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The input-output dataset¶
The IODataset
proposes two particular group names,
namely INPUT_GROUP
and OUTPUT_GROUP
.
This particular Dataset
is useful
for supervised machine learning and sensitivity analysis.
from __future__ import annotations
from gemseo.datasets.io_dataset import IODataset
First,
we instantiate the IODataset
:
dataset = IODataset()
and add some input and output variables
using the methods
add_input_variable()
and add_output_variable()
that are based on Dataset.add_variable()
:
dataset.add_input_variable("a", [[1.0, 2.0], [4.0, 5.0]])
dataset.add_input_variable("b", [[3.0], [6.0]])
dataset.add_output_variable("c", [[-1.0], [-2.0]])
as well as another variable:
dataset.add_variable("x", [[10.0], [20.0]])
dataset
We could also do the same with the methods
add_input_group()
and add_output_group()
.
dataset = IODataset()
dataset.add_input_group(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], ["a", "b"], {"a": 2, "b": 1}
)
dataset.add_output_group([[-1.0], [-2.0]], ["c"])
dataset.add_variable("x", [[10.0], [20.0]])
dataset
Then, we can easily access the names of the input and output variables:
dataset.input_names, dataset.output_names
(['a', 'b'], ['c'])
and those of all variables:
dataset.variable_names
['a', 'b', 'c', 'x']
The IODataset
provides also the number of samples:
dataset.n_samples
2
and the samples:
dataset.samples
[0, 1]
Lastly,
we can get the input data as an IODataset
view:
dataset.input_dataset
and the same for the output data:
dataset.output_dataset
Total running time of the script: (0 minutes 0.034 seconds)