class_docstr: Aggregator
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 response_column: Response variable column.
         Defaults to ``None``.
  :type response_column: str, 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 target_num_exemplars: Targeted number of exemplars
         Defaults to ``5000``.
  :type target_num_exemplars: int
  :param rel_tol_num_exemplars: Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents)
         Defaults to ``0.5``.
  :type rel_tol_num_exemplars: float
  :param transform: Transformation of training data
         Defaults to ``"normalize"``.
  :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
  :param categorical_encoding: Encoding scheme for categorical features
         Defaults to ``"auto"``.
  :type categorical_encoding: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
         "sort_by_response", "enum_limited"]
  :param save_mapping_frame: Whether to export the mapping of the aggregated frame
         Defaults to ``False``.
  :type save_mapping_frame: bool
  :param num_iteration_without_new_exemplar: The number of iterations to run before aggregator exits if the number
         of exemplars collected didn't change
         Defaults to ``500``.
  :type num_iteration_without_new_exemplar: int
  :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]]
      response_column=None,  # type: Optional[str]
      ignored_columns=None,  # type: Optional[List[str]]
      ignore_const_cols=True,  # type: bool
      target_num_exemplars=5000,  # type: int
      rel_tol_num_exemplars=0.5,  # type: float
      transform="normalize",  # type: Literal["none", "standardize", "normalize", "demean", "descale"]
      categorical_encoding="auto",  # type: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
      save_mapping_frame=False,  # type: bool
      num_iteration_without_new_exemplar=500,  # type: int
      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 response_column: Response variable column.
             Defaults to ``None``.
      :type response_column: str, 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 target_num_exemplars: Targeted number of exemplars
             Defaults to ``5000``.
      :type target_num_exemplars: int
      :param rel_tol_num_exemplars: Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents)
             Defaults to ``0.5``.
      :type rel_tol_num_exemplars: float
      :param transform: Transformation of training data
             Defaults to ``"normalize"``.
      :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
      :param categorical_encoding: Encoding scheme for categorical features
             Defaults to ``"auto"``.
      :type categorical_encoding: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
             "sort_by_response", "enum_limited"]
      :param save_mapping_frame: Whether to export the mapping of the aggregated frame
             Defaults to ``False``.
      :type save_mapping_frame: bool
      :param num_iteration_without_new_exemplar: The number of iterations to run before aggregator exits if the number
             of exemplars collected didn't change
             Defaults to ``500``.
      :type num_iteration_without_new_exemplar: int
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      """
      super(H2OAggregatorEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.response_column = response_column
      self.ignored_columns = ignored_columns
      self.ignore_const_cols = ignore_const_cols
      self.target_num_exemplars = target_num_exemplars
      self.rel_tol_num_exemplars = rel_tol_num_exemplars
      self.transform = transform
      self.categorical_encoding = categorical_encoding
      self.save_mapping_frame = save_mapping_frame
      self.num_iteration_without_new_exemplar = (
          num_iteration_without_new_exemplar
      )
      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 H2OAggregatorEstimator object from inherited base classes: H2OEstimator with example: obj = H2OAggregatorEstimator(model_id, training_frame, response_column, ignored_columns, ignore_const_cols, target_num_exemplars, rel_tol_num_exemplars, transform, categorical_encoding, save_mapping_frame, num_iteration_without_new_exemplar, export_checkpoints_dir)