class_docstr: Aggregator
docstr: Automatically export generated models to this directory.
  
  Type: ``str``.
  
  :examples:
  
  >>> import tempfile
  >>> from os import listdir
  >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
  >>> checkpoints_dir = tempfile.mkdtemp()
  >>> model = H2OAggregatorEstimator(target_num_exemplars=500, 
  ...                                rel_tol_num_exemplars=0.3,
  ...                                export_checkpoints_dir=checkpoints_dir)
  >>> model.train(training_frame=df)
  >>> new_df = model.aggregated_frame
  >>> new_df
  >>> len(listdir(checkpoints_dir))
docstr_tok: ["Automatically","export","generated","models","to","this","directory",".","Type",":","``","str","``",".",":","examples",":",">",">",">","import","tempfile",">",">",">","from","os","import","listdir",">",">",">","df","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/airlines\/allyears2k_headers.zip","''",")",">",">",">","checkpoints_dir","=","tempfile.mkdtemp","(",")",">",">",">","model","=","H2OAggregatorEstimator","(","target_num_exemplars=500",",","...","rel_tol_num_exemplars=0.3",",","...","export_checkpoints_dir=checkpoints_dir",")",">",">",">","model.train","(","training_frame=df",")",">",">",">","new_df","=","model.aggregated_frame",">",">",">","new_df",">",">",">","len","(","listdir","(","checkpoints_dir",")",")"]
code: def export_checkpoints_dir(self):
      """
      Automatically export generated models to this directory.
  
      Type: ``str``.
  
      :examples:
  
      >>> import tempfile
      >>> from os import listdir
      >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
      >>> checkpoints_dir = tempfile.mkdtemp()
      >>> model = H2OAggregatorEstimator(target_num_exemplars=500,
      ...                                rel_tol_num_exemplars=0.3,
      ...                                export_checkpoints_dir=checkpoints_dir)
      >>> model.train(training_frame=df)
      >>> new_df = model.aggregated_frame
      >>> new_df
      >>> len(listdir(checkpoints_dir))
      """
      return self._parms.get("export_checkpoints_dir")
  
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 22 for calling the H2OAggregatorEstimator obj's underlying member method code with example usage: obj.export_checkpoints_dir() and returns: self