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 matrixX'y. It is able to project both the training dataX and the targetsy. The training dataX is projected on the left singular vectors, whilethe targets are projected on the right singular vectors.

Read more in theUser 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 scaleX andy.

copybool, default=True

Whether to copyX andy in fit before applying centering, andpotentially scaling. IfFalse, 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 projectX intransform.

y_weights_ndarray of (n_targets, n_components)

The right singular vectors of the SVD of the cross-covariance matrix.Used to projectX intransform.

n_features_in_int

Number of features seen duringfit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas 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_decompositionimportPLSSVD>>>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 dataX_transformed ifyisnotNone,(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. Forexample, if the transformer outputs 3 features, then the feature namesout 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 infit.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

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

Set output container.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output oftransform andfit_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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible 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 dataX_transformed ifyisnotNone,(X_transformed,y_transformed) otherwise.