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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_mlearning_calibration_plot_calibration.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_calibration_plot_calibration.py:


Calibration of a polynomial regression
======================================

.. GENERATED FROM PYTHON SOURCE LINES 24-34

.. code-block:: default

    from __future__ import annotations

    import matplotlib.pyplot as plt
    from gemseo import configure_logger
    from gemseo.algos.design_space import DesignSpace
    from gemseo.mlearning.core.calibration import MLAlgoCalibration
    from gemseo.mlearning.quality_measures.mse_measure import MSEMeasure
    from gemseo.problems.dataset.rosenbrock import create_rosenbrock_dataset
    from matplotlib.tri import Triangulation








.. GENERATED FROM PYTHON SOURCE LINES 35-37

Load the dataset
----------------

.. GENERATED FROM PYTHON SOURCE LINES 37-39

.. code-block:: default

    dataset = create_rosenbrock_dataset(opt_naming=False, n_samples=25)








.. GENERATED FROM PYTHON SOURCE LINES 40-42

Define the measure
------------------

.. GENERATED FROM PYTHON SOURCE LINES 42-47

.. code-block:: default

    configure_logger()
    test_dataset = create_rosenbrock_dataset(opt_naming=False)
    measure_evaluation_method_name = "TEST"
    measure_options = {"test_data": test_dataset}








.. GENERATED FROM PYTHON SOURCE LINES 48-52

Calibrate the degree of the polynomial regression
-------------------------------------------------
Define and execute the calibration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 52-69

.. code-block:: default

    calibration_space = DesignSpace()
    calibration_space.add_variable("degree", 1, "integer", 1, 10, 1)
    calibration = MLAlgoCalibration(
        "PolynomialRegressor",
        dataset,
        ["degree"],
        calibration_space,
        MSEMeasure,
        measure_evaluation_method_name=measure_evaluation_method_name,
        measure_options=measure_options,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 10})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    degree = x_opt["degree"][0]
    f"optimal degree = {degree}; optimal criterion = {f_opt}"





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

 .. code-block:: none

        INFO - 08:25:07:  
        INFO - 08:25:07: *** Start DOEScenario execution ***
        INFO - 08:25:07: DOEScenario
        INFO - 08:25:07:    Disciplines: MLAlgoAssessor
        INFO - 08:25:07:    MDO formulation: DisciplinaryOpt
        INFO - 08:25:07: Optimization problem:
        INFO - 08:25:07:    minimize criterion(degree)
        INFO - 08:25:07:    with respect to degree
        INFO - 08:25:07:    over the design space:
        INFO - 08:25:07:    +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07:    | name   | lower_bound | value | upper_bound | type    |
        INFO - 08:25:07:    +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07:    | degree |      1      |   1   |      10     | integer |
        INFO - 08:25:07:    +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07: Solving optimization problem with algorithm fullfact:
        INFO - 08:25:07: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 08:25:07: ...  10%|█         | 1/10 [00:00<00:00, 32.15 it/sec, obj=5.89e+5]
        INFO - 08:25:07: ...  20%|██        | 2/10 [00:00<00:00, 46.48 it/sec, obj=1.73e+5]
        INFO - 08:25:07: ...  30%|███       | 3/10 [00:00<00:00, 54.92 it/sec, obj=3e+4]
        INFO - 08:25:07: ...  40%|████      | 4/10 [00:00<00:00, 60.40 it/sec, obj=1.1e-24]
        INFO - 08:25:07: ...  50%|█████     | 5/10 [00:00<00:00, 64.29 it/sec, obj=0.11]
        INFO - 08:25:07: ...  60%|██████    | 6/10 [00:00<00:00, 67.04 it/sec, obj=1.18e+3]
        INFO - 08:25:07: ...  70%|███████   | 7/10 [00:00<00:00, 69.18 it/sec, obj=6.9e+3]
        INFO - 08:25:07: ...  80%|████████  | 8/10 [00:00<00:00, 70.84 it/sec, obj=1.36e+4]
        INFO - 08:25:07: ...  90%|█████████ | 9/10 [00:00<00:00, 72.10 it/sec, obj=9.18e+4]
        INFO - 08:25:07: ... 100%|██████████| 10/10 [00:00<00:00, 73.18 it/sec, obj=1.63e+5]
        INFO - 08:25:07: Optimization result:
        INFO - 08:25:07:    Optimizer info:
        INFO - 08:25:07:       Status: None
        INFO - 08:25:07:       Message: None
        INFO - 08:25:07:       Number of calls to the objective function by the optimizer: 10
        INFO - 08:25:07:    Solution:
        INFO - 08:25:07:       Objective: 1.0957626812742524e-24
        INFO - 08:25:07:       Design space:
        INFO - 08:25:07:       +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07:       | name   | lower_bound | value | upper_bound | type    |
        INFO - 08:25:07:       +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07:       | degree |      1      |   4   |      10     | integer |
        INFO - 08:25:07:       +--------+-------------+-------+-------------+---------+
        INFO - 08:25:07: *** End DOEScenario execution (time: 0:00:00.147823) ***

    'optimal degree = 4; optimal criterion = 1.0957626812742524e-24'



.. GENERATED FROM PYTHON SOURCE LINES 70-72

Get the history
^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 72-74

.. code-block:: default

    calibration.dataset






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th>inputs</th>
          <th colspan="2" halign="left">outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>degree</th>
          <th>criterion</th>
          <th>learning</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1.0</td>
          <td>5.888317e+05</td>
          <td>8.200828e+05</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2.0</td>
          <td>1.732475e+05</td>
          <td>2.404571e+05</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3.0</td>
          <td>3.001292e+04</td>
          <td>1.645714e+04</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4.0</td>
          <td>1.095763e-24</td>
          <td>1.703801e-24</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5.0</td>
          <td>1.097877e-01</td>
          <td>1.391092e-23</td>
        </tr>
        <tr>
          <th>5</th>
          <td>6.0</td>
          <td>1.183264e+03</td>
          <td>2.332471e-24</td>
        </tr>
        <tr>
          <th>6</th>
          <td>7.0</td>
          <td>6.895919e+03</td>
          <td>1.401963e-23</td>
        </tr>
        <tr>
          <th>7</th>
          <td>8.0</td>
          <td>1.356307e+04</td>
          <td>5.192192e-23</td>
        </tr>
        <tr>
          <th>8</th>
          <td>9.0</td>
          <td>9.180547e+04</td>
          <td>8.964290e-23</td>
        </tr>
        <tr>
          <th>9</th>
          <td>10.0</td>
          <td>1.625259e+05</td>
          <td>8.767875e-23</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 75-77

Visualize the results
^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 77-89

.. code-block:: default

    degree = calibration.get_history("degree")
    criterion = calibration.get_history("criterion")
    learning = calibration.get_history("learning")

    plt.plot(degree, criterion, "-o", label="test", color="red")
    plt.plot(degree, learning, "-o", label="learning", color="blue")
    plt.xlabel("polynomial degree")
    plt.ylabel("quality")
    plt.axvline(x_opt["degree"], color="red", ls="--")
    plt.legend()
    plt.show()




.. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_001.png
   :alt: plot calibration
   :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 90-94

Calibrate the ridge penalty of the polynomial regression
--------------------------------------------------------
Define and execute the calibration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 94-111

.. code-block:: default

    calibration_space = DesignSpace()
    calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0)
    calibration = MLAlgoCalibration(
        "PolynomialRegressor",
        dataset,
        ["penalty_level"],
        calibration_space,
        MSEMeasure,
        measure_evaluation_method_name=measure_evaluation_method_name,
        measure_options=measure_options,
        degree=10,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 10})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    x_opt["penalty_level"][0], f_opt





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

 .. code-block:: none

        INFO - 08:25:07:  
        INFO - 08:25:07: *** Start DOEScenario execution ***
        INFO - 08:25:07: DOEScenario
        INFO - 08:25:07:    Disciplines: MLAlgoAssessor
        INFO - 08:25:07:    MDO formulation: DisciplinaryOpt
        INFO - 08:25:07: Optimization problem:
        INFO - 08:25:07:    minimize criterion(penalty_level)
        INFO - 08:25:07:    with respect to penalty_level
        INFO - 08:25:07:    over the design space:
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | name          | lower_bound | value | upper_bound | type  |
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | penalty_level |      0      |   0   |     100     | float |
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07: Solving optimization problem with algorithm fullfact:
        INFO - 08:25:07: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 08:25:07: ...  10%|█         | 1/10 [00:00<00:00, 70.03 it/sec, obj=1.63e+5]
        INFO - 08:25:07: ...  20%|██        | 2/10 [00:00<00:00, 76.22 it/sec, obj=3.25e+4]
        INFO - 08:25:07: ...  30%|███       | 3/10 [00:00<00:00, 79.01 it/sec, obj=1.78e+4]
        INFO - 08:25:07: ...  40%|████      | 4/10 [00:00<00:00, 80.63 it/sec, obj=1.72e+4]
        INFO - 08:25:07: ...  50%|█████     | 5/10 [00:00<00:00, 81.74 it/sec, obj=2e+4]
        INFO - 08:25:07: ...  60%|██████    | 6/10 [00:00<00:00, 82.50 it/sec, obj=2.35e+4]
        INFO - 08:25:07: ...  70%|███████   | 7/10 [00:00<00:00, 83.01 it/sec, obj=2.7e+4]
        INFO - 08:25:07: ...  80%|████████  | 8/10 [00:00<00:00, 83.42 it/sec, obj=3.03e+4]
        INFO - 08:25:07: ...  90%|█████████ | 9/10 [00:00<00:00, 83.79 it/sec, obj=3.33e+4]
        INFO - 08:25:07: ... 100%|██████████| 10/10 [00:00<00:00, 84.11 it/sec, obj=3.59e+4]
        INFO - 08:25:07: Optimization result:
        INFO - 08:25:07:    Optimizer info:
        INFO - 08:25:07:       Status: None
        INFO - 08:25:07:       Message: None
        INFO - 08:25:07:       Number of calls to the objective function by the optimizer: 10
        INFO - 08:25:07:    Solution:
        INFO - 08:25:07:       Objective: 17189.52649297074
        INFO - 08:25:07:       Design space:
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07:       | name          | lower_bound |       value       | upper_bound | type  |
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07:       | penalty_level |      0      | 33.33333333333333 |     100     | float |
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07: *** End DOEScenario execution (time: 0:00:00.129230) ***

    (33.33333333333333, 17189.52649297074)



.. GENERATED FROM PYTHON SOURCE LINES 112-114

Get the history
^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 114-116

.. code-block:: default

    calibration.dataset






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th>inputs</th>
          <th colspan="2" halign="left">outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>penalty_level</th>
          <th>criterion</th>
          <th>learning</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.000000</td>
          <td>162525.860760</td>
          <td>8.767875e-23</td>
        </tr>
        <tr>
          <th>1</th>
          <td>11.111111</td>
          <td>32506.221289</td>
          <td>1.087801e+03</td>
        </tr>
        <tr>
          <th>2</th>
          <td>22.222222</td>
          <td>17820.599507</td>
          <td>1.982580e+03</td>
        </tr>
        <tr>
          <th>3</th>
          <td>33.333333</td>
          <td>17189.526493</td>
          <td>2.690007e+03</td>
        </tr>
        <tr>
          <th>4</th>
          <td>44.444444</td>
          <td>19953.420378</td>
          <td>3.251453e+03</td>
        </tr>
        <tr>
          <th>5</th>
          <td>55.555556</td>
          <td>23493.269988</td>
          <td>3.703714e+03</td>
        </tr>
        <tr>
          <th>6</th>
          <td>66.666667</td>
          <td>27024.053276</td>
          <td>4.074147e+03</td>
        </tr>
        <tr>
          <th>7</th>
          <td>77.777778</td>
          <td>30303.486633</td>
          <td>4.382362e+03</td>
        </tr>
        <tr>
          <th>8</th>
          <td>88.888889</td>
          <td>33272.062306</td>
          <td>4.642448e+03</td>
        </tr>
        <tr>
          <th>9</th>
          <td>100.000000</td>
          <td>35934.745536</td>
          <td>4.864667e+03</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 117-119

Visualize the results
^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 119-131

.. code-block:: default

    penalty_level = calibration.get_history("penalty_level")
    criterion = calibration.get_history("criterion")
    learning = calibration.get_history("learning")

    plt.plot(penalty_level, criterion, "-o", label="test", color="red")
    plt.plot(penalty_level, learning, "-o", label="learning", color="blue")
    plt.axvline(x_opt["penalty_level"], color="red", ls="--")
    plt.xlabel("ridge penalty")
    plt.ylabel("quality")
    plt.legend()
    plt.show()




.. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_002.png
   :alt: plot calibration
   :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 132-136

Calibrate the lasso penalty of the polynomial regression
--------------------------------------------------------
Define and execute the calibration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 136-154

.. code-block:: default

    calibration_space = DesignSpace()
    calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0)
    calibration = MLAlgoCalibration(
        "PolynomialRegressor",
        dataset,
        ["penalty_level"],
        calibration_space,
        MSEMeasure,
        measure_evaluation_method_name=measure_evaluation_method_name,
        measure_options=measure_options,
        degree=10,
        l2_penalty_ratio=0.0,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 10})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    x_opt["penalty_level"][0], f_opt





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

 .. code-block:: none

        INFO - 08:25:07:  
        INFO - 08:25:07: *** Start DOEScenario execution ***
        INFO - 08:25:07: DOEScenario
        INFO - 08:25:07:    Disciplines: MLAlgoAssessor
        INFO - 08:25:07:    MDO formulation: DisciplinaryOpt
        INFO - 08:25:07: Optimization problem:
        INFO - 08:25:07:    minimize criterion(penalty_level)
        INFO - 08:25:07:    with respect to penalty_level
        INFO - 08:25:07:    over the design space:
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | name          | lower_bound | value | upper_bound | type  |
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | penalty_level |      0      |   0   |     100     | float |
        INFO - 08:25:07:    +---------------+-------------+-------+-------------+-------+
        INFO - 08:25:07: Solving optimization problem with algorithm fullfact:
        INFO - 08:25:07: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 08:25:07: ...  10%|█         | 1/10 [00:00<00:00, 71.55 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.301e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  20%|██        | 2/10 [00:00<00:00, 67.42 it/sec, obj=1.58e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  30%|███       | 3/10 [00:00<00:00, 66.39 it/sec, obj=3.15e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.284e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  40%|████      | 4/10 [00:00<00:00, 66.46 it/sec, obj=4.74e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  50%|█████     | 5/10 [00:00<00:00, 66.79 it/sec, obj=5.94e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  60%|██████    | 6/10 [00:00<00:00, 67.07 it/sec, obj=6.27e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  70%|███████   | 7/10 [00:00<00:00, 67.25 it/sec, obj=6.63e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.925e+03, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  80%|████████  | 8/10 [00:00<00:00, 67.36 it/sec, obj=6.93e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.746e+03, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...  90%|█████████ | 9/10 [00:00<00:00, 67.29 it/sec, obj=7.25e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.372e+03, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ... 100%|██████████| 10/10 [00:00<00:00, 67.26 it/sec, obj=7.57e+4]
        INFO - 08:25:07: Optimization result:
        INFO - 08:25:07:    Optimizer info:
        INFO - 08:25:07:       Status: None
        INFO - 08:25:07:       Message: None
        INFO - 08:25:07:       Number of calls to the objective function by the optimizer: 10
        INFO - 08:25:07:    Solution:
        INFO - 08:25:07:       Objective: 15775.989581125898
        INFO - 08:25:07:       Design space:
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07:       | name          | lower_bound |       value       | upper_bound | type  |
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07:       | penalty_level |      0      | 11.11111111111111 |     100     | float |
        INFO - 08:25:07:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:07: *** End DOEScenario execution (time: 0:00:00.160497) ***

    (11.11111111111111, 15775.989581125898)



.. GENERATED FROM PYTHON SOURCE LINES 155-157

Get the history
^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 157-159

.. code-block:: default

    calibration.dataset






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th>inputs</th>
          <th colspan="2" halign="left">outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>penalty_level</th>
          <th>criterion</th>
          <th>learning</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.000000</td>
          <td>162525.860760</td>
          <td>8.767875e-23</td>
        </tr>
        <tr>
          <th>1</th>
          <td>11.111111</td>
          <td>15775.989581</td>
          <td>1.814382e+03</td>
        </tr>
        <tr>
          <th>2</th>
          <td>22.222222</td>
          <td>31529.584354</td>
          <td>4.057302e+03</td>
        </tr>
        <tr>
          <th>3</th>
          <td>33.333333</td>
          <td>47420.249503</td>
          <td>5.792299e+03</td>
        </tr>
        <tr>
          <th>4</th>
          <td>44.444444</td>
          <td>59358.207437</td>
          <td>7.169565e+03</td>
        </tr>
        <tr>
          <th>5</th>
          <td>55.555556</td>
          <td>62656.171431</td>
          <td>7.278397e+03</td>
        </tr>
        <tr>
          <th>6</th>
          <td>66.666667</td>
          <td>66256.259889</td>
          <td>7.410137e+03</td>
        </tr>
        <tr>
          <th>7</th>
          <td>77.777778</td>
          <td>69336.190346</td>
          <td>7.540731e+03</td>
        </tr>
        <tr>
          <th>8</th>
          <td>88.888889</td>
          <td>72457.378777</td>
          <td>7.675963e+03</td>
        </tr>
        <tr>
          <th>9</th>
          <td>100.000000</td>
          <td>75749.793494</td>
          <td>7.816545e+03</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 160-162

Visualize the results
^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 162-174

.. code-block:: default

    penalty_level = calibration.get_history("penalty_level")
    criterion = calibration.get_history("criterion")
    learning = calibration.get_history("learning")

    plt.plot(penalty_level, criterion, "-o", label="test", color="red")
    plt.plot(penalty_level, learning, "-o", label="learning", color="blue")
    plt.axvline(x_opt["penalty_level"], color="red", ls="--")
    plt.xlabel("lasso penalty")
    plt.ylabel("quality")
    plt.legend()
    plt.show()




.. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_003.png
   :alt: plot calibration
   :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 175-179

Calibrate the elasticnet penalty of the polynomial regression
-------------------------------------------------------------
Define and execute the calibration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 179-197

.. code-block:: default

    calibration_space = DesignSpace()
    calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0)
    calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5)
    calibration = MLAlgoCalibration(
        "PolynomialRegressor",
        dataset,
        ["penalty_level", "l2_penalty_ratio"],
        calibration_space,
        MSEMeasure,
        measure_evaluation_method_name=measure_evaluation_method_name,
        measure_options=measure_options,
        degree=10,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 100})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0], f_opt





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

 .. code-block:: none

        INFO - 08:25:07:  
        INFO - 08:25:07: *** Start DOEScenario execution ***
        INFO - 08:25:07: DOEScenario
        INFO - 08:25:07:    Disciplines: MLAlgoAssessor
        INFO - 08:25:07:    MDO formulation: DisciplinaryOpt
        INFO - 08:25:07: Optimization problem:
        INFO - 08:25:07:    minimize criterion(penalty_level, l2_penalty_ratio)
        INFO - 08:25:07:    with respect to l2_penalty_ratio, penalty_level
        INFO - 08:25:07:    over the design space:
        INFO - 08:25:07:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | name             | lower_bound | value | upper_bound | type  |
        INFO - 08:25:07:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:07:    | penalty_level    |      0      |   0   |      40     | float |
        INFO - 08:25:07:    | l2_penalty_ratio |      0      |  0.5  |      1      | float |
        INFO - 08:25:07:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:07: Solving optimization problem with algorithm fullfact:
        INFO - 08:25:07: ...   0%|          | 0/100 [00:00<?, ?it]
        INFO - 08:25:07: ...   1%|          | 1/100 [00:00<00:01, 69.00 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...   2%|▏         | 2/100 [00:00<00:01, 66.35 it/sec, obj=4.14e+3]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.786e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...   3%|▎         | 3/100 [00:00<00:01, 66.31 it/sec, obj=1.34e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.325e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...   4%|▍         | 4/100 [00:00<00:01, 65.99 it/sec, obj=1.79e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.034e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...   5%|▌         | 5/100 [00:00<00:01, 66.13 it/sec, obj=2.39e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:07: ...   6%|▌         | 6/100 [00:00<00:01, 66.04 it/sec, obj=3.15e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.166e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...   7%|▋         | 7/100 [00:00<00:01, 66.25 it/sec, obj=3.91e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...   8%|▊         | 8/100 [00:00<00:01, 66.27 it/sec, obj=4.5e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.545e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...   9%|▉         | 9/100 [00:00<00:01, 66.46 it/sec, obj=4.95e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.811e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  10%|█         | 10/100 [00:00<00:01, 66.46 it/sec, obj=5.42e+4]
        INFO - 08:25:08: ...  11%|█         | 11/100 [00:00<00:01, 67.82 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.233e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  12%|█▏        | 12/100 [00:00<00:01, 67.49 it/sec, obj=1.35e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.032e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  13%|█▎        | 13/100 [00:00<00:01, 67.13 it/sec, obj=2.44e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.991e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  14%|█▍        | 14/100 [00:00<00:01, 67.01 it/sec, obj=3.28e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.326e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  15%|█▌        | 15/100 [00:00<00:01, 66.81 it/sec, obj=4.19e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.100e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  16%|█▌        | 16/100 [00:00<00:01, 66.73 it/sec, obj=4.76e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.636e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  17%|█▋        | 17/100 [00:00<00:01, 66.48 it/sec, obj=5.16e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.011e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  18%|█▊        | 18/100 [00:00<00:01, 66.42 it/sec, obj=5.52e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.376e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  19%|█▉        | 19/100 [00:00<00:01, 66.20 it/sec, obj=5.78e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.274e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  20%|██        | 20/100 [00:00<00:01, 66.15 it/sec, obj=5.98e+4]
        INFO - 08:25:08: ...  21%|██        | 21/100 [00:00<00:01, 66.78 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.416e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  22%|██▏       | 22/100 [00:00<00:01, 66.57 it/sec, obj=1.97e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.049e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  23%|██▎       | 23/100 [00:00<00:01, 66.48 it/sec, obj=3.17e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.114e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  24%|██▍       | 24/100 [00:00<00:01, 66.42 it/sec, obj=4.02e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.879e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  25%|██▌       | 25/100 [00:00<00:01, 66.38 it/sec, obj=4.59e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.346e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  26%|██▌       | 26/100 [00:00<00:01, 66.33 it/sec, obj=4.97e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.139e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  27%|██▋       | 27/100 [00:00<00:01, 66.31 it/sec, obj=5.3e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.088e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  28%|██▊       | 28/100 [00:00<00:01, 66.31 it/sec, obj=5.6e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.614e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  29%|██▉       | 29/100 [00:00<00:01, 66.32 it/sec, obj=5.89e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  30%|███       | 30/100 [00:00<00:01, 66.35 it/sec, obj=6.18e+4]
        INFO - 08:25:08: ...  31%|███       | 31/100 [00:00<00:01, 66.82 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.445e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  32%|███▏      | 32/100 [00:00<00:01, 66.73 it/sec, obj=2.43e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.489e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  33%|███▎      | 33/100 [00:00<00:01, 66.61 it/sec, obj=3.58e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.720e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  34%|███▍      | 34/100 [00:00<00:00, 66.57 it/sec, obj=4.29e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.355e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  35%|███▌      | 35/100 [00:00<00:00, 66.30 it/sec, obj=4.77e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.901e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  36%|███▌      | 36/100 [00:00<00:00, 66.28 it/sec, obj=5.12e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.912e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  37%|███▋      | 37/100 [00:00<00:00, 66.28 it/sec, obj=5.43e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.813e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  38%|███▊      | 38/100 [00:00<00:00, 66.25 it/sec, obj=5.74e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.575e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  39%|███▉      | 39/100 [00:00<00:00, 66.24 it/sec, obj=6.05e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.228e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  40%|████      | 40/100 [00:00<00:00, 66.23 it/sec, obj=6.35e+4]
        INFO - 08:25:08: ...  41%|████      | 41/100 [00:00<00:00, 66.59 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.024e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  42%|████▏     | 42/100 [00:00<00:00, 66.52 it/sec, obj=2.75e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.011e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  43%|████▎     | 43/100 [00:00<00:00, 66.46 it/sec, obj=3.82e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.113e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  44%|████▍     | 44/100 [00:00<00:00, 66.41 it/sec, obj=4.42e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.519e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  45%|████▌     | 45/100 [00:00<00:00, 66.34 it/sec, obj=4.9e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.055e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  46%|████▌     | 46/100 [00:00<00:00, 66.30 it/sec, obj=5.28e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.144e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  47%|████▋     | 47/100 [00:00<00:00, 66.24 it/sec, obj=5.61e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.279e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  48%|████▊     | 48/100 [00:00<00:00, 66.19 it/sec, obj=5.93e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.108e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  49%|████▉     | 49/100 [00:00<00:00, 66.13 it/sec, obj=6.24e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.238e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  50%|█████     | 50/100 [00:00<00:00, 66.09 it/sec, obj=6.54e+4]
        INFO - 08:25:08: ...  51%|█████     | 51/100 [00:00<00:00, 66.39 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.431e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  52%|█████▏    | 52/100 [00:00<00:00, 66.33 it/sec, obj=2.99e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.364e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  53%|█████▎    | 53/100 [00:00<00:00, 66.29 it/sec, obj=3.96e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.413e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  54%|█████▍    | 54/100 [00:00<00:00, 66.24 it/sec, obj=4.51e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.943e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  55%|█████▌    | 55/100 [00:00<00:00, 66.21 it/sec, obj=5e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.337e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  56%|█████▌    | 56/100 [00:00<00:00, 66.18 it/sec, obj=5.43e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.369e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  57%|█████▋    | 57/100 [00:00<00:00, 66.18 it/sec, obj=5.78e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.650e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  58%|█████▊    | 58/100 [00:00<00:00, 66.16 it/sec, obj=6.11e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.892e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  59%|█████▉    | 59/100 [00:00<00:00, 66.16 it/sec, obj=6.41e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.651e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  60%|██████    | 60/100 [00:00<00:00, 66.15 it/sec, obj=6.66e+4]
        INFO - 08:25:08: ...  61%|██████    | 61/100 [00:00<00:00, 66.39 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.740e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  62%|██████▏   | 62/100 [00:00<00:00, 66.35 it/sec, obj=3.18e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.649e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  63%|██████▎   | 63/100 [00:00<00:00, 66.31 it/sec, obj=4.07e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.844e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  64%|██████▍   | 64/100 [00:00<00:00, 66.27 it/sec, obj=4.6e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.984e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  65%|██████▌   | 65/100 [00:00<00:00, 66.23 it/sec, obj=5.09e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.131e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  66%|██████▌   | 66/100 [00:00<00:00, 66.19 it/sec, obj=5.53e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.602e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  67%|██████▋   | 67/100 [00:01<00:00, 66.11 it/sec, obj=5.92e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.732e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  68%|██████▊   | 68/100 [00:01<00:00, 66.07 it/sec, obj=6.25e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.134e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  69%|██████▉   | 69/100 [00:01<00:00, 66.02 it/sec, obj=6.52e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.496e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  70%|███████   | 70/100 [00:01<00:00, 65.96 it/sec, obj=6.71e+4]
        INFO - 08:25:08: ...  71%|███████   | 71/100 [00:01<00:00, 66.15 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.000e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:08: ...  72%|███████▏  | 72/100 [00:01<00:00, 66.07 it/sec, obj=3.32e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  73%|███████▎  | 73/100 [00:01<00:00, 66.02 it/sec, obj=4.15e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.148e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  74%|███████▍  | 74/100 [00:01<00:00, 65.97 it/sec, obj=4.69e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.706e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  75%|███████▌  | 75/100 [00:01<00:00, 65.92 it/sec, obj=5.19e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.195e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  76%|███████▌  | 76/100 [00:01<00:00, 65.88 it/sec, obj=5.63e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.466e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  77%|███████▋  | 77/100 [00:01<00:00, 65.85 it/sec, obj=6.01e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.580e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  78%|███████▊  | 78/100 [00:01<00:00, 65.83 it/sec, obj=6.32e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.258e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  79%|███████▉  | 79/100 [00:01<00:00, 65.81 it/sec, obj=6.55e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.020e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  80%|████████  | 80/100 [00:01<00:00, 65.80 it/sec, obj=6.72e+4]
        INFO - 08:25:09: ...  81%|████████  | 81/100 [00:01<00:00, 65.99 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.222e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  82%|████████▏ | 82/100 [00:01<00:00, 65.95 it/sec, obj=3.44e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.179e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  83%|████████▎ | 83/100 [00:01<00:00, 65.93 it/sec, obj=4.23e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  84%|████████▍ | 84/100 [00:01<00:00, 65.90 it/sec, obj=4.78e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  85%|████████▌ | 85/100 [00:01<00:00, 65.87 it/sec, obj=5.3e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.936e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  86%|████████▌ | 86/100 [00:01<00:00, 65.84 it/sec, obj=5.74e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.005e+05, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  87%|████████▋ | 87/100 [00:01<00:00, 65.81 it/sec, obj=6.09e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+05, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  88%|████████▊ | 88/100 [00:01<00:00, 65.79 it/sec, obj=6.35e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.877e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  89%|████████▉ | 89/100 [00:01<00:00, 65.77 it/sec, obj=6.53e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.866e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  90%|█████████ | 90/100 [00:01<00:00, 65.75 it/sec, obj=6.67e+4]
        INFO - 08:25:09: ...  91%|█████████ | 91/100 [00:01<00:00, 65.92 it/sec, obj=1.63e+5]
        INFO - 08:25:09: ...  92%|█████████▏| 92/100 [00:01<00:00, 66.08 it/sec, obj=6.89e+4]
        INFO - 08:25:09: ...  93%|█████████▎| 93/100 [00:01<00:00, 66.25 it/sec, obj=4.03e+4]
        INFO - 08:25:09: ...  94%|█████████▍| 94/100 [00:01<00:00, 66.42 it/sec, obj=2.71e+4]
        INFO - 08:25:09: ...  95%|█████████▌| 95/100 [00:01<00:00, 66.56 it/sec, obj=2.07e+4]
        INFO - 08:25:09: ...  96%|█████████▌| 96/100 [00:01<00:00, 66.72 it/sec, obj=1.78e+4]
        INFO - 08:25:09: ...  97%|█████████▋| 97/100 [00:01<00:00, 66.87 it/sec, obj=1.68e+4]
        INFO - 08:25:09: ...  98%|█████████▊| 98/100 [00:01<00:00, 67.03 it/sec, obj=1.69e+4]
        INFO - 08:25:09: ...  99%|█████████▉| 99/100 [00:01<00:00, 67.18 it/sec, obj=1.76e+4]
        INFO - 08:25:09: ... 100%|██████████| 100/100 [00:01<00:00, 67.33 it/sec, obj=1.87e+4]
        INFO - 08:25:09: Optimization result:
        INFO - 08:25:09:    Optimizer info:
        INFO - 08:25:09:       Status: None
        INFO - 08:25:09:       Message: None
        INFO - 08:25:09:       Number of calls to the objective function by the optimizer: 100
        INFO - 08:25:09:    Solution:
        INFO - 08:25:09:       Objective: 4136.820826715568
        INFO - 08:25:09:       Design space:
        INFO - 08:25:09:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:09:       | name             | lower_bound |       value       | upper_bound | type  |
        INFO - 08:25:09:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:09:       | penalty_level    |      0      | 4.444444444444445 |      40     | float |
        INFO - 08:25:09:       | l2_penalty_ratio |      0      |         0         |      1      | float |
        INFO - 08:25:09:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 08:25:09: *** End DOEScenario execution (time: 0:00:01.496497) ***

    (4.444444444444445, 0.0, 4136.820826715568)



.. GENERATED FROM PYTHON SOURCE LINES 198-200

Get the history
^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 200-202

.. code-block:: default

    calibration.dataset






.. 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 tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="2" halign="left">inputs</th>
          <th colspan="2" halign="left">outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>penalty_level</th>
          <th>l2_penalty_ratio</th>
          <th>criterion</th>
          <th>learning</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0.000000</td>
          <td>0.0</td>
          <td>162525.860760</td>
          <td>8.767875e-23</td>
        </tr>
        <tr>
          <th>1</th>
          <td>4.444444</td>
          <td>0.0</td>
          <td>4136.820827</td>
          <td>4.546714e+02</td>
        </tr>
        <tr>
          <th>2</th>
          <td>8.888889</td>
          <td>0.0</td>
          <td>13371.034446</td>
          <td>1.375915e+03</td>
        </tr>
        <tr>
          <th>3</th>
          <td>13.333333</td>
          <td>0.0</td>
          <td>17860.819693</td>
          <td>2.176736e+03</td>
        </tr>
        <tr>
          <th>4</th>
          <td>17.777778</td>
          <td>0.0</td>
          <td>23914.366014</td>
          <td>3.005032e+03</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>95</th>
          <td>22.222222</td>
          <td>1.0</td>
          <td>17820.599507</td>
          <td>1.982580e+03</td>
        </tr>
        <tr>
          <th>96</th>
          <td>26.666667</td>
          <td>1.0</td>
          <td>16816.595592</td>
          <td>2.285780e+03</td>
        </tr>
        <tr>
          <th>97</th>
          <td>31.111111</td>
          <td>1.0</td>
          <td>16894.821607</td>
          <td>2.561538e+03</td>
        </tr>
        <tr>
          <th>98</th>
          <td>35.555556</td>
          <td>1.0</td>
          <td>17602.178769</td>
          <td>2.812662e+03</td>
        </tr>
        <tr>
          <th>99</th>
          <td>40.000000</td>
          <td>1.0</td>
          <td>18674.751406</td>
          <td>3.041823e+03</td>
        </tr>
      </tbody>
    </table>
    <p>100 rows × 4 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 203-205

Visualize the results
^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 205-228

.. code-block:: default

    penalty_level = calibration.get_history("penalty_level").flatten()
    l2_penalty_ratio = calibration.get_history("l2_penalty_ratio").flatten()
    criterion = calibration.get_history("criterion").flatten()
    learning = calibration.get_history("learning").flatten()

    triang = Triangulation(penalty_level, l2_penalty_ratio)

    fig = plt.figure()
    ax = fig.add_subplot(1, 2, 1)
    ax.tricontourf(triang, criterion, cmap="Purples")
    ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
    ax.set_xlabel("penalty level")
    ax.set_ylabel("l2 penalty ratio")
    ax.set_title("Test measure")
    ax = fig.add_subplot(1, 2, 2)
    ax.tricontourf(triang, learning, cmap="Purples")
    ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
    ax.set_xlabel("penalty level")
    ax.set_ylabel("l2 penalty ratio")
    ax.set_title("Learning measure")

    plt.show()




.. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_004.png
   :alt: Test measure, Learning measure
   :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 229-231

Add an optimization stage
^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 231-269

.. code-block:: default

    calibration_space = DesignSpace()
    calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0)
    calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5)
    calibration = MLAlgoCalibration(
        "PolynomialRegressor",
        dataset,
        ["penalty_level", "l2_penalty_ratio"],
        calibration_space,
        MSEMeasure,
        measure_evaluation_method_name=measure_evaluation_method_name,
        measure_options=measure_options,
        degree=10,
    )
    calibration.execute({"algo": "NLOPT_COBYLA", "max_iter": 100})
    x_opt2 = calibration.optimal_parameters
    f_opt2 = calibration.optimal_criterion

    fig = plt.figure()
    ax = fig.add_subplot(1, 2, 1)
    ax.tricontourf(triang, criterion, cmap="Purples")
    ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
    ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red")
    ax.set_xlabel("penalty level")
    ax.set_ylabel("l2 penalty ratio")
    ax.set_title("Test measure")
    ax = fig.add_subplot(1, 2, 2)
    ax.tricontourf(triang, learning, cmap="Purples")
    ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
    ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red")
    ax.set_xlabel("penalty level")
    ax.set_ylabel("l2 penalty ratio")
    ax.set_title("Learning measure")
    plt.show()

    n_iterations = len(calibration.scenario.disciplines[0].cache)
    print(f"MSE with DOE: {f_opt} (100 evaluations)")
    print(f"MSE with OPT: {f_opt2} ({n_iterations} evaluations)")
    print(f"MSE reduction:{round((f_opt2 - f_opt) / f_opt * 100)}%")



.. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_005.png
   :alt: Test measure, Learning measure
   :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_005.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

        INFO - 08:25:09:  
        INFO - 08:25:09: *** Start MDOScenario execution ***
        INFO - 08:25:09: MDOScenario
        INFO - 08:25:09:    Disciplines: MLAlgoAssessor
        INFO - 08:25:09:    MDO formulation: DisciplinaryOpt
        INFO - 08:25:09: Optimization problem:
        INFO - 08:25:09:    minimize criterion(penalty_level, l2_penalty_ratio)
        INFO - 08:25:09:    with respect to l2_penalty_ratio, penalty_level
        INFO - 08:25:09:    over the design space:
        INFO - 08:25:09:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:09:    | name             | lower_bound | value | upper_bound | type  |
        INFO - 08:25:09:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:09:    | penalty_level    |      0      |   0   |      40     | float |
        INFO - 08:25:09:    | l2_penalty_ratio |      0      |  0.5  |      1      | float |
        INFO - 08:25:09:    +------------------+-------------+-------+-------------+-------+
        INFO - 08:25:09: Solving optimization problem with algorithm NLOPT_COBYLA:
        INFO - 08:25:09: ...   0%|          | 0/100 [00:00<?, ?it]
        INFO - 08:25:09: ...   1%|          | 1/100 [00:00<00:01, 68.38 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.287e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   2%|▏         | 2/100 [00:00<00:01, 64.81 it/sec, obj=4.06e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.952e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   3%|▎         | 3/100 [00:00<00:01, 63.85 it/sec, obj=4.28e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.118e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   4%|▍         | 4/100 [00:00<00:01, 63.42 it/sec, obj=5.16e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.089e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   5%|▌         | 5/100 [00:00<00:01, 63.25 it/sec, obj=4.66e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.973e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   6%|▌         | 6/100 [00:00<00:01, 63.19 it/sec, obj=3.63e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.402e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   7%|▋         | 7/100 [00:00<00:01, 63.00 it/sec, obj=3.03e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.059e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   8%|▊         | 8/100 [00:00<00:01, 62.81 it/sec, obj=2.03e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.428e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...   9%|▉         | 9/100 [00:00<00:01, 62.56 it/sec, obj=2.75e+3]
        INFO - 08:25:09: ...  10%|█         | 10/100 [00:00<00:01, 63.12 it/sec, obj=493]
        INFO - 08:25:09: ...  11%|█         | 11/100 [00:00<00:01, 64.48 it/sec, obj=1.63e+5]
        INFO - 08:25:09: ...  12%|█▏        | 12/100 [00:00<00:01, 65.69 it/sec, obj=1.63e+5]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.1.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.976e+04, tolerance: 2.850e+03
      model = cd_fast.enet_coordinate_descent(
        INFO - 08:25:09: ...  13%|█▎        | 13/100 [00:00<00:01, 65.35 it/sec, obj=5.23e+3]
        INFO - 08:25:09: ...  14%|█▍        | 14/100 [00:00<00:01, 65.70 it/sec, obj=493]
        INFO - 08:25:09: ...  15%|█▌        | 15/100 [00:00<00:01, 66.05 it/sec, obj=493]
        INFO - 08:25:09: ...  16%|█▌        | 16/100 [00:00<00:01, 66.32 it/sec, obj=493]
        INFO - 08:25:09: Optimization result:
        INFO - 08:25:09:    Optimizer info:
        INFO - 08:25:09:       Status: None
        INFO - 08:25:09:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
        INFO - 08:25:09:       Number of calls to the objective function by the optimizer: 17
        INFO - 08:25:09:    Solution:
        INFO - 08:25:09:       Objective: 493.1818200496802
        INFO - 08:25:09:       Design space:
        INFO - 08:25:09:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 08:25:09:       | name             | lower_bound |         value         | upper_bound | type  |
        INFO - 08:25:09:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 08:25:09:       | penalty_level    |      0      | 2.289834988289385e-15 |      40     | float |
        INFO - 08:25:09:       | l2_penalty_ratio |      0      |   0.5765298371174132  |      1      | float |
        INFO - 08:25:09:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 08:25:09: *** End MDOScenario execution (time: 0:00:00.254260) ***
    MSE with DOE: 4136.820826715568 (100 evaluations)
    MSE with OPT: 493.1818200496802 (1 evaluations)
    MSE reduction:-88%





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

   **Total running time of the script:** (0 minutes 3.027 seconds)


.. _sphx_glr_download_examples_mlearning_calibration_plot_calibration.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_calibration.py <plot_calibration.py>`

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

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


.. only:: html

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

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