scikit-learn/sklearn/decomposition/_sparse_pca.py

"""Matrix factorization with Sparse PCA."""
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause

import numpy as np

from ..utils import check_random_state
from ..utils.validation import check_is_fitted
from ..linear_model import ridge_regression
from ..base import BaseEstimator, TransformerMixin, _ClassNamePrefixFeaturesOutMixin
from ._dict_learning import dict_learning, dict_learning_online


class SparsePCA(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
    """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, 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 <n_jobs>`
        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 <random_state>`.

    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)
    0.9666...
    """

    def __init__(
        self,
        n_components=None,
        *,
        alpha=1,
        ridge_alpha=0.01,
        max_iter=1000,
        tol=1e-8,
        method="lars",
        n_jobs=None,
        U_init=None,
        V_init=None,
        verbose=False,
        random_state=None,
    ):
        self.n_components = n_components
        self.alpha = alpha
        self.ridge_alpha = ridge_alpha
        self.max_iter = max_iter
        self.tol = tol
        self.method = method
        self.n_jobs = n_jobs
        self.U_init = U_init
        self.V_init = V_init
        self.verbose = verbose
        self.random_state = random_state

    def fit(self, X, y=None):
        """Fit 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.
        """
        random_state = check_random_state(self.random_state)
        X = self._validate_data(X)

        self.mean_ = X.mean(axis=0)
        X = X - self.mean_

        if self.n_components is None:
            n_components = X.shape[1]
        else:
            n_components = self.n_components
        code_init = self.V_init.T if self.V_init is not None else None
        dict_init = self.U_init.T if self.U_init is not None else None
        Vt, _, E, self.n_iter_ = dict_learning(
            X.T,
            n_components,
            alpha=self.alpha,
            tol=self.tol,
            max_iter=self.max_iter,
            method=self.method,
            n_jobs=self.n_jobs,
            verbose=self.verbose,
            random_state=random_state,
            code_init=code_init,
            dict_init=dict_init,
            return_n_iter=True,
        )
        self.components_ = Vt.T
        components_norm = np.linalg.norm(self.components_, axis=1)[:, np.newaxis]
        components_norm[components_norm == 0] = 1
        self.components_ /= components_norm
        self.n_components_ = len(self.components_)

        self.error_ = E
        return self

    def transform(self, X):
        """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 : 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.
        """
        check_is_fitted(self)

        X = self._validate_data(X, reset=False)
        X = X - self.mean_

        U = ridge_regression(
            self.components_.T, X.T, self.ridge_alpha, solver="cholesky"
        )

        return U

    @property
    def _n_features_out(self):
        """Number of transformed output features."""
        return self.components_.shape[0]

    def _more_tags(self):
        return {
            "preserves_dtype": [np.float64, np.float32],
        }


class MiniBatchSparsePCA(SparsePCA):
    """Mini-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, 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.

    n_iter : int, default=100
        Number of iterations to perform for each mini batch.

    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 <n_jobs>`
        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 <random_state>`.

    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,
    ...                                  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
    """

    def __init__(
        self,
        n_components=None,
        *,
        alpha=1,
        ridge_alpha=0.01,
        n_iter=100,
        callback=None,
        batch_size=3,
        verbose=False,
        shuffle=True,
        n_jobs=None,
        method="lars",
        random_state=None,
    ):
        super().__init__(
            n_components=n_components,
            alpha=alpha,
            verbose=verbose,
            ridge_alpha=ridge_alpha,
            n_jobs=n_jobs,
            method=method,
            random_state=random_state,
        )
        self.n_iter = n_iter
        self.callback = callback
        self.batch_size = batch_size
        self.shuffle = shuffle

    def fit(self, X, y=None):
        """Fit 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 for API consistency by convention.

        Returns
        -------
        self : object
            Returns the instance itself.
        """
        random_state = check_random_state(self.random_state)
        X = self._validate_data(X)

        self.mean_ = X.mean(axis=0)
        X = X - self.mean_

        if self.n_components is None:
            n_components = X.shape[1]
        else:
            n_components = self.n_components
        Vt, _, self.n_iter_ = dict_learning_online(
            X.T,
            n_components,
            alpha=self.alpha,
            n_iter=self.n_iter,
            return_code=True,
            dict_init=None,
            verbose=self.verbose,
            callback=self.callback,
            batch_size=self.batch_size,
            shuffle=self.shuffle,
            n_jobs=self.n_jobs,
            method=self.method,
            random_state=random_state,
            return_n_iter=True,
        )
        self.components_ = Vt.T

        components_norm = np.linalg.norm(self.components_, axis=1)[:, np.newaxis]
        components_norm[components_norm == 0] = 1
        self.components_ /= components_norm
        self.n_components_ = len(self.components_)

        return self