new_contents: import sys
  import pandas as pd
  from sklearn.linear_model import PassiveAggressiveClassifier
  from sklearn.cross_validation import StratifiedShuffleSplit
  from sklearn.preprocessing import StandardScaler
  import itertools
  
  dataset = sys.argv[1]
  
  # Read the data set into memory
  input_data = pd.read_csv(dataset, compression='gzip', sep='\t')
  
  for (C, loss, fit_intercept) in itertools.product([0.01, 0.1, 0.5, 1.0, 10.0, 50.0, 100.0],
                                                    ['hinge', 'squared_hinge'],
                                                    [True, False]):
      for dataset_repeat in range(1, 31):
          # Divide the data set into a training and testing sets, each time with a different RNG seed
          training_indices, testing_indices = next(iter(StratifiedShuffleSplit(input_data['class'].values,
                                                                               n_iter=1,
                                                                               train_size=0.75,
                                                                               test_size=0.25,
                                                                               random_state=dataset_repeat)))
  
          training_features = input_data.loc[training_indices].drop('class', axis=1).values
          training_classes = input_data.loc[training_indices, 'class'].values
  
          testing_features = input_data.loc[testing_indices].drop('class', axis=1).values
          testing_classes = input_data.loc[testing_indices, 'class'].values
  
          ss = StandardScaler()
          training_features = ss.fit_transform(training_features.astype(float))
          testing_features = ss.transform(testing_features.astype(float))
  
          # Create and fit the model on the training data
          try:
              clf = PassiveAggressiveClassifier(C=C, loss=loss, fit_intercept)
              clf.fit(training_features, training_classes)
              testing_score = clf.score(testing_features, testing_classes)
          except KeyboardInterrupt:
              sys.exit(1)
          except:
              continue
  
          param_string = ''
          param_string += 'C={},'.format(C)
          param_string += 'loss={},'.format(loss)
          param_string += 'fit_intercept={}'.format(fit_intercept)
  
          out_text = '\t'.join([dataset.split('/')[-1][:-7],
                                'PassiveAggressiveClassifier',
                                param_string,
                                str(testing_score)])
  
          print(out_text)
  
subject: Add PassiveAggressiveClassifier to benchmark