`sklearn.cross_decomposition`.PLSRegression¶

class sklearn.cross_decomposition.PLSRegression(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True)[source]¶

PLS regression

PLSRegression is also known as PLS2 or PLS1, depending on the number of targets.

Read more in the User Guide.

New in version 0.8.

Parameters

n_componentsint, default=2: Number of components to keep. Should be in [1, min(n_samples, n_features, n_targets)].
scalebool, default=True: Whether to scale X and Y.
max_iterint, default=500: The maximum number of iterations of the power method when algorithm='nipals'. Ignored otherwise.
tolfloat, default=1e-06: The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of u_i - u_{i-1} is less than tol, where u corresponds to the left singular vector.
copybool, default=True: Whether to copy X and Y in fit before applying centering, and potentially scaling. If False, 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 cross-covariance matrices of each iteration.
y_weights_ndarray of shape (n_targets, n_components): The right singular vectors of the cross-covariance matrices of each iteration.
x_loadings_ndarray of shape (n_features, n_components): The loadings of X.
y_loadings_ndarray of shape (n_targets, n_components): The loadings of Y.
x_scores_ndarray of shape (n_samples, n_components): The transformed training samples.
y_scores_ndarray of shape (n_samples, n_components): The transformed training targets.
x_rotations_ndarray of shape (n_features, n_components): The projection matrix used to transform X.
y_rotations_ndarray of shape (n_features, n_components): The projection matrix used to transform Y.
coef_ndarray of shape (n_features, n_targets): The coefficients of the linear model such that Y is approximated as Y = X @ coef_.
n_iter_list of shape (n_components,): Number of iterations of the power method, for each component.
n_features_in_int: Number of features seen during fit.

Examples

>>> from sklearn.cross_decomposition import PLSRegression
>>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]]
>>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
>>> pls2 = PLSRegression(n_components=2)
>>> pls2.fit(X, Y)
PLSRegression()
>>> Y_pred = pls2.predict(X)

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.
`inverse_transform`(X)	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 \(R^2\) of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, Y, copy])	Apply the dimension reduction.

fit(X, Y)[source]¶

Fit model to data.

Parameters

Xarray-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.
Yarray-like of shape (n_samples,) or (n_samples, n_targets): Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

fit_transform(X, y=None)[source]¶

Learn and apply the dimension reduction on the train data.

Parameters

Xarray-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.
yarray-like of shape (n_samples, n_targets), default=None: 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.

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.

inverse_transform(X)[source]¶

Transform data back to its original space.

Parameters

Xarray-like of shape (n_samples, n_components): New data, where n_samples is the number of samples and n_components is the number of pls components.

Returns

x_reconstructedarray-like of shape (n_samples, n_features)

Notes

This transformation will only be exact if n_components=n_features.

predict(X, copy=True)[source]¶

Predict targets of given samples.

Parameters

Xarray-like of shape (n_samples, n_features): Samples.
copybool, default=True: Whether to copy X and Y, or perform in-place normalization.

Notes

This call requires the estimation of a matrix of shape (n_features, n_targets), which may be an issue in high dimensional space.

score(X, y, sample_weight=None)[source]¶

Return the coefficient of determination \(R^2\) of the prediction.

The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred) ** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: \(R^2\) of self.predict(X) wrt. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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, copy=True)[source]¶

Apply the dimension reduction.

Parameters

Xarray-like of shape (n_samples, n_features): Samples to transform.
Yarray-like of shape (n_samples, n_targets), default=None: Target vectors.
copybool, default=True: Whether to copy X and Y, or perform in-place normalization.

Returns

x_scores if Y is not given, (x_scores, y_scores) otherwise.

Examples using `sklearn.cross_decomposition.PLSRegression`¶

sklearn.cross_decomposition.PLSRegression¶

Examples using sklearn.cross_decomposition.PLSRegression¶

`sklearn.cross_decomposition`.PLSRegression¶

Examples using `sklearn.cross_decomposition.PLSRegression`¶