gemmr.estimators.NIPALSPLS

class gemmr.estimators.NIPALSPLS(n_components=2, *, scale=True, algorithm='nipals', max_iter=500, tol=1e-06, copy=True)

Identical to sklearn.cross_decomposition.PLSCanonical, except that fit creates additional attributes for compatibility with SVDPLS and SVDCCA:

corrs_

contains the canonical correlations

Type:np.ndarray (n_components,)

covs_

contains the covariances between scores. This is the quantity that is

maximized by PLS

Type:np.ndarray (n_components,)

assocs_

Identical to corrs_. assocs_` is the common identifier used in in SVDPLS, SVDCCA, NIPALSPLS and NIPALSCCA for the association strength that is optimized by each particular method

Type:np.ndarray (n_components,)

__init__(n_components=2, *, scale=True, algorithm='nipals', max_iter=500, tol=1e-06, copy=True)

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

Methods

__init__([n_components, scale, algorithm, …])	Initialize self.
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.
inverse_transform(X)	Transform data back to its original space.
predict(X[, copy])	Apply the dimension reduction learned on the train data.
score(X, y[, sample_weight])	Return the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.
transform(X[, Y, copy])	Apply the dimension reduction learned on the train data.

fit(X, Y)

Fit model to data.

Parameters:

• X (array-like of 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 of shape (n_samples, n_targets)) – Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.