docstr: Automated xgboost classifier
  
  :param logs_dir: Local directory to save logs and results. It defaults to
      "/tmp/auto_xgb_classifier_logs"
  :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
      The value will also be assigned to n_jobs in xgboost,
      which is the number of parallel threads used to run xgboost.
  :param name: Name of the auto xgboost classifier.
  :param xgb_configs: Other scikit learn xgboost parameters. You may refer to
     https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn
     for the parameter names to specify. Note that we will directly use cpus_per_trial value
     for n_jobs in xgboost and you shouldn't specify n_jobs again.
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code: def __init__(
      self,
      logs_dir="/tmp/auto_xgb_classifier_logs",
      cpus_per_trial=1,
      name=None,
      **xgb_configs,
  ):
      """
      Automated xgboost classifier
  
      :param logs_dir: Local directory to save logs and results. It defaults to
          "/tmp/auto_xgb_classifier_logs"
      :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
          The value will also be assigned to n_jobs in xgboost,
          which is the number of parallel threads used to run xgboost.
      :param name: Name of the auto xgboost classifier.
      :param xgb_configs: Other scikit learn xgboost parameters. You may refer to
         https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn
         for the parameter names to specify. Note that we will directly use cpus_per_trial value
         for n_jobs in xgboost and you shouldn't specify n_jobs again.
      """
      xgb_model_builder = XGBoostModelBuilder(
          model_type="classifier",
          cpus_per_trial=cpus_per_trial,
          **xgb_configs,
      )
      resources_per_trial = (
          {"cpu": cpus_per_trial} if cpus_per_trial else None
      )
      super().__init__(
          model_builder=xgb_model_builder,
          logs_dir=logs_dir,
          resources_per_trial=resources_per_trial,
          name=name,
      )
  
file_path: analytics-zoo/pyzoo/zoo/orca/automl/xgboost/auto_xgb.py
imports: from zoo.orca.automl.xgboost.XGBoost import XGBoostModelBuilder
  from zoo.orca.automl.auto_estimator import AutoEstimator
label_desc: Use image node_id 1 to create a new AutoXGBClassifier object from inherited base classes: AutoEstimator with example: obj = AutoXGBClassifier(logs_dir, cpus_per_trial, name)