class sklearn.cross_decomposition.CCA(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)
[source]
CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode=”B” and deflation_mode=”canonical”.
Read more in the User Guide.
Parameters: |
n_components : int, (default 2). number of components to keep. scale : boolean, (default True) whether to scale the data? max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop tol : non-negative real, default 1e-06. the tolerance used in the iterative algorithm copy : boolean Whether the deflation be done on a copy. Let the default value to True unless you don’t care about side effects |
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Attributes: |
x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_loadings_ : array, [p, n_components] X block loadings vectors. y_loadings_ : array, [q, n_components] Y block loadings vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. x_rotations_ : array, [p, n_components] X block to latents rotations. y_rotations_ : array, [q, n_components] Y block to latents rotations. n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. |
See also
For each component k, find the weights u, v that maximizes max corr(Xk u, Yk v), such that |u| = |v| = 1
Note that it maximizes only the correlations between the scores.
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.
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
>>> from sklearn.cross_decomposition import CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> cca = CCA(n_components=1) >>> cca.fit(X, Y) ... CCA(copy=True, max_iter=500, n_components=1, scale=True, tol=1e-06) >>> X_c, Y_c = cca.transform(X, Y)
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. |
predict (X[, copy]) | Apply the dimension reduction learned on the train data. |
score (X, y[, sample_weight]) | Returns 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 learned on the train data. |
__init__(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)
[source]
fit(X, Y)
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Fit model to data.
Parameters: |
X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples in the number of samples and n_features is the number of predictors. Y : array-like of response, shape = [n_samples, n_targets] Target vectors, where n_samples in the number of samples and n_targets is the number of response variables. |
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fit_transform(X, y=None, **fit_params)
[source]
Learn and apply the dimension reduction on the train data.
Parameters: |
X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. |
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Returns: |
x_scores if Y is not given, (x_scores, y_scores) otherwise. : |
get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
predict(X, copy=True)
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Apply the dimension reduction learned on the train data.
Parameters: |
X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. |
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This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space.
score(X, y, sample_weight=None)
[source]
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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: |
X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. |
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Returns: |
score : float R^2 of self.predict(X) wrt. y. |
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.
Returns: | self : |
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transform(X, Y=None, copy=True)
[source]
Apply the dimension reduction learned on the train data.
Parameters: |
X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. |
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Returns: |
x_scores if Y is not given, (x_scores, y_scores) otherwise. : |
sklearn.cross_decomposition.CCA
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html