.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/surrogate/plot_surrogate_discipline.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_surrogate_plot_surrogate_discipline.py: Create a surrogate discipline ============================= We want to build an :class:`.MDODiscipline` based on a regression model approximating the following discipline with two inputs and two outputs: - :math:`y_1=1+2x_1+3x_2` - :math:`y_2=-1-2x_1-3x_2` over the unit hypercube :math:`[0,1]\times[0,1]`. For that, we use a :class:`.SurrogateDiscipline` relying on a :class:`.MLRegressionAlgo` .. GENERATED FROM PYTHON SOURCE LINES 37-39 .. code-block:: default from __future__ import division, unicode_literals .. GENERATED FROM PYTHON SOURCE LINES 40-42 Import ------ .. GENERATED FROM PYTHON SOURCE LINES 42-55 .. code-block:: default from numpy import array from gemseo.api import ( configure_logger, create_design_space, create_discipline, create_scenario, create_surrogate, ) configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 56-60 Create the discipline to learn ------------------------------ We can implement this analytic discipline by means of the :class:`~gemseo.core.analytic_discipline.AnalyticDiscipline` class. .. GENERATED FROM PYTHON SOURCE LINES 60-65 .. code-block:: default expressions_dict = {"y_1": "1+2*x_1+3*x_2", "y_2": "-1-2*x_1-3*x_2"} discipline = create_discipline( "AnalyticDiscipline", name="func", expressions_dict=expressions_dict ) .. GENERATED FROM PYTHON SOURCE LINES 66-69 Create the input sampling space ------------------------------- We create the input sampling space by adding the variables one by one. .. GENERATED FROM PYTHON SOURCE LINES 69-73 .. code-block:: default design_space = create_design_space() design_space.add_variable("x_1", l_b=0.0, u_b=1.0) design_space.add_variable("x_2", l_b=0.0, u_b=1.0) .. GENERATED FROM PYTHON SOURCE LINES 74-79 Create the learning set ----------------------- We can build a learning set by means of a :class:`~gemseo.core.doe_scenario.DOEScenario` with a full factorial design of experiments. The number of samples can be equal to 9 for example. .. GENERATED FROM PYTHON SOURCE LINES 79-85 .. code-block:: default discipline.set_cache_policy(discipline.MEMORY_FULL_CACHE) scenario = create_scenario( [discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE" ) scenario.execute({"algo": "fullfact", "n_samples": 9}) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 12:57:07: INFO - 12:57:07: *** Start DOE Scenario execution *** INFO - 12:57:07: DOEScenario INFO - 12:57:07: Disciplines: func INFO - 12:57:07: MDOFormulation: DisciplinaryOpt INFO - 12:57:07: Algorithm: fullfact INFO - 12:57:07: Optimization problem: INFO - 12:57:07: Minimize: y_1(x_1, x_2) INFO - 12:57:07: With respect to: x_1, x_2 INFO - 12:57:07: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3 INFO - 12:57:07: Final number of samples for DOE = 9 vs 9 requested INFO - 12:57:07: DOE sampling: 0%| | 0/9 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_surrogate_discipline.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_