gemseo.mlearning.transformers.dimension_reduction.pca module#
The Principal Component Analysis (PCA) to reduce the dimension of a variable.
The PCA
class wraps the PCA from Scikit-learn.
Dependence#
This dimension reduction algorithm relies on the PCA class of the scikit-learn library.
- class PCA(name='', n_components=None, scale=False, **parameters)[source]#
Bases:
BaseDimensionReduction
Principal component dimension reduction algorithm.
- Parameters:
name (str) --
A name for this transformer.
By default it is set to "".
n_components (int | None) -- The number of components of the latent space. If
None
, use the maximum number allowed by the technique, typicallymin(n_samples, n_features)
.scale (bool) --
Whether to scale the data before applying the PCA.
By default it is set to False.
**parameters (float | str | bool | None) -- The optional parameters for sklearn PCA constructor.
- compute_jacobian(data, *args, **kwargs)#
Force a NumPy array to be at least 2D and evaluate the function
f
.f
expects a 2D array shaped as(n_points, input_dimension)
and returns a nD arrays shaped as(..., n_points, output_dimension)
or(..., n_points, output_dimension, input_dimension)
.If the original
data
is a 1D array shaped as(input_dimension,)
, then this wrapper returns a (n-1)D array shaped as(..., output_dimension)
or(..., output_dimension, intput_dimension)
.
- compute_jacobian_inverse(data, *args, **kwargs)#
Force a NumPy array to be at least 2D and evaluate the function
f
.f
expects a 2D array shaped as(n_points, input_dimension)
and returns a nD arrays shaped as(..., n_points, output_dimension)
or(..., n_points, output_dimension, input_dimension)
.If the original
data
is a 1D array shaped as(input_dimension,)
, then this wrapper returns a (n-1)D array shaped as(..., output_dimension)
or(..., output_dimension, intput_dimension)
.
- inverse_transform(data, *args, **kwargs)#
Force a NumPy array to be at least 2D and evaluate the function
f
.f
expects a 2D array shaped as(n_points, input_dimension)
and returns a nD arrays shaped as(..., n_points, output_dimension)
or(..., n_points, output_dimension, input_dimension)
.If the original
data
is a 1D array shaped as(input_dimension,)
, then this wrapper returns a (n-1)D array shaped as(..., output_dimension)
or(..., output_dimension, intput_dimension)
.
- transform(data, *args, **kwargs)#
Force a NumPy array to be at least 2D and evaluate the function
f
.f
expects a 2D array shaped as(n_points, input_dimension)
and returns a nD arrays shaped as(..., n_points, output_dimension)
or(..., n_points, output_dimension, input_dimension)
.If the original
data
is a 1D array shaped as(input_dimension,)
, then this wrapper returns a (n-1)D array shaped as(..., output_dimension)
or(..., output_dimension, intput_dimension)
.
- property components: RealArray#
The principal components.