scikits.learn.pls.CCA

class scikits.learn.pls.CCA(n_components=2, scale=True, algorithm='nipals', max_iter=500, tol=9.9999999999999995e-07, copy=True)

CCA Canonical Correlation Analysis. CCA inherits from PLS with mode=”B” and deflation_mode=”canonical”.

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) :

Training vectors, where n_samples in the number of samples and q is the number of response variables.

n_components: int, number of components to keep. (default 2). :

scale: boolean, scale data? (default True) :

algorithm: str “nipals” or “svd” the algorithm used to estimate the :

weights, it will be called “n_components” time ie.: for each iteration of the outer loop.

max_iter: an integer, the maximum number of iterations (default 500) of the :

NIPALS inner loop (used only if algorithm=”nipals”)

tol: a not negative real, the tolerance used in the iterative algorithm :

default 1e-06.

copy: boolean, should the deflation been made on a copy? Let the default :

value to True unless you don’t care about side effect

See also

PLSCanonical, PLSSVD

Notes

For each component k, find the weights u, v that maximizes max corr(Xk u, Yk v), such that |u| = |v| = 1

Note that it maximizes only the correlations between the scores.

The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.

The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score.

References

Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.

In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.

Attributes

x_weights_: array, [p, n_components]	X block weights vectors.
y_weights_: array, [q, n_components]	Y block weights vectors.
x_loadings_: array, [p, n_components]	X block loadings vectors.
y_loadings_: array, [q, n_components]	Y block loadings vectors.
x_scores_: array, [n_samples, n_components]	X scores.
y_scores_: array, [n_samples, n_components]	Y scores.
x_rotations_: array, [p, n_components]	X block to latents rotations.
y_rotations_: array, [q, n_components]	Y block to latents rotations.

Methods

__init__(n_components=2, scale=True, algorithm='nipals', max_iter=500, tol=9.9999999999999995e-07, copy=True)

predict(X, copy=True)

Apply the dimension reduction learned 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.

copy: X has to be normalize, do it on a copy or in place

with side effect!

Notes

This call require the estimation of a p x q matrix, which may be an issue in high dimensional space.

transform(X, Y=None, copy=True)

Apply the dimension reduction learned 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.

copy: X and Y have to be normalize, do it on a copy or in place

with side effect!

Returns :x_scores if Y is not given, (x_scores, y_scores) otherwise. :