0PhFdZddlmZmZddlZddlmZmZm Z m Z ddl m Z ddl mZddlmZmZdd lmZdd lmZmZmZd d lmZmZGd dee eZGddeZGddeZdS)z%Matrix factorization with Sparse PCA.)IntegralRealN) BaseEstimatorClassNamePrefixFeaturesOutMixinTransformerMixin _fit_context)ridge_regression)check_random_state)Interval StrOptions)svd_flip) check_arraycheck_is_fitted validate_data)MiniBatchDictionaryLearning dict_learningc `eZdZUdZdeedddgeedddgeedddgeedddgeedddgedd hgedgd gd gd Ze e d < dddddddddddZ e dddZ dZdZedZfdZxZS)_BaseSparsePCAz/Base class for SparsePCA and MiniBatchSparsePCANrleftclosedgrlarscdverbose random_state n_componentsalpha ridge_alphamax_itertolmethodn_jobsrr_parameter_constraints{Gz?:0yE>F)r r!r"r#r$r%rrc||_||_||_||_||_||_||_||_| |_dSNr) selfrr r!r"r#r$r%rrs a/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/decomposition/_sparse_pca.py__init__z_BaseSparsePCA.__init__'sL) &     (T)prefer_skip_nested_validationct|j}t||}|d|_||jz }|j|jd}n|j}||||S)aFit the model from data in X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. y : Ignored Not used, present here for API consistency by convention. Returns ------- self : object Returns the instance itself. raxisNr)r rrmeanmean_rshape_fit)r,Xyrrs r-fitz_BaseSparsePCA.fit>su$*$*;<< $ " "VVV^^  N   $71:LL,LyyL,777r/ct|t||d}||jz }t|jj|j|jd}|S)aLeast 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 : ndarray 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 : ndarray of shape (n_samples, n_components) Transformed data. F)resetcholesky)solver)rrr5r components_Tr!)r,r8Us r- transformz_BaseSparsePCA.transform]s`*  $ / / /  N    T%5j   r/cbt|t|}||jz|jzS)aTransform data from the latent space to the original space. This inversion is an approximation due to the loss of information induced by the forward decomposition. .. versionadded:: 1.2 Parameters ---------- X : ndarray of shape (n_samples, n_components) Data in the latent space. Returns ------- X_original : ndarray of shape (n_samples, n_features) Reconstructed data in the original space. )rrr?r5)r,r8s r-inverse_transformz _BaseSparsePCA.inverse_transform}s2$  NND$$ 22r/c&|jjdS)z&Number of transformed output features.r)r?r6)r,s r-_n_features_outz_BaseSparsePCA._n_features_outs%a((r/cdt}ddg|j_|S)Nfloat64float32)super__sklearn_tags__transformer_tagspreserves_dtype)r,tags __class__s r-rKz_BaseSparsePCA.__sklearn_tags__s-ww''))1:I0F- r/r+)__name__ __module__ __qualname____doc__r rrr r&dict__annotations__r.r r:rBrDpropertyrFrK __classcell__rOs@r-rrs99xx!T&IIIJ(4d6:::; sD@@@AXh4???@sD8889:vtn--.T";'( $ $D   ) ))))).\555888658<@333.))X)r/rc eZdZUdZiejdejgdejgdZee d< dddddd dddd dd fd Z d Z xZ S) SparsePCAauSparse 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 `. Parameters ---------- n_components : int, default=None Number of sparse atoms to extract. If None, then ``n_components`` is set to ``n_features``. alpha : float, default=1 Sparsity controlling parameter. Higher values lead to sparser components. ridge_alpha : float, default=0.01 Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method. max_iter : int, default=1000 Maximum number of iterations to perform. tol : float, default=1e-8 Tolerance for the stopping condition. method : {'lars', 'cd'}, default='lars' Method to be used for optimization. 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, 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 ` for more details. U_init : ndarray of shape (n_samples, n_components), default=None Initial values for the loadings for warm restart scenarios. Only used if `U_init` and `V_init` are not None. V_init : ndarray of shape (n_components, n_features), default=None Initial values for the components for warm restart scenarios. Only used if `U_init` and `V_init` are not None. verbose : int or bool, default=False Controls the verbosity; the higher, the more messages. Defaults to 0. random_state : int, RandomState instance or None, default=None Used during dictionary learning. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. Attributes ---------- components_ : ndarray of shape (n_components, n_features) Sparse components extracted from the data. error_ : ndarray Vector of errors at each iteration. n_components_ : int Estimated number of components. .. versionadded:: 0.23 n_iter_ : int Number of iterations run. mean_ : ndarray of shape (n_features,) Per-feature empirical mean, estimated from the training set. Equal to ``X.mean(axis=0)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PCA : Principal Component Analysis implementation. MiniBatchSparsePCA : Mini batch variant of `SparsePCA` that is faster but less accurate. DictionaryLearning : Generic dictionary learning problem using a sparse code. 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) np.float64(0.9666...) N)U_initV_initr&rr'r(r)rF) r r!r"r#r$r%r[r\rrc xt|||||||| |  ||_| |_dSNr)rJr.r[r\) r,rr r!r"r#r$r%r[r\rrrOs r-r.zSparsePCA.__init__sU %#%    r/c|j |jjnd}|j |jjnd}t|j||j|j|j|j|j|j |||d \}}}|_ t||d\}}|j|_ tj|j dddtjf} d| | dk<|xj | zc_ t#|j |_||_|S)z Specialized `fit` for SparsePCA.NT) r r#r"r$r%rr code_init dict_init return_n_iter)u_based_decisionrr2r)r\r@r[rr r#r"r$r%rn_iter_rr?nplinalgnormnewaxislen n_components_error_) r,r8rrr`racode dictionaryEcomponents_norms r-r7zSparsePCA._fit4s &*[%`. Parameters ---------- n_components : int, default=None Number of sparse atoms to extract. If None, then ``n_components`` is set to ``n_features``. alpha : int, default=1 Sparsity controlling parameter. Higher values lead to sparser components. ridge_alpha : float, default=0.01 Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method. max_iter : int, default=1_000 Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. .. versionadded:: 1.2 callback : callable, default=None Callable that gets invoked every five iterations. batch_size : int, default=3 The number of features to take in each mini batch. verbose : int or bool, default=False Controls the verbosity; the higher, the more messages. Defaults to 0. shuffle : bool, default=True Whether to shuffle the data before splitting it in batches. n_jobs : int, 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 ` for more details. method : {'lars', 'cd'}, default='lars' Method to be used for optimization. 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, 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 `. tol : float, default=1e-3 Control early stopping based on the norm of the differences in the dictionary between 2 steps. To disable early stopping based on changes in the dictionary, set `tol` to 0.0. .. versionadded:: 1.1 max_no_improvement : int or None, default=10 Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. To disable convergence detection based on cost function, set `max_no_improvement` to `None`. .. versionadded:: 1.1 Attributes ---------- components_ : ndarray of shape (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_ : ndarray of shape (n_features,) Per-feature empirical mean, estimated from the training set. Equal to ``X.mean(axis=0)``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- DictionaryLearning : Find a dictionary that sparsely encodes data. IncrementalPCA : Incremental principal components analysis. PCA : Principal component analysis. SparsePCA : Sparse Principal Components Analysis. TruncatedSVD : Dimensionality reduction using truncated SVD. 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, ... max_iter=10, 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) np.float64(0.9...) rNrrrboolean)r"callback batch_sizeshufflemax_no_improvementr&r'r(FTrgMbP? ) r r!r"rtrurrvr%r$rr#rwc t||||| | | ||  ||_||_||_| |_dSr^)rJr.rtrurvrw)r,rr r!r"rtrurrvr%r$rr#rwrOs r-r.zMiniBatchSparsePCA.__init__sf" %#%  ! $ "4r/c^d|jz}t||j|jd|j|j|j|j|||j|j|j|j |j }| d| |j ||j j |jc|_|_t"j|jdddt"jf}d||dk<|xj|zc_t+|j|_|S) z)Specialized `fit` for MiniBatchSparsePCA.lasso_N)rr r"rarurvr% fit_algorithmrtransform_algorithmtransform_alpharrtr#rwdefault)rBrr2r)r$rr r"rurvr%rrtr#rw set_outputr:r@rBrdr?rerfrgrhrirj)r,r8rrr~estros r-r7zMiniBatchSparsePCA._fits'4)%*]L;+% 3 JL]#6   " +++  ),qs););)=s{&$,)..)9.BB111bj=Q011,- O+ !122 r/r+)rPrQrRrSrr&r rcallablerTrUr.r7rWrXs@r-rrrrSsEEN$  /$Xh4???@8$x!T&AAAB;'x!T&III4P $$$D5 5555555Br/rr)rSnumbersrrnumpyrebaserrrr linear_modelr utilsr utils._param_validationr r utils.extmathrutils.validationrrr_dict_learningrrrrZrrr/r-rs++ #""""""" ,+++++&&&&&&::::::::$$$$$$JJJJJJJJJJFFFFFFFFDDDDD46F DDDNqqqqqqqqhQQQQQQQQQQr/