.. 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 ` 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 26-42 .. code-block:: default from __future__ import absolute_import, division, print_function, unicode_literals from builtins import round import matplotlib.pyplot as plt from future import standard_library from matplotlib.tri import Triangulation 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 standard_library.install_aliases() .. GENERATED FROM PYTHON SOURCE LINES 43-45 Load the dataset ---------------- .. GENERATED FROM PYTHON SOURCE LINES 45-47 .. code-block:: default dataset = RosenbrockDataset(opt_naming=False, n_samples=25) .. GENERATED FROM PYTHON SOURCE LINES 48-50 Define the measure ------------------ .. GENERATED FROM PYTHON SOURCE LINES 50-54 .. code-block:: default configure_logger() test_dataset = RosenbrockDataset(opt_naming=False) measure_options = {"method": "test", "test_data": test_dataset} .. GENERATED FROM PYTHON SOURCE LINES 55-59 Calibrate the degree of the polynomial regression ------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 59-75 .. code-block:: default calibration_space = DesignSpace() calibration_space.add_variable("degree", 1, "integer", 1, 10, 1) calibration = MLAlgoCalibration( "PolynomialRegression", 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 optimal degree: 4.0 optimal criterion: 1.270764892025432e-24 .. GENERATED FROM PYTHON SOURCE LINES 76-78 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 78-80 .. code-block:: default print(calibration.dataset.export_to_dataframe()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none inputs outputs degree criterion learning 0 0 0 0 1.0 5.888317e+05 8.200828e+05 1 2.0 1.732475e+05 2.404571e+05 2 3.0 3.001292e+04 1.645714e+04 3 4.0 1.270765e-24 2.042323e-24 4 5.0 1.097877e-01 5.385996e-24 5 6.0 1.183264e+03 6.075900e-25 6 7.0 6.895919e+03 2.103320e-23 7 8.0 1.356307e+04 2.622494e-23 8 9.0 9.180547e+04 4.803723e-23 9 10.0 1.625259e+05 4.449178e-23 .. GENERATED FROM PYTHON SOURCE LINES 81-83 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 83-95 .. 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:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_001.png :alt: plot calibration :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 96-100 Calibrate the ridge penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 100-117 .. code-block:: default calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0) calibration = MLAlgoCalibration( "PolynomialRegression", 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 optimal penalty_level: 33.33333333333333 optimal criterion: 17189.5264929348 .. GENERATED FROM PYTHON SOURCE LINES 118-120 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 120-122 .. code-block:: default print(calibration.dataset.export_to_dataframe()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none inputs outputs penalty_level criterion learning 0 0 0 0 0.000000 162525.860760 4.449178e-23 1 11.111111 32506.221289 1.087801e+03 2 22.222222 17820.599507 1.982580e+03 3 33.333333 17189.526493 2.690007e+03 4 44.444444 19953.420378 3.251453e+03 5 55.555556 23493.269988 3.703714e+03 6 66.666667 27024.053276 4.074147e+03 7 77.777778 30303.486633 4.382362e+03 8 88.888889 33272.062305 4.642448e+03 9 100.000000 35934.745536 4.864667e+03 .. GENERATED FROM PYTHON SOURCE LINES 123-125 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 125-137 .. 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:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_002.png :alt: plot calibration :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 138-142 Calibrate the lasso penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 142-160 .. code-block:: default calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0) calibration = MLAlgoCalibration( "PolynomialRegression", 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 /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53014.11952527281, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46312.77905592318, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22836.762840616968, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19844.062995126646, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10539.652780551347, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11515.535179016297, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9925.218372422736, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8745.639387524861, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6371.9844124363735, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( optimal penalty_level: 11.11111111111111 optimal criterion: 15775.98958112584 .. GENERATED FROM PYTHON SOURCE LINES 161-163 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 163-165 .. code-block:: default print(calibration.dataset.export_to_dataframe()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none inputs outputs penalty_level criterion learning 0 0 0 0 0.000000 162525.860760 4.449178e-23 1 11.111111 15775.989581 1.814382e+03 2 22.222222 31529.584354 4.057302e+03 3 33.333333 47420.249503 5.792299e+03 4 44.444444 59358.207437 7.169565e+03 5 55.555556 62656.171431 7.278397e+03 6 66.666667 66256.259889 7.410137e+03 7 77.777778 69336.190346 7.540731e+03 8 88.888889 72457.378777 7.675963e+03 9 100.000000 75749.793494 7.816545e+03 .. GENERATED FROM PYTHON SOURCE LINES 166-168 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 168-180 .. 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:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_003.png :alt: plot calibration :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 181-185 Calibrate the elasticnet penalty of the polynomial regression ------------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 185-204 .. 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( "PolynomialRegression", 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 /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29043.941873262476, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17861.242207899086, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23248.369020079124, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40337.592310345935, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46312.77905592318, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 41663.11234403636, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19678.93630810287, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25448.221246517962, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28112.625869984273, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52327.379969754176, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60323.82550357153, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49912.85388970988, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53260.502122664984, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51004.324032207056, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46356.76266880178, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40105.01306439645, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43759.23050117445, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 32737.6383574511, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54160.34846162713, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 70494.26467152775, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71136.63093822205, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68792.4637923982, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63459.39289327263, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51385.13766746219, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30876.534229431825, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26141.488265132284, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24190.211027816127, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64450.33236753119, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74891.63916846788, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77196.15850180795, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73552.79972675092, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59011.058667527715, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49116.956434006184, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48133.69228252996, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45754.86056797807, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42282.73810280081, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 70242.51600023739, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80113.69706210528, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81128.3587537658, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75191.01350819836, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60549.874831144545, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61441.387341868096, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62785.52398195918, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61079.903585404085, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 72380.74554461434, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74313.77388861046, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83635.41756075562, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84127.30452105973, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79426.0212278234, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73365.24131195752, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73686.88102281588, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76500.00157023198, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78920.80461385961, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86505.78991687924, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77400.21108157154, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86487.02367866725, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88435.62342717669, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79838.89049765863, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81308.80235178744, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86023.17726375742, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87320.20191975817, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91344.83957996666, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84962.34972659523, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79998.05160984234, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88966.87593987946, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91475.65007848587, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87062.52528431122, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91948.95998352996, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94655.4499929364, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95795.91888194041, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 92577.41520459115, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80204.40914621703, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82215.63093180019, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91794.83776150897, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94456.9098907501, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95065.41953052203, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99355.28336318409, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100496.24127551212, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100955.79720587714, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98772.7683944503, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98656.18825020781, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( optimal penalty_level: 4.444444444444445 optimal l2_penalty_ratio: 0.0 optimal criterion: 4136.820826715572 .. GENERATED FROM PYTHON SOURCE LINES 205-207 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 207-209 .. code-block:: default print(calibration.dataset.export_to_dataframe()) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none inputs outputs penalty_level l2_penalty_ratio criterion learning 0 0 0 0 0 0.000000 0.0 162525.860760 4.449178e-23 1 4.444444 0.0 4136.820827 4.546714e+02 2 8.888889 0.0 13371.034446 1.375915e+03 3 13.333333 0.0 17860.819693 2.176736e+03 4 17.777778 0.0 23914.366014 3.005032e+03 .. ... ... ... ... 95 22.222222 1.0 17820.599507 1.982580e+03 96 26.666667 1.0 16816.595592 2.285780e+03 97 31.111111 1.0 16894.821607 2.561538e+03 98 35.555556 1.0 17602.178769 2.812662e+03 99 40.000000 1.0 18674.751406 3.041823e+03 [100 rows x 4 columns] .. GENERATED FROM PYTHON SOURCE LINES 210-212 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 212-235 .. 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:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_004.png :alt: Test measure, Learning measure :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 236-238 Add an optimization stage ^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 238-276 .. 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( "PolynomialRegression", dataset, ["penalty_level", "l2_penalty_ratio"], calibration_space, MSEMeasure, measure_options, degree=10, use_doe=False, ) 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("MSE with DOE: {} (100 evaluations)".format(f_opt)) print("MSE with OPT: {} ({} evaluations)".format(f_opt2, n_iterations)) print("MSE reduction:{}%".format(round((f_opt2 - f_opt) / f_opt * 100))) .. image:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_005.png :alt: Test measure, Learning measure :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82866.87944006122, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89524.2977591, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71179.70573387566, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80888.49322377346, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79727.48832857361, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74015.51423530807, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60591.135974715275, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14284.518255657937, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31213.890569437783, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11514.853703010187, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3718.3940922714746, tolerance: 2850.2270000000003 model = cd_fast.enet_coordinate_descent( MSE with DOE: 4136.820826715572 (100 evaluations) MSE with OPT: 478.14128242553414 (64 evaluations) MSE reduction:-88% .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.492 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 ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_calibration.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_