Note
Click here to download the full example code
Rosenbrock dataset¶
This Dataset
contains 100 evaluations
of the well-known Rosenbrock function:
\[f(x,y)=(1-x)^2+100(y-x^2)^2\]
This function is known for its global minimum at point (1,1), its banana valley and the difficulty to reach its minimum.
This Dataset
is based on a full-factorial
design of experiments.
More information about the Rosenbrock function
from gemseo.api import configure_logger
from gemseo.api import load_dataset
from gemseo.post.dataset.yvsx import YvsX
from gemseo.post.dataset.zvsxy import ZvsXY
from matplotlib import pyplot as plt
configure_logger()
Out:
<RootLogger root (INFO)>
Load Rosenbrock dataset¶
We can easily load this dataset by means of the
load_dataset()
function of the API:
dataset = load_dataset("RosenbrockDataset")
print(dataset)
Out:
Rosenbrock
Number of samples: 100
Number of variables: 2
Variables names and sizes by group:
design_parameters: x (2)
functions: rosen (1)
Number of dimensions (total = 3) by group:
design_parameters: 2
functions: 1
Show the input and output data¶
print(dataset.get_data_by_group("design_parameters"))
print(dataset.get_data_by_group("functions"))
Out:
[[-2. -2. ]
[-1.55555556 -2. ]
[-1.11111111 -2. ]
[-0.66666667 -2. ]
[-0.22222222 -2. ]
[ 0.22222222 -2. ]
[ 0.66666667 -2. ]
[ 1.11111111 -2. ]
[ 1.55555556 -2. ]
[ 2. -2. ]
[-2. -1.55555556]
[-1.55555556 -1.55555556]
[-1.11111111 -1.55555556]
[-0.66666667 -1.55555556]
[-0.22222222 -1.55555556]
[ 0.22222222 -1.55555556]
[ 0.66666667 -1.55555556]
[ 1.11111111 -1.55555556]
[ 1.55555556 -1.55555556]
[ 2. -1.55555556]
[-2. -1.11111111]
[-1.55555556 -1.11111111]
[-1.11111111 -1.11111111]
[-0.66666667 -1.11111111]
[-0.22222222 -1.11111111]
[ 0.22222222 -1.11111111]
[ 0.66666667 -1.11111111]
[ 1.11111111 -1.11111111]
[ 1.55555556 -1.11111111]
[ 2. -1.11111111]
[-2. -0.66666667]
[-1.55555556 -0.66666667]
[-1.11111111 -0.66666667]
[-0.66666667 -0.66666667]
[-0.22222222 -0.66666667]
[ 0.22222222 -0.66666667]
[ 0.66666667 -0.66666667]
[ 1.11111111 -0.66666667]
[ 1.55555556 -0.66666667]
[ 2. -0.66666667]
[-2. -0.22222222]
[-1.55555556 -0.22222222]
[-1.11111111 -0.22222222]
[-0.66666667 -0.22222222]
[-0.22222222 -0.22222222]
[ 0.22222222 -0.22222222]
[ 0.66666667 -0.22222222]
[ 1.11111111 -0.22222222]
[ 1.55555556 -0.22222222]
[ 2. -0.22222222]
[-2. 0.22222222]
[-1.55555556 0.22222222]
[-1.11111111 0.22222222]
[-0.66666667 0.22222222]
[-0.22222222 0.22222222]
[ 0.22222222 0.22222222]
[ 0.66666667 0.22222222]
[ 1.11111111 0.22222222]
[ 1.55555556 0.22222222]
[ 2. 0.22222222]
[-2. 0.66666667]
[-1.55555556 0.66666667]
[-1.11111111 0.66666667]
[-0.66666667 0.66666667]
[-0.22222222 0.66666667]
[ 0.22222222 0.66666667]
[ 0.66666667 0.66666667]
[ 1.11111111 0.66666667]
[ 1.55555556 0.66666667]
[ 2. 0.66666667]
[-2. 1.11111111]
[-1.55555556 1.11111111]
[-1.11111111 1.11111111]
[-0.66666667 1.11111111]
[-0.22222222 1.11111111]
[ 0.22222222 1.11111111]
[ 0.66666667 1.11111111]
[ 1.11111111 1.11111111]
[ 1.55555556 1.11111111]
[ 2. 1.11111111]
[-2. 1.55555556]
[-1.55555556 1.55555556]
[-1.11111111 1.55555556]
[-0.66666667 1.55555556]
[-0.22222222 1.55555556]
[ 0.22222222 1.55555556]
[ 0.66666667 1.55555556]
[ 1.11111111 1.55555556]
[ 1.55555556 1.55555556]
[ 2. 1.55555556]
[-2. 2. ]
[-1.55555556 2. ]
[-1.11111111 2. ]
[-0.66666667 2. ]
[-0.22222222 2. ]
[ 0.22222222 2. ]
[ 0.66666667 2. ]
[ 1.11111111 2. ]
[ 1.55555556 2. ]
[ 2. 2. ]]
[[3.60900000e+03]
[1.95995260e+03]
[1.05069974e+03]
[6.00308642e+02]
[4.21490779e+02]
[4.20601890e+02]
[5.97641975e+02]
[1.04625530e+03]
[1.95373038e+03]
[3.60100000e+03]
[3.09541975e+03]
[1.58683874e+03]
[7.82935681e+02]
[4.02777778e+02]
[2.59076513e+02]
[2.58187624e+02]
[4.00111111e+02]
[7.78491236e+02]
[1.58061652e+03]
[3.08741975e+03]
[2.62134568e+03]
[1.25323106e+03]
[5.54677793e+02]
[2.44753086e+02]
[1.36168419e+02]
[1.35279531e+02]
[2.42086420e+02]
[5.50233349e+02]
[1.24700884e+03]
[2.61334568e+03]
[2.18677778e+03]
[9.59129553e+02]
[3.65926078e+02]
[1.26234568e+02]
[5.27664990e+01]
[5.18776101e+01]
[1.23567901e+02]
[3.61481634e+02]
[9.52907331e+02]
[2.17877778e+03]
[1.79171605e+03]
[7.04534217e+02]
[2.16680537e+02]
[4.72222222e+01]
[8.87075141e+00]
[7.98186252e+00]
[4.45555556e+01]
[2.12236092e+02]
[6.98311995e+02]
[1.78371605e+03]
[1.43616049e+03]
[4.89445054e+02]
[1.06941168e+02]
[7.71604938e+00]
[4.48117665e+00]
[3.59228776e+00]
[5.04938272e+00]
[1.02496723e+02]
[4.83222832e+02]
[1.42816049e+03]
[1.12011111e+03]
[3.13862064e+02]
[3.67079713e+01]
[7.71604938e+00]
[3.95977747e+01]
[3.87088858e+01]
[5.04938272e+00]
[3.22635269e+01]
[3.07639841e+02]
[1.11211111e+03]
[8.43567901e+02]
[1.77785246e+02]
[5.98094803e+00]
[4.72222222e+01]
[1.14220546e+02]
[1.13331657e+02]
[4.45555556e+01]
[1.53650358e+00]
[1.71563024e+02]
[8.35567901e+02]
[6.06530864e+02]
[8.12146014e+01]
[1.47600975e+01]
[1.26234568e+02]
[2.28349489e+02]
[2.27460601e+02]
[1.23567901e+02]
[1.03156531e+01]
[7.49923792e+01]
[5.98530864e+02]
[4.09000000e+02]
[2.41501296e+01]
[6.30454199e+01]
[2.44753086e+02]
[3.81984606e+02]
[3.81095717e+02]
[2.42086420e+02]
[5.86009755e+01]
[1.79279073e+01]
[4.01000000e+02]]
Load the data with an input-output naming¶
dataset = load_dataset("RosenbrockDataset", opt_naming=False)
print(dataset)
Out:
Rosenbrock
Number of samples: 100
Number of variables: 2
Variables names and sizes by group:
inputs: x (2)
outputs: rosen (1)
Number of dimensions (total = 3) by group:
inputs: 2
outputs: 1
Plot the data¶
ZvsXY(dataset, x="x", x_comp=0, y="x", y_comp=1, z="rosen").execute(
save=False, show=False
)
YvsX(dataset, x="x", x_comp=0, y="rosen").execute(save=False, show=False)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()
Total running time of the script: ( 0 minutes 0.310 seconds)