val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?alpha:int ->
?ridge_alpha:float ->
?n_iter:int ->
?callback:[ `Callable of Py.Object.t | `None ] ->
?batch_size:int ->
?verbose:int ->
?shuffle:bool ->
?n_jobs:[ `Int of int | `None ] ->
?method_:[ `Lars | `Cd ] ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?normalize_components:string ->
unit ->
tMini-batch Sparse Principal Components Analysis
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 : int, 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.
n_iter : int, number of iterations to perform for each mini batch
callback : callable or None, optional (default: None) callable that gets invoked every five iterations
batch_size : int, the number of features to take in each mini batch
verbose : int Controls the verbosity; the higher, the more messages. Defaults to 0.
shuffle : boolean, whether to shuffle the data before splitting it in batches
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.
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.
random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
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.
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 MiniBatchSparsePCA >>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0) >>> transformer = MiniBatchSparsePCA(n_components=5, batch_size=50, ... random_state=0) >>> transformer.fit(X) MiniBatchSparsePCA(...) >>> X_transformed = transformer.transform(X) >>> X_transformed.shape (200, 5) >>> # most values in the components_ are zero (sparsity) >>> np.mean(transformer.components_ == 0) 0.94
See also -------- PCA SparsePCA DictionaryLearning
val fit : ?y:Py.Object.t -> x:Ndarray.t -> t -> tFit 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:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit 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 : numpy array of shape n_samples, n_features Training set.
y : numpy array of shape n_samples Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
val get_params : ?deep:bool -> t -> Py.Object.tGet 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 -> t -> tSet 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.
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 n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.