content: import os
  import random
  import shutil
  
  import numpy as np
  
  import autosklearn.automl
  from autosklearn.constants import *
  
  
  class AutoSklearnClassifier(autosklearn.automl.AutoML):
      """This class implements the classification task. It must not be pickled!
  
      Parameters
      ----------
      time_left_for_this_task : int, optional (default=3600)
          Time limit in seconds for the search for appropriate classification
          models. By increasing this value, *auto-sklearn* will find better
          configurations.
  
      per_run_time_limit : int, optional (default=360)
          Time limit for a single call to machine learning model.
  
      initial_configurations_via_metalearning : int, optional (default=25)
  
      ensemble_size : int, optional (default=50)
  
      ensemble_nbest : int, optional (default=50)
  
      seed : int, optional (default=1)
  
      ml_memory_limit : int, optional (3000)
          Memory limit for the machine learning algorithm. If the machine
          learning algorithm allocates tries to allocate more memory,
          its evaluation will be stopped.
      """
  
      def __init__(self, time_left_for_this_task=3600,
                   per_run_time_limit=360,
                   initial_configurations_via_metalearning=25,
                   ensemble_size=50, ensemble_nbest=50, seed=1,
                   ml_memory_limit=3000):
          random_number = random.randint(0, 10000)
  
          pid = os.getpid()
          output_dir = "/tmp/autosklearn_output_%d_%d" % (pid, random_number)
          tmp_dir = "/tmp/autosklearn_tmp_%d_%d" % (pid, random_number)
          os.makedirs(output_dir)
          os.makedirs(tmp_dir)
  
          super(AutoSklearnClassifier, self).__init__(
              tmp_dir, output_dir, time_left_for_this_task, per_run_time_limit,
              log_dir=tmp_dir,
              initial_configurations_via_metalearning=initial_configurations_via_metalearning,
              ensemble_size=ensemble_size, ensemble_nbest=ensemble_nbest,
              seed=seed, ml_memory_limit=ml_memory_limit)
  
      def __del__(self):
          self._delete_output_directories()
  
      def _create_output_directories(self):
          os.makedirs(self.output_dir)
          os.makedirs(self.tmp_dir)
  
      def _delete_output_directories(self):
          shutil.rmtree(self.tmp_dir)
          shutil.rmtree(self.output_dir)
  
      def fit(self, X, y, metric='acc_metric', feat_type=None):
          """Fit *autosklearn* to given training set (X, y).
  
          X : array-like or sparse matrix of shape = [n_samples, n_features]
              The training input samples.
  
          y : array-like, shape = [n_samples] or [n_samples, n_outputs]
              The target classes.
  
          metric : str, optional (default='acc_metric')
              The metric to optimize for. Can be one of: ['acc_metric',
              'auc_metric', 'bac_metric', 'f1_metric', 'pac_metric']
  
          feat_type : list, optional (default=None)
              List of :python:`len(X.shape[1])` describing if an attribute is
              continuous or categorical. Categorical attributes will
              automatically 1Hot encoded.
          """
          # Fit is supposed to be idempotent!
          self._delete_output_directories()
          self._create_output_directories()
  
          y = np.atleast_1d(y)
  
          if y.ndim == 1:
              # reshape is necessary to preserve the data contiguity against vs
              # [:, np.newaxis] that does not.
              y = np.reshape(y, (-1, 1))
  
          self.n_outputs_ = y.shape[1]
  
          y = np.copy(y)
  
          self.classes_ = []
          self.n_classes_ = []
  
          for k in xrange(self.n_outputs_):
              classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True)
              self.classes_.append(classes_k)
              self.n_classes_.append(classes_k.shape[0])
  
          self.n_classes_ = np.array(self.n_classes_, dtype=np.int)
  
          if self.n_outputs_ > 1:
              task = MULTILABEL_CLASSIFICATION
          else:
              if len(self.classes_[0]) == 2:
                  task = BINARY_CLASSIFICATION
              else:
                  task = MULTICLASS_CLASSIFICATION
  
          # TODO: fix metafeatures calculation to allow this!
          if y.shape[1] == 1:
              y = y.flatten()
  
          return super(AutoSklearnClassifier, self).fit(X, y, task, metric,
                                                     feat_type)
  
      def predict(self, X):
          """Predict class for X.
  
          Parameters
          ----------
          X : array-like or sparse matrix of shape = [n_samples, n_features]
  
          Returns
          -------
          y : array of shape = [n_samples] or [n_samples, n_outputs]
              The predicted classes.
          """
          return super(AutoSklearnClassifier, self).predict(X)
  
  
  class AutoSklearnRegressor(autosklearn.automl.AutoML):
      def __init__(self, **kwargs):
          raise NotImplementedError()