class_docstr: Implementation of the SoftImpute algorithm from:
  "Spectral Regularization Algorithms for Learning Large Incomplete Matrices"
  by Mazumder, Hastie, and Tibshirani.
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docstr: Parameters
  ----------
  shrinkage_value : float
      Value by which we shrink singular values on each iteration. If
      omitted then the default value will be the maximum singular
      value of the initialized matrix (zeros for missing values) divided
      by 50.
  
  convergence_threshold : float
      Minimum ration difference between iterations (as a fraction of
      the Frobenius norm of the current solution) before stopping.
  
  max_iters : int
      Maximum number of SVD iterations
  
  max_rank : int, optional
      Perform a truncated SVD on each iteration with this value as its
      rank.
  
  n_power_iterations : int
      Number of power iterations to perform with randomized SVD
  
  init_fill_method : str
      How to initialize missing values of data matrix, default is
      to fill them with zeros.
  
  min_value : float
      Smallest allowable value in the solution
  
  max_value : float
      Largest allowable value in the solution
  
  normalizer : object
      Any object (such as BiScaler) with fit() and transform() methods
  
  verbose : bool
      Print debugging info
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code: def __init__(
      self,
      shrinkage_value=None,
      convergence_threshold=0.001,
      max_iters=100,
      max_rank=None,
      n_power_iterations=1,
      init_fill_method="zero",
      min_value=None,
      max_value=None,
      normalizer=None,
      verbose=True,
  ):
      """
      Parameters
      ----------
      shrinkage_value : float
          Value by which we shrink singular values on each iteration. If
          omitted then the default value will be the maximum singular
          value of the initialized matrix (zeros for missing values) divided
          by 50.
  
      convergence_threshold : float
          Minimum ration difference between iterations (as a fraction of
          the Frobenius norm of the current solution) before stopping.
  
      max_iters : int
          Maximum number of SVD iterations
  
      max_rank : int, optional
          Perform a truncated SVD on each iteration with this value as its
          rank.
  
      n_power_iterations : int
          Number of power iterations to perform with randomized SVD
  
      init_fill_method : str
          How to initialize missing values of data matrix, default is
          to fill them with zeros.
  
      min_value : float
          Smallest allowable value in the solution
  
      max_value : float
          Largest allowable value in the solution
  
      normalizer : object
          Any object (such as BiScaler) with fit() and transform() methods
  
      verbose : bool
          Print debugging info
      """
      Solver.__init__(
          self,
          fill_method=init_fill_method,
          min_value=min_value,
          max_value=max_value,
          normalizer=normalizer,
      )
      self.shrinkage_value = shrinkage_value
      self.convergence_threshold = convergence_threshold
      self.max_iters = max_iters
      self.max_rank = max_rank
      self.n_power_iterations = n_power_iterations
      self.verbose = verbose
  
file_path: fancyimpute/fancyimpute/soft_impute.py
imports: import numpy
  from sklearn.utils.extmath import randomized_svd
  from sklearn.utils import check_array
  from .common import masked_mae
  from .solver import Solver
label_desc: Use image node_id 1 to create a new SoftImpute object from inherited base classes: Solver with example: obj = SoftImpute(shrinkage_value, convergence_threshold, max_iters, max_rank, n_power_iterations, init_fill_method, min_value, max_value, normalizer, verbose)