sklearn.cross_decomposition.PLSSVD

class sklearn.cross_decomposition.PLSSVD(n_components=2, scale=True, copy=True)

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 in-place 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

PLSCanonical, CCA

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.

__init__(n_components=2, scale=True, copy=True)

Initialize self. See help(type(self)) for accurate signature.

fit(X, Y)

Fit model to data.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

Y : array-like, 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)

Learn and apply the dimension reduction on the train data.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

y : array-like, 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)

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)

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)

Apply the dimension reduction learned on the train data.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

Y : array-like, shape = [n_samples, n_targets]

Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.