba</th>
          <th>numpy</th>
          <th>onnxruntime1</th>
          <th>python</th>
          <th>sklearn</th>
        </tr>
        <tr>
          <th>size</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>0.000038</td>
          <td>0.000090</td>
          <td>0.000248</td>
          <td>0.000168</td>
          <td>0.000265</td>
        </tr>
        <tr>
          <th>10</th>
          <td>0.000041</td>
          <td>0.000095</td>
          <td>0.000244</td>
          <td>0.000153</td>
          <td>0.000269</td>
        </tr>
        <tr>
          <th>100</th>
          <td>0.000054</td>
          <td>0.000110</td>
          <td>0.000275</td>
          <td>0.000168</td>
          <td>0.000288</td>
        </tr>
        <tr>
          <th>1000</th>
          <td>0.000160</td>
          <td>0.000222</td>
          <td>0.000455</td>
          <td>0.000297</td>
          <td>0.000472</td>
        </tr>
        <tr>
          <th>10000</th>
          <td>0.001669</td>
          <td>0.001811</td>
          <td>0.001318</td>
          <td>0.001868</td>
          <td>0.002411</td>
        </tr>
        <tr>
          <th>100000</th>
          <td>0.012811</td>
          <td>0.014375</td>
          <td>0.006431</td>
          <td>0.015843</td>
          <td>0.017375</td>
        </tr>
        <tr>
          <th>200000</th>
          <td>0.025214</td>
          <td>0.028564</td>
          <td>0.012378</td>
          <td>0.030088</td>
          <td>0.040088</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 118-119

Graph.

.. GENERATED FROM PYTHON SOURCE LINES 119-129

.. code-block:: default

    fig, ax = plt.subplots(1, 2, figsize=(10, 4))
    piv.plot(title="Speedup PCA with ONNX (lower better)",
             logx=True, logy=True, ax=ax[0])
    piv2 = piv.copy()
    for c in piv2.columns:
        piv2[c] /= piv['sklearn']
    print(piv2)
    piv2.plot(title="baseline=scikit-learn (lower better)",
              logx=True, logy=True, ax=ax[1])
    plt.show()



.. image-sg:: /gyexamples/images/sphx_glr_plot_speedup_pca_001.png
   :alt: Speedup PCA with ONNX (lower better), baseline=scikit-learn (lower better)
   :srcset: /gyexamples/images/sphx_glr_plot_speedup_pca_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    name       numba     numpy  onnxruntime1    python  sklearn
    size                                                       
    1       0.143822  0.341372      0.936087  0.633956      1.0
    10      0.152444  0.353136      0.906268  0.568480      1.0
    100     0.186291  0.381746      0.955312  0.582807      1.0
    1000    0.337874  0.469534      0.964486  0.629264      1.0
    10000   0.692215  0.751055      0.546553  0.774775      1.0
    100000  0.737314  0.827304      0.370113  0.911817      1.0
    200000  0.628970  0.712530      0.308767  0.750558      1.0





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

   **Total running time of the script:** ( 2 minutes  9.585 seconds)


.. _sphx_glr_download_gyexamples_plot_speedup_pca.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_speedup_pca.py <plot_speedup_pca.py>`

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

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


.. only:: html

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

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