sklearn.cross_decomposition
.PLSSVD¶

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 crosscovariance matrix X’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.
New 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
andY
. copybool, default=True
Whether to copy
X
andY
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 crosscovariance matrix. Used to project
X
intransform
. y_weights_ndarray of (n_targets, n_components)
The right singular vectors of the SVD of the crosscovariance matrix. Used to project
X
intransform
. x_scores_ndarray of shape (n_samples, n_components)
The transformed training samples.
Deprecated since version 0.24:
x_scores_
is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). You can just calltransform
on the training data instead. y_scores_ndarray of shape (n_samples, n_components)
The transformed training targets.
Deprecated since version 0.24:
y_scores_
is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). You can just calltransform
on the training data instead.
See also
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))
Methods
fit
(X, Y)Fit model to data.
fit_transform
(X[, y])Learn and apply the dimensionality reduction.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X[, Y])Apply the dimensionality reduction.

fit
(X, Y)[source]¶ Fit model to data.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training samples.
 Yarraylike of shape (n_samples,) or (n_samples, n_targets)
Targets.

fit_transform
(X, y=None)[source]¶ Learn and apply the dimensionality reduction.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training samples.
 yarraylike of shape (n_samples,) or (n_samples, n_targets), default=None
Targets.
 Returns
 outarraylike or tuple of arraylike
The transformed data
X_tranformed
ifY
is not None,(X_transformed, Y_transformed)
otherwise.

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_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
 Xarraylike of shape (n_samples, n_features)
Samples to be transformed.
 Yarraylike of shape (n_samples,) or (n_samples, n_targets), default=None
Targets.
 Returns
 outarraylike or tuple of arraylike
The transformed data
X_tranformed
ifY
is not None,(X_transformed, Y_transformed)
otherwise.