Note
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Calibration of a polynomial regression¶
from __future__ import annotations
import matplotlib.pyplot as plt
from gemseo.algos.design_space import DesignSpace
from gemseo.api import configure_logger
from gemseo.mlearning.core.calibration import MLAlgoCalibration
from gemseo.mlearning.qual_measure.mse_measure import MSEMeasure
from gemseo.problems.dataset.rosenbrock import RosenbrockDataset
from matplotlib.tri import Triangulation
Load the dataset¶
dataset = RosenbrockDataset(opt_naming=False, n_samples=25)
Define the measure¶
configure_logger()
test_dataset = RosenbrockDataset(opt_naming=False)
measure_options = {"method": "test", "test_data": test_dataset}
Calibrate the degree of the polynomial regression¶
Define and execute the calibration¶
calibration_space = DesignSpace()
calibration_space.add_variable("degree", 1, "integer", 1, 10, 1)
calibration = MLAlgoCalibration(
"PolynomialRegressor",
dataset,
["degree"],
calibration_space,
MSEMeasure,
measure_options,
)
calibration.execute({"algo": "fullfact", "n_samples": 10})
x_opt = calibration.optimal_parameters
f_opt = calibration.optimal_criterion
print("optimal degree:", x_opt["degree"][0])
print("optimal criterion:", f_opt)
INFO - 13:29:21:
INFO - 13:29:21: *** Start DOEScenario execution ***
INFO - 13:29:21: DOEScenario
INFO - 13:29:21: Disciplines: MLAlgoAssessor
INFO - 13:29:21: MDO formulation: DisciplinaryOpt
INFO - 13:29:21: Optimization problem:
INFO - 13:29:21: minimize criterion(degree)
INFO - 13:29:21: with respect to degree
INFO - 13:29:21: over the design space:
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: | degree | 1 | 1 | 10 | integer |
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: Solving optimization problem with algorithm fullfact:
INFO - 13:29:21: ... 0%| | 0/10 [00:00<?, ?it]
INFO - 13:29:21: ... 10%|█ | 1/10 [00:00<00:00, 170.47 it/sec, obj=5.89e+5]
INFO - 13:29:21: ... 20%|██ | 2/10 [00:00<00:00, 227.91 it/sec, obj=1.73e+5]
INFO - 13:29:21: ... 30%|███ | 3/10 [00:00<00:00, 252.46 it/sec, obj=3e+4]
INFO - 13:29:21: ... 40%|████ | 4/10 [00:00<00:00, 265.11 it/sec, obj=1.1e-24]
INFO - 13:29:21: ... 50%|█████ | 5/10 [00:00<00:00, 273.70 it/sec, obj=0.11]
INFO - 13:29:21: ... 60%|██████ | 6/10 [00:00<00:00, 278.01 it/sec, obj=1.18e+3]
INFO - 13:29:21: ... 70%|███████ | 7/10 [00:00<00:00, 277.25 it/sec, obj=6.9e+3]
INFO - 13:29:21: ... 80%|████████ | 8/10 [00:00<00:00, 277.24 it/sec, obj=1.36e+4]
INFO - 13:29:21: ... 90%|█████████ | 9/10 [00:00<00:00, 278.40 it/sec, obj=9.18e+4]
INFO - 13:29:21: ... 100%|██████████| 10/10 [00:00<00:00, 277.89 it/sec, obj=1.63e+5]
INFO - 13:29:21: Optimization result:
INFO - 13:29:21: Optimizer info:
INFO - 13:29:21: Status: None
INFO - 13:29:21: Message: None
INFO - 13:29:21: Number of calls to the objective function by the optimizer: 10
INFO - 13:29:21: Solution:
INFO - 13:29:21: Objective: 1.0957626812742524e-24
INFO - 13:29:21: Design space:
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: | degree | 1 | 4 | 10 | integer |
INFO - 13:29:21: +--------+-------------+-------+-------------+---------+
INFO - 13:29:21: *** End DOEScenario execution (time: 0:00:00.045441) ***
optimal degree: 4
optimal criterion: 1.0957626812742524e-24
Get the history¶
print(calibration.dataset.export_to_dataframe())
inputs outputs
degree criterion learning
0 0 0
0 1.0 5.888317e+05 8.200828e+05
1 2.0 1.732475e+05 2.404571e+05
2 3.0 3.001292e+04 1.645714e+04
3 4.0 1.095763e-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
Visualize the results¶
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()
Calibrate the ridge penalty of the polynomial regression¶
Define and execute the calibration¶
calibration_space = DesignSpace()
calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0)
calibration = MLAlgoCalibration(
"PolynomialRegressor",
dataset,
["penalty_level"],
calibration_space,
MSEMeasure,
measure_options,
degree=10,
)
calibration.execute({"algo": "fullfact", "n_samples": 10})
x_opt = calibration.optimal_parameters
f_opt = calibration.optimal_criterion
print("optimal penalty_level:", x_opt["penalty_level"][0])
print("optimal criterion:", f_opt)
INFO - 13:29:21:
INFO - 13:29:21: *** Start DOEScenario execution ***
INFO - 13:29:21: DOEScenario
INFO - 13:29:21: Disciplines: MLAlgoAssessor
INFO - 13:29:21: MDO formulation: DisciplinaryOpt
INFO - 13:29:21: Optimization problem:
INFO - 13:29:21: minimize criterion(penalty_level)
INFO - 13:29:21: with respect to penalty_level
INFO - 13:29:21: over the design space:
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: | penalty_level | 0 | 0 | 100 | float |
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: Solving optimization problem with algorithm fullfact:
INFO - 13:29:21: ... 0%| | 0/10 [00:00<?, ?it]
INFO - 13:29:21: ... 10%|█ | 1/10 [00:00<00:00, 171.64 it/sec, obj=1.63e+5]
INFO - 13:29:21: ... 20%|██ | 2/10 [00:00<00:00, 221.92 it/sec, obj=3.25e+4]
INFO - 13:29:21: ... 30%|███ | 3/10 [00:00<00:00, 251.27 it/sec, obj=1.78e+4]
INFO - 13:29:21: ... 40%|████ | 4/10 [00:00<00:00, 268.82 it/sec, obj=1.72e+4]
INFO - 13:29:21: ... 50%|█████ | 5/10 [00:00<00:00, 280.35 it/sec, obj=2e+4]
INFO - 13:29:21: ... 60%|██████ | 6/10 [00:00<00:00, 288.46 it/sec, obj=2.35e+4]
INFO - 13:29:21: ... 70%|███████ | 7/10 [00:00<00:00, 295.39 it/sec, obj=2.7e+4]
INFO - 13:29:21: ... 80%|████████ | 8/10 [00:00<00:00, 300.47 it/sec, obj=3.03e+4]
INFO - 13:29:21: ... 90%|█████████ | 9/10 [00:00<00:00, 304.33 it/sec, obj=3.33e+4]
INFO - 13:29:21: ... 100%|██████████| 10/10 [00:00<00:00, 307.47 it/sec, obj=3.59e+4]
INFO - 13:29:21: Optimization result:
INFO - 13:29:21: Optimizer info:
INFO - 13:29:21: Status: None
INFO - 13:29:21: Message: None
INFO - 13:29:21: Number of calls to the objective function by the optimizer: 10
INFO - 13:29:21: Solution:
INFO - 13:29:21: Objective: 17189.52649297338
INFO - 13:29:21: Design space:
INFO - 13:29:21: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:21: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:21: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:21: | penalty_level | 0 | 33.33333333333333 | 100 | float |
INFO - 13:29:21: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:21: *** End DOEScenario execution (time: 0:00:00.041565) ***
optimal penalty_level: 33.33333333333333
optimal criterion: 17189.52649297338
Get the history¶
print(calibration.dataset.export_to_dataframe())
inputs outputs
penalty_level criterion learning
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 33272.062305 4.642448e+03
9 100.000000 35934.745536 4.864667e+03
Visualize the results¶
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()
Calibrate the lasso penalty of the polynomial regression¶
Define and execute the calibration¶
calibration_space = DesignSpace()
calibration_space.add_variable("penalty_level", 1, "float", 0.0, 100.0, 0.0)
calibration = MLAlgoCalibration(
"PolynomialRegressor",
dataset,
["penalty_level"],
calibration_space,
MSEMeasure,
measure_options,
degree=10,
l2_penalty_ratio=0.0,
)
calibration.execute({"algo": "fullfact", "n_samples": 10})
x_opt = calibration.optimal_parameters
f_opt = calibration.optimal_criterion
print("optimal penalty_level:", x_opt["penalty_level"][0])
print("optimal criterion:", f_opt)
INFO - 13:29:21:
INFO - 13:29:21: *** Start DOEScenario execution ***
INFO - 13:29:21: DOEScenario
INFO - 13:29:21: Disciplines: MLAlgoAssessor
INFO - 13:29:21: MDO formulation: DisciplinaryOpt
INFO - 13:29:21: Optimization problem:
INFO - 13:29:21: minimize criterion(penalty_level)
INFO - 13:29:21: with respect to penalty_level
INFO - 13:29:21: over the design space:
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: | penalty_level | 0 | 0 | 100 | float |
INFO - 13:29:21: +---------------+-------------+-------+-------------+-------+
INFO - 13:29:21: Solving optimization problem with algorithm fullfact:
INFO - 13:29:21: ... 0%| | 0/10 [00:00<?, ?it]
INFO - 13:29:21: ... 10%|█ | 1/10 [00:00<00:00, 170.05 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.301e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:21: ... 20%|██ | 2/10 [00:00<00:00, 146.86 it/sec, obj=1.58e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 30%|███ | 3/10 [00:00<00:00, 147.67 it/sec, obj=3.15e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.284e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 40%|████ | 4/10 [00:00<00:00, 149.46 it/sec, obj=4.74e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 50%|█████ | 5/10 [00:00<00:00, 150.29 it/sec, obj=5.94e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 60%|██████ | 6/10 [00:00<00:00, 151.56 it/sec, obj=6.27e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 70%|███████ | 7/10 [00:00<00:00, 152.80 it/sec, obj=6.63e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.925e+03, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 80%|████████ | 8/10 [00:00<00:00, 153.65 it/sec, obj=6.93e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.746e+03, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 90%|█████████ | 9/10 [00:00<00:00, 153.88 it/sec, obj=7.25e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.372e+03, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 100%|██████████| 10/10 [00:00<00:00, 154.08 it/sec, obj=7.57e+4]
INFO - 13:29:22: Optimization result:
INFO - 13:29:22: Optimizer info:
INFO - 13:29:22: Status: None
INFO - 13:29:22: Message: None
INFO - 13:29:22: Number of calls to the objective function by the optimizer: 10
INFO - 13:29:22: Solution:
INFO - 13:29:22: Objective: 15775.989581125898
INFO - 13:29:22: Design space:
INFO - 13:29:22: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:22: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: | penalty_level | 0 | 11.11111111111111 | 100 | float |
INFO - 13:29:22: +---------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: *** End DOEScenario execution (time: 0:00:00.073803) ***
optimal penalty_level: 11.11111111111111
optimal criterion: 15775.989581125898
Get the history¶
print(calibration.dataset.export_to_dataframe())
inputs outputs
penalty_level criterion learning
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
Visualize the results¶
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()
Calibrate the elasticnet penalty of the polynomial regression¶
Define and execute the calibration¶
calibration_space = DesignSpace()
calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0)
calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5)
calibration = MLAlgoCalibration(
"PolynomialRegressor",
dataset,
["penalty_level", "l2_penalty_ratio"],
calibration_space,
MSEMeasure,
measure_options,
degree=10,
)
calibration.execute({"algo": "fullfact", "n_samples": 100})
x_opt = calibration.optimal_parameters
f_opt = calibration.optimal_criterion
print("optimal penalty_level:", x_opt["penalty_level"][0])
print("optimal l2_penalty_ratio:", x_opt["l2_penalty_ratio"][0])
print("optimal criterion:", f_opt)
INFO - 13:29:22:
INFO - 13:29:22: *** Start DOEScenario execution ***
INFO - 13:29:22: DOEScenario
INFO - 13:29:22: Disciplines: MLAlgoAssessor
INFO - 13:29:22: MDO formulation: DisciplinaryOpt
INFO - 13:29:22: Optimization problem:
INFO - 13:29:22: minimize criterion(penalty_level, l2_penalty_ratio)
INFO - 13:29:22: with respect to l2_penalty_ratio, penalty_level
INFO - 13:29:22: over the design space:
INFO - 13:29:22: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:22: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:22: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:22: | penalty_level | 0 | 0 | 40 | float |
INFO - 13:29:22: | l2_penalty_ratio | 0 | 0.5 | 1 | float |
INFO - 13:29:22: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:22: Solving optimization problem with algorithm fullfact:
INFO - 13:29:22: ... 0%| | 0/100 [00:00<?, ?it]
INFO - 13:29:22: ... 1%| | 1/100 [00:00<00:00, 164.12 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 2%|▏ | 2/100 [00:00<00:00, 151.56 it/sec, obj=4.14e+3]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.786e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 3%|▎ | 3/100 [00:00<00:00, 145.96 it/sec, obj=1.34e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.325e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 4%|▍ | 4/100 [00:00<00:00, 146.13 it/sec, obj=1.79e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.034e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 5%|▌ | 5/100 [00:00<00:00, 146.61 it/sec, obj=2.39e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.631e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 6%|▌ | 6/100 [00:00<00:00, 145.66 it/sec, obj=3.15e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.166e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 7%|▋ | 7/100 [00:00<00:00, 146.17 it/sec, obj=3.91e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 8%|▊ | 8/100 [00:00<00:00, 146.81 it/sec, obj=4.5e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.545e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 9%|▉ | 9/100 [00:00<00:00, 146.26 it/sec, obj=4.95e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.811e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 10%|█ | 10/100 [00:00<00:00, 147.03 it/sec, obj=5.42e+4]
INFO - 13:29:22: ... 11%|█ | 11/100 [00:00<00:00, 152.69 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.233e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 12%|█▏ | 12/100 [00:00<00:00, 151.13 it/sec, obj=1.35e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.032e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 13%|█▎ | 13/100 [00:00<00:00, 149.36 it/sec, obj=2.44e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.991e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 14%|█▍ | 14/100 [00:00<00:00, 147.90 it/sec, obj=3.28e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.326e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 15%|█▌ | 15/100 [00:00<00:00, 147.30 it/sec, obj=4.19e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.100e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 16%|█▌ | 16/100 [00:00<00:00, 146.33 it/sec, obj=4.76e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.636e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 17%|█▋ | 17/100 [00:00<00:00, 145.55 it/sec, obj=5.16e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.011e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 18%|█▊ | 18/100 [00:00<00:00, 145.42 it/sec, obj=5.52e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.376e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 19%|█▉ | 19/100 [00:00<00:00, 145.18 it/sec, obj=5.78e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.274e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 20%|██ | 20/100 [00:00<00:00, 144.98 it/sec, obj=5.98e+4]
INFO - 13:29:22: ... 21%|██ | 21/100 [00:00<00:00, 148.34 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.416e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 22%|██▏ | 22/100 [00:00<00:00, 147.16 it/sec, obj=1.97e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.049e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 23%|██▎ | 23/100 [00:00<00:00, 146.20 it/sec, obj=3.17e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.114e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 24%|██▍ | 24/100 [00:00<00:00, 145.40 it/sec, obj=4.02e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.879e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 25%|██▌ | 25/100 [00:00<00:00, 144.78 it/sec, obj=4.59e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.346e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 26%|██▌ | 26/100 [00:00<00:00, 144.64 it/sec, obj=4.97e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.139e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 27%|██▋ | 27/100 [00:00<00:00, 143.87 it/sec, obj=5.3e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.088e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 28%|██▊ | 28/100 [00:00<00:00, 143.69 it/sec, obj=5.6e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.614e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 29%|██▉ | 29/100 [00:00<00:00, 143.73 it/sec, obj=5.89e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 30%|███ | 30/100 [00:00<00:00, 143.62 it/sec, obj=6.18e+4]
INFO - 13:29:22: ... 31%|███ | 31/100 [00:00<00:00, 145.90 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.445e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 32%|███▏ | 32/100 [00:00<00:00, 145.01 it/sec, obj=2.43e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.489e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 33%|███▎ | 33/100 [00:00<00:00, 144.41 it/sec, obj=3.58e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.720e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 34%|███▍ | 34/100 [00:00<00:00, 144.25 it/sec, obj=4.29e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.355e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 35%|███▌ | 35/100 [00:00<00:00, 143.86 it/sec, obj=4.77e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.901e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 36%|███▌ | 36/100 [00:00<00:00, 143.88 it/sec, obj=5.12e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.912e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 37%|███▋ | 37/100 [00:00<00:00, 143.83 it/sec, obj=5.43e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.813e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 38%|███▊ | 38/100 [00:00<00:00, 143.40 it/sec, obj=5.74e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.575e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 39%|███▉ | 39/100 [00:00<00:00, 143.32 it/sec, obj=6.05e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.228e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 40%|████ | 40/100 [00:00<00:00, 143.25 it/sec, obj=6.35e+4]
INFO - 13:29:22: ... 41%|████ | 41/100 [00:00<00:00, 144.97 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.024e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 42%|████▏ | 42/100 [00:00<00:00, 144.52 it/sec, obj=2.75e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.011e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 43%|████▎ | 43/100 [00:00<00:00, 143.87 it/sec, obj=3.82e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.113e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 44%|████▍ | 44/100 [00:00<00:00, 143.50 it/sec, obj=4.42e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.519e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 45%|████▌ | 45/100 [00:00<00:00, 143.29 it/sec, obj=4.9e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.055e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 46%|████▌ | 46/100 [00:00<00:00, 142.93 it/sec, obj=5.28e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.144e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 47%|████▋ | 47/100 [00:00<00:00, 142.89 it/sec, obj=5.61e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.279e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 48%|████▊ | 48/100 [00:00<00:00, 142.86 it/sec, obj=5.93e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.108e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 49%|████▉ | 49/100 [00:00<00:00, 142.65 it/sec, obj=6.24e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.238e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 50%|█████ | 50/100 [00:00<00:00, 142.71 it/sec, obj=6.54e+4]
INFO - 13:29:22: ... 51%|█████ | 51/100 [00:00<00:00, 144.09 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.431e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 52%|█████▏ | 52/100 [00:00<00:00, 143.85 it/sec, obj=2.99e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.364e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 53%|█████▎ | 53/100 [00:00<00:00, 143.63 it/sec, obj=3.96e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.413e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 54%|█████▍ | 54/100 [00:00<00:00, 143.17 it/sec, obj=4.51e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.943e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 55%|█████▌ | 55/100 [00:00<00:00, 142.99 it/sec, obj=5e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.337e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 56%|█████▌ | 56/100 [00:00<00:00, 142.95 it/sec, obj=5.43e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.369e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 57%|█████▋ | 57/100 [00:00<00:00, 142.75 it/sec, obj=5.78e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.650e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 58%|█████▊ | 58/100 [00:00<00:00, 142.73 it/sec, obj=6.11e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.892e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 59%|█████▉ | 59/100 [00:00<00:00, 142.76 it/sec, obj=6.41e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.651e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 60%|██████ | 60/100 [00:00<00:00, 142.58 it/sec, obj=6.66e+4]
INFO - 13:29:22: ... 61%|██████ | 61/100 [00:00<00:00, 143.74 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.740e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 62%|██████▏ | 62/100 [00:00<00:00, 143.33 it/sec, obj=3.18e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.649e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 63%|██████▎ | 63/100 [00:00<00:00, 143.23 it/sec, obj=4.07e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.844e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 64%|██████▍ | 64/100 [00:00<00:00, 143.03 it/sec, obj=4.6e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.984e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 65%|██████▌ | 65/100 [00:00<00:00, 142.71 it/sec, obj=5.09e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.131e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 66%|██████▌ | 66/100 [00:00<00:00, 142.56 it/sec, obj=5.53e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.602e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 67%|██████▋ | 67/100 [00:00<00:00, 142.48 it/sec, obj=5.92e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.732e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 68%|██████▊ | 68/100 [00:00<00:00, 142.25 it/sec, obj=6.25e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.134e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 69%|██████▉ | 69/100 [00:00<00:00, 142.24 it/sec, obj=6.52e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.496e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 70%|███████ | 70/100 [00:00<00:00, 142.22 it/sec, obj=6.71e+4]
INFO - 13:29:22: ... 71%|███████ | 71/100 [00:00<00:00, 143.21 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.000e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 72%|███████▏ | 72/100 [00:00<00:00, 143.05 it/sec, obj=3.32e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 73%|███████▎ | 73/100 [00:00<00:00, 142.92 it/sec, obj=4.15e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.148e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 74%|███████▍ | 74/100 [00:00<00:00, 142.79 it/sec, obj=4.69e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.706e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 75%|███████▌ | 75/100 [00:00<00:00, 142.68 it/sec, obj=5.19e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.195e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 76%|███████▌ | 76/100 [00:00<00:00, 142.55 it/sec, obj=5.63e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.466e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 77%|███████▋ | 77/100 [00:00<00:00, 142.43 it/sec, obj=6.01e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.580e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 78%|███████▊ | 78/100 [00:00<00:00, 142.36 it/sec, obj=6.32e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.258e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 79%|███████▉ | 79/100 [00:00<00:00, 142.26 it/sec, obj=6.55e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.020e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 80%|████████ | 80/100 [00:00<00:00, 142.20 it/sec, obj=6.72e+4]
INFO - 13:29:22: ... 81%|████████ | 81/100 [00:00<00:00, 143.06 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.222e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 82%|████████▏ | 82/100 [00:00<00:00, 142.87 it/sec, obj=3.44e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.179e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 83%|████████▎ | 83/100 [00:00<00:00, 142.75 it/sec, obj=4.23e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 84%|████████▍ | 84/100 [00:00<00:00, 142.62 it/sec, obj=4.78e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 85%|████████▌ | 85/100 [00:00<00:00, 142.47 it/sec, obj=5.3e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.936e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 86%|████████▌ | 86/100 [00:00<00:00, 142.41 it/sec, obj=5.74e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.005e+05, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 87%|████████▋ | 87/100 [00:00<00:00, 142.31 it/sec, obj=6.09e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+05, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 88%|████████▊ | 88/100 [00:00<00:00, 142.26 it/sec, obj=6.35e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.877e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 89%|████████▉ | 89/100 [00:00<00:00, 142.12 it/sec, obj=6.53e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.866e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:22: ... 90%|█████████ | 90/100 [00:00<00:00, 142.03 it/sec, obj=6.67e+4]
INFO - 13:29:22: ... 91%|█████████ | 91/100 [00:00<00:00, 142.80 it/sec, obj=1.63e+5]
INFO - 13:29:22: ... 92%|█████████▏| 92/100 [00:00<00:00, 143.69 it/sec, obj=6.89e+4]
INFO - 13:29:22: ... 93%|█████████▎| 93/100 [00:00<00:00, 144.57 it/sec, obj=4.03e+4]
INFO - 13:29:22: ... 94%|█████████▍| 94/100 [00:00<00:00, 145.46 it/sec, obj=2.71e+4]
INFO - 13:29:22: ... 95%|█████████▌| 95/100 [00:00<00:00, 146.31 it/sec, obj=2.07e+4]
INFO - 13:29:22: ... 96%|█████████▌| 96/100 [00:00<00:00, 147.18 it/sec, obj=1.78e+4]
INFO - 13:29:22: ... 97%|█████████▋| 97/100 [00:00<00:00, 148.03 it/sec, obj=1.68e+4]
INFO - 13:29:22: ... 98%|█████████▊| 98/100 [00:00<00:00, 148.89 it/sec, obj=1.69e+4]
INFO - 13:29:22: ... 99%|█████████▉| 99/100 [00:00<00:00, 149.73 it/sec, obj=1.76e+4]
INFO - 13:29:22: ... 100%|██████████| 100/100 [00:00<00:00, 150.57 it/sec, obj=1.87e+4]
INFO - 13:29:22: Optimization result:
INFO - 13:29:22: Optimizer info:
INFO - 13:29:22: Status: None
INFO - 13:29:22: Message: None
INFO - 13:29:22: Number of calls to the objective function by the optimizer: 100
INFO - 13:29:22: Solution:
INFO - 13:29:22: Objective: 4136.820826715568
INFO - 13:29:22: Design space:
INFO - 13:29:22: +------------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:22: +------------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: | penalty_level | 0 | 4.444444444444445 | 40 | float |
INFO - 13:29:22: | l2_penalty_ratio | 0 | 0 | 1 | float |
INFO - 13:29:22: +------------------+-------------+-------------------+-------------+-------+
INFO - 13:29:22: *** End DOEScenario execution (time: 0:00:00.673772) ***
optimal penalty_level: 4.444444444444445
optimal l2_penalty_ratio: 0.0
optimal criterion: 4136.820826715568
Get the history¶
print(calibration.dataset.export_to_dataframe())
inputs outputs
l2_penalty_ratio penalty_level criterion learning
0 0 0 0
0 0.0 0.000000 162525.860760 8.767875e-23
1 0.0 4.444444 4136.820827 4.546714e+02
2 0.0 8.888889 13371.034446 1.375915e+03
3 0.0 13.333333 17860.819693 2.176736e+03
4 0.0 17.777778 23914.366014 3.005032e+03
.. ... ... ... ...
95 1.0 22.222222 17820.599507 1.982580e+03
96 1.0 26.666667 16816.595592 2.285780e+03
97 1.0 31.111111 16894.821607 2.561538e+03
98 1.0 35.555556 17602.178769 2.812662e+03
99 1.0 40.000000 18674.751406 3.041823e+03
[100 rows x 4 columns]
Visualize the results¶
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()
Add an optimization stage¶
calibration_space = DesignSpace()
calibration_space.add_variable("penalty_level", 1, "float", 0.0, 40.0, 0.0)
calibration_space.add_variable("l2_penalty_ratio", 1, "float", 0.0, 1.0, 0.5)
calibration = MLAlgoCalibration(
"PolynomialRegressor",
dataset,
["penalty_level", "l2_penalty_ratio"],
calibration_space,
MSEMeasure,
measure_options,
degree=10,
)
calibration.execute({"algo": "NLOPT_COBYLA", "max_iter": 100})
x_opt2 = calibration.optimal_parameters
f_opt2 = calibration.optimal_criterion
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.tricontourf(triang, criterion, cmap="Purples")
ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red")
ax.set_xlabel("penalty level")
ax.set_ylabel("l2 penalty ratio")
ax.set_title("Test measure")
ax = fig.add_subplot(1, 2, 2)
ax.tricontourf(triang, learning, cmap="Purples")
ax.scatter(x_opt["penalty_level"][0], x_opt["l2_penalty_ratio"][0])
ax.scatter(x_opt2["penalty_level"][0], x_opt2["l2_penalty_ratio"][0], color="red")
ax.set_xlabel("penalty level")
ax.set_ylabel("l2 penalty ratio")
ax.set_title("Learning measure")
plt.show()
n_iterations = len(calibration.scenario.disciplines[0].cache)
print(f"MSE with DOE: {f_opt} (100 evaluations)")
print(f"MSE with OPT: {f_opt2} ({n_iterations} evaluations)")
print(f"MSE reduction:{round((f_opt2 - f_opt) / f_opt * 100)}%")
INFO - 13:29:23:
INFO - 13:29:23: *** Start MDOScenario execution ***
INFO - 13:29:23: MDOScenario
INFO - 13:29:23: Disciplines: MLAlgoAssessor
INFO - 13:29:23: MDO formulation: DisciplinaryOpt
INFO - 13:29:23: Optimization problem:
INFO - 13:29:23: minimize criterion(penalty_level, l2_penalty_ratio)
INFO - 13:29:23: with respect to l2_penalty_ratio, penalty_level
INFO - 13:29:23: over the design space:
INFO - 13:29:23: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:23: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:23: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:23: | penalty_level | 0 | 0 | 40 | float |
INFO - 13:29:23: | l2_penalty_ratio | 0 | 0.5 | 1 | float |
INFO - 13:29:23: +------------------+-------------+-------+-------------+-------+
INFO - 13:29:23: Solving optimization problem with algorithm NLOPT_COBYLA:
INFO - 13:29:23: ... 0%| | 0/100 [00:00<?, ?it]
INFO - 13:29:23: ... 1%| | 1/100 [00:00<00:00, 151.78 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.287e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 2%|▏ | 2/100 [00:00<00:00, 138.27 it/sec, obj=4.06e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.952e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 3%|▎ | 3/100 [00:00<00:00, 137.03 it/sec, obj=4.28e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.118e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 4%|▍ | 4/100 [00:00<00:00, 134.00 it/sec, obj=5.16e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.089e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 5%|▌ | 5/100 [00:00<00:00, 132.66 it/sec, obj=4.66e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.973e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 6%|▌ | 6/100 [00:00<00:00, 131.51 it/sec, obj=3.63e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.402e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 7%|▋ | 7/100 [00:00<00:00, 130.84 it/sec, obj=3.03e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.059e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 8%|▊ | 8/100 [00:00<00:00, 130.03 it/sec, obj=2.03e+4]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.428e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 9%|▉ | 9/100 [00:00<00:00, 129.01 it/sec, obj=2.75e+3]
INFO - 13:29:23: ... 10%|█ | 10/100 [00:00<00:00, 132.24 it/sec, obj=493]
INFO - 13:29:23: ... 11%|█ | 11/100 [00:00<00:00, 137.83 it/sec, obj=1.63e+5]
INFO - 13:29:23: ... 12%|█▏ | 12/100 [00:00<00:00, 143.06 it/sec, obj=1.63e+5]
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/develop/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.976e+04, tolerance: 2.850e+03
model = cd_fast.enet_coordinate_descent(
INFO - 13:29:23: ... 13%|█▎ | 13/100 [00:00<00:00, 141.09 it/sec, obj=5.23e+3]
INFO - 13:29:23: ... 14%|█▍ | 14/100 [00:00<00:00, 142.81 it/sec, obj=493]
INFO - 13:29:23: ... 15%|█▌ | 15/100 [00:00<00:00, 144.40 it/sec, obj=493]
INFO - 13:29:23: ... 16%|█▌ | 16/100 [00:00<00:00, 145.78 it/sec, obj=493]
INFO - 13:29:23: Optimization result:
INFO - 13:29:23: Optimizer info:
INFO - 13:29:23: Status: None
INFO - 13:29:23: Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
INFO - 13:29:23: Number of calls to the objective function by the optimizer: 17
INFO - 13:29:23: Solution:
INFO - 13:29:23: Objective: 493.1818200496802
INFO - 13:29:23: Design space:
INFO - 13:29:23: +------------------+-------------+-----------------------+-------------+-------+
INFO - 13:29:23: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:23: +------------------+-------------+-----------------------+-------------+-------+
INFO - 13:29:23: | penalty_level | 0 | 2.289834988289385e-15 | 40 | float |
INFO - 13:29:23: | l2_penalty_ratio | 0 | 0.5765298371174132 | 1 | float |
INFO - 13:29:23: +------------------+-------------+-----------------------+-------------+-------+
INFO - 13:29:23: *** End MDOScenario execution (time: 0:00:00.122211) ***
MSE with DOE: 4136.820826715568 (100 evaluations)
MSE with OPT: 493.1818200496802 (1 evaluations)
MSE reduction:-88%
Total running time of the script: ( 0 minutes 1.839 seconds)