sklearn.cross_decomposition
.PLSSVD¶
-
class
sklearn.cross_decomposition.
PLSSVD
(n_components=2, scale=True, copy=True)[source]¶ Partial Least Square SVD
Simply perform a svd on the crosscovariance matrix: X’Y There are no iterative deflation here.
Read more in the User Guide.
New in version 0.8.
- Parameters
n_components : int, default 2
Number of components to keep.
scale : boolean, default True
Whether to scale X and Y.
copy : boolean, default True
Whether to copy X and Y, or perform in-place computations.
Attributes
x_weights_
(array, [p, n_components]) X block weights vectors.
y_weights_
(array, [q, n_components]) Y block weights vectors.
x_scores_
(array, [n_samples, n_components]) X scores.
y_scores_
(array, [n_samples, n_components]) Y scores.
See also
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]]) >>> plsca = PLSSVD(n_components=2) >>> plsca.fit(X, Y) PLSSVD() >>> X_c, Y_c = plsca.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 dimension reduction on the train data.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X[, Y])Apply the dimension reduction learned on the train data.
-
__init__
(n_components=2, scale=True, copy=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit
(X, Y)[source]¶ 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.
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fit_transform
(X, y=None)[source]¶ Learn and apply the dimension reduction on the train 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.
- Returns
x_scores if Y is not given, (x_scores, y_scores) otherwise.
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get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params : mapping of string to any
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 pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.
-
transform
(X, Y=None)[source]¶ Apply the dimension reduction learned on the train 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.