Class TruncatedSVDScikitsLearnNode

Dimensionality reduction using truncated SVD (aka LSA).

This node has been automatically generated by wrapping the ``sklearn.decomposition.truncated_svd.TruncatedSVD`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This transformer performs linear dimensionality reduction by means of
truncated singular value decomposition (SVD). It is very similar to PCA,
but operates on sample vectors directly, instead of on a covariance matrix.
This means it can work with scipy.sparse matrices efficiently.

In particular, truncated SVD works on term count/tf-idf 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 algorithm: a fast randomized SVD solver, and
a "naive" algorithm that uses ARPACK as an eigensolver on (X * X.T) or
(X.T * X), whichever is more efficient.

Read more in the :ref:`User Guide <LSA>`.

**Parameters**

n_components : int, 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.

algorithm : string, 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_iter : int, optional
    Number of iterations for randomized SVD solver. Not used by ARPACK.

random_state : int or RandomState, optional
    (Seed for) pseudo-random number generator. If not given, the
    numpy.random singleton is used.

tol : float, optional
    Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
    SVD solver.

**Attributes**

``components_`` : array, shape (n_components, n_features)

``explained_variance_ratio_`` : array, [n_components]
    Percentage of variance explained by each of the selected components.

``explained_variance_`` : array, [n_components]
    The variance of the training samples transformed by a projection to
    each component.

**Examples**

>>> from sklearn.decomposition import TruncatedSVD
>>> from sklearn.random_projection import sparse_random_matrix
>>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42)
>>> svd = TruncatedSVD(n_components=5, random_state=42)
>>> svd.fit(X) # doctest: +NORMALIZE_WHITESPACE
TruncatedSVD(algorithm='randomized', n_components=5, n_iter=5,
        random_state=42, tol=0.0)
>>> print(svd.explained_variance_ratio_) # doctest: +ELLIPSIS
[ 0.0782... 0.0552... 0.0544... 0.0499... 0.0413...]
>>> print(svd.explained_variance_ratio_.sum()) # doctest: +ELLIPSIS
0.279...

See also

PCA
RandomizedPCA

**References**

Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061

**Notes**

SVD suffers from a problem called "sign indeterminancy", 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.

Overrides: object.__init__

Perform dimensionality reduction on X.

This node has been automatically generated by wrapping the sklearn.decomposition.truncated_svd.TruncatedSVD class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

New data.

Returns

X_new : array, shape (n_samples, n_components)

Reduced version of X. This will always be a dense array.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit LSI model on training data X.

This node has been automatically generated by wrapping the sklearn.decomposition.truncated_svd.TruncatedSVD class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Training data.

Returns

self : object

Returns the transformer object.

Overrides: Node.stop_training