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

.. only:: html

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

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

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

.. _sphx_glr_gyexamples_plot_opml_random_forest_reg.py:


.. _l-example-tree-ensemble-reg-bench:

Benchmark Random Forests, Tree Ensemble, (AoS and SoA)
======================================================

The script compares different implementations for the operator
TreeEnsembleRegressor.

* *baseline*: RandomForestRegressor from :epkg:`scikit-learn`
* *ort*: :epkg:`onnxruntime`,
* *mlprodict*: an implementation based on an array of structures,
  every structure describes a node,
* *mlprodict2* similar implementation but instead of having an
  array of structures, it relies on a structure of arrays,
  it parallelizes by blocks of 128 observations and inside
  every block, goes through trees then through observations
  (double loop),
* *mlprodict3*: parallelizes by trees, this implementation
  is faster when the depth is higher than 10.

.. contents::
    :local:

A structure of arrays has better performance:
`Case study: Comparing Arrays of Structures and Structures of
Arrays Data Layouts for a Compute-Intensive Loop
<https://software.intel.com/content/www/us/en/develop/articles/
a-case-study-comparing-aos-arrays-of-structures-and-soa-structures-of-arrays-data-layouts.html>`_.
See also `AoS and SoA <https://en.wikipedia.org/wiki/AoS_and_SoA>`_.

.. faqref::
    :title: Profile the execution

    :epkg:`py-spy` can be used to profile the execution
    of a program. The profile is more informative if the
    code is compiled with debug information.

    ::

        py-spy record --native -r 10 -o plot_random_forest_reg.svg -- python plot_random_forest_reg.py

Import
++++++

.. GENERATED FROM PYTHON SOURCE LINES 46-62

.. code-block:: default

    import warnings
    from time import perf_counter as time
    from multiprocessing import cpu_count
    import numpy
    from numpy.random import rand
    from numpy.testing import assert_almost_equal
    import pandas
    import matplotlib.pyplot as plt
    from sklearn import config_context
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.utils._testing import ignore_warnings
    from skl2onnx import convert_sklearn
    from skl2onnx.common.data_types import FloatTensorType
    from onnxruntime import InferenceSession
    from mlprodict.onnxrt import OnnxInference








.. GENERATED FROM PYTHON SOURCE LINES 63-64

Available optimisation on this machine.

.. GENERATED FROM PYTHON SOURCE LINES 64-69

.. code-block:: default


    from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
    print(code_optimisation())






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

 .. code-block:: none

    AVX-omp=8




.. GENERATED FROM PYTHON SOURCE LINES 70-72

Versions
++++++++

.. GENERATED FROM PYTHON SOURCE LINES 72-97

.. code-block:: default



    def version():
        from datetime import datetime
        import sklearn
        import numpy
        import onnx
        import onnxruntime
        import skl2onnx
        import mlprodict
        df = pandas.DataFrame([
            {"name": "date", "version": str(datetime.now())},
            {"name": "numpy", "version": numpy.__version__},
            {"name": "scikit-learn", "version": sklearn.__version__},
            {"name": "onnx", "version": onnx.__version__},
            {"name": "onnxruntime", "version": onnxruntime.__version__},
            {"name": "skl2onnx", "version": skl2onnx.__version__},
            {"name": "mlprodict", "version": mlprodict.__version__},
        ])
        return df


    version()







.. 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></th>
          <th>name</th>
          <th>version</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>date</td>
          <td>2023-02-04 05:51:41.345444</td>
        </tr>
        <tr>
          <th>1</th>
          <td>numpy</td>
          <td>1.23.5</td>
        </tr>
        <tr>
          <th>2</th>
          <td>scikit-learn</td>
          <td>1.2.1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>onnx</td>
          <td>1.13.0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>onnxruntime</td>
          <td>1.13.1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>skl2onnx</td>
          <td>1.13.1</td>
        </tr>
        <tr>
          <th>6</th>
          <td>mlprodict</td>
          <td>0.9.1887</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 98-100

Implementations to benchmark
++++++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 100-140

.. code-block:: default


    def fcts_model(X, y, max_depth, n_estimators, n_jobs):
        "RandomForestClassifier."
        rf = RandomForestRegressor(max_depth=max_depth, n_estimators=n_estimators,
                                   n_jobs=n_jobs)
        rf.fit(X, y)

        initial_types = [('X', FloatTensorType([None, X.shape[1]]))]
        onx = convert_sklearn(rf, initial_types=initial_types)
        sess = InferenceSession(onx.SerializeToString())
        outputs = [o.name for o in sess.get_outputs()]
        oinf = OnnxInference(onx, runtime="python")
        oinf.sequence_[0].ops_._init(numpy.float32, 1)
        name = outputs[0]
        oinf2 = OnnxInference(onx, runtime="python")
        oinf2.sequence_[0].ops_._init(numpy.float32, 2)
        oinf3 = OnnxInference(onx, runtime="python")
        oinf3.sequence_[0].ops_._init(numpy.float32, 3)

        def predict_skl_predict(X, model=rf):
            return rf.predict(X)

        def predict_onnxrt_predict(X, sess=sess):
            return sess.run(outputs[:1], {'X': X})[0]

        def predict_onnx_inference(X, oinf=oinf):
            return oinf.run({'X': X})[name]

        def predict_onnx_inference2(X, oinf2=oinf2):
            return oinf2.run({'X': X})[name]

        def predict_onnx_inference3(X, oinf3=oinf3):
            return oinf3.run({'X': X})[name]

        return {'predict': (
            predict_skl_predict, predict_onnxrt_predict,
            predict_onnx_inference, predict_onnx_inference2,
            predict_onnx_inference3)}









.. GENERATED FROM PYTHON SOURCE LINES 141-143

Benchmarks
++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 143-261

.. code-block:: default


    def allow_configuration(**kwargs):
        return True


    def bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
              methods, repeat=10, verbose=False):
        res = []
        for nfeat in n_features:

            ntrain = 50000
            X_train = numpy.empty((ntrain, nfeat)).astype(numpy.float32)
            X_train[:, :] = rand(ntrain, nfeat)[:, :]
            eps = rand(ntrain) - 0.5
            y_train = X_train.sum(axis=1) + eps

            for n_jobs in n_jobss:
                for max_depth in max_depths:
                    for n_estimators in n_estimatorss:
                        fcts = fcts_model(X_train, y_train,
                                          max_depth, n_estimators, n_jobs)

                        for n in n_obs:
                            for method in methods:

                                fct1, fct2, fct3, fct4, fct5 = fcts[method]

                                if not allow_configuration(
                                        n=n, nfeat=nfeat, max_depth=max_depth,
                                        n_estimator=n_estimators, n_jobs=n_jobs,
                                        method=method):
                                    continue

                                obs = dict(n_obs=n, nfeat=nfeat,
                                           max_depth=max_depth,
                                           n_estimators=n_estimators,
                                           method=method,
                                           n_jobs=n_jobs)

                                # creates different inputs to avoid caching
                                Xs = []
                                for r in range(repeat):
                                    x = numpy.empty((n, nfeat))
                                    x[:, :] = rand(n, nfeat)[:, :]
                                    Xs.append(x.astype(numpy.float32))

                                # measures the baseline
                                with config_context(assume_finite=True):
                                    st = time()
                                    repeated = 0
                                    for X in Xs:
                                        p1 = fct1(X)
                                        repeated += 1
                                        if time() - st >= 1:
                                            break  # stops if longer than a second
                                    end = time()
                                    obs["time_skl"] = (end - st) / repeated

                                # measures the new implementation
                                st = time()
                                r2 = 0
                                for X in Xs:
                                    p2 = fct2(X)
                                    r2 += 1
                                    if r2 >= repeated:
                                        break
                                end = time()
                                obs["time_ort"] = (end - st) / r2

                                # measures the other new implementation
                                st = time()
                                r2 = 0
                                for X in Xs:
                                    p2 = fct3(X)
                                    r2 += 1
                                    if r2 >= repeated:
                                        break
                                end = time()
                                obs["time_mlprodict"] = (end - st) / r2

                                # measures the other new implementation 2
                                st = time()
                                r2 = 0
                                for X in Xs:
                                    p2 = fct4(X)
                                    r2 += 1
                                    if r2 >= repeated:
                                        break
                                end = time()
                                obs["time_mlprodict2"] = (end - st) / r2

                                # measures the other new implementation 3
                                st = time()
                                r2 = 0
                                for X in Xs:
                                    p2 = fct5(X)
                                    r2 += 1
                                    if r2 >= repeated:
                                        break
                                end = time()
                                obs["time_mlprodict3"] = (end - st) / r2

                                # final
                                res.append(obs)
                                if verbose and (len(res) % 1 == 0 or n >= 10000):
                                    print("bench", len(res), ":", obs)

                                # checks that both produce the same outputs
                                if n <= 10000:
                                    if len(p1.shape) == 1 and len(p2.shape) == 2:
                                        p2 = p2.ravel()
                                    try:
                                        assert_almost_equal(
                                            p1.ravel(), p2.ravel(), decimal=5)
                                    except AssertionError as e:
                                        warnings.warn(str(e))
        return res








.. GENERATED FROM PYTHON SOURCE LINES 262-264

Graphs
++++++

.. GENERATED FROM PYTHON SOURCE LINES 264-332

.. code-block:: default



    def plot_rf_models(dfr):

        def autolabel(ax, rects):
            for rect in rects:
                height = rect.get_height()
                ax.annotate(f'{height:1.1f}x',
                            xy=(rect.get_x() + rect.get_width() / 2, height),
                            xytext=(0, 3),  # 3 points vertical offset
                            textcoords="offset points",
                            ha='center', va='bottom',
                            fontsize=8)

        engines = [_.split('_')[-1] for _ in dfr.columns if _.startswith("time_")]
        engines = [_ for _ in engines if _ != 'skl']
        for engine in engines:
            dfr[f"speedup_{engine}"] = dfr["time_skl"] / dfr[f"time_{engine}"]
        print(dfr.tail().T)

        ncols = 4
        fig, axs = plt.subplots(len(engines), ncols, figsize=(
            14, 4 * len(engines)), sharey=True)

        row = 0
        for row, engine in enumerate(engines):
            pos = 0
            name = f"RandomForestRegressor - {engine}"
            for max_depth in sorted(set(dfr.max_depth)):
                for nf in sorted(set(dfr.nfeat)):
                    for est in sorted(set(dfr.n_estimators)):
                        for n_jobs in sorted(set(dfr.n_jobs)):
                            sub = dfr[(dfr.max_depth == max_depth) &
                                      (dfr.nfeat == nf) &
                                      (dfr.n_estimators == est) &
                                      (dfr.n_jobs == n_jobs)]
                            ax = axs[row, pos]
                            labels = sub.n_obs
                            means = sub[f"speedup_{engine}"]

                            x = numpy.arange(len(labels))
                            width = 0.90

                            rects1 = ax.bar(x, means, width, label='Speedup')
                            if pos == 0:
                                ax.set_yscale('log')
                                ax.set_ylim([0.1, max(dfr[f"speedup_{engine}"])])

                            if pos == 0:
                                ax.set_ylabel('Speedup')
                            ax.set_title(
                                '%s\ndepth %d - %d features\n %d estimators %d '
                                'jobs' % (name, max_depth, nf, est, n_jobs))
                            if row == len(engines) - 1:
                                ax.set_xlabel('batch size')
                            ax.set_xticks(x)
                            ax.set_xticklabels(labels)
                            autolabel(ax, rects1)
                            for tick in ax.xaxis.get_major_ticks():
                                tick.label.set_fontsize(8)
                            for tick in ax.yaxis.get_major_ticks():
                                tick.label.set_fontsize(8)
                            pos += 1

        fig.tight_layout()
        return fig, ax









.. GENERATED FROM PYTHON SOURCE LINES 333-335

Run benchs
++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 335-364

.. code-block:: default


    @ignore_warnings(category=FutureWarning)
    def run_bench(repeat=100, verbose=False):
        n_obs = [1, 10, 100, 1000, 10000]
        methods = ['predict']
        n_features = [30]
        max_depths = [6, 8, 10, 12]
        n_estimatorss = [100]
        n_jobss = [cpu_count()]

        start = time()
        results = bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
                        methods, repeat=repeat, verbose=verbose)
        end = time()

        results_df = pandas.DataFrame(results)
        print("Total time = %0.3f sec cpu=%d\n" % (end - start, cpu_count()))

        # plot the results
        return results_df


    name = "plot_random_forest_reg"
    df = run_bench(verbose=True)
    df.to_csv(f"{name}.csv", index=False)
    df.to_excel(f"{name}.xlsx", index=False)
    fig, ax = plot_rf_models(df)
    fig.savefig(f"{name}.png")
    plt.show()



.. image-sg:: /gyexamples/images/sphx_glr_plot_opml_random_forest_reg_001.png
   :alt: RandomForestRegressor - ort depth 6 - 30 features  100 estimators 8 jobs, RandomForestRegressor - ort depth 8 - 30 features  100 estimators 8 jobs, RandomForestRegressor - ort depth 10 - 30 features  100 estimators 8 jobs, RandomForestRegressor - ort depth 12 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict depth 6 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict depth 8 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict depth 10 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict depth 12 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict2 depth 6 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict2 depth 8 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict2 depth 10 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict2 depth 12 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict3 depth 6 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict3 depth 8 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict3 depth 10 - 30 features  100 estimators 8 jobs, RandomForestRegressor - mlprodict3 depth 12 - 30 features  100 estimators 8 jobs
   :srcset: /gyexamples/images/sphx_glr_plot_opml_random_forest_reg_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    bench 1 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04274427462466216, 'time_ort': 6.223437473333131e-05, 'time_mlprodict': 0.006505213750642724, 'time_mlprodict2': 0.006133636333591615, 'time_mlprodict3': 0.006030462420312688}
    bench 2 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04139167800080031, 'time_ort': 0.0003842888819053769, 'time_mlprodict': 0.012418290758505464, 'time_mlprodict2': 0.0001894340803846717, 'time_mlprodict3': 0.012340408395975827}
    bench 3 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.045885168090039355, 'time_ort': 0.00047946963670917535, 'time_mlprodict': 0.012305136224974624, 'time_mlprodict2': 0.007038850136185912, 'time_mlprodict3': 0.012557338637469167}
    bench 4 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06382088743703207, 'time_ort': 0.0029533385022659786, 'time_mlprodict': 0.041088564685196616, 'time_mlprodict2': 0.011018224620784167, 'time_mlprodict3': 0.03776891368761426}
    bench 5 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.08683001716660026, 'time_ort': 0.015782713249791414, 'time_mlprodict': 0.736361120827496, 'time_mlprodict2': 0.02297877207941686, 'time_mlprodict3': 0.9753920404182281}
    bench 6 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04192163804448986, 'time_ort': 6.702054330768685e-05, 'time_mlprodict': 0.006381189208089684, 'time_mlprodict2': 0.006371027251589112, 'time_mlprodict3': 0.0018173234566347674}
    bench 7 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.042143816623138264, 'time_ort': 0.0006803655414842069, 'time_mlprodict': 0.012322351249167696, 'time_mlprodict2': 0.000588648957394374, 'time_mlprodict3': 0.012241991668512734}
    bench 8 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04502972130380247, 'time_ort': 0.0008717101705058113, 'time_mlprodict': 0.012853026612783256, 'time_mlprodict2': 0.007448452828532975, 'time_mlprodict3': 0.012698329914280253}
    bench 9 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0660240198703832, 'time_ort': 0.003177096186846029, 'time_mlprodict': 0.07283168956200825, 'time_mlprodict2': 0.016596586247032974, 'time_mlprodict3': 0.07363988437282387}
    bench 10 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0887689823381758, 'time_ort': 0.02344617566753489, 'time_mlprodict': 0.6914812588268736, 'time_mlprodict2': 0.033903778491852186, 'time_mlprodict3': 0.7002630396649087}
    bench 11 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04237027762671156, 'time_ort': 0.00010302728818108638, 'time_mlprodict': 0.00621231846162118, 'time_mlprodict2': 0.006183585418815103, 'time_mlprodict3': 0.0032372772499608495}
    bench 12 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043248251958478555, 'time_ort': 0.0009172522938267017, 'time_mlprodict': 0.012395918873759607, 'time_mlprodict2': 0.000969078416043582, 'time_mlprodict3': 0.01256808833568357}
    bench 13 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.05404166847246846, 'time_ort': 0.001302548261408351, 'time_mlprodict': 0.013250650004728845, 'time_mlprodict2': 0.008324564207884433, 'time_mlprodict3': 0.013145678002681387}
    bench 14 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06621509043907281, 'time_ort': 0.006222726879059337, 'time_mlprodict': 0.09657030319067417, 'time_mlprodict2': 0.02533866231533466, 'time_mlprodict3': 0.06031203343445668}
    bench 15 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09021130333227727, 'time_ort': 0.03384415574449425, 'time_mlprodict': 0.8070350550891211, 'time_mlprodict2': 0.0613647821592167, 'time_mlprodict3': 0.7027815164183266}
    bench 16 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04291177958172435, 'time_ort': 8.826204187547167e-05, 'time_mlprodict': 0.006334640498001439, 'time_mlprodict2': 0.006213610497070476, 'time_mlprodict3': 0.006276590705965646}
    bench 17 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043733895477919796, 'time_ort': 0.0010634556048266265, 'time_mlprodict': 0.008363155212820224, 'time_mlprodict2': 0.0012041385255187101, 'time_mlprodict3': 0.012366141997399214}
    bench 18 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.05578579688962135, 'time_ort': 0.0017745679425489572, 'time_mlprodict': 0.013762781221885234, 'time_mlprodict2': 0.009069721499044035, 'time_mlprodict3': 0.013756523555558588}
    bench 19 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06995020406320691, 'time_ort': 0.009843397132741908, 'time_mlprodict': 0.08748375320186218, 'time_mlprodict2': 0.030305100868766508, 'time_mlprodict3': 0.10346798666287213}
    bench 20 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09470866154879332, 'time_ort': 0.07119774518915536, 'time_mlprodict': 1.0159253080968151, 'time_mlprodict2': 0.10117462536701086, 'time_mlprodict3': 1.08055495608344}
    Total time = 389.856 sec cpu=8

                                15         16        17        18        19
    n_obs                        1         10       100      1000     10000
    nfeat                       30         30        30        30        30
    max_depth                   12         12        12        12        12
    n_estimators               100        100       100       100       100
    method                 predict    predict   predict   predict   predict
    n_jobs                       8          8         8         8         8
    time_skl              0.042912   0.043734  0.055786   0.06995  0.094709
    time_ort              0.000088   0.001063  0.001775  0.009843  0.071198
    time_mlprodict        0.006335   0.008363  0.013763  0.087484  1.015925
    time_mlprodict2       0.006214   0.001204   0.00907  0.030305  0.101175
    time_mlprodict3       0.006277   0.012366  0.013757  0.103468  1.080555
    speedup_ort         486.186119  41.124326  31.43627  7.106307   1.33022
    speedup_mlprodict     6.774146   5.229354  4.053381  0.799579  0.093224
    speedup_mlprodict2    6.906094  36.319655  6.150773  2.308199  0.936091
    speedup_mlprodict3    6.836797   3.536584  4.055225  0.676056  0.087648
    somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:323: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
      tick.label.set_fontsize(8)
    somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:325: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
      tick.label.set_fontsize(8)





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

   **Total running time of the script:** ( 6 minutes  41.805 seconds)


.. _sphx_glr_download_gyexamples_plot_opml_random_forest_reg.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_opml_random_forest_reg.py <plot_opml_random_forest_reg.py>`

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

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


.. only:: html

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

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