content: import os
  import cPickle
  import matplotlib.pyplot as plt
  from datasets import DatasetManager
  
  
  def plot_cost(results, data_name, plot_label):
      plt.figure(plot_label)
      plt.ylabel('Accuracy (m)', fontsize=30)
      plt.xlabel('Epoch', fontsize=30)
      plt.yscale('symlog')
      plt.tick_params(axis='both', which='major', labelsize=20)
      plt.grid(True)
      for i in range(1, 2, 1):
  
          y, x = zip(*results[i][data_name])
          name = results[i]['Name']
          plt.plot(x, y, label=name, linewidth=5.0)
      plt.legend(fontsize='xx-large')
  
  
  def get_metrics(test_set_y, predicted_values, model_name):
      for i in xrange(len(predicted_values)):
          print predicted_values[i][1]
  
  
  if __name__ == '__main__':
      """
      seed = 50
      with open(os.path.join('experimentation',  'cinvestav_testbed_experiment_results_' + str(seed)), 'rb') as f:
          results = cPickle.load(f)
  
      plot_cost(
          results=results,
          data_name='cost_train',
          plot_label='Cost on train phase')
      plot_cost(
          results=results,
          data_name='cost_valid',
          plot_label='Cost on valid phase')
      plot_cost(
          results=results,
          data_name='cost_test',
          plot_label='Cost on test phase')
      plt.show()
  
      """
      seed = 50
      dataset, result = DatasetManager.read_dataset2('test_cleaned_dataset.csv', shared=True, seed=seed)
      with open(os.path.join('trained_models',  'Logistic Regressionbrandeis_university.save'), 'rb') as f:
          model = cPickle.load(f)
  
      predicted_values = model.predict(dataset)
      get_metrics(
          test_set_y=result,
          predicted_values=predicted_values,
          model_name='Logistic Regression'
      )
  
  