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.
Parameters : X : array-like of predictors, shape = [n_samples, p]
Training vector, where n_samples is the number of samples and p is the number of predictors. X will be centered before any analysis.
Y : array-like of response, shape = [n_samples, q]
Training vector, where n_samples is the number of samples and q is the number of response variables. X will be centered before any analysis.
n_components : int, (default 2).
number of components to keep.
scale : boolean, (default True)
whether to scale X and Y.
See also
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. Methods
fit(X, Y) 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)¶
- fit_transform(X, y=None, **fit_params)¶
Learn and apply the dimension reduction on the train data.
Parameters : X : array-like of predictors, shape = [n_samples, p]
Training vectors, where n_samples in the number of samples and p is the number of predictors.
Y : array-like of response, shape = [n_samples, q], optional
Training vectors, where n_samples in the number of samples and q 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 former 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.