class_docstr: Singular Value Decomposition
docstr: :param model_id: Destination id for this model; auto-generated if not specified.
         Defaults to ``None``.
  :type model_id: Union[None, str, H2OEstimator], optional
  :param training_frame: Id of the training data frame.
         Defaults to ``None``.
  :type training_frame: Union[None, str, H2OFrame], optional
  :param validation_frame: Id of the validation data frame.
         Defaults to ``None``.
  :type validation_frame: Union[None, str, H2OFrame], optional
  :param ignored_columns: Names of columns to ignore for training.
         Defaults to ``None``.
  :type ignored_columns: List[str], optional
  :param ignore_const_cols: Ignore constant columns.
         Defaults to ``True``.
  :type ignore_const_cols: bool
  :param score_each_iteration: Whether to score during each iteration of model training.
         Defaults to ``False``.
  :type score_each_iteration: bool
  :param transform: Transformation of training data
         Defaults to ``"none"``.
  :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
  :param svd_method: Method for computing SVD (Caution: Randomized is currently experimental and unstable)
         Defaults to ``"gram_s_v_d"``.
  :type svd_method: Literal["gram_s_v_d", "power", "randomized"]
  :param nv: Number of right singular vectors
         Defaults to ``1``.
  :type nv: int
  :param max_iterations: Maximum iterations
         Defaults to ``1000``.
  :type max_iterations: int
  :param seed: RNG seed for k-means++ initialization
         Defaults to ``-1``.
  :type seed: int
  :param keep_u: Save left singular vectors?
         Defaults to ``True``.
  :type keep_u: bool
  :param u_name: Frame key to save left singular vectors
         Defaults to ``None``.
  :type u_name: str, optional
  :param use_all_factor_levels: Whether first factor level is included in each categorical expansion
         Defaults to ``True``.
  :type use_all_factor_levels: bool
  :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
         Defaults to ``0.0``.
  :type max_runtime_secs: float
  :param export_checkpoints_dir: Automatically export generated models to this directory.
         Defaults to ``None``.
  :type export_checkpoints_dir: str, optional
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code: def __init__(
      self,
      model_id=None,  # type: Optional[Union[None, str, H2OEstimator]]
      training_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      validation_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      ignored_columns=None,  # type: Optional[List[str]]
      ignore_const_cols=True,  # type: bool
      score_each_iteration=False,  # type: bool
      transform="none",  # type: Literal["none", "standardize", "normalize", "demean", "descale"]
      svd_method="gram_s_v_d",  # type: Literal["gram_s_v_d", "power", "randomized"]
      nv=1,  # type: int
      max_iterations=1000,  # type: int
      seed=-1,  # type: int
      keep_u=True,  # type: bool
      u_name=None,  # type: Optional[str]
      use_all_factor_levels=True,  # type: bool
      max_runtime_secs=0.0,  # type: float
      export_checkpoints_dir=None,  # type: Optional[str]
  ):
      """
      :param model_id: Destination id for this model; auto-generated if not specified.
             Defaults to ``None``.
      :type model_id: Union[None, str, H2OEstimator], optional
      :param training_frame: Id of the training data frame.
             Defaults to ``None``.
      :type training_frame: Union[None, str, H2OFrame], optional
      :param validation_frame: Id of the validation data frame.
             Defaults to ``None``.
      :type validation_frame: Union[None, str, H2OFrame], optional
      :param ignored_columns: Names of columns to ignore for training.
             Defaults to ``None``.
      :type ignored_columns: List[str], optional
      :param ignore_const_cols: Ignore constant columns.
             Defaults to ``True``.
      :type ignore_const_cols: bool
      :param score_each_iteration: Whether to score during each iteration of model training.
             Defaults to ``False``.
      :type score_each_iteration: bool
      :param transform: Transformation of training data
             Defaults to ``"none"``.
      :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
      :param svd_method: Method for computing SVD (Caution: Randomized is currently experimental and unstable)
             Defaults to ``"gram_s_v_d"``.
      :type svd_method: Literal["gram_s_v_d", "power", "randomized"]
      :param nv: Number of right singular vectors
             Defaults to ``1``.
      :type nv: int
      :param max_iterations: Maximum iterations
             Defaults to ``1000``.
      :type max_iterations: int
      :param seed: RNG seed for k-means++ initialization
             Defaults to ``-1``.
      :type seed: int
      :param keep_u: Save left singular vectors?
             Defaults to ``True``.
      :type keep_u: bool
      :param u_name: Frame key to save left singular vectors
             Defaults to ``None``.
      :type u_name: str, optional
      :param use_all_factor_levels: Whether first factor level is included in each categorical expansion
             Defaults to ``True``.
      :type use_all_factor_levels: bool
      :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
             Defaults to ``0.0``.
      :type max_runtime_secs: float
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      """
      super(H2OSingularValueDecompositionEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.validation_frame = validation_frame
      self.ignored_columns = ignored_columns
      self.ignore_const_cols = ignore_const_cols
      self.score_each_iteration = score_each_iteration
      self.transform = transform
      self.svd_method = svd_method
      self.nv = nv
      self.max_iterations = max_iterations
      self.seed = seed
      self.keep_u = keep_u
      self.u_name = u_name
      self.use_all_factor_levels = use_all_factor_levels
      self.max_runtime_secs = max_runtime_secs
      self.export_checkpoints_dir = export_checkpoints_dir
      self._parms["_rest_version"] = 99
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 1 to create a new H2OSingularValueDecompositionEstimator object from inherited base classes: H2OEstimator with example: obj = H2OSingularValueDecompositionEstimator(model_id, training_frame, validation_frame, ignored_columns, ignore_const_cols, score_each_iteration, transform, svd_method, nv, max_iterations, seed, keep_u, u_name, use_all_factor_levels, max_runtime_secs, export_checkpoints_dir)