gemseo / mlearning / transform / dimension_reduction

pls module¶

The Partial Least Square (PLS) regression to reduce the dimension of a variable.

The PLS class wraps the PCA from Scikit-learn.

Dependence¶

This dimension reduction algorithm relies on the PLSRegression class of the scikit-learn library.

class gemseo.mlearning.transform.dimension_reduction.pls.PLS(name='PLS', n_components=None, **parameters)[source]¶

Bases: DimensionReduction

Partial Least Square regression.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “PLS”.
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 (float | int | bool) – The optional parameters for sklearn PCA constructor.

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 (Union[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 (Union[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 description is missing.
**kwargs (Any) – The description is missing.

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 description is missing.
**kwargs (Any) – The description is missing.

Returns:

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

Return type:

Any

CROSSED: Final[bool] = True¶: 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.