old_contents: from operator import itemgetter
  
  import numpy as np
  import sklearn.cross_validation
  import sklearn.datasets
  import sklearn.metrics
  
  import autosklearn.classification
  
  
  # Utility function to report best scores
  # from http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py
  def report(grid_scores, n_top=3):
      top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]
      for i, score in enumerate(top_scores):
          print("Model with rank: {0}".format(i + 1))
          print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
              score.mean_validation_score,
              np.std(score.cv_validation_scores)))
          print("Parameters: {0}".format(score.parameters))
          print("")
  
  
  def main():
      digits = sklearn.datasets.load_digits()
      X = digits.data
      y = digits.target
      X_train, X_test, y_train, y_test = \
          sklearn.cross_validation.train_test_split(X, y, random_state=1)
  
      automl = autosklearn.classification.AutoSklearnClassifier(
          time_left_for_this_task=120, per_run_time_limit=30,
          tmp_folder='/tmp/autoslearn_holdout_example_tmp',
          output_folder='/tmp/autosklearn_holdout_example_out',
      automl.fit(X_train, y_train, dataset_name='digits')
  
      # Print the best models together with their scores - if all scores are
      # unreasonably bad (around 0.0) you should have a look into the logging
      # file to figure out the error
      report(automl.grid_scores_)
      # Print the final ensemble constructed by auto-sklearn.
      print(automl.show_models())
      predictions = automl.predict(X_test)
      # Print statistics about the auto-sklearn run such as number of
      # iterations, number of models failed with a time out.
      print(automl.sprint_statistics())
      print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
  
  
  if __name__ == '__main__':
      main()
  
new_contents: from operator import itemgetter
  
  import numpy as np
  import sklearn.cross_validation
  import sklearn.datasets
  import sklearn.metrics
  
  import autosklearn.classification
  
  
  # Utility function to report best scores
  # from http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py
  def report(grid_scores, n_top=3):
      top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]
      for i, score in enumerate(top_scores):
          print("Model with rank: {0}".format(i + 1))
          print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
              score.mean_validation_score,
              np.std(score.cv_validation_scores)))
          print("Parameters: {0}".format(score.parameters))
          print("")
  
  
  def main():
      digits = sklearn.datasets.load_digits()
      X = digits.data
      y = digits.target
      X_train, X_test, y_train, y_test = \
          sklearn.cross_validation.train_test_split(X, y, random_state=1)
  
      automl = autosklearn.classification.AutoSklearnClassifier(
          time_left_for_this_task=120, per_run_time_limit=30,
          tmp_folder='/tmp/autoslearn_holdout_example_tmp',
          output_folder='/tmp/autosklearn_holdout_example_out')
      automl.fit(X_train, y_train, dataset_name='digits')
  
      # Print the best models together with their scores - if all scores are
      # unreasonably bad (around 0.0) you should have a look into the logging
      # file to figure out the error
      report(automl.grid_scores_)
      # Print the final ensemble constructed by auto-sklearn.
      print(automl.show_models())
      predictions = automl.predict(X_test)
      # Print statistics about the auto-sklearn run such as number of
      # iterations, number of models failed with a time out.
      print(automl.sprint_statistics())
      print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
  
  
  if __name__ == '__main__':
      main()
  
subject: FIX syntax error in example