sklearn.cross_decomposition
.PLSSVD¶
- class sklearn.cross_decomposition.PLSSVD(n_components=2, *, scale=True, copy=True)[source]¶
Partial Least Square SVD.
This transformer simply performs a SVD on the cross-covariance matrix
X'Y
. It is able to project both the training dataX
and the targetsY
. The training dataX
is projected on the left singular vectors, while the targets are projected on the right singular vectors.Read more in the User Guide.
New in version 0.8.
- Parameters
- n_componentsint, default=2
The number of components to keep. Should be in
[1, min(n_samples, n_features, n_targets)]
.- scalebool, default=True
Whether to scale
X
andY
.- copybool, default=True
Whether to copy
X
andY
in fit before applying centering, and potentially scaling. IfFalse
, these operations will be done inplace, modifying both arrays.
- Attributes
- x_weights_ndarray of shape (n_features, n_components)
The left singular vectors of the SVD of the cross-covariance matrix. Used to project
X
intransform
.- y_weights_ndarray of (n_targets, n_components)
The right singular vectors of the SVD of the cross-covariance matrix. Used to project
X
intransform
.- n_features_in_int
Number of features seen during fit.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
PLSCanonical
Partial Least Squares transformer and regressor.
CCA
Canonical Correlation Analysis.
Examples
>>> import numpy as np >>> from sklearn.cross_decomposition import PLSSVD >>> X = np.array([[0., 0., 1.], ... [1., 0., 0.], ... [2., 2., 2.], ... [2., 5., 4.]]) >>> Y = np.array([[0.1, -0.2], ... [0.9, 1.1], ... [6.2, 5.9], ... [11.9, 12.3]]) >>> pls = PLSSVD(n_components=2).fit(X, Y) >>> X_c, Y_c = pls.transform(X, Y) >>> X_c.shape, Y_c.shape ((4, 2), (4, 2))
Methods
fit
(X, Y)Fit model to data.
fit_transform
(X[, y])Learn and apply the dimensionality reduction.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X[, Y])Apply the dimensionality reduction.
- fit(X, Y)[source]¶
Fit model to data.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training samples.
- Yarray-like of shape (n_samples,) or (n_samples, n_targets)
Targets.
- Returns
- selfobject
Fitted estimator.
- fit_transform(X, y=None)[source]¶
Learn and apply the dimensionality reduction.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training samples.
- yarray-like of shape (n_samples,) or (n_samples, n_targets), default=None
Targets.
- Returns
- outarray-like or tuple of array-like
The transformed data
X_tranformed
ifY is not None
,(X_transformed, Y_transformed)
otherwise.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters
- input_featuresarray-like of str or None, default=None
Only used to validate feature names with the names seen in
fit
.
- Returns
- feature_names_outndarray of str objects
Transformed feature names.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
- transform(X, Y=None)[source]¶
Apply the dimensionality reduction.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Samples to be transformed.
- Yarray-like of shape (n_samples,) or (n_samples, n_targets), default=None
Targets.
- Returns
- x_scoresarray-like or tuple of array-like
The transformed data
X_tranformed
ifY is not None
,(X_transformed, Y_transformed)
otherwise.