text: from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
      UniformIntegerHyperparameter, CategoricalHyperparameter
  
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE
  
  
  class AdaboostRegressor(AutoSklearnRegressionAlgorithm):
      def __init__(self, n_estimators, learning_rate, loss, max_depth, random_state=None):
          self.n_estimators = n_estimators
          self.learning_rate = learning_rate
          self.loss = loss
          self.random_state = random_state
          self.max_depth = max_depth
          self.estimator = None
  
      def fit(self, X, y):
          import sklearn.ensemble
          import sklearn.tree
  
          self.n_estimators = int(self.n_estimators)
          self.learning_rate = float(self.learning_rate)
          self.max_depth = int(self.max_depth)
          base_estimator = sklearn.tree.DecisionTreeRegressor(
              max_depth=self.max_depth)
  
          self.estimator = sklearn.ensemble.AdaBoostRegressor(
              base_estimator=base_estimator,
              n_estimators=self.n_estimators,
              learning_rate=self.learning_rate,
              loss=self.loss,
              random_state=self.random_state
          )
  
          if y.ndim == 2 and y.shape[1] == 1:
              y = y.flatten()
  
          self.estimator.fit(X, y)
          return self
  
      def predict(self, X):
          if self.estimator is None:
              raise NotImplementedError
          return self.estimator.predict(X)
  
      @staticmethod
      def get_properties(dataset_properties=None):
          return {'shortname': 'AB',
                  'name': 'AdaBoost Regressor',
                  'handles_regression': True,
                  'handles_classification': False,
                  'handles_multiclass': False,
                  'handles_multilabel': False,
                  'handles_multioutput': False,
                  'is_deterministic': True,
                  'input': (DENSE, SPARSE, UNSIGNED_DATA),
                  'output': (PREDICTIONS, )}
  
      @staticmethod
      def get_hyperparameter_search_space(dataset_properties=None):
          cs = ConfigurationSpace()
  
          # base_estimator = Constant(name="base_estimator", value="None")
          n_estimators = UniformIntegerHyperparameter(
              name="n_estimators", lower=50, upper=500, default_value=50,
              log=False)
          learning_rate = UniformFloatHyperparameter(
              name="learning_rate", lower=0.01, upper=2, default_value=0.1,
              log=True)
          loss = CategoricalHyperparameter(
              name="loss", choices=["linear", "square", "exponential"],
              default_value="linear")
          max_depth = UniformIntegerHyperparameter(
              name="max_depth", lower=1, upper=10, default_value=1, log=False)
  
          cs.add_hyperparameters([n_estimators, learning_rate, loss, max_depth])
          return cs
  