sklearn.cross_decomposition
.CCA¶

class
sklearn.cross_decomposition.
CCA
(n_components=2, scale=True, max_iter=500, tol=1e06, 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 : nonnegative real, default 1e06.
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
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_ (arraylike) Number of iterations of the NIPALS inner loop for each component. See also
Notes
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.
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.
In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
Examples
>>> 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=1e06) >>> X_c, Y_c = cca.transform(X, Y)
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. 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. 
fit
(X, Y)[source]¶ Fit model to data.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : arraylike, shape = [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: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
y : arraylike, 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.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
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.

predict
(X, copy=True)[source]¶ Apply the dimension reduction learned on the train data.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
copy : boolean, default True
Whether to copy X and Y, or perform inplace normalization.
Notes
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 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: X : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
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

transform
(X, Y=None, copy=True)[source]¶ Apply the dimension reduction learned on the train data.
Parameters: X : arraylike, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y : arraylike, shape = [n_samples, n_targets]
Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
copy : boolean, default True
Whether to copy X and Y, or perform inplace normalization.
Returns: x_scores if Y is not given, (x_scores, y_scores) otherwise.
