.. 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

        Click :ref:`here <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 24-32

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 33-35

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

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 38-40

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

.. GENERATED FROM PYTHON SOURCE LINES 40-44

.. code-block:: default

    configure_logger()
    test_dataset = RosenbrockDataset(opt_naming=False)
    measure_options = {"method": "test", "test_data": test_dataset}








.. GENERATED FROM PYTHON SOURCE LINES 45-49

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

.. GENERATED FROM PYTHON SOURCE LINES 49-65

.. 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_options,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 10})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    print("optimal degree:", x_opt["degree"][0])
    print("optimal criterion:", f_opt)





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

 Out:

 .. code-block:: none

        INFO - 10:06:25:  
        INFO - 10:06:25: *** Start DOEScenario execution ***
        INFO - 10:06:25: DOEScenario
        INFO - 10:06:25:    Disciplines: MLAlgoAssessor
        INFO - 10:06:25:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:25: Optimization problem:
        INFO - 10:06:25:    minimize criterion(degree)
        INFO - 10:06:25:    with respect to degree
        INFO - 10:06:25:    over the design space:
        INFO - 10:06:25:    +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25:    | name   | lower_bound | value | upper_bound | type    |
        INFO - 10:06:25:    +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25:    | degree |      1      |   1   |      10     | integer |
        INFO - 10:06:25:    +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:25: Full factorial design required. Number of samples along each direction for a design vector of size 1 with 10 samples: 10
        INFO - 10:06:25: Final number of samples for DOE = 10 vs 10 requested
        INFO - 10:06:25: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 10:06:25: ... 100%|██████████| 10/10 [00:00<00:00, 295.61 it/sec, obj=1.63e+5]
        INFO - 10:06:25: Optimization result:
        INFO - 10:06:25:    Optimizer info:
        INFO - 10:06:25:       Status: None
        INFO - 10:06:25:       Message: None
        INFO - 10:06:25:       Number of calls to the objective function by the optimizer: 10
        INFO - 10:06:25:    Solution:
        INFO - 10:06:25:       Objective: 1.0957626812742524e-24
        INFO - 10:06:25:       Design space:
        INFO - 10:06:25:       +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25:       | name   | lower_bound | value | upper_bound | type    |
        INFO - 10:06:25:       +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25:       | degree |      1      |   4   |      10     | integer |
        INFO - 10:06:25:       +--------+-------------+-------+-------------+---------+
        INFO - 10:06:25: *** End DOEScenario execution (time: 0:00:00.042435) ***
    optimal degree: 4
    optimal criterion: 1.0957626812742524e-24




.. GENERATED FROM PYTHON SOURCE LINES 66-68

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

.. GENERATED FROM PYTHON SOURCE LINES 68-70

.. code-block:: default

    print(calibration.dataset.export_to_dataframe())





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

 Out:

 .. code-block:: none

            outputs inputs       outputs
          criterion degree      learning
                  0      0             0
    0  5.888317e+05    1.0  8.200828e+05
    1  1.732475e+05    2.0  2.404571e+05
    2  3.001292e+04    3.0  1.645714e+04
    3  1.095763e-24    4.0  1.703801e-24
    4  1.097877e-01    5.0  1.391092e-23
    5  1.183264e+03    6.0  2.332471e-24
    6  6.895919e+03    7.0  1.401963e-23
    7  1.356307e+04    8.0  5.192192e-23
    8  9.180547e+04    9.0  8.964290e-23
    9  1.625259e+05   10.0  8.767875e-23




.. GENERATED FROM PYTHON SOURCE LINES 71-73

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

.. GENERATED FROM PYTHON SOURCE LINES 73-85

.. 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 86-90

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

.. GENERATED FROM PYTHON SOURCE LINES 90-107

.. 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_options,
        degree=10,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 10})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    print("optimal penalty_level:", x_opt["penalty_level"][0])
    print("optimal criterion:", f_opt)





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

 Out:

 .. code-block:: none

        INFO - 10:06:26:  
        INFO - 10:06:26: *** Start DOEScenario execution ***
        INFO - 10:06:26: DOEScenario
        INFO - 10:06:26:    Disciplines: MLAlgoAssessor
        INFO - 10:06:26:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:26: Optimization problem:
        INFO - 10:06:26:    minimize criterion(penalty_level)
        INFO - 10:06:26:    with respect to penalty_level
        INFO - 10:06:26:    over the design space:
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | name          | lower_bound | value | upper_bound | type  |
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | penalty_level |      0      |   0   |     100     | float |
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:26: Full factorial design required. Number of samples along each direction for a design vector of size 1 with 10 samples: 10
        INFO - 10:06:26: Final number of samples for DOE = 10 vs 10 requested
        INFO - 10:06:26: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 10:06:26: ... 100%|██████████| 10/10 [00:00<00:00, 318.34 it/sec, obj=3.59e+4]
        INFO - 10:06:26: Optimization result:
        INFO - 10:06:26:    Optimizer info:
        INFO - 10:06:26:       Status: None
        INFO - 10:06:26:       Message: None
        INFO - 10:06:26:       Number of calls to the objective function by the optimizer: 10
        INFO - 10:06:26:    Solution:
        INFO - 10:06:26:       Objective: 17189.52649297074
        INFO - 10:06:26:       Design space:
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26:       | name          | lower_bound |       value       | upper_bound | type  |
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26:       | penalty_level |      0      | 33.33333333333333 |     100     | float |
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26: *** End DOEScenario execution (time: 0:00:00.039811) ***
    optimal penalty_level: 33.33333333333333
    optimal criterion: 17189.52649297074




.. GENERATED FROM PYTHON SOURCE LINES 108-110

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

.. GENERATED FROM PYTHON SOURCE LINES 110-112

.. code-block:: default

    print(calibration.dataset.export_to_dataframe())





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

 Out:

 .. code-block:: none

             outputs                      inputs
           criterion      learning penalty_level
                   0             0             0
    0  162525.860760  8.767875e-23      0.000000
    1   32506.221289  1.087801e+03     11.111111
    2   17820.599507  1.982580e+03     22.222222
    3   17189.526493  2.690007e+03     33.333333
    4   19953.420378  3.251453e+03     44.444444
    5   23493.269988  3.703714e+03     55.555556
    6   27024.053276  4.074147e+03     66.666667
    7   30303.486633  4.382362e+03     77.777778
    8   33272.062306  4.642448e+03     88.888889
    9   35934.745536  4.864667e+03    100.000000




.. GENERATED FROM PYTHON SOURCE LINES 113-115

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

.. GENERATED FROM PYTHON SOURCE LINES 115-127

.. 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 128-132

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

.. GENERATED FROM PYTHON SOURCE LINES 132-150

.. 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_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
    print("optimal penalty_level:", x_opt["penalty_level"][0])
    print("optimal criterion:", f_opt)





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

 Out:

 .. code-block:: none

        INFO - 10:06:26:  
        INFO - 10:06:26: *** Start DOEScenario execution ***
        INFO - 10:06:26: DOEScenario
        INFO - 10:06:26:    Disciplines: MLAlgoAssessor
        INFO - 10:06:26:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:26: Optimization problem:
        INFO - 10:06:26:    minimize criterion(penalty_level)
        INFO - 10:06:26:    with respect to penalty_level
        INFO - 10:06:26:    over the design space:
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | name          | lower_bound | value | upper_bound | type  |
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | penalty_level |      0      |   0   |     100     | float |
        INFO - 10:06:26:    +---------------+-------------+-------+-------------+-------+
        INFO - 10:06:26: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:26: Full factorial design required. Number of samples along each direction for a design vector of size 1 with 10 samples: 10
        INFO - 10:06:26: Final number of samples for DOE = 10 vs 10 requested
        INFO - 10:06:26: ...   0%|          | 0/10 [00:00<?, ?it]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:26: ... 100%|██████████| 10/10 [00:00<00:00, 157.01 it/sec, obj=7.57e+4]
        INFO - 10:06:26: Optimization result:
        INFO - 10:06:26:    Optimizer info:
        INFO - 10:06:26:       Status: None
        INFO - 10:06:26:       Message: None
        INFO - 10:06:26:       Number of calls to the objective function by the optimizer: 10
        INFO - 10:06:26:    Solution:
        INFO - 10:06:26:       Objective: 15775.989581125898
        INFO - 10:06:26:       Design space:
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26:       | name          | lower_bound |       value       | upper_bound | type  |
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26:       | penalty_level |      0      | 11.11111111111111 |     100     | float |
        INFO - 10:06:26:       +---------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:26: *** End DOEScenario execution (time: 0:00:00.072162) ***
    optimal penalty_level: 11.11111111111111
    optimal criterion: 15775.989581125898




.. GENERATED FROM PYTHON SOURCE LINES 151-153

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

.. GENERATED FROM PYTHON SOURCE LINES 153-155

.. code-block:: default

    print(calibration.dataset.export_to_dataframe())





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

 Out:

 .. code-block:: none

             outputs                      inputs
           criterion      learning penalty_level
                   0             0             0
    0  162525.860760  8.767875e-23      0.000000
    1   15775.989581  1.814382e+03     11.111111
    2   31529.584354  4.057302e+03     22.222222
    3   47420.249503  5.792299e+03     33.333333
    4   59358.207437  7.169565e+03     44.444444
    5   62656.171431  7.278397e+03     55.555556
    6   66256.259889  7.410137e+03     66.666667
    7   69336.190346  7.540731e+03     77.777778
    8   72457.378777  7.675963e+03     88.888889
    9   75749.793494  7.816545e+03    100.000000




.. GENERATED FROM PYTHON SOURCE LINES 156-158

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

.. GENERATED FROM PYTHON SOURCE LINES 158-170

.. 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 171-175

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

.. GENERATED FROM PYTHON SOURCE LINES 175-194

.. 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_options,
        degree=10,
    )
    calibration.execute({"algo": "fullfact", "n_samples": 100})
    x_opt = calibration.optimal_parameters
    f_opt = calibration.optimal_criterion
    print("optimal penalty_level:", x_opt["penalty_level"][0])
    print("optimal l2_penalty_ratio:", x_opt["l2_penalty_ratio"][0])
    print("optimal criterion:", f_opt)





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

 Out:

 .. code-block:: none

        INFO - 10:06:26:  
        INFO - 10:06:26: *** Start DOEScenario execution ***
        INFO - 10:06:26: DOEScenario
        INFO - 10:06:26:    Disciplines: MLAlgoAssessor
        INFO - 10:06:26:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:26: Optimization problem:
        INFO - 10:06:26:    minimize criterion(penalty_level, l2_penalty_ratio)
        INFO - 10:06:26:    with respect to l2_penalty_ratio, penalty_level
        INFO - 10:06:26:    over the design space:
        INFO - 10:06:26:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | name             | lower_bound | value | upper_bound | type  |
        INFO - 10:06:26:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:26:    | penalty_level    |      0      |   0   |      40     | float |
        INFO - 10:06:26:    | l2_penalty_ratio |      0      |  0.5  |      1      | float |
        INFO - 10:06:26:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:26: Solving optimization problem with algorithm fullfact:
        INFO - 10:06:26: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 100 samples: 10
        INFO - 10:06:26: Final number of samples for DOE = 100 vs 100 requested
        INFO - 10:06:26: ...   0%|          | 0/100 [00:00<?, ?it]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:26: ...  16%|█▌        | 16/100 [00:00<00:00, 945.90 it/sec, obj=4.76e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:26: ...  32%|███▏      | 32/100 [00:00<00:00, 474.38 it/sec, obj=2.43e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:26: ...  48%|████▊     | 48/100 [00:00<00:00, 312.31 it/sec, obj=5.93e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:26: ...  64%|██████▍   | 64/100 [00:00<00:00, 234.10 it/sec, obj=4.6e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:27: ...  79%|███████▉  | 79/100 [00:00<00:00, 187.79 it/sec, obj=6.55e+4]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:27: ...  98%|█████████▊| 98/100 [00:00<00:00, 157.41 it/sec, obj=1.69e+4]
        INFO - 10:06:27: ... 100%|██████████| 100/100 [00:00<00:00, 155.90 it/sec, obj=1.87e+4]
        INFO - 10:06:27: Optimization result:
        INFO - 10:06:27:    Optimizer info:
        INFO - 10:06:27:       Status: None
        INFO - 10:06:27:       Message: None
        INFO - 10:06:27:       Number of calls to the objective function by the optimizer: 100
        INFO - 10:06:27:    Solution:
        INFO - 10:06:27:       Objective: 4136.820826715568
        INFO - 10:06:27:       Design space:
        INFO - 10:06:27:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:27:       | name             | lower_bound |       value       | upper_bound | type  |
        INFO - 10:06:27:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:27:       | penalty_level    |      0      | 4.444444444444445 |      40     | float |
        INFO - 10:06:27:       | l2_penalty_ratio |      0      |         0         |      1      | float |
        INFO - 10:06:27:       +------------------+-------------+-------------------+-------------+-------+
        INFO - 10:06:27: *** End DOEScenario execution (time: 0:00:00.650620) ***
    optimal penalty_level: 4.444444444444445
    optimal l2_penalty_ratio: 0.0
    optimal criterion: 4136.820826715568




.. GENERATED FROM PYTHON SOURCE LINES 195-197

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

.. GENERATED FROM PYTHON SOURCE LINES 197-199

.. code-block:: default

    print(calibration.dataset.export_to_dataframe())





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

 Out:

 .. code-block:: none

              outputs           inputs       outputs        inputs
            criterion l2_penalty_ratio      learning penalty_level
                    0                0             0             0
    0   162525.860760              0.0  8.767875e-23      0.000000
    1     4136.820827              0.0  4.546714e+02      4.444444
    2    13371.034446              0.0  1.375915e+03      8.888889
    3    17860.819693              0.0  2.176736e+03     13.333333
    4    23914.366014              0.0  3.005032e+03     17.777778
    ..            ...              ...           ...           ...
    95   17820.599507              1.0  1.982580e+03     22.222222
    96   16816.595592              1.0  2.285780e+03     26.666667
    97   16894.821607              1.0  2.561538e+03     31.111111
    98   17602.178769              1.0  2.812662e+03     35.555556
    99   18674.751406              1.0  3.041823e+03     40.000000

    [100 rows x 4 columns]




.. GENERATED FROM PYTHON SOURCE LINES 200-202

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

.. GENERATED FROM PYTHON SOURCE LINES 202-225

.. 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 226-228

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

.. GENERATED FROM PYTHON SOURCE LINES 228-265

.. 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_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

 Out:

 .. code-block:: none

        INFO - 10:06:27:  
        INFO - 10:06:27: *** Start MDOScenario execution ***
        INFO - 10:06:27: MDOScenario
        INFO - 10:06:27:    Disciplines: MLAlgoAssessor
        INFO - 10:06:27:    MDO formulation: DisciplinaryOpt
        INFO - 10:06:27: Optimization problem:
        INFO - 10:06:27:    minimize criterion(penalty_level, l2_penalty_ratio)
        INFO - 10:06:27:    with respect to l2_penalty_ratio, penalty_level
        INFO - 10:06:27:    over the design space:
        INFO - 10:06:27:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:27:    | name             | lower_bound | value | upper_bound | type  |
        INFO - 10:06:27:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:27:    | penalty_level    |      0      |   0   |      40     | float |
        INFO - 10:06:27:    | l2_penalty_ratio |      0      |  0.5  |      1      | float |
        INFO - 10:06:27:    +------------------+-------------+-------+-------------+-------+
        INFO - 10:06:27: Solving optimization problem with algorithm NLOPT_COBYLA:
        INFO - 10:06:27: ...   0%|          | 0/100 [00:00<?, ?it]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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(
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.1/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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 - 10:06:27: ...  15%|█▌        | 15/100 [00:00<00:00, 962.49 it/sec, obj=493]
        INFO - 10:06:27: ...  16%|█▌        | 16/100 [00:00<00:00, 903.00 it/sec, obj=493]
        INFO - 10:06:27: Optimization result:
        INFO - 10:06:27:    Optimizer info:
        INFO - 10:06:27:       Status: None
        INFO - 10:06:27:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
        INFO - 10:06:27:       Number of calls to the objective function by the optimizer: 17
        INFO - 10:06:27:    Solution:
        INFO - 10:06:27:       Objective: 493.1818200496802
        INFO - 10:06:27:       Design space:
        INFO - 10:06:27:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 10:06:27:       | name             | lower_bound |         value         | upper_bound | type  |
        INFO - 10:06:27:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 10:06:27:       | penalty_level    |      0      | 2.289834988289385e-15 |      40     | float |
        INFO - 10:06:27:       | l2_penalty_ratio |      0      |   0.5765298371174132  |      1      | float |
        INFO - 10:06:27:       +------------------+-------------+-----------------------+-------------+-------+
        INFO - 10:06:27: *** End MDOScenario execution (time: 0:00:00.120520) ***
    MSE with DOE: 4136.820826715568 (100 evaluations)
    MSE with OPT: 493.1818200496802 (2 evaluations)
    MSE reduction:-88%





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

   **Total running time of the script:** ( 0 minutes  1.761 seconds)


.. _sphx_glr_download_examples_mlearning_calibration_plot_calibration.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: 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>`_