class_docstr: This class is available only for Uplift DRF model.
  This class is essentially an API for the AUUC object.
docstr: Retrieve uplift values for each bin. 
  
  :param metric: AUUC metric type. One of:
  
      - "qini"
      - "lift"
      - "gain"
      - "AUTO" (default; defaults to "qini") 
  
  :returns: a list of uplift values.
  
  :examples:
  
  >>> from h2o.estimators import H2OUpliftRandomForestEstimator
  >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv")
  >>> treatment_column = "treatment"
  >>> response_column = "conversion"
  >>> train[treatment_column] = train[treatment_column].asfactor()
  >>> train[response_column] = train[response_column].asfactor()
  >>> predictors = ["f1", "f2", "f3", "f4", "f5", "f6"]
  >>>
  >>> uplift_model = H2OUpliftRandomForestEstimator(ntrees=10, 
  ...                                               max_depth=5,
  ...                                               treatment_column=treatment_column,
  ...                                               uplift_metric="kl",
  ...                                               distribution="bernoulli",
  ...                                               min_rows=10,
  ...                                               auuc_type="gain")
  >>> uplift_model.train(y=response_column, x=predictors, training_frame=train)
  >>> perf = uplift_model.model_performance()
  >>> perf.uplift()
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code: def uplift(self, metric="AUTO"):
      """
      Retrieve uplift values for each bin.
  
      :param metric: AUUC metric type. One of:
  
          - "qini"
          - "lift"
          - "gain"
          - "AUTO" (default; defaults to "qini")
  
      :returns: a list of uplift values.
  
      :examples:
  
      >>> from h2o.estimators import H2OUpliftRandomForestEstimator
      >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv")
      >>> treatment_column = "treatment"
      >>> response_column = "conversion"
      >>> train[treatment_column] = train[treatment_column].asfactor()
      >>> train[response_column] = train[response_column].asfactor()
      >>> predictors = ["f1", "f2", "f3", "f4", "f5", "f6"]
      >>>
      >>> uplift_model = H2OUpliftRandomForestEstimator(ntrees=10,
      ...                                               max_depth=5,
      ...                                               treatment_column=treatment_column,
      ...                                               uplift_metric="kl",
      ...                                               distribution="bernoulli",
      ...                                               min_rows=10,
      ...                                               auuc_type="gain")
      >>> uplift_model.train(y=response_column, x=predictors, training_frame=train)
      >>> perf = uplift_model.model_performance()
      >>> perf.uplift()
      """
      assert metric in ["AUTO", "qini", "lift", "gain"]
      if metric == "AUTO":
          metric = "qini"
      return self._metric_json["thresholds_and_metric_scores"][metric]
  
imports: import h2o
  from h2o.model import MetricsBase
  from h2o.plot import get_matplotlib_pyplot
label_desc: Use image node_id 9 for calling the H2OBinomialUpliftModelMetrics obj's underlying member method code with example usage: obj.uplift(metric) and returns: self