sklearn.decomposition
.TruncatedSVD¶

class
sklearn.decomposition.
TruncatedSVD
(n_components=2, *, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)[source]¶ Dimensionality reduction using truncated SVD (aka LSA).
This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with sparse matrices efficiently.
In particular, truncated SVD works on term count/tfidf matrices as returned by the vectorizers in
sklearn.feature_extraction.text
. In that context, it is known as latent semantic analysis (LSA).This estimator supports two algorithms: a fast randomized SVD solver, and a “naive” algorithm that uses ARPACK as an eigensolver on
X * X.T
orX.T * X
, whichever is more efficient.Read more in the User Guide.
 Parameters
 n_componentsint, default = 2
Desired dimensionality of output data. Must be strictly less than the number of features. The default value is useful for visualisation. For LSA, a value of 100 is recommended.
 algorithmstring, default = “randomized”
SVD solver to use. Either “arpack” for the ARPACK wrapper in SciPy (scipy.sparse.linalg.svds), or “randomized” for the randomized algorithm due to Halko (2009).
 n_iterint, optional (default 5)
Number of iterations for randomized SVD solver. Not used by ARPACK. The default is larger than the default in
randomized_svd
to handle sparse matrices that may have large slowly decaying spectrum. random_stateint, RandomState instance, default=None
Used during randomized svd. Pass an int for reproducible results across multiple function calls. See Glossary.
 tolfloat, optional
Tolerance for ARPACK. 0 means machine precision. Ignored by randomized SVD solver.
 Attributes
 components_array, shape (n_components, n_features)
 explained_variance_array, shape (n_components,)
The variance of the training samples transformed by a projection to each component.
 explained_variance_ratio_array, shape (n_components,)
Percentage of variance explained by each of the selected components.
 singular_values_array, shape (n_components,)
The singular values corresponding to each of the selected components. The singular values are equal to the 2norms of the
n_components
variables in the lowerdimensional space.
See also
Notes
SVD suffers from a problem called “sign indeterminacy”, which means the sign of the
components_
and the output from transform depend on the algorithm and random state. To work around this, fit instances of this class to data once, then keep the instance around to do transformations.References
Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf
Examples
>>> from sklearn.decomposition import TruncatedSVD >>> from scipy.sparse import random as sparse_random >>> from sklearn.random_projection import sparse_random_matrix >>> X = sparse_random(100, 100, density=0.01, format='csr', ... random_state=42) >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42) >>> svd.fit(X) TruncatedSVD(n_components=5, n_iter=7, random_state=42) >>> print(svd.explained_variance_ratio_) [0.0646... 0.0633... 0.0639... 0.0535... 0.0406...] >>> print(svd.explained_variance_ratio_.sum()) 0.286... >>> print(svd.singular_values_) [1.553... 1.512... 1.510... 1.370... 1.199...]
Methods
fit
(X[, y])Fit LSI model on training data X.
fit_transform
(X[, y])Fit LSI model to X and perform dimensionality reduction on X.
get_params
([deep])Get parameters for this estimator.
Transform X back to its original space.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Perform dimensionality reduction on X.

__init__
(n_components=2, *, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y=None)[source]¶ Fit LSI model on training data X.
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
Training data.
 yIgnored
 Returns
 selfobject
Returns the transformer object.

fit_transform
(X, y=None)[source]¶ Fit LSI model to X and perform dimensionality reduction on X.
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
Training data.
 yIgnored
 Returns
 X_newarray, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

inverse_transform
(X)[source]¶ Transform X back to its original space.
Returns an array X_original whose transform would be X.
 Parameters
 Xarraylike, shape (n_samples, n_components)
New data.
 Returns
 X_originalarray, shape (n_samples, n_features)
Note that this is always a dense array.

set_params
(**params)[source]¶ 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
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.