PLSSVD (original) (raw)

class sklearn.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 data X and the targetsY. 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 Xhas 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

import numpy as np from sklearn.cross_decomposition import 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=None, Y=None)[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.

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

Targets.

Deprecated since version 1.5: Y is deprecated in 1.5 and will be removed in 1.7. Use y instead.

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 APIfor 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, 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.

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

Targets.

Deprecated since version 1.5: Y is deprecated in 1.5 and will be removed in 1.7. Use y instead.

Returns:

x_scoresarray-like or tuple of array-like

The transformed data X_transformed if Y is not None,(X_transformed, Y_transformed) otherwise.