package sklearn

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sklearn
- Sklearn

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type tag = [

| `CCA

]

type t =
  [ `BaseEstimator
  | `CCA
  | `MultiOutputMixin
  | `Object
  | `RegressorMixin
  | `TransformerMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_regressor : t -> [ `RegressorMixin ] Obj.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_multi_output : t -> [ `MultiOutputMixin ] Obj.t

val create : 
  ?n_components:int ->
  ?scale:bool ->
  ?max_iter:int ->
  ?tol:float ->
  ?copy:bool ->
  unit ->
  t

CCA Canonical Correlation Analysis.

CCA inherits from PLS with mode='B' and deflation_mode='canonical'.

Read more in the :ref:`User Guide <cross_decomposition>`.

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

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.

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.

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(n_components=1) >>> X_c, Y_c = cca.transform(X, Y)

References ----------

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.

See also -------- PLSCanonical PLSSVD

val fit : 
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

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.

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val inverse_transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Transform data back to its original space.

Parameters ---------- X : array-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_reconstructed : array-like of shape (n_samples, n_features)

Notes ----- This transformation will only be exact if n_components=n_features

val predict : 
  ?copy:bool ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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.

copy : boolean, default True Whether to copy X and Y, or perform in-place normalization.

Notes ----- This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space.

val score : 
  ?sample_weight:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  float

Return 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 : array-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, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

val transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?copy:bool ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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.

copy : boolean, 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.

val x_weights_ : t -> Py.Object.t

Attribute x_weights_: get value or raise Not_found if None.

val x_weights_opt : t -> Py.Object.t option

Attribute x_weights_: get value as an option.

val y_weights_ : t -> Py.Object.t

Attribute y_weights_: get value or raise Not_found if None.

val y_weights_opt : t -> Py.Object.t option

Attribute y_weights_: get value as an option.

val x_loadings_ : t -> Py.Object.t

Attribute x_loadings_: get value or raise Not_found if None.

val x_loadings_opt : t -> Py.Object.t option

Attribute x_loadings_: get value as an option.

val y_loadings_ : t -> Py.Object.t

Attribute y_loadings_: get value or raise Not_found if None.

val y_loadings_opt : t -> Py.Object.t option

Attribute y_loadings_: get value as an option.

val x_scores_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute x_scores_: get value or raise Not_found if None.

val x_scores_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute x_scores_: get value as an option.

val y_scores_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute y_scores_: get value or raise Not_found if None.

val y_scores_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute y_scores_: get value as an option.

val x_rotations_ : t -> Py.Object.t

Attribute x_rotations_: get value or raise Not_found if None.

val x_rotations_opt : t -> Py.Object.t option

Attribute x_rotations_: get value as an option.

val y_rotations_ : t -> Py.Object.t

Attribute y_rotations_: get value or raise Not_found if None.

val y_rotations_opt : t -> Py.Object.t option

Attribute y_rotations_: get value as an option.

val n_iter_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.