parameters: (self,
                 example_id_column,
                 feature_columns,
                 weight_column_name=None,
                 model_dir=None,
                 head=None,
                 l1_regularization=0.0,
                 l2_regularization=1.0,
                 num_loss_partitions=None,
                 config=None,
                 feature_engineering_fn=None)
docstring: Construct a `_SDCAEstimator` estimator object.
  
      Args:
        example_id_column: A string defining the feature column name representing
          example ids. Used to initialize the underlying SDCA optimizer.
        feature_columns: An iterable containing all the feature columns used by
          the model. All items in the set should be instances of classes derived
          from `FeatureColumn`.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        model_dir: Directory to save model parameters, graph etc. This can also be
          used to load checkpoints from the directory into an estimator to
          continue  training a previously saved model.
        head: type of head. Currently, _BinaryLogisticHead and _BinarySvmHead are
          supported for classification and _RegressionHead for regression. It
          should be a subclass of _SingleHead.
        l1_regularization: L1-regularization parameter. Refers to global L1
          regularization (across all examples).
        l2_regularization: L2-regularization parameter. Refers to global L2
          regularization (across all examples).
        num_loss_partitions: number of partitions of the (global) loss function
          optimized by the underlying optimizer (SDCAOptimizer).
        config: `RunConfig` object to configure the runtime settings.
        feature_engineering_fn: Feature engineering function. Takes features and
          labels which are the output of `input_fn` and returns features and
          labels which will be fed into the model.
  
      Returns:
        A `_SDCAEstimator` estimator.
  
      Raises:
        ValueError: if head is not supported by SDCA.
docstring_summary: Construct a `_SDCAEstimator` estimator object.
docstring_tokens: Construct a _SDCAEstimator estimator object .
function: def __init__(self,
                 example_id_column,
                 feature_columns,
                 weight_column_name=None,
                 model_dir=None,
                 head=None,
                 l1_regularization=0.0,
                 l2_regularization=1.0,
                 num_loss_partitions=None,
                 config=None,
                 feature_engineering_fn=None):
      """Construct a `_SDCAEstimator` estimator object.
  
      Args:
        example_id_column: A string defining the feature column name representing
          example ids. Used to initialize the underlying SDCA optimizer.
        feature_columns: An iterable containing all the feature columns used by
          the model. All items in the set should be instances of classes derived
          from `FeatureColumn`.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        model_dir: Directory to save model parameters, graph etc. This can also be
          used to load checkpoints from the directory into an estimator to
          continue  training a previously saved model.
        head: type of head. Currently, _BinaryLogisticHead and _BinarySvmHead are
          supported for classification and _RegressionHead for regression. It
          should be a subclass of _SingleHead.
        l1_regularization: L1-regularization parameter. Refers to global L1
          regularization (across all examples).
        l2_regularization: L2-regularization parameter. Refers to global L2
          regularization (across all examples).
        num_loss_partitions: number of partitions of the (global) loss function
          optimized by the underlying optimizer (SDCAOptimizer).
        config: `RunConfig` object to configure the runtime settings.
        feature_engineering_fn: Feature engineering function. Takes features and
          labels which are the output of `input_fn` and returns features and
          labels which will be fed into the model.
  
      Returns:
        A `_SDCAEstimator` estimator.
  
      Raises:
        ValueError: if head is not supported by SDCA.
      """
      self._feature_columns = tuple(feature_columns or [])
      assert self._feature_columns
  
      if not _head_is_valid_for_sdca(head):
        raise ValueError(
            "head type: {} is not supported. Supported head types: "
            "_BinaryLogisticHead, _BinarySvmHead and _RegressionHead.".format(
                type(head)))
      assert head.logits_dimension == 1
  
      params = {
          "head": head,
          "feature_columns": feature_columns,
          "example_id_column": example_id_column,
          "num_loss_partitions": num_loss_partitions,
          "l1_regularization": l1_regularization,
          "l2_regularization": l2_regularization,
          "weight_column_name": weight_column_name,
          "update_weights_hook": _SdcaUpdateWeightsHook(),
      }
  
      super(_SDCAEstimator, self).__init__(
          model_fn=sdca_model_fn,
          model_dir=model_dir,
          config=config,
          params=params,
          feature_engineering_fn=feature_engineering_fn)
function_tokens: def __init__ ( self , example_id_column , feature_columns , weight_column_name = None , model_dir = None , head = None , l1_regularization = 0.0 , l2_regularization = 1.0 , num_loss_partitions = None , config = None , feature_engineering_fn = None ) : self . _feature_columns = tuple ( feature_columns or [ ] ) assert self . _feature_columns if not _head_is_valid_for_sdca ( head ) : raise ValueError ( "head type: {} is not supported. Supported head types: " "_BinaryLogisticHead, _BinarySvmHead and _RegressionHead." . format ( type ( head ) ) ) assert head . logits_dimension == 1 params = { "head" : head , "feature_columns" : feature_columns , "example_id_column" : example_id_column , "num_loss_partitions" : num_loss_partitions , "l1_regularization" : l1_regularization , "l2_regularization" : l2_regularization , "weight_column_name" : weight_column_name , "update_weights_hook" : _SdcaUpdateWeightsHook ( ) , } super ( _SDCAEstimator , self ) . __init__ ( model_fn = sdca_model_fn , model_dir = model_dir , config = config , params = params , feature_engineering_fn = feature_engineering_fn )