class_docstr: Cox Proportional Hazards
  
  Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset.
class_docstr_tok: ["Cox","Proportional","Hazards","Trains","a","Cox","Proportional","Hazards","Model","(","CoxPH",")","on","an","H2O","dataset","."]
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 start_column: Start Time Column.
         Defaults to ``None``.
  :type start_column: str, optional
  :param stop_column: Stop Time Column.
         Defaults to ``None``.
  :type stop_column: str, 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 weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent
         to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating
         that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do
         not increase the size of the data frame. This is typically the number of times a row is repeated, but
         non-integer values are supported as well. During training, rows with higher weights matter more, due to
         the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at
         that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
         Defaults to ``None``.
  :type weights_column: str, optional
  :param offset_column: Offset column. This will be added to the combination of columns before applying the link
         function.
         Defaults to ``None``.
  :type offset_column: str, optional
  :param stratify_by: List of columns to use for stratification.
         Defaults to ``None``.
  :type stratify_by: List[str], optional
  :param ties: Method for Handling Ties.
         Defaults to ``"efron"``.
  :type ties: Literal["efron", "breslow"]
  :param init: Coefficient starting value.
         Defaults to ``0.0``.
  :type init: float
  :param lre_min: Minimum log-relative error.
         Defaults to ``9.0``.
  :type lre_min: float
  :param max_iterations: Maximum number of iterations.
         Defaults to ``20``.
  :type max_iterations: int
  :param interactions: A list of predictor column indices to interact. All pairwise combinations will be computed
         for the list.
         Defaults to ``None``.
  :type interactions: List[str], optional
  :param interaction_pairs: A list of pairwise (first order) column interactions.
         Defaults to ``None``.
  :type interaction_pairs: List[tuple], optional
  :param interactions_only: A list of columns that should only be used to create interactions but should not
         itself participate in model training.
         Defaults to ``None``.
  :type interactions_only: List[str], optional
  :param use_all_factor_levels: (Internal. For development only!) Indicates whether to use all factor levels.
         Defaults to ``False``.
  :type use_all_factor_levels: bool
  :param export_checkpoints_dir: Automatically export generated models to this directory.
         Defaults to ``None``.
  :type export_checkpoints_dir: str, optional
  :param single_node_mode: Run on a single node to reduce the effect of network overhead (for smaller datasets)
         Defaults to ``False``.
  :type single_node_mode: bool
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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]]
      start_column=None,  # type: Optional[str]
      stop_column=None,  # type: Optional[str]
      response_column=None,  # type: Optional[str]
      ignored_columns=None,  # type: Optional[List[str]]
      weights_column=None,  # type: Optional[str]
      offset_column=None,  # type: Optional[str]
      stratify_by=None,  # type: Optional[List[str]]
      ties="efron",  # type: Literal["efron", "breslow"]
      init=0.0,  # type: float
      lre_min=9.0,  # type: float
      max_iterations=20,  # type: int
      interactions=None,  # type: Optional[List[str]]
      interaction_pairs=None,  # type: Optional[List[tuple]]
      interactions_only=None,  # type: Optional[List[str]]
      use_all_factor_levels=False,  # type: bool
      export_checkpoints_dir=None,  # type: Optional[str]
      single_node_mode=False,  # type: bool
  ):
      """
      :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 start_column: Start Time Column.
             Defaults to ``None``.
      :type start_column: str, optional
      :param stop_column: Stop Time Column.
             Defaults to ``None``.
      :type stop_column: str, 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 weights_column: Column with observation weights. Giving some observation a weight of zero is equivalent
             to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating
             that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do
             not increase the size of the data frame. This is typically the number of times a row is repeated, but
             non-integer values are supported as well. During training, rows with higher weights matter more, due to
             the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at
             that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
             Defaults to ``None``.
      :type weights_column: str, optional
      :param offset_column: Offset column. This will be added to the combination of columns before applying the link
             function.
             Defaults to ``None``.
      :type offset_column: str, optional
      :param stratify_by: List of columns to use for stratification.
             Defaults to ``None``.
      :type stratify_by: List[str], optional
      :param ties: Method for Handling Ties.
             Defaults to ``"efron"``.
      :type ties: Literal["efron", "breslow"]
      :param init: Coefficient starting value.
             Defaults to ``0.0``.
      :type init: float
      :param lre_min: Minimum log-relative error.
             Defaults to ``9.0``.
      :type lre_min: float
      :param max_iterations: Maximum number of iterations.
             Defaults to ``20``.
      :type max_iterations: int
      :param interactions: A list of predictor column indices to interact. All pairwise combinations will be computed
             for the list.
             Defaults to ``None``.
      :type interactions: List[str], optional
      :param interaction_pairs: A list of pairwise (first order) column interactions.
             Defaults to ``None``.
      :type interaction_pairs: List[tuple], optional
      :param interactions_only: A list of columns that should only be used to create interactions but should not
             itself participate in model training.
             Defaults to ``None``.
      :type interactions_only: List[str], optional
      :param use_all_factor_levels: (Internal. For development only!) Indicates whether to use all factor levels.
             Defaults to ``False``.
      :type use_all_factor_levels: bool
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      :param single_node_mode: Run on a single node to reduce the effect of network overhead (for smaller datasets)
             Defaults to ``False``.
      :type single_node_mode: bool
      """
      super(H2OCoxProportionalHazardsEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.start_column = start_column
      self.stop_column = stop_column
      self.response_column = response_column
      self.ignored_columns = ignored_columns
      self.weights_column = weights_column
      self.offset_column = offset_column
      self.stratify_by = stratify_by
      self.ties = ties
      self.init = init
      self.lre_min = lre_min
      self.max_iterations = max_iterations
      self.interactions = interactions
      self.interaction_pairs = interaction_pairs
      self.interactions_only = interactions_only
      self.use_all_factor_levels = use_all_factor_levels
      self.export_checkpoints_dir = export_checkpoints_dir
      self.single_node_mode = single_node_mode
  
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 H2OCoxProportionalHazardsEstimator object from inherited base classes: H2OEstimator with example: obj = H2OCoxProportionalHazardsEstimator(model_id, training_frame, start_column, stop_column, response_column, ignored_columns, weights_column, offset_column, stratify_by, ties, init, lre_min, max_iterations, interactions, interaction_pairs, interactions_only, use_all_factor_levels, export_checkpoints_dir, single_node_mode)