8.21.2. sklearn.pls.PLSCanonical¶
- class sklearn.pls.PLSCanonical(n_components=2, scale=True, algorithm='nipals', max_iter=500, tol=1e-06, copy=True)¶
PLS canonical. PLSCanonical inherits from PLS with mode=”A” 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 : string, “nipals” or “svd”
The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop.
max_iter : an integer, (default 500)
the maximum number of iterations of the NIPALS inner loop (used only if algorithm=”nipals”)
tol : non-negative real, default 1e-06
the tolerance used in the iterative algorithm
copy : boolean, default True
Whether the deflation should be done on a copy. Let the default value to True unless you don’t care about side effect
Notes
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.
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
Examples
>>> from sklearn.pls import PLSCanonical, PLSRegression, CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> X_c, Y_c = plsca.transform(X, Y)
Attributes
x_weights_ array, shape = [p, n_components] X block weights vectors. y_weights_ array, shape = [q, n_components] Y block weights vectors. x_loadings_ array, shape = [p, n_components] X block loadings vectors. y_loadings_ array, shape = [q, n_components] Y block loadings vectors. x_scores_ array, shape = [n_samples, n_components] X scores. y_scores_ array, shape = [n_samples, n_components] Y scores. x_rotations_ array, shape = [p, n_components] X block to latents rotations. y_rotations_ array, shape = [q, n_components] Y block to latents rotations. Methods
fit(X, Y) predict(X[, copy]) Apply the dimension reduction learned on the train data. set_params(**params) Set the parameters of the estimator. transform(X[, Y, copy]) Apply the dimension reduction learned on the train data. - __init__(n_components=2, scale=True, algorithm='nipals', max_iter=500, tol=1e-06, 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 : boolean
Whether to copy X and Y, or perform in-place normalization.
Notes
This call require the estimation of a p x q matrix, which may be an issue in high dimensional space.
- set_params(**params)¶
Set the parameters of the 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, 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 : boolean
Whether to copy X and Y, or perform in-place normalization.
Returns : x_scores if Y is not given, (x_scores, y_scores) otherwise. :