docstr: Retrieve normalized area under uplift curve (AUUC) value for the specified metrics in model params.
  
  If all are ``False`` (default), then return the training metric normalized AUUC value.
  If more than one options is set to ``True``, then return a dictionary of metrics where the 
  keys are "train" and "valid".
  
  :param metric: AUUC metric type ("qini", "lift", "gain", default is None which means metric set in parameters)
  :param bool train: If True, return the AUUC value for the training data.
  :param bool valid: If True, return the AUUC value for the validation data.
  :param metric: AUUC metric type. One of:
  
      - "qini"
      - "lift"
      - "gain"
      - "None" (default; metric set in parameters)
  
  :returns: Normalized AUUC value for the specified key(s).
  
  :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)
  >>> uplift_model.auuc_normalized() # <- Default: return training metric value
  >>> uplift_model.auuc_normalized(train=True,  valid=True)
docstr_tok: ["Retrieve","normalized","area","under","uplift","curve","(","AUUC",")","value","for","the","specified","metrics","in","model","params",".","If","all","are","``","False","``","(","default",")",",","then","return","the","training","metric","normalized","AUUC","value",".","If","more","than","one","options","is","set","to","``","True","``",",","then","return","a","dictionary","of","metrics","where","the","keys","are","``","train","''","and","``","valid","''",".",":","param","metric",":","AUUC","metric","type","(","``","qini","''",",","``","lift","''",",","``","gain","''",",","default","is","None","which","means","metric","set","in","parameters",")",":","param","bool","train",":","If","True",",","return","the","AUUC","value","for","the","training","data",".",":","param","bool","valid",":","If","True",",","return","the","AUUC","value","for","the","validation","data",".",":","param","metric",":","AUUC","metric","type",".","One","of",":","-","``","qini","''","-","``","lift","''","-","``","gain","''","-","``","None","''","(","default",";","metric","set","in","parameters",")",":","returns",":","Normalized","AUUC","value","for","the","specified","key","(","s",")",".",":","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",")",">",">",">","uplift_model.auuc_normalized","(",")","#","<","-","Default",":","return","training","metric","value",">",">",">","uplift_model.auuc_normalized","(","train=True",",","valid=True",")"]
code: def auuc_normalized(self, metric=None, train=False, valid=False):
      """
      Retrieve normalized area under uplift curve (AUUC) value for the specified metrics in model params.
  
      If all are ``False`` (default), then return the training metric normalized AUUC value.
      If more than one options is set to ``True``, then return a dictionary of metrics where the
      keys are "train" and "valid".
  
      :param metric: AUUC metric type ("qini", "lift", "gain", default is None which means metric set in parameters)
      :param bool train: If True, return the AUUC value for the training data.
      :param bool valid: If True, return the AUUC value for the validation data.
      :param metric: AUUC metric type. One of:
  
          - "qini"
          - "lift"
          - "gain"
          - "None" (default; metric set in parameters)
  
      :returns: Normalized AUUC value for the specified key(s).
  
      :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)
      >>> uplift_model.auuc_normalized() # <- Default: return training metric value
      >>> uplift_model.auuc_normalized(train=True,  valid=True)
      """
      assert metric in [None, "qini", "lift", "gain"], (
          "AUUC metric "
          + metric
          + " should be None, 'qini','lift' or 'gain'."
      )
      return self._delegate_to_metrics(
          metric=metric,
          method="auuc_normalized",
          train=train,
          valid=valid,
      )
  
file_path: h2o-3/h2o-py/h2o/model/models/uplift.py
imports: from h2o.utils.compatibility import *
  from h2o.model import ModelBase
  from h2o.utils.metaclass import deprecated_params_order
label_desc: Use image node_id 2 for calling the H2OBinomialUpliftModel obj's underlying member method code with example usage: obj.auuc_normalized(metric, train, valid) and returns: self