sklearn.cross_decomposition
.PLSSVD¶

class
sklearn.cross_decomposition.
PLSSVD
(n_components=2, scale=True, copy=True)[source]¶ Partial Least Square SVD
Simply perform a svd on the crosscovariance matrix: X’Y There are no iterative deflation here.
Read more in the User Guide.
New in version 0.8.
 Parameters
 n_componentsint, default 2
Number of components to keep.
 scaleboolean, default True
Whether to scale X and Y.
 copyboolean, default True
Whether to copy X and Y, or perform inplace computations.
 Attributes
 x_weights_array, [p, n_components]
X block weights vectors.
 y_weights_array, [q, n_components]
Y block weights vectors.
 x_scores_array, [n_samples, n_components]
X scores.
 y_scores_array, [n_samples, n_components]
Y scores.
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]]) >>> plsca = PLSSVD(n_components=2) >>> plsca.fit(X, Y) PLSSVD() >>> X_c, Y_c = plsca.transform(X, Y) >>> X_c.shape, Y_c.shape ((4, 2), (4, 2))
Methods
fit
(self, X, Y)Fit model to data.
fit_transform
(self, X[, y])Learn and apply the dimension reduction on the train data.
get_params
(self[, deep])Get parameters for this estimator.
set_params
(self, \*\*params)Set the parameters of this estimator.
transform
(self, X[, Y])Apply the dimension reduction learned on the train data.

__init__
(self, n_components=2, scale=True, copy=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, Y)[source]¶ Fit model to data.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
 Yarraylike of shape (n_samples, n_targets)
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

fit_transform
(self, X, y=None)[source]¶ Learn and apply the dimension reduction on the train data.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
 yarraylike of shape (n_samples, n_targets)
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
 Returns
 x_scores if Y is not given, (x_scores, y_scores) otherwise.

get_params
(self, 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
 paramsmapping of string to any
Parameter names mapped to their values.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.

transform
(self, X, Y=None)[source]¶ Apply the dimension reduction learned on the train data.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
 Yarraylike of shape (n_samples, n_targets)
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.