parameters: (self,
                 learner_config,
                 examples_per_layer,
                 head,
                 ranking_model_pair_keys,
                 num_trees=None,
                 feature_columns=None,
                 weight_column_name=None,
                 model_dir=None,
                 config=None,
                 label_keys=None,
                 logits_modifier_function=None,
                 center_bias=False,
                 output_leaf_index=False,
                 num_quantiles=100)
docstring: Initializes a GradientBoostedDecisionTreeRanker instance.
  
      This is an estimator that can be trained off the pairwise data and can be
      used for inference on non-paired data. This is essentially LambdaMart.
      Args:
        learner_config: A config for the learner.
        examples_per_layer: Number of examples to accumulate before growing a
          layer. It can also be a function that computes the number of examples
          based on the depth of the layer that's being built.
        head: `Head` instance.
        ranking_model_pair_keys: Keys to distinguish between features for left and
          right part of the training pairs for ranking. For example, for an
          Example with features "a.f1" and "b.f1", the keys would be ("a", "b").
        num_trees: An int, number of trees to build.
        feature_columns: A list of feature columns.
        weight_column_name: Name of the column for weights, or None if not
          weighted.
        model_dir: Directory for model exports, etc.
        config: `RunConfig` object to configure the runtime settings.
        label_keys: Optional list of strings with size `[n_classes]` defining the
          label vocabulary. Only supported for `n_classes` > 2.
        logits_modifier_function: A modifier function for the logits.
        center_bias: Whether a separate tree should be created for first fitting
          the bias.
        output_leaf_index: whether to output leaf indices along with predictions
          during inference. The leaf node indexes are available in predictions
          dict by the key 'leaf_index'. It is a Tensor of rank 2 and its shape is
          [batch_size, num_trees]. For example, result_iter =
          classifier.predict(...)
          for result_dict in result_iter: # access leaf index list by
            result_dict["leaf_index"] # which contains one leaf index per tree
        num_quantiles: Number of quantiles to build for numeric feature values.
  
      Raises:
        ValueError: If learner_config is not valid.
docstring_summary: Initializes a GradientBoostedDecisionTreeRanker instance.
docstring_tokens: Initializes a GradientBoostedDecisionTreeRanker instance .
function: def __init__(self,
                 learner_config,
                 examples_per_layer,
                 head,
                 ranking_model_pair_keys,
                 num_trees=None,
                 feature_columns=None,
                 weight_column_name=None,
                 model_dir=None,
                 config=None,
                 label_keys=None,
                 logits_modifier_function=None,
                 center_bias=False,
                 output_leaf_index=False,
                 num_quantiles=100):
      """Initializes a GradientBoostedDecisionTreeRanker instance.
  
      This is an estimator that can be trained off the pairwise data and can be
      used for inference on non-paired data. This is essentially LambdaMart.
      Args:
        learner_config: A config for the learner.
        examples_per_layer: Number of examples to accumulate before growing a
          layer. It can also be a function that computes the number of examples
          based on the depth of the layer that's being built.
        head: `Head` instance.
        ranking_model_pair_keys: Keys to distinguish between features for left and
          right part of the training pairs for ranking. For example, for an
          Example with features "a.f1" and "b.f1", the keys would be ("a", "b").
        num_trees: An int, number of trees to build.
        feature_columns: A list of feature columns.
        weight_column_name: Name of the column for weights, or None if not
          weighted.
        model_dir: Directory for model exports, etc.
        config: `RunConfig` object to configure the runtime settings.
        label_keys: Optional list of strings with size `[n_classes]` defining the
          label vocabulary. Only supported for `n_classes` > 2.
        logits_modifier_function: A modifier function for the logits.
        center_bias: Whether a separate tree should be created for first fitting
          the bias.
        output_leaf_index: whether to output leaf indices along with predictions
          during inference. The leaf node indexes are available in predictions
          dict by the key 'leaf_index'. It is a Tensor of rank 2 and its shape is
          [batch_size, num_trees]. For example, result_iter =
          classifier.predict(...)
          for result_dict in result_iter: # access leaf index list by
            result_dict["leaf_index"] # which contains one leaf index per tree
        num_quantiles: Number of quantiles to build for numeric feature values.
  
      Raises:
        ValueError: If learner_config is not valid.
      """
  
      def _model_fn(features, labels, mode, config):
        return model.ranking_model_builder(
            features=features,
            labels=labels,
            mode=mode,
            config=config,
            params={
                'head': head,
                'n_classes': 2,
                'feature_columns': feature_columns,
                'learner_config': learner_config,
                'num_trees': num_trees,
                'weight_column_name': weight_column_name,
                'examples_per_layer': examples_per_layer,
                'center_bias': center_bias,
                'logits_modifier_function': logits_modifier_function,
                'use_core_libs': True,
                'output_leaf_index': output_leaf_index,
                'ranking_model_pair_keys': ranking_model_pair_keys,
                'override_global_step_value': None,
                'num_quantiles': num_quantiles,
            },
            output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC)
  
      super(CoreGradientBoostedDecisionTreeRanker, self).__init__(
          model_fn=_model_fn, model_dir=model_dir, config=config)
function_tokens: def __init__ ( self , learner_config , examples_per_layer , head , ranking_model_pair_keys , num_trees = None , feature_columns = None , weight_column_name = None , model_dir = None , config = None , label_keys = None , logits_modifier_function = None , center_bias = False , output_leaf_index = False , num_quantiles = 100 ) : def _model_fn ( features , labels , mode , config ) : return model . ranking_model_builder ( features = features , labels = labels , mode = mode , config = config , params = { 'head' : head , 'n_classes' : 2 , 'feature_columns' : feature_columns , 'learner_config' : learner_config , 'num_trees' : num_trees , 'weight_column_name' : weight_column_name , 'examples_per_layer' : examples_per_layer , 'center_bias' : center_bias , 'logits_modifier_function' : logits_modifier_function , 'use_core_libs' : True , 'output_leaf_index' : output_leaf_index , 'ranking_model_pair_keys' : ranking_model_pair_keys , 'override_global_step_value' : None , 'num_quantiles' : num_quantiles , } , output_type = model . ModelBuilderOutputType . ESTIMATOR_SPEC ) super ( CoreGradientBoostedDecisionTreeRanker , self ) . __init__ ( model_fn = _model_fn , model_dir = model_dir , config = config )