315 lines
11 KiB
Python
315 lines
11 KiB
Python
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"""Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
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"""
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# Author: Lars Buitinck
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# Olivier Grisel <olivier.grisel@ensta.org>
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# Michael Becker <mike@beckerfuffle.com>
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# License: 3-clause BSD.
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from numbers import Integral, Real
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import numpy as np
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import scipy.sparse as sp
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from scipy.sparse.linalg import svds
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from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin
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from ..utils import check_array, check_random_state
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from ..utils._arpack import _init_arpack_v0
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from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip
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from ..utils.sparsefuncs import mean_variance_axis
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from ..utils.validation import check_is_fitted
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from ..utils._param_validation import Interval, StrOptions
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__all__ = ["TruncatedSVD"]
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class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
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"""Dimensionality reduction using truncated SVD (aka LSA).
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This transformer performs linear dimensionality reduction by means of
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truncated singular value decomposition (SVD). Contrary to PCA, this
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estimator does not center the data before computing the singular value
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decomposition. This means it can work with sparse matrices
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efficiently.
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In particular, truncated SVD works on term count/tf-idf matrices as
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returned by the vectorizers in :mod:`sklearn.feature_extraction.text`. In
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that context, it is known as latent semantic analysis (LSA).
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This estimator supports two algorithms: a fast randomized SVD solver, and
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a "naive" algorithm that uses ARPACK as an eigensolver on `X * X.T` or
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`X.T * X`, whichever is more efficient.
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Read more in the :ref:`User Guide <LSA>`.
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Parameters
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----------
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n_components : int, default=2
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Desired dimensionality of output data.
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If algorithm='arpack', must be strictly less than the number of features.
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If algorithm='randomized', must be less than or equal to the number of features.
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The default value is useful for visualisation. For LSA, a value of
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100 is recommended.
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algorithm : {'arpack', 'randomized'}, default='randomized'
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SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
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(scipy.sparse.linalg.svds), or "randomized" for the randomized
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algorithm due to Halko (2009).
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n_iter : int, default=5
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Number of iterations for randomized SVD solver. Not used by ARPACK. The
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default is larger than the default in
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:func:`~sklearn.utils.extmath.randomized_svd` to handle sparse
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matrices that may have large slowly decaying spectrum.
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n_oversamples : int, default=10
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Number of oversamples for randomized SVD solver. Not used by ARPACK.
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See :func:`~sklearn.utils.extmath.randomized_svd` for a complete
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description.
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.. versionadded:: 1.1
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power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto'
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Power iteration normalizer for randomized SVD solver.
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Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`
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for more details.
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.. versionadded:: 1.1
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random_state : int, RandomState instance or None, default=None
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Used during randomized svd. Pass an int for reproducible results across
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multiple function calls.
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See :term:`Glossary <random_state>`.
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tol : float, default=0.0
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Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
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SVD solver.
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Attributes
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----------
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components_ : ndarray of shape (n_components, n_features)
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The right singular vectors of the input data.
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explained_variance_ : ndarray of shape (n_components,)
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The variance of the training samples transformed by a projection to
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each component.
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explained_variance_ratio_ : ndarray of shape (n_components,)
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Percentage of variance explained by each of the selected components.
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singular_values_ : ndarray of shape (n_components,)
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The singular values corresponding to each of the selected components.
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The singular values are equal to the 2-norms of the ``n_components``
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variables in the lower-dimensional space.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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DictionaryLearning : Find a dictionary that sparsely encodes data.
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FactorAnalysis : A simple linear generative model with
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Gaussian latent variables.
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IncrementalPCA : Incremental principal components analysis.
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KernelPCA : Kernel Principal component analysis.
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NMF : Non-Negative Matrix Factorization.
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PCA : Principal component analysis.
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Notes
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-----
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SVD suffers from a problem called "sign indeterminacy", which means the
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sign of the ``components_`` and the output from transform depend on the
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algorithm and random state. To work around this, fit instances of this
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class to data once, then keep the instance around to do transformations.
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References
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----------
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:arxiv:`Halko, et al. (2009). "Finding structure with randomness:
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Stochastic algorithms for constructing approximate matrix decompositions"
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<0909.4061>`
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Examples
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--------
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>>> from sklearn.decomposition import TruncatedSVD
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>>> from scipy.sparse import csr_matrix
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>>> import numpy as np
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>>> np.random.seed(0)
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>>> X_dense = np.random.rand(100, 100)
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>>> X_dense[:, 2 * np.arange(50)] = 0
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>>> X = csr_matrix(X_dense)
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>>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
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>>> svd.fit(X)
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TruncatedSVD(n_components=5, n_iter=7, random_state=42)
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>>> print(svd.explained_variance_ratio_)
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[0.0157... 0.0512... 0.0499... 0.0479... 0.0453...]
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>>> print(svd.explained_variance_ratio_.sum())
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0.2102...
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>>> print(svd.singular_values_)
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[35.2410... 4.5981... 4.5420... 4.4486... 4.3288...]
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"""
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_parameter_constraints: dict = {
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"n_components": [Interval(Integral, 1, None, closed="left")],
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"algorithm": [StrOptions({"arpack", "randomized"})],
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"n_iter": [Interval(Integral, 0, None, closed="left")],
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"n_oversamples": [Interval(Integral, 1, None, closed="left")],
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"power_iteration_normalizer": [StrOptions({"auto", "OR", "LU", "none"})],
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"random_state": ["random_state"],
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"tol": [Interval(Real, 0, None, closed="left")],
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}
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def __init__(
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self,
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n_components=2,
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*,
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algorithm="randomized",
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n_iter=5,
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n_oversamples=10,
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power_iteration_normalizer="auto",
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random_state=None,
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tol=0.0,
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):
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self.algorithm = algorithm
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self.n_components = n_components
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self.n_iter = n_iter
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self.n_oversamples = n_oversamples
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self.power_iteration_normalizer = power_iteration_normalizer
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self.random_state = random_state
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self.tol = tol
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def fit(self, X, y=None):
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"""Fit model on training data X.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training data.
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y : Ignored
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Not used, present here for API consistency by convention.
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Returns
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-------
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self : object
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Returns the transformer object.
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"""
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# param validation is done in fit_transform
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self.fit_transform(X)
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return self
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def fit_transform(self, X, y=None):
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"""Fit model to X and perform dimensionality reduction on X.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training data.
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y : Ignored
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Not used, present here for API consistency by convention.
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Returns
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-------
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X_new : ndarray of shape (n_samples, n_components)
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Reduced version of X. This will always be a dense array.
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"""
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self._validate_params()
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X = self._validate_data(X, accept_sparse=["csr", "csc"], ensure_min_features=2)
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random_state = check_random_state(self.random_state)
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if self.algorithm == "arpack":
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v0 = _init_arpack_v0(min(X.shape), random_state)
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U, Sigma, VT = svds(X, k=self.n_components, tol=self.tol, v0=v0)
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# svds doesn't abide by scipy.linalg.svd/randomized_svd
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# conventions, so reverse its outputs.
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Sigma = Sigma[::-1]
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U, VT = svd_flip(U[:, ::-1], VT[::-1])
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elif self.algorithm == "randomized":
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if self.n_components > X.shape[1]:
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raise ValueError(
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f"n_components({self.n_components}) must be <="
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f" n_features({X.shape[1]})."
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)
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U, Sigma, VT = randomized_svd(
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X,
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self.n_components,
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n_iter=self.n_iter,
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n_oversamples=self.n_oversamples,
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power_iteration_normalizer=self.power_iteration_normalizer,
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random_state=random_state,
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)
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self.components_ = VT
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# As a result of the SVD approximation error on X ~ U @ Sigma @ V.T,
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# X @ V is not the same as U @ Sigma
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if self.algorithm == "randomized" or (
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self.algorithm == "arpack" and self.tol > 0
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):
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X_transformed = safe_sparse_dot(X, self.components_.T)
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else:
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X_transformed = U * Sigma
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# Calculate explained variance & explained variance ratio
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self.explained_variance_ = exp_var = np.var(X_transformed, axis=0)
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if sp.issparse(X):
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_, full_var = mean_variance_axis(X, axis=0)
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full_var = full_var.sum()
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else:
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full_var = np.var(X, axis=0).sum()
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self.explained_variance_ratio_ = exp_var / full_var
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self.singular_values_ = Sigma # Store the singular values.
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return X_transformed
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def transform(self, X):
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"""Perform dimensionality reduction on X.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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New data.
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Returns
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-------
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X_new : ndarray of shape (n_samples, n_components)
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Reduced version of X. This will always be a dense array.
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"""
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check_is_fitted(self)
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X = self._validate_data(X, accept_sparse=["csr", "csc"], reset=False)
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return safe_sparse_dot(X, self.components_.T)
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def inverse_transform(self, X):
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"""Transform X back to its original space.
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Returns an array X_original whose transform would be X.
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Parameters
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----------
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X : array-like of shape (n_samples, n_components)
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New data.
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Returns
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-------
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X_original : ndarray of shape (n_samples, n_features)
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Note that this is always a dense array.
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"""
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X = check_array(X)
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return np.dot(X, self.components_)
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def _more_tags(self):
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return {"preserves_dtype": [np.float64, np.float32]}
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@property
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def _n_features_out(self):
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"""Number of transformed output features."""
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return self.components_.shape[0]
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