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PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLSC2A in [Wegelin 2000]. This node has been automatically generated by wrapping the ``sklearn.cross_decomposition.pls_.PLSCanonical`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This class inherits from PLS with mode="A" and deflation_mode="canonical", norm_y_weights=True and algorithm="nipals", but svd should provide similar results up to numerical errors. Read more in the :ref:`User Guide <cross_decomposition>`. **Parameters** scale : boolean, scale data? (default True) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : nonnegative real, default 1e06 the tolerance used in the iterative algorithm copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect n_components : int, number of components to keep. (default 2). **Attributes** ``x_weights_`` : array, shape = [p, n_components] X block weights vectors. ``y_weights_`` : array, shape = [q, n_components] Y block weights vectors. ``x_loadings_`` : array, shape = [p, n_components] X block loadings vectors. ``y_loadings_`` : array, shape = [q, n_components] Y block loadings vectors. ``x_scores_`` : array, shape = [n_samples, n_components] X scores. ``y_scores_`` : array, shape = [n_samples, n_components] Y scores. ``x_rotations_`` : array, shape = [p, n_components] X block to latents rotations. ``y_rotations_`` : array, shape = [q, n_components] Y block to latents rotations. ``n_iter_`` : arraylike Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm provided is "svd". **Notes** Matrices:: T: ``x_scores_`` U: ``y_scores_`` W: ``x_weights_`` C: ``y_weights_`` P: ``x_loadings_`` Q: ``y_loadings__`` Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) ``x_rotations_`` = W (P.T W)^(1) ``y_rotations_`` = C (Q.T C)^(1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimize:: max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that ``u = v = 1`` Note that it maximizes both the correlations between the scores and the intrablock variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score. This performs a canonical symmetric version of the PLS regression. But slightly different than the CCA. This is mostly used for modeling. This implementation provides the same results that the "plspm" package provided in the R language (Rproject), using the function plsca(X, Y). Results are equal or collinear with the function ``pls(..., mode = "canonical")`` of the "mixOmics" package. The difference relies in the fact that mixOmics implementation does not exactly implement the Wold algorithm since it does not normalize y_weights to one. **Examples** >>> from sklearn.cross_decomposition import PLSCanonical >>> 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]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e06) >>> X_c, Y_c = plsca.transform(X, Y) **References** Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also CCA PLSSVD














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLSC2A in [Wegelin 2000]. This node has been automatically generated by wrapping the ``sklearn.cross_decomposition.pls_.PLSCanonical`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This class inherits from PLS with mode="A" and deflation_mode="canonical", norm_y_weights=True and algorithm="nipals", but svd should provide similar results up to numerical errors. Read more in the :ref:`User Guide <cross_decomposition>`. **Parameters** scale : boolean, scale data? (default True) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : nonnegative real, default 1e06 the tolerance used in the iterative algorithm copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect n_components : int, number of components to keep. (default 2). **Attributes** ``x_weights_`` : array, shape = [p, n_components] X block weights vectors. ``y_weights_`` : array, shape = [q, n_components] Y block weights vectors. ``x_loadings_`` : array, shape = [p, n_components] X block loadings vectors. ``y_loadings_`` : array, shape = [q, n_components] Y block loadings vectors. ``x_scores_`` : array, shape = [n_samples, n_components] X scores. ``y_scores_`` : array, shape = [n_samples, n_components] Y scores. ``x_rotations_`` : array, shape = [p, n_components] X block to latents rotations. ``y_rotations_`` : array, shape = [q, n_components] Y block to latents rotations. ``n_iter_`` : arraylike Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm provided is "svd". **Notes** Matrices:: T: ``x_scores_`` U: ``y_scores_`` W: ``x_weights_`` C: ``y_weights_`` P: ``x_loadings_`` Q: ``y_loadings__`` Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) ``x_rotations_`` = W (P.T W)^(1) ``y_rotations_`` = C (Q.T C)^(1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimize:: max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that ``u = v = 1`` Note that it maximizes both the correlations between the scores and the intrablock variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score. This performs a canonical symmetric version of the PLS regression. But slightly different than the CCA. This is mostly used for modeling. This implementation provides the same results that the "plspm" package provided in the R language (Rproject), using the function plsca(X, Y). Results are equal or collinear with the function ``pls(..., mode = "canonical")`` of the "mixOmics" package. The difference relies in the fact that mixOmics implementation does not exactly implement the Wold algorithm since it does not normalize y_weights to one. **Examples** >>> from sklearn.cross_decomposition import PLSCanonical >>> 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]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e06) >>> X_c, Y_c = plsca.transform(X, Y) **References** Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the twoblock case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also CCA PLSSVD




Apply the dimension reduction learned on the train data. This node has been automatically generated by wrapping the sklearn.cross_decomposition.pls_.PLSCanonical class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns x_scores if Y is not given, (x_scores, y_scores) otherwise.



Fit model to data. This node has been automatically generated by wrapping the sklearn.cross_decomposition.pls_.PLSCanonical class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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