Constraints history

In this example, we illustrate the use of the ConstraintsHistory plot on the Sobieski’s SSBJ problem.

from gemseo.api import configure_logger
from gemseo.api import create_discipline
from gemseo.api import create_scenario
from gemseo.problems.sobieski.core.problem import SobieskiProblem
from matplotlib import pyplot as plt

Import

The first step is to import some functions from the API and a method to get the design space.

configure_logger()

Out:

<RootLogger root (INFO)>

Description

The ConstraintsHistory post-processing plots the constraints functions history in line charts with violation indication by color on the background.

This plot is more precise than the constraint plot provided by the opt_history_view but scales less with the number of constraints.

Create disciplines

At this point, we instantiate the disciplines of Sobieski’s SSBJ problem: Propulsion, Aerodynamics, Structure and Mission

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

Create design space

We also read the design space from the SobieskiProblem.

design_space = SobieskiProblem().design_space

Create and execute scenario

The next step is to build an MDO scenario in order to maximize the range, encoded ‘y_4’, with respect to the design parameters, while satisfying the inequality constraints ‘g_1’, ‘g_2’ and ‘g_3’. We can use the MDF formulation, the SLSQP optimization algorithm and a maximum number of iterations equal to 100.

scenario = create_scenario(
    disciplines,
    formulation="MDF",
    objective_name="y_4",
    maximize_objective=True,
    design_space=design_space,
)
scenario.set_differentiation_method("user")
all_constraints = ["g_1", "g_2", "g_3"]
for constraint in all_constraints:
    scenario.add_constraint(constraint, "ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 10})

Out:

    INFO - 07:14:23:
    INFO - 07:14:23: *** Start MDOScenario execution ***
    INFO - 07:14:23: MDOScenario
    INFO - 07:14:23:    Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiStructure SobieskiMission
    INFO - 07:14:23:    MDO formulation: MDF
    INFO - 07:14:23: Optimization problem:
    INFO - 07:14:23:    minimize -y_4(x_shared, x_1, x_2, x_3)
    INFO - 07:14:23:    with respect to x_1, x_2, x_3, x_shared
    INFO - 07:14:23:    subject to constraints:
    INFO - 07:14:23:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 07:14:23:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 07:14:23:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 07:14:23:    over the design space:
    INFO - 07:14:23:    +----------+-------------+-------+-------------+-------+
    INFO - 07:14:23:    | name     | lower_bound | value | upper_bound | type  |
    INFO - 07:14:23:    +----------+-------------+-------+-------------+-------+
    INFO - 07:14:23:    | x_shared |     0.01    |  0.05 |     0.09    | float |
    INFO - 07:14:23:    | x_shared |    30000    | 45000 |    60000    | float |
    INFO - 07:14:23:    | x_shared |     1.4     |  1.6  |     1.8     | float |
    INFO - 07:14:23:    | x_shared |     2.5     |  5.5  |     8.5     | float |
    INFO - 07:14:23:    | x_shared |      40     |   55  |      70     | float |
    INFO - 07:14:23:    | x_shared |     500     |  1000 |     1500    | float |
    INFO - 07:14:23:    | x_1      |     0.1     |  0.25 |     0.4     | float |
    INFO - 07:14:23:    | x_1      |     0.75    |   1   |     1.25    | float |
    INFO - 07:14:23:    | x_2      |     0.75    |   1   |     1.25    | float |
    INFO - 07:14:23:    | x_3      |     0.1     |  0.5  |      1      | float |
    INFO - 07:14:23:    +----------+-------------+-------+-------------+-------+
    INFO - 07:14:23: Solving optimization problem with algorithm SLSQP:
    INFO - 07:14:23: ...   0%|          | 0/10 [00:00<?, ?it]
    INFO - 07:14:23: ...  20%|██        | 2/10 [00:00<00:00, 41.92 it/sec, obj=-2.12e+3]
 WARNING - 07:14:23: MDAJacobi has reached its maximum number of iterations but the normed residual 1.0259352902248124e-06 is still above the tolerance 1e-06.
    INFO - 07:14:23: ...  30%|███       | 3/10 [00:00<00:00, 23.68 it/sec, obj=-3.8e+3]
    INFO - 07:14:23: ...  40%|████      | 4/10 [00:00<00:00, 17.13 it/sec, obj=-3.96e+3]
    INFO - 07:14:23: ...  50%|█████     | 5/10 [00:00<00:00, 13.42 it/sec, obj=-3.96e+3]
    INFO - 07:14:24: ...  60%|██████    | 6/10 [00:00<00:00, 11.03 it/sec, obj=-3.96e+3]
    INFO - 07:14:24: ...  70%|███████   | 7/10 [00:01<00:00,  9.54 it/sec, obj=-4.81e+3]
    INFO - 07:14:24: ...  90%|█████████ | 9/10 [00:01<00:00,  7.94 it/sec, obj=-3.87e+3]
    INFO - 07:14:24: ... 100%|██████████| 10/10 [00:01<00:00,  7.54 it/sec, obj=-4.64e+3]
    INFO - 07:14:24: Optimization result:
    INFO - 07:14:24:    Optimizer info:
    INFO - 07:14:24:       Status: None
    INFO - 07:14:24:       Message: Maximum number of iterations reached. GEMSEO Stopped the driver
    INFO - 07:14:24:       Number of calls to the objective function by the optimizer: 12
    INFO - 07:14:24:    Solution:
    INFO - 07:14:24:       The solution is feasible.
    INFO - 07:14:24:       Objective: -3963.5118239326903
    INFO - 07:14:24:       Standardized constraints:
    INFO - 07:14:24:          g_1 = [-0.01808064 -0.03336052 -0.04426042 -0.05184355 -0.05733364 -0.13720861
    INFO - 07:14:24:  -0.10279139]
    INFO - 07:14:24:          g_2 = 9.785920617177979e-06
    INFO - 07:14:24:          g_3 = [-7.67233630e-01 -2.32766370e-01  8.55509121e-05 -1.83255000e-01]
    INFO - 07:14:24:       Design space:
    INFO - 07:14:24:       +----------+-------------+---------------------+-------------+-------+
    INFO - 07:14:24:       | name     | lower_bound |        value        | upper_bound | type  |
    INFO - 07:14:24:       +----------+-------------+---------------------+-------------+-------+
    INFO - 07:14:24:       | x_shared |     0.01    | 0.06000244648015432 |     0.09    | float |
    INFO - 07:14:24:       | x_shared |    30000    |        60000        |    60000    | float |
    INFO - 07:14:24:       | x_shared |     1.4     |         1.4         |     1.8     | float |
    INFO - 07:14:24:       | x_shared |     2.5     |         2.5         |     8.5     | float |
    INFO - 07:14:24:       | x_shared |      40     |          70         |      70     | float |
    INFO - 07:14:24:       | x_shared |     500     |         1500        |     1500    | float |
    INFO - 07:14:24:       | x_1      |     0.1     |  0.3999997783130735 |     0.4     | float |
    INFO - 07:14:24:       | x_1      |     0.75    |         0.75        |     1.25    | float |
    INFO - 07:14:24:       | x_2      |     0.75    |         0.75        |     1.25    | float |
    INFO - 07:14:24:       | x_3      |     0.1     |  0.1562581125267868 |      1      | float |
    INFO - 07:14:24:       +----------+-------------+---------------------+-------------+-------+
    INFO - 07:14:24: *** End MDOScenario execution (time: 0:00:01.340613) ***

{'max_iter': 10, 'algo': 'SLSQP'}

Post-process scenario

Lastly, we post-process the scenario by means of the ConstraintsHistory plot which plots the history of constraints passed as argument by the user. Each constraint history is represented by a subplot where the value of the constraints is drawn by a line. Moreover, the background color represents a qualitative view of these values: active areas are white, violated ones are red and satisfied ones are green.

Tip

Each post-processing method requires different inputs and offers a variety of customization options. Use the API function get_post_processing_options_schema() to print a table with the options for any post-processing algorithm. Or refer to our dedicated page: Post-processing algorithms.

scenario.post_process(
    "ConstraintsHistory", constraint_names=all_constraints, save=False, show=False
)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()