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 inSVDPLS
,SVDCCA
,NIPALSPLS
andNIPALSCCA
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)
Methods
__init__
([n_components, scale, algorithm, ...])fit
(X, Y)Fit model to data.
fit_transform
(X[, y])Learn and apply the dimension reduction on the train data.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_metadata_routing
()Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
inverse_transform
(X[, Y])Transform data back to its original space.
predict
(X[, copy])Predict targets of given samples.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
set_predict_request
(*[, copy])Request metadata passed to the
predict
method.set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.set_transform_request
(*[, copy])Request metadata passed to the
transform
method.transform
(X[, Y, copy])Apply the dimension reduction.
Attributes
param