Dataset from a NumPy array#

In this example, we will see how to build a Dataset from an NumPy array.

from __future__ import annotations

from numpy import concatenate
from numpy.random import default_rng

from gemseo import configure_logger
from gemseo.datasets.dataset import Dataset

configure_logger()

rng = default_rng(1)

Let us consider three parameters \(x_1\), \(x_2\) and \(x_3\) of size 1, 2 and 3 respectively. We generate 5 random samples of the inputs where

  • x_1 is stored in the first column,

  • x_2 is stored in the 2nd and 3rd columns

and 5 random samples of the outputs:

n_samples = 5
inputs = rng.random((n_samples, 3))
outputs = rng.random((n_samples, 3))
data = concatenate((inputs, outputs), 1)

A dataset with default names#

We create a Dataset from the NumPy array only and let GEMSEO give default names to its columns:

dataset = Dataset.from_array(data)
dataset
GROUP parameters
VARIABLE x_0 x_1 x_2 x_3 x_4 x_5
COMPONENT 0 0 0 0 0 0
0 0.511822 0.950464 0.144160 0.453498 0.134042 0.403113
1 0.948649 0.311831 0.423326 0.203455 0.262313 0.750365
2 0.827703 0.409199 0.549594 0.280409 0.485191 0.980737
3 0.027559 0.753513 0.538143 0.961657 0.724790 0.541227
4 0.329732 0.788429 0.303195 0.276891 0.160652 0.969925


A dataset with custom names#

We can also pass the names and sizes of the variables:

names_to_sizes = {"x_1": 1, "x_2": 2, "y_1": 3}
dataset = Dataset.from_array(data, ["x_1", "x_2", "y_1"], names_to_sizes)
dataset
GROUP parameters
VARIABLE x_1 x_2 y_1
COMPONENT 0 0 1 0 1 2
0 0.511822 0.950464 0.144160 0.453498 0.134042 0.403113
1 0.948649 0.311831 0.423326 0.203455 0.262313 0.750365
2 0.827703 0.409199 0.549594 0.280409 0.485191 0.980737
3 0.027559 0.753513 0.538143 0.961657 0.724790 0.541227
4 0.329732 0.788429 0.303195 0.276891 0.160652 0.969925


Warning

The number of variables names must be equal to the number of columns of the data array. Otherwise, the user has to specify the sizes of the different variables by means of a dictionary and be careful that the total size is equal to this number of columns.

A dataset with custom groups#

We can also use the notions of groups of variables:

groups = {"x_1": "inputs", "x_2": "inputs", "y_1": "outputs"}
dataset = Dataset.from_array(data, ["x_1", "x_2", "y_1"], names_to_sizes, groups)
dataset
GROUP inputs outputs
VARIABLE x_1 x_2 y_1
COMPONENT 0 0 1 0 1 2
0 0.511822 0.950464 0.144160 0.453498 0.134042 0.403113
1 0.948649 0.311831 0.423326 0.203455 0.262313 0.750365
2 0.827703 0.409199 0.549594 0.280409 0.485191 0.980737
3 0.027559 0.753513 0.538143 0.961657 0.724790 0.541227
4 0.329732 0.788429 0.303195 0.276891 0.160652 0.969925


Note

The groups are specified by means of a dictionary where indices are the variables names and values are the groups. If a variable is missing, the default group Dataset.DEFAULT_GROUP is considered.

Total running time of the script: (0 minutes 0.017 seconds)

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