gemseo / mlearning / transformers / dimension_reduction

dimension_reduction module¶

Dimension reduction as a generic transformer.

The DimensionReduction class implements the concept of dimension reduction.

See also

pca

class gemseo.mlearning.transformers.dimension_reduction.dimension_reduction.DimensionReduction(name='DimensionReduction', n_components=None, **parameters)[source]¶

Bases: Transformer

Dimension reduction.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “DimensionReduction”.
n_components (int | None) – The number of components of the latent space. If None, use the maximum number allowed by the technique, typically min(n_samples, n_features).
**parameters (bool | int | float | str | None) – The parameters of the transformer.

compute_jacobian(data)¶

Compute the Jacobian of transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed, shaped as (n_observations, n_features) or (n_features, ).
Returns:: The Jacobian matrix, shaped according to data.
Return type:: NoReturn

compute_jacobian_inverse(data)¶

Compute the Jacobian of the inverse_transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed, shaped as (n_observations, n_features) or (n_features, ).
Returns:: The Jacobian matrix, shaped according to data..
Return type:: NoReturn

duplicate()¶

Duplicate the current object.

Returns:: A deepcopy of the current instance.
Return type:: Transformer

fit(data, *args)¶

Fit the transformer to the data.

Parameters:

data (ndarray) – The data to be fitted, shaped as (n_observations, n_features) or (n_observations, ).
args (float | int | str) –

Return type:

None

fit_transform(data, *args)¶

Fit the transformer to the data and transform the data.

Parameters:

data (ndarray) – The data to be transformed, shaped as (n_observations, n_features) or (n_observations, ).
args (float | int | str) –

Returns:

The transformed data, shaped as data.

Return type:

ndarray

inverse_transform(data)¶

Perform an inverse transform on the data.

Parameters:: data (ndarray) – The data to be inverse transformed, shaped as (n_observations, n_features) or (n_features, ).
Returns:: The inverse transformed data, shaped as data.
Return type:: NoReturn

abstract transform(data)¶

Transform the data.

Parameters:: data (ndarray) – The data to be transformed, shaped as (n_observations, n_features) or (n_features, ).
Returns:: The transformed data, shaped as data.
Return type:: ndarray

CROSSED: ClassVar[bool] = False¶: Whether the fit() method requires two data arrays.

property is_fitted: bool¶: Whether the transformer has been fitted from some data.

property n_components: int¶: The number of components.

name: str¶: The name of the transformer.

property parameters: dict[str, Union[bool, int, float, numpy.ndarray, str, NoneType]]¶: The parameters of the transformer.

Examples using DimensionReduction¶

KL-SVD on Burgers equation

PCA on Burgers equation