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.
Parameters:  n_components : int, default 2
Number of components to keep.
 scale : boolean, default True
Whether to scale X and Y.
 copy : boolean, 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(copy=True, n_components=2, scale=True) >>> X_c, Y_c = plsca.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 dimension reduction on the train data. get_params
([deep])Get parameters for this estimator. set_params
(**params)Set the parameters of this estimator. transform
(X[, Y])Apply the dimension reduction learned on the train data. 
fit
(X, Y)[source]¶ Fit model to data.
Parameters:  X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
 Y : arraylike, 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
(X, y=None)[source]¶ Learn and apply the dimension reduction on the train data.
Parameters:  X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
 y : arraylike, 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
(deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
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 pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns:  self

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