package sklearn
Module Decomposition.MiniBatchSparsePCASource
Source
val create :
?n_components:int ->
?alpha:int ->
?ridge_alpha:float ->
?n_iter:int ->
?callback:Py.Object.t ->
?batch_size:int ->
?verbose:int ->
?shuffle:bool ->
?n_jobs:int ->
?method_:[ `Lars | `Cd ] ->
?random_state:int ->
?normalize_components:[ `Deprecated ] ->
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, default=None Used for random shuffling when ``shuffle`` is set to ``True``, during online 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.
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 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
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.
Source
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.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 : 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.
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.
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.
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.
Attribute components_: get value or raise Not_found if None.
Attribute components_: get value as an option.
Attribute mean_: get value or raise Not_found if None.
Pretty-print the object to a formatter.