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 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

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)[source]
fit(X, Y)[source]

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)[source]

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)[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 : 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.