class_docstr: 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 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 algorithms: 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 (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_state : int, RandomState instance or None, optional, default = None
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  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_ : 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 2-norms of the ``n_components``
      variables in the lower-dimensional space.
  
  Examples
  --------
  >>> from mars.learn.decomposition import TruncatedSVD
  >>> import mars.tensor as mt
  >>> from sklearn.random_projection import sparse_random_matrix
  >>> X = mt.tensor(sparse_random_matrix(100, 100, density=0.01, random_state=42))
  >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
  >>> svd.fit(X)  # doctest: +NORMALIZE_WHITESPACE
  TruncatedSVD(algorithm='randomized', n_components=5, n_iter=7,
          random_state=42, tol=0.0)
  >>> print(svd.explained_variance_ratio_)  # doctest: +ELLIPSIS
  [0.0606... 0.0584... 0.0497... 0.0434... 0.0372...]
  >>> print(svd.explained_variance_ratio_.sum())  # doctest: +ELLIPSIS
  0.249...
  >>> print(svd.singular_values_)  # doctest: +ELLIPSIS
  [2.5841... 2.5245... 2.3201... 2.1753... 2.0443...]
  
  See also
  --------
  PCA
  
  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
  
  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.
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docstr: Fit LSI model on training data X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      Training data.
  session : session to run
  
  y : Ignored
  
  Returns
  -------
  self : object
      Returns the transformer object.
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code: def fit(self, X, y=None, session=None):
      """Fit LSI model on training data X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          Training data.
      session : session to run
  
      y : Ignored
  
      Returns
      -------
      self : object
          Returns the transformer object.
      """
      self.fit_transform(X, session=session)
      return self
  
file_path: mars/mars/learn/decomposition/_truncated_svd.py
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from ...None import tensor
  from ...tensor.linalg import randomized_svd
  from ...tensor.utils import check_random_state
  from ...core import ExecutableTuple
  from ..utils import check_array
label_desc: Use image node_id 2 for calling the TruncatedSVD obj's underlying member method code with example usage: obj.fit(X, y, session) and returns: self