Note
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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
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
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
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)