.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/scenario/plot_doe_scenario.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_scenario_plot_doe_scenario.py: Create a DOE Scenario ===================== .. GENERATED FROM PYTHON SOURCE LINES 26-39 .. code-block:: Python from __future__ import annotations from gemseo import configure_logger from gemseo import create_design_space from gemseo import create_discipline from gemseo import create_scenario from gemseo import get_available_doe_algorithms from gemseo import get_available_post_processings configure_logger() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 40-61 Let :math:`(P)` be a simple optimization problem: .. math:: (P) = \left\{ \begin{aligned} & \underset{x\in\mathbb{N}^2}{\text{minimize}} & & f(x) = x_1 + x_2 \\ & \text{subject to} & & -5 \leq x \leq 5 \end{aligned} \right. In this example, we will see how to use |g| to solve this problem :math:`(P)` by means of a Design Of Experiments (DOE) Define the discipline --------------------- Firstly, by means of the :func:`.create_discipline` API function, we create an :class:`.MDODiscipline` of :class:`.AnalyticDiscipline` type from a Python function: .. GENERATED FROM PYTHON SOURCE LINES 62-66 .. code-block:: Python expressions = {"y": "x1+x2"} discipline = create_discipline("AnalyticDiscipline", expressions=expressions) .. GENERATED FROM PYTHON SOURCE LINES 67-75 Now, we want to minimize this :class:`.MDODiscipline` over a design of experiments (DOE). Define the design space ----------------------- For that, by means of the :func:`.create_design_space` API function, we define the :class:`.DesignSpace` :math:`[-5, 5]\times[-5, 5]` by using its :meth:`.DesignSpace.add_variable` method. .. GENERATED FROM PYTHON SOURCE LINES 75-80 .. code-block:: Python design_space = create_design_space() design_space.add_variable("x1", l_b=-5, u_b=5, var_type="integer") design_space.add_variable("x2", l_b=-5, u_b=5, var_type="integer") .. GENERATED FROM PYTHON SOURCE LINES 81-86 Define the DOE scenario ----------------------- Then, by means of the :func:`.create_scenario` API function, we define a :class:`.DOEScenario` from the :class:`.MDODiscipline` and the :class:`.DesignSpace` defined above: .. GENERATED FROM PYTHON SOURCE LINES 86-91 .. code-block:: Python scenario = create_scenario( discipline, "DisciplinaryOpt", "y", design_space, scenario_type="DOE" ) .. GENERATED FROM PYTHON SOURCE LINES 92-99 Execute the DOE scenario ------------------------ Lastly, we solve the :class:`.OptimizationProblem` included in the :class:`.DOEScenario` defined above by minimizing the objective function over a design of experiments included in the :class:`.DesignSpace`. Precisely, we choose a `full factorial design `_ of size :math:`11^2`: .. GENERATED FROM PYTHON SOURCE LINES 99-102 .. code-block:: Python scenario.execute({"algo": "fullfact", "n_samples": 11**2}) .. rst-class:: sphx-glr-script-out .. code-block:: none INFO - 00:10:53: INFO - 00:10:53: *** Start DOEScenario execution *** INFO - 00:10:53: DOEScenario INFO - 00:10:53: Disciplines: AnalyticDiscipline INFO - 00:10:53: MDO formulation: DisciplinaryOpt INFO - 00:10:53: Optimization problem: INFO - 00:10:53: minimize y(x1, x2) INFO - 00:10:53: with respect to x1, x2 INFO - 00:10:53: over the design space: INFO - 00:10:53: +------+-------------+-------+-------------+---------+ INFO - 00:10:53: | Name | Lower bound | Value | Upper bound | Type | INFO - 00:10:53: +------+-------------+-------+-------------+---------+ INFO - 00:10:53: | x1 | -5 | None | 5 | integer | INFO - 00:10:53: | x2 | -5 | None | 5 | integer | INFO - 00:10:53: +------+-------------+-------+-------------+---------+ INFO - 00:10:53: Solving optimization problem with algorithm fullfact: INFO - 00:10:53: 1%| | 1/121 [00:00<00:00, 398.09 it/sec, obj=-10] INFO - 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00:10:54: 95%|█████████▌| 115/121 [00:00<00:00, 1469.48 it/sec, obj=4] INFO - 00:10:54: 96%|█████████▌| 116/121 [00:00<00:00, 1469.73 it/sec, obj=5] INFO - 00:10:54: 97%|█████████▋| 117/121 [00:00<00:00, 1470.74 it/sec, obj=6] INFO - 00:10:54: 98%|█████████▊| 118/121 [00:00<00:00, 1471.78 it/sec, obj=7] INFO - 00:10:54: 98%|█████████▊| 119/121 [00:00<00:00, 1472.72 it/sec, obj=8] INFO - 00:10:54: 99%|█████████▉| 120/121 [00:00<00:00, 1473.24 it/sec, obj=9] INFO - 00:10:54: 100%|██████████| 121/121 [00:00<00:00, 1474.06 it/sec, obj=10] INFO - 00:10:54: Optimization result: INFO - 00:10:54: Optimizer info: INFO - 00:10:54: Status: None INFO - 00:10:54: Message: None INFO - 00:10:54: Number of calls to the objective function by the optimizer: 121 INFO - 00:10:54: Solution: INFO - 00:10:54: Objective: -10.0 INFO - 00:10:54: Design space: INFO - 00:10:54: +------+-------------+-------+-------------+---------+ INFO - 00:10:54: | Name | Lower bound | Value | Upper bound | Type | INFO - 00:10:54: +------+-------------+-------+-------------+---------+ INFO - 00:10:54: | x1 | -5 | -5 | 5 | integer | INFO - 00:10:54: | x2 | -5 | -5 | 5 | integer | INFO - 00:10:54: +------+-------------+-------+-------------+---------+ INFO - 00:10:54: *** End DOEScenario execution (time: 0:00:00.094521) *** {'eval_jac': False, 'n_samples': 121, 'algo': 'fullfact'} .. GENERATED FROM PYTHON SOURCE LINES 103-105 The optimum results can be found in the execution log. It is also possible to access them with :attr:`.Scenario.optimization_result`: .. GENERATED FROM PYTHON SOURCE LINES 105-109 .. code-block:: Python optimization_result = scenario.optimization_result f"The solution of P is (x*, f(x*)) = ({optimization_result.x_opt}, {optimization_result.f_opt})" .. rst-class:: sphx-glr-script-out .. code-block:: none 'The solution of P is (x*, f(x*)) = ([-5. -5.], -10.0)' .. GENERATED FROM PYTHON SOURCE LINES 110-113 Available DOE algorithms ------------------------ In order to get the list of available DOE algorithms, use: .. GENERATED FROM PYTHON SOURCE LINES 113-115 .. code-block:: Python get_available_doe_algorithms() .. rst-class:: sphx-glr-script-out .. code-block:: none ['CustomDOE', 'DiagonalDOE', 'OT_SOBOL', 'OT_RANDOM', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_MONTE_CARLO', 'OT_FACTORIAL', 'OT_COMPOSITE', 'OT_AXIAL', 'OT_OPT_LHS', 'OT_LHS', 'OT_LHSC', 'OT_FULLFACT', 'OT_SOBOL_INDICES', 'fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs', 'Halton', 'LHS', 'MC', 'PoissonDisk', 'Sobol'] .. GENERATED FROM PYTHON SOURCE LINES 116-119 Available post-processing ------------------------- In order to get the list of available post-processing algorithms, use: .. GENERATED FROM PYTHON SOURCE LINES 119-121 .. code-block:: Python get_available_post_processings() .. rst-class:: sphx-glr-script-out .. code-block:: none ['Animation', 'BasicHistory', 'Compromise', 'ConstraintsHistory', 'Correlations', 'DataVersusModel', 'GradientSensitivity', 'HighTradeOff', 'MultiObjectiveDiagram', 'ObjConstrHist', 'OptHistoryView', 'ParallelCoordinates', 'ParetoFront', 'Petal', 'QuadApprox', 'Radar', 'RadarChart', 'Robustness', 'SOM', 'ScatterPareto', 'ScatterPlotMatrix', 'TopologyView', 'VariableInfluence'] .. GENERATED FROM PYTHON SOURCE LINES 122-128 You can also look at the examples: .. raw:: html
Examples
.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.108 seconds) .. _sphx_glr_download_examples_scenario_plot_doe_scenario.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_doe_scenario.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_doe_scenario.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_