The optimisation dataset

The OptimizationDataset proposes several particular group names, namely DESIGN_GROUP, OBJECTIVE_GROUP, OBSERVABLE_GROUP, and CONSTRAINT_GROUP. This particular Dataset is useful to post-process an optimization history.

from __future__ import annotations

from gemseo.datasets.optimization_dataset import OptimizationDataset

First, we instantiate the OptimizationDataset:

dataset = OptimizationDataset()

and add some data of interest using the methods add_design_variable(), add_constraint_variable(), add_objective_variable(), and add_observable_variable() that are based on Dataset.add_variable():

dataset.add_design_variable("x", [[1.0, 2.0], [4.0, 5.0]])
dataset.add_design_variable("z", [[3.0], [6.0]])
dataset.add_objective_variable("f", [[-1.0], [-2.0]])
dataset.add_constraint_variable("c", [[-0.5], [0.1]])
dataset.add_observable_variable("o", [[-3.0], [8.0]])

as well as another variable:

dataset.add_variable("a", [[10.0], [20.0]])
print(dataset)

GROUP     designs           objectives constraints observables. parameters
VARIABLE        x         z          f           c            o          a
COMPONENT       0    1    0          0           0            0          0
1             1.0  2.0  3.0       -1.0        -0.5         -3.0       10.0
2             4.0  5.0  6.0       -2.0         0.1          8.0       20.0

We could also do the same with the methods add_design_group(), add_constraint_group(), add_objective_group(), and add_observable_group()

dataset = OptimizationDataset()
dataset.add_design_group(
    [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], ["x", "y"], {"x": 2, "y": 1}
)
dataset.add_objective_group([[-1.0], [-2.0]], ["f"])
dataset.add_constraint_group([[-0.5], [0.1]], ["c"])
dataset.add_observable_group([[-3.0], [8.0]], ["o"])
dataset.add_variable("a", [[10.0], [20.0]])
print(dataset)

GROUP     designs           objectives constraints observables. parameters
VARIABLE        x         y          f           c            o          a
COMPONENT       0    1    0          0           0            0          0
1             1.0  2.0  3.0       -1.0        -0.5         -3.0       10.0
2             4.0  5.0  6.0       -2.0         0.1          8.0       20.0

Then, we can easily access the names of the input and output variables:

print(dataset.design_variable_names)
print(dataset.constraint_names)
print(dataset.objective_names)
print(dataset.observable_names)

['x', 'y']
['c']
['f']
['o']

and those of all variables:

print(dataset.variable_names)

['a', 'c', 'f', 'o', 'x', 'y']

The OptimizationDataset provides also the number of iterations:

print(dataset.n_iterations)

2

and the iterations:

print(dataset.iterations)

[1, 2]

Lastly, we can get the design data as an OptimizationDataset view:

print(dataset.design_dataset)

GROUP     designs
VARIABLE        x         y
COMPONENT       0    1    0
1             1.0  2.0  3.0
2             4.0  5.0  6.0

and the same for the other data groups:

print(dataset.constraint_dataset)
print(dataset.objective_dataset)
print(dataset.observable_dataset)

GROUP     constraints
VARIABLE            c
COMPONENT           0
1                -0.5
2                 0.1
GROUP     objectives
VARIABLE           f
COMPONENT          0
1               -1.0
2               -2.0
GROUP     observables.
VARIABLE             o
COMPONENT            0
1                 -3.0
2                  8.0