.. 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 24-35 .. code-block:: Python from __future__ import annotations import matplotlib.pyplot as plt from matplotlib.tri import Triangulation from gemseo.algos.design_space import DesignSpace from gemseo.mlearning.core.calibration import MLAlgoCalibration from gemseo.mlearning.regression.quality.mse_measure import MSEMeasure from gemseo.problems.dataset.rosenbrock import create_rosenbrock_dataset .. GENERATED FROM PYTHON SOURCE LINES 36-38 Load the dataset ---------------- .. GENERATED FROM PYTHON SOURCE LINES 38-40 .. code-block:: Python dataset = create_rosenbrock_dataset(opt_naming=False, n_samples=25) .. GENERATED FROM PYTHON SOURCE LINES 41-43 Define the measure ------------------ .. GENERATED FROM PYTHON SOURCE LINES 43-47 .. code-block:: Python test_dataset = create_rosenbrock_dataset(opt_naming=False) measure_evaluation_method_name = "TEST" measure_options = {"test_data": test_dataset} .. GENERATED FROM PYTHON SOURCE LINES 48-52 Calibrate the degree of the polynomial regression ------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 52-69 .. code-block:: 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_name="PYDOE_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 - 16:21:57: *** Start DOEScenario execution *** INFO - 16:21:57: DOEScenario INFO - 16:21:57: Disciplines: MLAlgoAssessor INFO - 16:21:57: MDO formulation: DisciplinaryOpt INFO - 16:21:57: Optimization problem: INFO - 16:21:57: minimize criterion(degree) INFO - 16:21:57: with respect to degree INFO - 16:21:57: over the design space: INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: | degree | 1 | 1 | 10 | integer | INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: Solving optimization problem with algorithm PYDOE_FULLFACT: INFO - 16:21:57: 10%|█ | 1/10 [00:00<00:00, 24.08 it/sec, feas=True, obj=5.89e+5] INFO - 16:21:57: 20%|██ | 2/10 [00:00<00:00, 41.88 it/sec, feas=True, obj=1.73e+5] INFO - 16:21:57: 30%|███ | 3/10 [00:00<00:00, 55.79 it/sec, feas=True, obj=3e+4] INFO - 16:21:57: 40%|████ | 4/10 [00:00<00:00, 66.88 it/sec, feas=True, obj=1.1e-24] INFO - 16:21:57: 50%|█████ | 5/10 [00:00<00:00, 75.96 it/sec, feas=True, obj=0.11] INFO - 16:21:57: 60%|██████ | 6/10 [00:00<00:00, 83.50 it/sec, feas=True, obj=1.18e+3] INFO - 16:21:57: 70%|███████ | 7/10 [00:00<00:00, 89.70 it/sec, feas=True, obj=6.9e+3] INFO - 16:21:57: 80%|████████ | 8/10 [00:00<00:00, 95.06 it/sec, feas=True, obj=1.36e+4] INFO - 16:21:57: 90%|█████████ | 9/10 [00:00<00:00, 99.70 it/sec, feas=True, obj=9.18e+4] INFO - 16:21:57: 100%|██████████| 10/10 [00:00<00:00, 103.58 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:57: Optimization result: INFO - 16:21:57: Optimizer info: INFO - 16:21:57: Status: None INFO - 16:21:57: Message: None INFO - 16:21:57: Solution: INFO - 16:21:57: Objective: 1.0957626812742524e-24 INFO - 16:21:57: Design space: INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: | degree | 1 | 4 | 10 | integer | INFO - 16:21:57: +--------+-------------+-------+-------------+---------+ INFO - 16:21:57: *** End DOEScenario execution *** 'optimal degree = 4; optimal criterion = 1.0957626812742524e-24' .. GENERATED FROM PYTHON SOURCE LINES 70-72 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 72-74 .. code-block:: Python calibration.dataset .. raw:: html
GROUP inputs outputs
VARIABLE degree criterion learning
COMPONENT 0 0 0
0 1 5.888317e+05 8.200828e+05
1 2 1.732475e+05 2.404571e+05
2 3 3.001292e+04 1.645714e+04
3 4 1.095763e-24 1.703801e-24
4 5 1.097877e-01 1.391092e-23
5 6 1.183264e+03 2.332471e-24
6 7 6.895919e+03 1.401963e-23
7 8 1.356307e+04 5.192192e-23
8 9 9.180547e+04 8.964290e-23
9 10 1.625259e+05 8.767875e-23


.. GENERATED FROM PYTHON SOURCE LINES 75-77 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 77-89 .. 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 90-94 Calibrate the ridge penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 94-111 .. 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_name="PYDOE_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 - 16:21:57: *** Start DOEScenario execution *** INFO - 16:21:57: DOEScenario INFO - 16:21:57: Disciplines: MLAlgoAssessor INFO - 16:21:57: MDO formulation: DisciplinaryOpt INFO - 16:21:57: Optimization problem: INFO - 16:21:57: minimize criterion(penalty_level) INFO - 16:21:57: with respect to penalty_level INFO - 16:21:57: over the design space: INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | penalty_level | 0 | 0 | 100 | float | INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: Solving optimization problem with algorithm PYDOE_FULLFACT: INFO - 16:21:57: 10%|█ | 1/10 [00:00<00:00, 112.50 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:57: 20%|██ | 2/10 [00:00<00:00, 127.16 it/sec, feas=True, obj=3.25e+4] INFO - 16:21:57: 30%|███ | 3/10 [00:00<00:00, 135.23 it/sec, feas=True, obj=1.78e+4] INFO - 16:21:57: 40%|████ | 4/10 [00:00<00:00, 131.01 it/sec, feas=True, obj=1.72e+4] INFO - 16:21:57: 50%|█████ | 5/10 [00:00<00:00, 135.78 it/sec, feas=True, obj=2e+4] INFO - 16:21:57: 60%|██████ | 6/10 [00:00<00:00, 139.08 it/sec, feas=True, obj=2.35e+4] INFO - 16:21:57: 70%|███████ | 7/10 [00:00<00:00, 141.72 it/sec, feas=True, obj=2.7e+4] INFO - 16:21:57: 80%|████████ | 8/10 [00:00<00:00, 143.86 it/sec, feas=True, obj=3.03e+4] INFO - 16:21:57: 90%|█████████ | 9/10 [00:00<00:00, 145.54 it/sec, feas=True, obj=3.33e+4] INFO - 16:21:57: 100%|██████████| 10/10 [00:00<00:00, 146.20 it/sec, feas=True, obj=3.59e+4] INFO - 16:21:57: Optimization result: INFO - 16:21:57: Optimizer info: INFO - 16:21:57: Status: None INFO - 16:21:57: Message: None INFO - 16:21:57: Solution: INFO - 16:21:57: Objective: 17189.52649297074 INFO - 16:21:57: Design space: INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: | penalty_level | 0 | 33.33333333333333 | 100 | float | INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: *** End DOEScenario execution *** (np.float64(33.33333333333333), np.float64(17189.52649297074)) .. GENERATED FROM PYTHON SOURCE LINES 112-114 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 114-116 .. 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 117-119 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 119-131 .. 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 132-136 Calibrate the lasso penalty of the polynomial regression -------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 136-154 .. 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_name="PYDOE_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 - 16:21:57: *** Start DOEScenario execution *** INFO - 16:21:57: DOEScenario INFO - 16:21:57: Disciplines: MLAlgoAssessor INFO - 16:21:57: MDO formulation: DisciplinaryOpt INFO - 16:21:57: Optimization problem: INFO - 16:21:57: minimize criterion(penalty_level) INFO - 16:21:57: with respect to penalty_level INFO - 16:21:57: over the design space: INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | penalty_level | 0 | 0 | 100 | float | INFO - 16:21:57: +---------------+-------------+-------+-------------+-------+ INFO - 16:21:57: Solving optimization problem with algorithm PYDOE_FULLFACT: INFO - 16:21:57: 10%|█ | 1/10 [00:00<00:00, 113.27 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:57: 20%|██ | 2/10 [00:00<00:00, 115.59 it/sec, feas=True, obj=1.58e+4] INFO - 16:21:57: 30%|███ | 3/10 [00:00<00:00, 117.98 it/sec, feas=True, obj=3.15e+4] INFO - 16:21:57: 40%|████ | 4/10 [00:00<00:00, 119.63 it/sec, feas=True, obj=4.74e+4] INFO - 16:21:57: 50%|█████ | 5/10 [00:00<00:00, 121.05 it/sec, feas=True, obj=5.94e+4] INFO - 16:21:57: 60%|██████ | 6/10 [00:00<00:00, 122.12 it/sec, feas=True, obj=6.27e+4] INFO - 16:21:57: 70%|███████ | 7/10 [00:00<00:00, 122.88 it/sec, feas=True, obj=6.63e+4] INFO - 16:21:57: 80%|████████ | 8/10 [00:00<00:00, 120.46 it/sec, feas=True, obj=6.93e+4] INFO - 16:21:57: 90%|█████████ | 9/10 [00:00<00:00, 113.78 it/sec, feas=True, obj=7.25e+4] INFO - 16:21:57: 100%|██████████| 10/10 [00:00<00:00, 114.68 it/sec, feas=True, obj=7.57e+4] INFO - 16:21:57: Optimization result: INFO - 16:21:57: Optimizer info: INFO - 16:21:57: Status: None INFO - 16:21:57: Message: None INFO - 16:21:57: Solution: INFO - 16:21:57: Objective: 15775.989581125898 INFO - 16:21:57: Design space: INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: | penalty_level | 0 | 11.11111111111111 | 100 | float | INFO - 16:21:57: +---------------+-------------+-------------------+-------------+-------+ INFO - 16:21:57: *** End DOEScenario execution *** (np.float64(11.11111111111111), np.float64(15775.989581125898)) .. GENERATED FROM PYTHON SOURCE LINES 155-157 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 157-159 .. 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 160-162 Visualize the results ^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 162-174 .. 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 175-179 Calibrate the elasticnet penalty of the polynomial regression ------------------------------------------------------------- Define and execute the calibration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 179-197 .. 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_name="PYDOE_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 - 16:21:57: *** Start DOEScenario execution *** INFO - 16:21:57: DOEScenario INFO - 16:21:57: Disciplines: MLAlgoAssessor INFO - 16:21:57: MDO formulation: DisciplinaryOpt INFO - 16:21:57: Optimization problem: INFO - 16:21:57: minimize criterion(penalty_level, l2_penalty_ratio) INFO - 16:21:57: with respect to l2_penalty_ratio, penalty_level INFO - 16:21:57: over the design space: INFO - 16:21:57: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:57: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:57: | penalty_level | 0 | 0 | 40 | float | INFO - 16:21:57: | l2_penalty_ratio | 0 | 0.5 | 1 | float | INFO - 16:21:57: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:57: Solving optimization problem with algorithm PYDOE_FULLFACT: INFO - 16:21:57: 1%| | 1/100 [00:00<00:00, 107.61 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:57: 2%|▏ | 2/100 [00:00<00:00, 111.35 it/sec, feas=True, obj=4.14e+3] INFO - 16:21:57: 3%|▎ | 3/100 [00:00<00:00, 113.92 it/sec, feas=True, obj=1.34e+4] INFO - 16:21:57: 4%|▍ | 4/100 [00:00<00:00, 113.85 it/sec, feas=True, obj=1.79e+4] INFO - 16:21:57: 5%|▌ | 5/100 [00:00<00:00, 115.02 it/sec, feas=True, obj=2.39e+4] INFO - 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16:21:58: 88%|████████▊ | 88/100 [00:00<00:00, 122.10 it/sec, feas=True, obj=6.35e+4] INFO - 16:21:58: 89%|████████▉ | 89/100 [00:00<00:00, 122.08 it/sec, feas=True, obj=6.53e+4] INFO - 16:21:58: 90%|█████████ | 90/100 [00:00<00:00, 122.07 it/sec, feas=True, obj=6.67e+4] INFO - 16:21:58: 91%|█████████ | 91/100 [00:00<00:00, 122.39 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:58: 92%|█████████▏| 92/100 [00:00<00:00, 122.39 it/sec, feas=True, obj=6.89e+4] INFO - 16:21:58: 93%|█████████▎| 93/100 [00:00<00:00, 122.70 it/sec, feas=True, obj=4.03e+4] INFO - 16:21:58: 94%|█████████▍| 94/100 [00:00<00:00, 123.00 it/sec, feas=True, obj=2.71e+4] INFO - 16:21:58: 95%|█████████▌| 95/100 [00:00<00:00, 123.30 it/sec, feas=True, obj=2.07e+4] INFO - 16:21:58: 96%|█████████▌| 96/100 [00:00<00:00, 123.59 it/sec, feas=True, obj=1.78e+4] INFO - 16:21:58: 97%|█████████▋| 97/100 [00:00<00:00, 123.81 it/sec, feas=True, obj=1.68e+4] INFO - 16:21:58: 98%|█████████▊| 98/100 [00:00<00:00, 124.06 it/sec, feas=True, obj=1.69e+4] INFO - 16:21:58: 99%|█████████▉| 99/100 [00:00<00:00, 124.31 it/sec, feas=True, obj=1.76e+4] INFO - 16:21:58: 100%|██████████| 100/100 [00:00<00:00, 124.52 it/sec, feas=True, obj=1.87e+4] INFO - 16:21:58: Optimization result: INFO - 16:21:58: Optimizer info: INFO - 16:21:58: Status: None INFO - 16:21:58: Message: None INFO - 16:21:58: Solution: INFO - 16:21:58: Objective: 4136.820826715568 INFO - 16:21:58: Design space: INFO - 16:21:58: +------------------+-------------+-------------------+-------------+-------+ INFO - 16:21:58: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:58: +------------------+-------------+-------------------+-------------+-------+ INFO - 16:21:58: | penalty_level | 0 | 4.444444444444445 | 40 | float | INFO - 16:21:58: | l2_penalty_ratio | 0 | 0 | 1 | float | INFO - 16:21:58: +------------------+-------------+-------------------+-------------+-------+ INFO - 16:21:58: *** End DOEScenario execution *** (np.float64(4.444444444444445), np.float64(0.0), np.float64(4136.820826715568)) .. GENERATED FROM PYTHON SOURCE LINES 198-200 Get the history ^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 200-202 .. 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 203-205 Visualize the results ^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 205-228 .. 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 229-231 Add an optimization stage ^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 231-269 .. 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("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 - 16:21:58: *** Start MDOScenario execution *** INFO - 16:21:58: MDOScenario INFO - 16:21:58: Disciplines: MLAlgoAssessor INFO - 16:21:58: MDO formulation: DisciplinaryOpt INFO - 16:21:58: Optimization problem: INFO - 16:21:58: minimize criterion(penalty_level, l2_penalty_ratio) INFO - 16:21:58: with respect to l2_penalty_ratio, penalty_level INFO - 16:21:58: over the design space: INFO - 16:21:58: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:58: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:58: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:58: | penalty_level | 0 | 0 | 40 | float | INFO - 16:21:58: | l2_penalty_ratio | 0 | 0.5 | 1 | float | INFO - 16:21:58: +------------------+-------------+-------+-------------+-------+ INFO - 16:21:58: Solving optimization problem with algorithm NLOPT_COBYLA: INFO - 16:21:58: 1%| | 1/100 [00:00<00:01, 98.10 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:58: 2%|▏ | 2/100 [00:00<00:01, 95.37 it/sec, feas=True, obj=4.06e+4] INFO - 16:21:58: 3%|▎ | 3/100 [00:00<00:00, 100.93 it/sec, feas=True, obj=4.28e+4] INFO - 16:21:58: 4%|▍ | 4/100 [00:00<00:00, 104.28 it/sec, feas=True, obj=5.16e+4] INFO - 16:21:58: 5%|▌ | 5/100 [00:00<00:00, 106.19 it/sec, feas=True, obj=4.66e+4] INFO - 16:21:58: 6%|▌ | 6/100 [00:00<00:00, 107.49 it/sec, feas=True, obj=3.63e+4] INFO - 16:21:58: 7%|▋ | 7/100 [00:00<00:00, 108.30 it/sec, feas=True, obj=3.03e+4] INFO - 16:21:58: 8%|▊ | 8/100 [00:00<00:00, 109.14 it/sec, feas=True, obj=2.03e+4] INFO - 16:21:58: 9%|▉ | 9/100 [00:00<00:00, 109.65 it/sec, feas=True, obj=2.75e+3] INFO - 16:21:58: 10%|█ | 10/100 [00:00<00:00, 111.33 it/sec, feas=True, obj=493] INFO - 16:21:58: 11%|█ | 11/100 [00:00<00:00, 114.02 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:58: 12%|█▏ | 12/100 [00:00<00:00, 116.58 it/sec, feas=True, obj=1.63e+5] INFO - 16:21:58: 13%|█▎ | 13/100 [00:00<00:00, 116.45 it/sec, feas=True, obj=5.23e+3] INFO - 16:21:58: 14%|█▍ | 14/100 [00:00<00:00, 117.28 it/sec, feas=True, obj=493] INFO - 16:21:58: 15%|█▌ | 15/100 [00:00<00:00, 118.05 it/sec, feas=True, obj=493] INFO - 16:21:58: 16%|█▌ | 16/100 [00:00<00:00, 118.71 it/sec, feas=True, obj=493] INFO - 16:21:58: Optimization result: INFO - 16:21:58: Optimizer info: INFO - 16:21:58: Status: None INFO - 16:21:58: Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO stopped the driver. INFO - 16:21:58: Solution: INFO - 16:21:58: Objective: 493.1818200496802 INFO - 16:21:58: Design space: INFO - 16:21:58: +------------------+-------------+-----------------------+-------------+-------+ INFO - 16:21:58: | Name | Lower bound | Value | Upper bound | Type | INFO - 16:21:58: +------------------+-------------+-----------------------+-------------+-------+ INFO - 16:21:58: | penalty_level | 0 | 2.289834988289385e-15 | 40 | float | INFO - 16:21:58: | l2_penalty_ratio | 0 | 0.5765298371174132 | 1 | float | INFO - 16:21:58: +------------------+-------------+-----------------------+-------------+-------+ INFO - 16:21:58: *** End MDOScenario execution *** 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 1.550 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 ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_calibration.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_