PLSSVD#

classsklearn.cross_decomposition.PLSSVD(n_components=2, *, scale=True, copy=True)[source] #

Partial Least Square SVD.

This transformer simply performs a SVD on the cross-covariance matrix X'y. It is able to project both the training data X and the targets y. The training data X is projected on the left singular vectors, while the targets are projected on the right singular vectors.

Read more in the User Guide.

Added in version 0.8.

Parameters:

n_componentsint, default=2

The number of components to keep. Should be in [1, min(n_samples, n_features, n_targets)].

scalebool, default=True

Whether to scale X and y.

copybool, default=True

Whether to copy X and y in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays.

Attributes:

x_weights_ndarray of shape (n_features, n_components)

The left singular vectors of the SVD of the cross-covariance matrix. Used to project X in transform.

y_weights_ndarray of (n_targets, n_components)

The right singular vectors of the SVD of the cross-covariance matrix. Used to project X in transform.

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

See also

PLSCanonical

Partial Least Squares transformer and regressor.

CCA

Canonical Correlation Analysis.

Examples

>>> importnumpyasnp
>>> fromsklearn.cross_decompositionimport PLSSVD
>>> X = np.array([[0., 0., 1.],
...  [1., 0., 0.],
...  [2., 2., 2.],
...  [2., 5., 4.]])
>>> y = np.array([[0.1, -0.2],
...  [0.9, 1.1],
...  [6.2, 5.9],
...  [11.9, 12.3]])
>>> pls = PLSSVD(n_components=2).fit(X, y)
>>> X_c, y_c = pls.transform(X, y)
>>> X_c.shape, y_c.shape
((4, 2), (4, 2))

fit(X, y)[source] #

Fit model to data.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training samples.

yarray-like of shape (n_samples,) or (n_samples, n_targets)

Targets.

Returns:

selfobject

Fitted estimator.

fit_transform(X, y=None)[source] #

Learn and apply the dimensionality reduction.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training samples.

yarray-like of shape (n_samples,) or (n_samples, n_targets), default=None

Targets.

Returns:

outarray-like or tuple of array-like

The transformed data X_transformed if y is not None, (X_transformed, y_transformed) otherwise.

get_feature_names_out(input_features=None)[source] #

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None

Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source] #

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source] #

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_output(*, transform=None)[source] #

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{"default", "pandas", "polars"}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source] #

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X, y=None)[source] #

Apply the dimensionality reduction.

Parameters:

Xarray-like of shape (n_samples, n_features)

Samples to be transformed.

yarray-like of shape (n_samples,) or (n_samples, n_targets), default=None

Targets.

Returns:

x_scoresarray-like or tuple of array-like

The transformed data X_transformed if y is not None, (X_transformed, y_transformed) otherwise.