gemseo / mlearning / transformers / dimension_reduction

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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 gemseo.mlearning.transformers.dimension_reduction.pca.PCA(name='', n_components=None, scale=False, **parameters)[source]

Bases: DimensionReduction

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, typically min(n_samples, n_features).

  • scale (bool) –

    Whether to scale the data before applying the PCA.

    By default it is set to False.

  • **parameters (float | int | str | bool | None) – The optional parameters for sklearn PCA constructor.

compute_jacobian(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

compute_jacobian_inverse(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

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, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

transform(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

  • data (ndarray) – A 1D or 2D NumPy array.

  • *args (Any) – The positional arguments.

  • **kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

CROSSED: ClassVar[bool] = False

Whether the fit() method requires two data arrays.

property components: ndarray

The principal components.

property data_is_scaled: bool

Whether the transformer scales the data before reducing its dimension.

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, bool | int | float | ndarray | str | None]

The parameters of the transformer.

Examples using PCA

PCA on Burgers equation

PCA on Burgers equation