gemmr.estimators.SVDPLS

class gemmr.estimators.SVDPLS(n_components=1, covariance='empirical', scale=False, std_ddof=0, calc_loadings=False)

Partial Least Squares estimators based on singular value decomposition.

Parameters:

• n_components (int >= 1) – number of between-set components to estimate

• covariance (str) – must be ‘empirical’

• scale (bool) – whether to divide each feature by its standard deviation before fitting

• std_ddof (int >= 0) – when calculating standard deviations and covariances, they are normalized by 1 / (n - std_ddof)

covs_

contains the covariances between scores. This is the quantity that is maximized by PLS

Type:

np.ndarray (n_components,)

assocs_

Identical to covs_. 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,)

corrs_

Pearson correlations between X and Y scores for each component

Type:

np.ndarray (n_components_,)

__init__(n_components=1, covariance='empirical', scale=False, std_ddof=0, calc_loadings=False)

Methods

__init__([n_components, covariance, scale, ...])
fit(X, Y[, copy, x_align_ref])	Fit the estimator.
fit_transform(X, Y, **fit_params)	Fit the estimator and return the resulting scores
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
get_x_loadings(X)
get_y_loadings(Y)
set_fit_request(*[, copy, x_align_ref])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, copy])	Request metadata passed to the transform method.
transform(X[, Y, copy])	Apply the previously fitted estimator to new data.