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.. _gradients_sensitivity:

Gradient sensitivity
********************

Preliminaries: instantiation and execution of the MDO scenario
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Let's start with the following code lines which instantiate and execute the :class:`~gemseo.core.mdo_scenario.MDOScenario` :

.. code::

   from gemseo.api import create_discipline, create_scenario

   formulation = 'MDF'

   disciplines = create_discipline(["SobieskiPropulsion", "SobieskiAerodynamics",
                                    "SobieskiMission", "SobieskiStructure"])

   scenario = create_scenario(disciplines,
                              formulation=formulation,
                              objective_name="y_4",
                              maximize_objective=True,
                              design_space="design_space.txt")

   scenario.set_differentiation_method("user")

   algo_options = {'max_iter': 10, 'algo': "SLSQP"}
   for constraint in ["g_1","g_2","g_3"]:
       scenario.add_constraint(constraint, 'ineq')

   scenario.execute(algo_options)

GradientSensitivity
~~~~~~~~~~~~~~~~~~~

Description
-----------

The **GradientSensitivity** post processing
builds histograms of derivatives of objective and constraints

The plot method considers the derivatives at the last iteration.
The iteration can be changed in option. The x- and y- figure sizes
can also be modified in option.
It is possible either to save the plot, to show the plot or both.

Options
-------

- **extension**, :code:`str` - file extension
- **figsize_x**, :code:`int` - size of figure in horizontal direction (inches)
- **figsize_y**, :code:`int` - size of figure in vertical direction (inches)
- **file_path**, :code:`str` - the base paths of the files to export
- **iteration**, :code:`int` -  the iteration to plot sensitivities, if negative, use optimum
- **save**, :code:`bool` - if True, exports plot to pdf
- **show**, :code:`bool` - if True, displays the plot windows

Case of the MDF formulation
~~~~~~~~~~~~~~~~~~~~~~~~~~~

To visualize the gradient sensitivity plot of the :code:`scenario`,
we use the :meth:`~gemseo.api.execute_post` API method with the keyword :code:`"GradientSensitivity"`
and additional arguments concerning the type of display (file, screen, both):

.. code::

    scenario.post_process(“GradientSensitivity”, save=True, show=False,file_path=“mdf” )

The figure :ref:`fig-ssbj-mdf-grad-sens` shows the total derivatives of the objective and constraints with
respect to the :term:`design variables`: :math:`\frac{d f}{d x_i}`:

-  a large value means that the function is sensitive to the variable,
-  a null value means that, at the optimal solution, the function does
   not depend on the variable,
-  a negative value means that the function decreases when the variable
   is increased.

.. _fig-ssbj-mdf-grad-sens:

.. figure:: /_images/postprocessing/mdf_gradient_sensitivity.png
    :scale: 50 %

    Gradient sensitivity on the Sobieski use case for the MDF formulation

:math:`x_0` (wing-taper ratio) and :math:`x_2` (Mach number) appear to
be the most important for the gradient of the objective function.

The ``g_1_0`` to ``g_1_4`` are very similar, since they all quantify the
stress in various sections. ``g_1_5`` and ``g_1_6`` correspond to the
lower and upper bounds of the twist , therefore their sensitivities are
opposite. ``g_2`` is a function of only :math:`x_0` ; :math:`x_0` is the
only variable that influences its gradient.