.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/mlearning/calibration/plot_calibration.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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 24-36 .. code-block:: Python from __future__ import annotations import matplotlib.pyplot as plt from matplotlib.tri import Triangulation from gemseo import configure_logger from gemseo.algos.design_space import DesignSpace from gemseo.mlearning.core.calibration import MLAlgoCalibration from gemseo.mlearning.quality_measures.mse_measure import MSEMeasure from gemseo.problems.dataset.rosenbrock import create_rosenbrock_dataset .. GENERATED FROM PYTHON SOURCE LINES 37-39 Load the dataset ---------------- .. GENERATED FROM PYTHON SOURCE LINES 39-41 .. code-block:: Python dataset = create_rosenbrock_dataset(opt_naming=False, n_samples=25) .. GENERATED FROM PYTHON SOURCE LINES 42-44 Define the measure ------------------ .. GENERATED FROM PYTHON SOURCE LINES 44-49 .. code-block:: Python configure_logger() test_dataset = create_rosenbrock_dataset(opt_naming=False) measure_evaluation_method_name = "TEST" measure_options = {"test_data": test_dataset} .. GENERATED FROM PYTHON SOURCE LINES 50-54 Calibrate the degree of the polynomial regression ------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 54-71 .. code-block:: Python calibration_space = DesignSpace() calibration_space.add_variable("degree", 1, "integer", 1, 10, 1) calibration = MLAlgoCalibration( "PolynomialRegressor", dataset, ["degree"], calibration_space, MSEMeasure, measure_evaluation_method_name=measure_evaluation_method_name, measure_options=measure_options, ) calibration.execute({"algo": "fullfact", "n_samples": 10}) x_opt = calibration.optimal_parameters f_opt = calibration.optimal_criterion degree = x_opt["degree"][0] f"optimal degree = {degree}; optimal criterion = {f_opt}" .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:56:23: INFO - 13:56:23: *** Start DOEScenario execution *** INFO - 13:56:23: DOEScenario INFO - 13:56:23: Disciplines: MLAlgoAssessor INFO - 13:56:23: MDO formulation: DisciplinaryOpt INFO - 13:56:23: Optimization problem: INFO - 13:56:23: minimize criterion(degree) INFO - 13:56:23: with respect to degree INFO - 13:56:23: over the design space: INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: | degree | 1 | 1 | 10 | integer | INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: Solving optimization problem with algorithm fullfact: INFO - 13:56:23: 10%|█ | 1/10 [00:00<00:00, 32.73 it/sec, obj=5.89e+5] INFO - 13:56:23: 20%|██ | 2/10 [00:00<00:00, 45.99 it/sec, obj=1.73e+5] INFO - 13:56:23: 30%|███ | 3/10 [00:00<00:00, 53.45 it/sec, obj=3e+4] INFO - 13:56:23: 40%|████ | 4/10 [00:00<00:00, 58.20 it/sec, obj=1.1e-24] INFO - 13:56:23: 50%|█████ | 5/10 [00:00<00:00, 61.60 it/sec, obj=0.11] INFO - 13:56:23: 60%|██████ | 6/10 [00:00<00:00, 64.09 it/sec, obj=1.18e+3] INFO - 13:56:23: 70%|███████ | 7/10 [00:00<00:00, 65.94 it/sec, obj=6.9e+3] INFO - 13:56:23: 80%|████████ | 8/10 [00:00<00:00, 67.35 it/sec, obj=1.36e+4] INFO - 13:56:23: 90%|█████████ | 9/10 [00:00<00:00, 68.51 it/sec, obj=9.18e+4] INFO - 13:56:23: 100%|██████████| 10/10 [00:00<00:00, 69.36 it/sec, obj=1.63e+5] INFO - 13:56:23: Optimization result: INFO - 13:56:23: Optimizer info: INFO - 13:56:23: Status: None INFO - 13:56:23: Message: None INFO - 13:56:23: Number of calls to the objective function by the optimizer: 10 INFO - 13:56:23: Solution: INFO - 13:56:23: Objective: 1.0957626812742524e-24 INFO - 13:56:23: Design space: INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: | degree | 1 | 4 | 10 | integer | INFO - 13:56:23: +--------+-------------+-------+-------------+---------+ INFO - 13:56:23: *** End DOEScenario execution (time: 0:00:00.156533) *** 'optimal degree = 4; optimal criterion = 1.0957626812742524e-24' .. GENERATED FROM PYTHON SOURCE LINES 72-74 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 74-76 .. code-block:: Python calibration.dataset .. raw:: html
GROUP inputs outputs
VARIABLE degree criterion learning
COMPONENT 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 1.095763e-24 1.703801e-24
4 5.0 1.097877e-01 1.391092e-23
5 6.0 1.183264e+03 2.332471e-24
6 7.0 6.895919e+03 1.401963e-23
7 8.0 1.356307e+04 5.192192e-23
8 9.0 9.180547e+04 8.964290e-23
9 10.0 1.625259e+05 8.767875e-23


.. GENERATED FROM PYTHON SOURCE LINES 77-79 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 79-91 .. code-block:: Python 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 92-96 Calibrate the ridge penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 96-113 .. code-block:: Python calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0) calibration = MLAlgoCalibration( "PolynomialRegressor", dataset, ["penalty_level"], calibration_space, MSEMeasure, measure_evaluation_method_name=measure_evaluation_method_name, measure_options=measure_options, degree=10, ) calibration.execute({"algo": "fullfact", "n_samples": 10}) x_opt = calibration.optimal_parameters f_opt = calibration.optimal_criterion x_opt["penalty_level"][0], f_opt .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:56:23: INFO - 13:56:23: *** Start DOEScenario execution *** INFO - 13:56:23: DOEScenario INFO - 13:56:23: Disciplines: MLAlgoAssessor INFO - 13:56:23: MDO formulation: DisciplinaryOpt INFO - 13:56:23: Optimization problem: INFO - 13:56:23: minimize criterion(penalty_level) INFO - 13:56:23: with respect to penalty_level INFO - 13:56:23: over the design space: INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: | penalty_level | 0 | 0 | 100 | float | INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: Solving optimization problem with algorithm fullfact: INFO - 13:56:23: 10%|█ | 1/10 [00:00<00:00, 64.02 it/sec, obj=1.63e+5] INFO - 13:56:23: 20%|██ | 2/10 [00:00<00:00, 69.86 it/sec, obj=3.25e+4] INFO - 13:56:23: 30%|███ | 3/10 [00:00<00:00, 72.00 it/sec, obj=1.78e+4] INFO - 13:56:23: 40%|████ | 4/10 [00:00<00:00, 73.27 it/sec, obj=1.72e+4] INFO - 13:56:23: 50%|█████ | 5/10 [00:00<00:00, 74.46 it/sec, obj=2e+4] INFO - 13:56:23: 60%|██████ | 6/10 [00:00<00:00, 75.14 it/sec, obj=2.35e+4] INFO - 13:56:23: 70%|███████ | 7/10 [00:00<00:00, 75.48 it/sec, obj=2.7e+4] INFO - 13:56:23: 80%|████████ | 8/10 [00:00<00:00, 75.72 it/sec, obj=3.03e+4] INFO - 13:56:23: 90%|█████████ | 9/10 [00:00<00:00, 75.93 it/sec, obj=3.33e+4] INFO - 13:56:23: 100%|██████████| 10/10 [00:00<00:00, 76.20 it/sec, obj=3.59e+4] INFO - 13:56:23: Optimization result: INFO - 13:56:23: Optimizer info: INFO - 13:56:23: Status: None INFO - 13:56:23: Message: None INFO - 13:56:23: Number of calls to the objective function by the optimizer: 10 INFO - 13:56:23: Solution: INFO - 13:56:23: Objective: 17189.52649297074 INFO - 13:56:23: Design space: INFO - 13:56:23: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:23: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:23: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:23: | penalty_level | 0 | 33.33333333333333 | 100 | float | INFO - 13:56:23: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:23: *** End DOEScenario execution (time: 0:00:00.144071) *** (33.33333333333333, 17189.52649297074) .. GENERATED FROM PYTHON SOURCE LINES 114-116 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 116-118 .. code-block:: Python calibration.dataset .. raw:: html
GROUP inputs outputs
VARIABLE penalty_level criterion learning
COMPONENT 0 0 0
0 0.000000 162525.860760 8.767875e-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 33272.062306 4.642448e+03
9 100.000000 35934.745536 4.864667e+03


.. GENERATED FROM PYTHON SOURCE LINES 119-121 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 121-133 .. code-block:: Python 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 134-138 Calibrate the lasso penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 138-156 .. code-block:: Python calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0) calibration = MLAlgoCalibration( "PolynomialRegressor", dataset, ["penalty_level"], calibration_space, MSEMeasure, measure_evaluation_method_name=measure_evaluation_method_name, measure_options=measure_options, degree=10, l2_penalty_ratio=0.0, ) calibration.execute({"algo": "fullfact", "n_samples": 10}) x_opt = calibration.optimal_parameters f_opt = calibration.optimal_criterion x_opt["penalty_level"][0], f_opt .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:56:23: INFO - 13:56:23: *** Start DOEScenario execution *** INFO - 13:56:23: DOEScenario INFO - 13:56:23: Disciplines: MLAlgoAssessor INFO - 13:56:23: MDO formulation: DisciplinaryOpt INFO - 13:56:23: Optimization problem: INFO - 13:56:23: minimize criterion(penalty_level) INFO - 13:56:23: with respect to penalty_level INFO - 13:56:23: over the design space: INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: | penalty_level | 0 | 0 | 100 | float | INFO - 13:56:23: +---------------+-------------+-------+-------------+-------+ INFO - 13:56:23: Solving optimization problem with algorithm fullfact: INFO - 13:56:23: 10%|█ | 1/10 [00:00<00:00, 63.21 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.301e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:23: 20%|██ | 2/10 [00:00<00:00, 59.70 it/sec, obj=1.58e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:23: 30%|███ | 3/10 [00:00<00:00, 59.57 it/sec, obj=3.15e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.284e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:23: 40%|████ | 4/10 [00:00<00:00, 59.75 it/sec, obj=4.74e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 50%|█████ | 5/10 [00:00<00:00, 59.82 it/sec, obj=5.94e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 60%|██████ | 6/10 [00:00<00:00, 59.60 it/sec, obj=6.27e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 70%|███████ | 7/10 [00:00<00:00, 59.32 it/sec, obj=6.63e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.925e+03, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 80%|████████ | 8/10 [00:00<00:00, 59.19 it/sec, obj=6.93e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.746e+03, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 90%|█████████ | 9/10 [00:00<00:00, 59.49 it/sec, obj=7.25e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:24: 100%|██████████| 10/10 [00:00<00:00, 59.74 it/sec, obj=7.57e+4] INFO - 13:56:24: Optimization result: INFO - 13:56:24: Optimizer info: INFO - 13:56:24: Status: None INFO - 13:56:24: Message: None INFO - 13:56:24: Number of calls to the objective function by the optimizer: 10 INFO - 13:56:24: Solution: INFO - 13:56:24: Objective: 15775.989581125898 INFO - 13:56:24: Design space: INFO - 13:56:24: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:24: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:24: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:24: | penalty_level | 0 | 11.11111111111111 | 100 | float | INFO - 13:56:24: +---------------+-------------+-------------------+-------------+-------+ INFO - 13:56:24: *** End DOEScenario execution (time: 0:00:00.179818) *** (11.11111111111111, 15775.989581125898) .. GENERATED FROM PYTHON SOURCE LINES 157-159 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 159-161 .. code-block:: Python calibration.dataset .. raw:: html
GROUP inputs outputs
VARIABLE penalty_level criterion learning
COMPONENT 0 0 0
0 0.000000 162525.860760 8.767875e-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 162-164 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 164-176 .. code-block:: Python 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 177-181 Calibrate the elasticnet penalty of the polynomial regression ------------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 181-199 .. code-block:: Python calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0) calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5) calibration = MLAlgoCalibration( "PolynomialRegressor", dataset, ["penalty_level", "l2_penalty_ratio"], calibration_space, MSEMeasure, measure_evaluation_method_name=measure_evaluation_method_name, measure_options=measure_options, degree=10, ) calibration.execute({"algo": "fullfact", "n_samples": 100}) x_opt = calibration.optimal_parameters f_opt = calibration.optimal_criterion x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0], f_opt .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:56:24: INFO - 13:56:24: *** Start DOEScenario execution *** INFO - 13:56:24: DOEScenario INFO - 13:56:24: Disciplines: MLAlgoAssessor INFO - 13:56:24: MDO formulation: DisciplinaryOpt INFO - 13:56:24: Optimization problem: INFO - 13:56:24: minimize criterion(penalty_level, l2_penalty_ratio) INFO - 13:56:24: with respect to l2_penalty_ratio, penalty_level INFO - 13:56:24: over the design space: INFO - 13:56:24: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:24: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:24: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:24: | penalty_level | 0 | 0 | 40 | float | INFO - 13:56:24: | l2_penalty_ratio | 0 | 0.5 | 1 | float | INFO - 13:56:24: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:24: Solving optimization problem with algorithm fullfact: INFO - 13:56:24: 1%| | 1/100 [00:00<00:01, 61.63 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 2%|▏ | 2/100 [00:00<00:01, 59.28 it/sec, obj=4.14e+3] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.786e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 3%|▎ | 3/100 [00:00<00:01, 59.46 it/sec, obj=1.34e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.325e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 4%|▍ | 4/100 [00:00<00:01, 59.41 it/sec, obj=1.79e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.034e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 5%|▌ | 5/100 [00:00<00:01, 59.52 it/sec, obj=2.39e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 6%|▌ | 6/100 [00:00<00:01, 59.38 it/sec, obj=3.15e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.166e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 7%|▋ | 7/100 [00:00<00:01, 59.51 it/sec, obj=3.91e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 8%|▊ | 8/100 [00:00<00:01, 59.16 it/sec, obj=4.5e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.545e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 9%|▉ | 9/100 [00:00<00:01, 59.17 it/sec, obj=4.95e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.811e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 10%|█ | 10/100 [00:00<00:01, 59.16 it/sec, obj=5.42e+4] INFO - 13:56:24: 11%|█ | 11/100 [00:00<00:01, 60.51 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.233e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 12%|█▏ | 12/100 [00:00<00:01, 60.18 it/sec, obj=1.35e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.032e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 13%|█▎ | 13/100 [00:00<00:01, 59.96 it/sec, obj=2.44e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.991e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 14%|█▍ | 14/100 [00:00<00:01, 59.90 it/sec, obj=3.28e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.326e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 15%|█▌ | 15/100 [00:00<00:01, 59.77 it/sec, obj=4.19e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:24: 16%|█▌ | 16/100 [00:00<00:01, 59.71 it/sec, obj=4.76e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.636e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 17%|█▋ | 17/100 [00:00<00:01, 59.61 it/sec, obj=5.16e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.011e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 18%|█▊ | 18/100 [00:00<00:01, 59.61 it/sec, obj=5.52e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.376e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 19%|█▉ | 19/100 [00:00<00:01, 59.59 it/sec, obj=5.78e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.274e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 20%|██ | 20/100 [00:00<00:01, 59.65 it/sec, obj=5.98e+4] INFO - 13:56:24: 21%|██ | 21/100 [00:00<00:01, 60.37 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.416e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 22%|██▏ | 22/100 [00:00<00:01, 60.20 it/sec, obj=1.97e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.049e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 23%|██▎ | 23/100 [00:00<00:01, 60.06 it/sec, obj=3.17e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.114e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 24%|██▍ | 24/100 [00:00<00:01, 59.99 it/sec, obj=4.02e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.879e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 25%|██▌ | 25/100 [00:00<00:01, 59.95 it/sec, obj=4.59e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.346e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 26%|██▌ | 26/100 [00:00<00:01, 59.89 it/sec, obj=4.97e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.139e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 27%|██▋ | 27/100 [00:00<00:01, 59.84 it/sec, obj=5.3e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.088e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 28%|██▊ | 28/100 [00:00<00:01, 59.83 it/sec, obj=5.6e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.614e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 29%|██▉ | 29/100 [00:00<00:01, 59.80 it/sec, obj=5.89e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 30%|███ | 30/100 [00:00<00:01, 59.80 it/sec, obj=6.18e+4] INFO - 13:56:24: 31%|███ | 31/100 [00:00<00:01, 60.29 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:24: 32%|███▏ | 32/100 [00:00<00:01, 60.20 it/sec, obj=2.43e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.489e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 33%|███▎ | 33/100 [00:00<00:01, 60.11 it/sec, obj=3.58e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.720e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 34%|███▍ | 34/100 [00:00<00:01, 60.05 it/sec, obj=4.29e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.355e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 35%|███▌ | 35/100 [00:00<00:01, 60.00 it/sec, obj=4.77e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.901e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 36%|███▌ | 36/100 [00:00<00:01, 59.96 it/sec, obj=5.12e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.912e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 37%|███▋ | 37/100 [00:00<00:01, 59.91 it/sec, obj=5.43e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.813e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 38%|███▊ | 38/100 [00:00<00:01, 59.85 it/sec, obj=5.74e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.575e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 39%|███▉ | 39/100 [00:00<00:01, 59.80 it/sec, obj=6.05e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.228e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 40%|████ | 40/100 [00:00<00:01, 59.74 it/sec, obj=6.35e+4] INFO - 13:56:24: 41%|████ | 41/100 [00:00<00:00, 60.07 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.024e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 42%|████▏ | 42/100 [00:00<00:00, 59.98 it/sec, obj=2.75e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.011e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:24: 43%|████▎ | 43/100 [00:00<00:00, 59.89 it/sec, obj=3.82e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.113e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 44%|████▍ | 44/100 [00:00<00:00, 59.57 it/sec, obj=4.42e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.519e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 45%|████▌ | 45/100 [00:00<00:00, 59.29 it/sec, obj=4.9e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.055e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 46%|████▌ | 46/100 [00:00<00:00, 59.19 it/sec, obj=5.28e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.144e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 47%|████▋ | 47/100 [00:00<00:00, 59.16 it/sec, obj=5.61e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:25: 48%|████▊ | 48/100 [00:00<00:00, 59.14 it/sec, obj=5.93e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.108e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 49%|████▉ | 49/100 [00:00<00:00, 59.12 it/sec, obj=6.24e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.238e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 50%|█████ | 50/100 [00:00<00:00, 59.07 it/sec, obj=6.54e+4] INFO - 13:56:25: 51%|█████ | 51/100 [00:00<00:00, 59.33 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.431e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 52%|█████▏ | 52/100 [00:00<00:00, 59.24 it/sec, obj=2.99e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.364e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 53%|█████▎ | 53/100 [00:00<00:00, 59.15 it/sec, obj=3.96e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.413e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 54%|█████▍ | 54/100 [00:00<00:00, 59.10 it/sec, obj=4.51e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.943e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 55%|█████▌ | 55/100 [00:00<00:00, 59.02 it/sec, obj=5e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.337e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 56%|█████▌ | 56/100 [00:00<00:00, 58.98 it/sec, obj=5.43e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.369e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 57%|█████▋ | 57/100 [00:00<00:00, 58.96 it/sec, obj=5.78e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.650e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 58%|█████▊ | 58/100 [00:00<00:00, 58.96 it/sec, obj=6.11e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.892e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 59%|█████▉ | 59/100 [00:01<00:00, 58.98 it/sec, obj=6.41e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.651e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 60%|██████ | 60/100 [00:01<00:00, 59.00 it/sec, obj=6.66e+4] INFO - 13:56:25: 61%|██████ | 61/100 [00:01<00:00, 59.26 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.740e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 62%|██████▏ | 62/100 [00:01<00:00, 59.21 it/sec, obj=3.18e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.649e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 63%|██████▎ | 63/100 [00:01<00:00, 59.19 it/sec, obj=4.07e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:25: 64%|██████▍ | 64/100 [00:01<00:00, 59.11 it/sec, obj=4.6e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.984e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 65%|██████▌ | 65/100 [00:01<00:00, 59.10 it/sec, obj=5.09e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.131e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 66%|██████▌ | 66/100 [00:01<00:00, 59.07 it/sec, obj=5.53e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.602e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 67%|██████▋ | 67/100 [00:01<00:00, 59.06 it/sec, obj=5.92e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.732e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 68%|██████▊ | 68/100 [00:01<00:00, 59.03 it/sec, obj=6.25e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.134e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 69%|██████▉ | 69/100 [00:01<00:00, 59.01 it/sec, obj=6.52e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.496e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 70%|███████ | 70/100 [00:01<00:00, 58.96 it/sec, obj=6.71e+4] INFO - 13:56:25: 71%|███████ | 71/100 [00:01<00:00, 59.16 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.000e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 72%|███████▏ | 72/100 [00:01<00:00, 59.12 it/sec, obj=3.32e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 73%|███████▎ | 73/100 [00:01<00:00, 59.07 it/sec, obj=4.15e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.148e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 74%|███████▍ | 74/100 [00:01<00:00, 59.05 it/sec, obj=4.69e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.706e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 75%|███████▌ | 75/100 [00:01<00:00, 59.01 it/sec, obj=5.19e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.195e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 76%|███████▌ | 76/100 [00:01<00:00, 59.00 it/sec, obj=5.63e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.466e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 77%|███████▋ | 77/100 [00:01<00:00, 58.95 it/sec, obj=6.01e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.580e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 78%|███████▊ | 78/100 [00:01<00:00, 58.91 it/sec, obj=6.32e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:25: 79%|███████▉ | 79/100 [00:01<00:00, 58.88 it/sec, obj=6.55e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.020e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 80%|████████ | 80/100 [00:01<00:00, 58.85 it/sec, obj=6.72e+4] INFO - 13:56:25: 81%|████████ | 81/100 [00:01<00:00, 59.03 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.222e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 82%|████████▏ | 82/100 [00:01<00:00, 58.96 it/sec, obj=3.44e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.179e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 83%|████████▎ | 83/100 [00:01<00:00, 58.93 it/sec, obj=4.23e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 84%|████████▍ | 84/100 [00:01<00:00, 58.88 it/sec, obj=4.78e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 85%|████████▌ | 85/100 [00:01<00:00, 58.87 it/sec, obj=5.3e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.936e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 86%|████████▌ | 86/100 [00:01<00:00, 58.84 it/sec, obj=5.74e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.005e+05, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 87%|████████▋ | 87/100 [00:01<00:00, 58.80 it/sec, obj=6.09e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+05, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 88%|████████▊ | 88/100 [00:01<00:00, 58.75 it/sec, obj=6.35e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.877e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:25: 89%|████████▉ | 89/100 [00:01<00:00, 58.74 it/sec, obj=6.53e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:25: 90%|█████████ | 90/100 [00:01<00:00, 58.69 it/sec, obj=6.67e+4] INFO - 13:56:25: 91%|█████████ | 91/100 [00:01<00:00, 58.85 it/sec, obj=1.63e+5] INFO - 13:56:25: 92%|█████████▏| 92/100 [00:01<00:00, 59.02 it/sec, obj=6.89e+4] INFO - 13:56:25: 93%|█████████▎| 93/100 [00:01<00:00, 59.19 it/sec, obj=4.03e+4] INFO - 13:56:25: 94%|█████████▍| 94/100 [00:01<00:00, 59.35 it/sec, obj=2.71e+4] INFO - 13:56:25: 95%|█████████▌| 95/100 [00:01<00:00, 59.51 it/sec, obj=2.07e+4] INFO - 13:56:25: 96%|█████████▌| 96/100 [00:01<00:00, 59.66 it/sec, obj=1.78e+4] INFO - 13:56:25: 97%|█████████▋| 97/100 [00:01<00:00, 59.82 it/sec, obj=1.68e+4] INFO - 13:56:25: 98%|█████████▊| 98/100 [00:01<00:00, 59.97 it/sec, obj=1.69e+4] INFO - 13:56:25: 99%|█████████▉| 99/100 [00:01<00:00, 60.12 it/sec, obj=1.76e+4] INFO - 13:56:25: 100%|██████████| 100/100 [00:01<00:00, 60.26 it/sec, obj=1.87e+4] INFO - 13:56:25: Optimization result: INFO - 13:56:25: Optimizer info: INFO - 13:56:25: Status: None INFO - 13:56:25: Message: None INFO - 13:56:25: Number of calls to the objective function by the optimizer: 100 INFO - 13:56:25: Solution: INFO - 13:56:25: Objective: 4136.820826715568 INFO - 13:56:25: Design space: INFO - 13:56:25: +------------------+-------------+-------------------+-------------+-------+ INFO - 13:56:25: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:25: +------------------+-------------+-------------------+-------------+-------+ INFO - 13:56:25: | penalty_level | 0 | 4.444444444444445 | 40 | float | INFO - 13:56:25: | l2_penalty_ratio | 0 | 0 | 1 | float | INFO - 13:56:25: +------------------+-------------+-------------------+-------------+-------+ INFO - 13:56:25: *** End DOEScenario execution (time: 0:00:01.673399) *** (4.444444444444445, 0.0, 4136.820826715568) .. GENERATED FROM PYTHON SOURCE LINES 200-202 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 202-204 .. code-block:: Python calibration.dataset .. raw:: html
GROUP inputs outputs
VARIABLE penalty_level l2_penalty_ratio criterion learning
COMPONENT 0 0 0 0
0 0.000000 0.0 162525.860760 8.767875e-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 × 4 columns



.. GENERATED FROM PYTHON SOURCE LINES 205-207 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 207-230 .. code-block:: Python 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 231-233 Add an optimization stage ^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 233-271 .. code-block:: Python calibration_space = DesignSpace() calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0) calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5) calibration = MLAlgoCalibration( "PolynomialRegressor", dataset, ["penalty_level", "l2_penalty_ratio"], calibration_space, MSEMeasure, measure_evaluation_method_name=measure_evaluation_method_name, measure_options=measure_options, degree=10, ) calibration.execute({"algo": "NLOPT_COBYLA", "max_iter": 100}) x_opt2 = calibration.optimal_parameters f_opt2 = calibration.optimal_criterion fig = plt.figure() ax = fig.add_subplot(1, 2, 1) ax.tricontourf(triang, criterion, cmap="Purples") ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0]) ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red") ax.set_xlabel("penalty level") ax.set_ylabel("l2 penalty ratio") ax.set_title("Test measure") ax = fig.add_subplot(1, 2, 2) ax.tricontourf(triang, learning, cmap="Purples") ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0]) ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red") ax.set_xlabel("penalty level") ax.set_ylabel("l2 penalty ratio") ax.set_title("Learning measure") plt.show() n_iterations = len(calibration.scenario.disciplines[0].cache) print(f"MSE with DOE: {f_opt} (100 evaluations)") print(f"MSE with OPT: {f_opt2} ({n_iterations} evaluations)") print(f"MSE reduction:{round((f_opt2 - f_opt) / f_opt * 100)}%") .. image-sg:: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_005.png :alt: Test measure, Learning measure :srcset: /examples/mlearning/calibration/images/sphx_glr_plot_calibration_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 13:56:26: INFO - 13:56:26: *** Start MDOScenario execution *** INFO - 13:56:26: MDOScenario INFO - 13:56:26: Disciplines: MLAlgoAssessor INFO - 13:56:26: MDO formulation: DisciplinaryOpt INFO - 13:56:26: Optimization problem: INFO - 13:56:26: minimize criterion(penalty_level, l2_penalty_ratio) INFO - 13:56:26: with respect to l2_penalty_ratio, penalty_level INFO - 13:56:26: over the design space: INFO - 13:56:26: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:26: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:26: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:26: | penalty_level | 0 | 0 | 40 | float | INFO - 13:56:26: | l2_penalty_ratio | 0 | 0.5 | 1 | float | INFO - 13:56:26: +------------------+-------------+-------+-------------+-------+ INFO - 13:56:26: Solving optimization problem with algorithm NLOPT_COBYLA: INFO - 13:56:26: 1%| | 1/100 [00:00<00:01, 57.97 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.287e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 2%|▏ | 2/100 [00:00<00:01, 56.14 it/sec, obj=4.06e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.952e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 3%|▎ | 3/100 [00:00<00:01, 55.53 it/sec, obj=4.28e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.118e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 4%|▍ | 4/100 [00:00<00:01, 55.52 it/sec, obj=5.16e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.089e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 5%|▌ | 5/100 [00:00<00:01, 55.24 it/sec, obj=4.66e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.973e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 6%|▌ | 6/100 [00:00<00:01, 54.83 it/sec, obj=3.63e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.402e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 7%|▋ | 7/100 [00:00<00:01, 54.51 it/sec, obj=3.03e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.059e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 8%|▊ | 8/100 [00:00<00:01, 54.53 it/sec, obj=2.03e+4] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.428e+04, tolerance: 2.850e+03 model = cd_fast.enet_coordinate_descent( INFO - 13:56:26: 9%|▉ | 9/100 [00:00<00:01, 54.54 it/sec, obj=2.75e+3] INFO - 13:56:26: 10%|█ | 10/100 [00:00<00:01, 55.27 it/sec, obj=493] INFO - 13:56:26: 11%|█ | 11/100 [00:00<00:01, 56.62 it/sec, obj=1.63e+5] INFO - 13:56:26: 12%|█▏ | 12/100 [00:00<00:01, 57.83 it/sec, obj=1.63e+5] /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.3.0/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:678: 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 - 13:56:26: 13%|█▎ | 13/100 [00:00<00:01, 57.55 it/sec, obj=5.23e+3] INFO - 13:56:26: 14%|█▍ | 14/100 [00:00<00:01, 57.85 it/sec, obj=493] INFO - 13:56:26: 15%|█▌ | 15/100 [00:00<00:01, 58.14 it/sec, obj=493] INFO - 13:56:26: 16%|█▌ | 16/100 [00:00<00:01, 58.30 it/sec, obj=493] INFO - 13:56:26: Optimization result: INFO - 13:56:26: Optimizer info: INFO - 13:56:26: Status: None INFO - 13:56:26: Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver INFO - 13:56:26: Number of calls to the objective function by the optimizer: 17 INFO - 13:56:26: Solution: INFO - 13:56:26: Objective: 493.1818200496802 INFO - 13:56:26: Design space: INFO - 13:56:26: +------------------+-------------+-----------------------+-------------+-------+ INFO - 13:56:26: | Name | Lower bound | Value | Upper bound | Type | INFO - 13:56:26: +------------------+-------------+-----------------------+-------------+-------+ INFO - 13:56:26: | penalty_level | 0 | 2.289834988289385e-15 | 40 | float | INFO - 13:56:26: | l2_penalty_ratio | 0 | 0.5765298371174132 | 1 | float | INFO - 13:56:26: +------------------+-------------+-----------------------+-------------+-------+ INFO - 13:56:26: *** End MDOScenario execution (time: 0:00:00.288948) *** MSE with DOE: 4136.820826715568 (100 evaluations) MSE with OPT: 493.1818200496802 (1 evaluations) MSE reduction:-88% .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.320 seconds) .. _sphx_glr_download_examples_mlearning_calibration_plot_calibration.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_calibration.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_calibration.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_