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package sklearn

sklearn
- Sklearn

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type tag = [

| `SparsePCA

]

type t = [ `BaseEstimator | `Object | `SparsePCA | `TransformerMixin ] Obj.t

val of_pyobject : Py.Object.t -> t

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

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

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

val create : 
  ?n_components:int ->
  ?alpha:float ->
  ?ridge_alpha:float ->
  ?max_iter:int ->
  ?tol:float ->
  ?method_:[ `Lars | `Cd ] ->
  ?n_jobs:int ->
  ?u_init:[> `ArrayLike ] Np.Obj.t ->
  ?v_init:[> `ArrayLike ] Np.Obj.t ->
  ?verbose:int ->
  ?random_state:int ->
  ?normalize_components:[ `Deprecated ] ->
  unit ->
  t

Sparse Principal Components Analysis (SparsePCA)

Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.

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

Parameters ---------- n_components : int, Number of sparse atoms to extract.

alpha : float, Sparsity controlling parameter. Higher values lead to sparser components.

ridge_alpha : float, Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.

max_iter : int, Maximum number of iterations to perform.

tol : float, Tolerance for the stopping condition.

method : 'lars', 'cd' lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse.

n_jobs : int or None, optional (default=None) Number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

U_init : array of shape (n_samples, n_components), Initial values for the loadings for warm restart scenarios.

V_init : array of shape (n_components, n_features), Initial values for the components for warm restart scenarios.

verbose : int Controls the verbosity; the higher, the more messages. Defaults to 0.

random_state : int, RandomState instance, default=None Used during dictionary learning. Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`.

normalize_components : 'deprecated' This parameter does not have any effect. The components are always normalized.

.. versionadded:: 0.20

.. deprecated:: 0.22 ``normalize_components`` is deprecated in 0.22 and will be removed in 0.24.

Attributes ---------- components_ : array, n_components, n_features Sparse components extracted from the data.

error_ : array Vector of errors at each iteration.

n_components_ : int Estimated number of components.

.. versionadded:: 0.23

n_iter_ : int Number of iterations run.

mean_ : array, shape (n_features,) Per-feature empirical mean, estimated from the training set. Equal to ``X.mean(axis=0)``.

Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_friedman1 >>> from sklearn.decomposition import SparsePCA >>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0) >>> transformer = SparsePCA(n_components=5, random_state=0) >>> transformer.fit(X) SparsePCA(...) >>> X_transformed = transformer.transform(X) >>> X_transformed.shape (200, 5) >>> # most values in the components_ are zero (sparsity) >>> np.mean(transformer.components_ == 0) 0.9666...

See also -------- PCA MiniBatchSparsePCA DictionaryLearning

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

Fit the model from data in X.

Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features.

y : Ignored

Returns ------- self : object Returns the instance itself.

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

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

Least Squares projection of the data onto the sparse components.

To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the `ridge_alpha` parameter.

Note that Sparse PCA components orthogonality is not enforced as in PCA hence one cannot use a simple linear projection.

Parameters ---------- X : array of shape (n_samples, n_features) Test data to be transformed, must have the same number of features as the data used to train the model.

Returns ------- X_new array, shape (n_samples, n_components) Transformed data.

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

Attribute components_: get value or raise Not_found if None.

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

Attribute components_: get value as an option.

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

Attribute error_: get value or raise Not_found if None.

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

Attribute error_: get value as an option.

val n_components_ : t -> int

Attribute n_components_: get value or raise Not_found if None.

val n_components_opt : t -> int option

Attribute n_components_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

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

Attribute mean_: get value or raise Not_found if None.

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

Attribute mean_: 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.