Note

Go to the end to download the full example code

# Correlations¶

In this example, we illustrate the use of the `Correlations`

plot
on the Sobieski’s SSBJ problem.

```
from __future__ import annotations
from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.problems.sobieski.core.design_space import SobieskiDesignSpace
```

## Import¶

The first step is to import some high-level functions and a method to get the design space.

```
configure_logger()
```

```
<RootLogger root (INFO)>
```

## Description¶

A correlation coefficient indicates whether there is a linear relationship between 2 quantities \(x\) and \(y\), in which case it equals 1 or -1. It is the normalized covariance between the two quantities:

The **Correlations** post-processing builds scatter plots of correlated variables
among design variables, output functions, and constraints.

The plot method considers all variable correlations greater than 95%. A different threshold value and/or a sublist of variable names can be passed as options.

## Create disciplines¶

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

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

## Create design space¶

We also create the `SobieskiDesignSpace`

.

```
design_space = SobieskiDesignSpace()
```

## 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,
"MDF",
"y_4",
design_space,
maximize_objective=True,
)
scenario.set_differentiation_method()
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, constraint_type="ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 10})
```

```
INFO - 13:58:23:
INFO - 13:58:23: *** Start MDOScenario execution ***
INFO - 13:58:23: MDOScenario
INFO - 13:58:23: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:58:23: MDO formulation: MDF
INFO - 13:58:23: Optimization problem:
INFO - 13:58:23: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:58:23: with respect to x_1, x_2, x_3, x_shared
INFO - 13:58:23: subject to constraints:
INFO - 13:58:23: g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:58:23: g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:58:23: g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 13:58:23: over the design space:
INFO - 13:58:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:58:23: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:58:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:58:23: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:58:23: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:58:23: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:58:23: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:58:23: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:58:23: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:58:23: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:58:23: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:58:23: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:58:23: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:58:23: +-------------+-------------+-------+-------------+-------+
INFO - 13:58:23: Solving optimization problem with algorithm SLSQP:
INFO - 13:58:24: 10%|█ | 1/10 [00:00<00:00, 10.07 it/sec, obj=-536]
INFO - 13:58:24: 20%|██ | 2/10 [00:00<00:01, 7.09 it/sec, obj=-2.12e+3]
WARNING - 13:58:24: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7130677857005655e-05 is still above the tolerance 1e-06.
INFO - 13:58:24: 30%|███ | 3/10 [00:00<00:01, 5.93 it/sec, obj=-3.75e+3]
INFO - 13:58:24: 40%|████ | 4/10 [00:00<00:01, 5.65 it/sec, obj=-3.96e+3]
INFO - 13:58:24: 50%|█████ | 5/10 [00:00<00:00, 5.53 it/sec, obj=-3.96e+3]
INFO - 13:58:24: Optimization result:
INFO - 13:58:24: Optimizer info:
INFO - 13:58:24: Status: 8
INFO - 13:58:24: Message: Positive directional derivative for linesearch
INFO - 13:58:24: Number of calls to the objective function by the optimizer: 6
INFO - 13:58:24: Solution:
INFO - 13:58:24: The solution is feasible.
INFO - 13:58:24: Objective: -3963.408265187933
INFO - 13:58:24: Standardized constraints:
INFO - 13:58:24: g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346 -0.05732607 -0.13720865
INFO - 13:58:24: -0.10279135]
INFO - 13:58:24: g_2 = 3.333278582928756e-06
INFO - 13:58:24: g_3 = [-7.67181773e-01 -2.32818227e-01 8.30379541e-07 -1.83255000e-01]
INFO - 13:58:24: Design space:
INFO - 13:58:24: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:58:24: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:58:24: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:58:24: | x_shared[0] | 0.01 | 0.06000083331964572 | 0.09 | float |
INFO - 13:58:24: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:58:24: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 13:58:24: | x_shared[3] | 2.5 | 2.5 | 8.5 | float |
INFO - 13:58:24: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:58:24: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 13:58:24: | x_1[0] | 0.1 | 0.4 | 0.4 | float |
INFO - 13:58:24: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 13:58:24: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 13:58:24: | x_3 | 0.1 | 0.1562448753887276 | 1 | float |
INFO - 13:58:24: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:58:24: *** End MDOScenario execution (time: 0:00:01.033515) ***
{'max_iter': 10, 'algo': 'SLSQP'}
```

## Post-process scenario¶

Lastly, we post-process the scenario by means of the `Correlations`

plot which provides scatter plots of correlated variables among design
variables, outputs functions and constraints any of the constraint or
objective functions w.r.t. optimization iterations or sampling snapshots.
This method requires the list of functions names to plot.

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("Correlations", save=False, show=True)
```

```
INFO - 13:58:24: Detected 41 correlations > 0.95
<gemseo.post.correlations.Correlations object at 0x7f28cf303700>
```

**Total running time of the script:** (0 minutes 3.210 seconds)