parameters: ()
docstring: Plot the scores
docstring_summary: Plot the scores
docstring_tokens: Plot the scores
function: def plot_final_scores():
      ''' Plot the scores '''
      font = {
        'size'   : 12
      }
      mpl.rc('font', **font)
      fig, ax = plt.subplots( nrows=1, ncols=1, figsize=(7,4) )  # create figure & 1 axis
      outfiles = [
          RESULT_DIR + 'seq2seq_sample_imagenet_%s_iter_20000.json',
          RESULT_DIR + 'seq2seq_teacher_imagenet_%s_iter_5000.json',
          RESULT_DIR + '%s_stop_agent.json',
          RESULT_DIR + '%s_random_agent.json'
      ]
      for split in ['val_seen']:
          ev = Evaluation([split])
          for i,outfile in enumerate(outfiles):
              score_summary,scores = ev.score(outfile % split)
              if i == 1:
                  method = 'Teacher-forcing'
                  ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C1')
              elif i == 0:
                  method = 'Student-forcing'
                  ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, alpha=0.7, normed=True, color='C0')
              elif i == 2:
                  method = 'Start locations'
                  ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C3')
              elif i == 3:
                  method = 'Random agent'
                  ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C2')
      ax.set_title('Val Seen Navigation Error')
      ax.set_xlabel('Error (m)')
      ax.set_ylabel('Frequency')
      ax.set_ylim([0,0.14])
      ax.set_xlim([0,30])
      plt.axvline(x=3, color='black', linestyle='--')
      legend = ax.legend(loc='upper right')
      plt.tight_layout()
      plt.savefig('%s/val_seen_error.png' % (PLOT_DIR))
      plt.close(fig)
function_tokens: def plot_final_scores ( ) : font = { 'size' : 12 } mpl . rc ( 'font' , * * font ) fig , ax = plt . subplots ( nrows = 1 , ncols = 1 , figsize = ( 7 , 4 ) ) # create figure & 1 axis outfiles = [ RESULT_DIR + 'seq2seq_sample_imagenet_%s_iter_20000.json' , RESULT_DIR + 'seq2seq_teacher_imagenet_%s_iter_5000.json' , RESULT_DIR + '%s_stop_agent.json' , RESULT_DIR + '%s_random_agent.json' ] for split in [ 'val_seen' ] : ev = Evaluation ( [ split ] ) for i , outfile in enumerate ( outfiles ) : score_summary , scores = ev . score ( outfile % split ) if i == 1 : method = 'Teacher-forcing' ax . hist ( scores [ 'nav_errors' ] , bins = range ( 0 , 30 , 3 ) , label = method , normed = True , histtype = 'step' , linewidth = 2.5 , color = 'C1' ) elif i == 0 : method = 'Student-forcing' ax . hist ( scores [ 'nav_errors' ] , bins = range ( 0 , 30 , 3 ) , label = method , alpha = 0.7 , normed = True , color = 'C0' ) elif i == 2 : method = 'Start locations' ax . hist ( scores [ 'nav_errors' ] , bins = range ( 0 , 30 , 3 ) , label = method , normed = True , histtype = 'step' , linewidth = 2.5 , color = 'C3' ) elif i == 3 : method = 'Random agent' ax . hist ( scores [ 'nav_errors' ] , bins = range ( 0 , 30 , 3 ) , label = method , normed = True , histtype = 'step' , linewidth = 2.5 , color = 'C2' ) ax . set_title ( 'Val Seen Navigation Error' ) ax . set_xlabel ( 'Error (m)' ) ax . set_ylabel ( 'Frequency' ) ax . set_ylim ( [ 0 , 0.14 ] ) ax . set_xlim ( [ 0 , 30 ] ) plt . axvline ( x = 3 , color = 'black' , linestyle = '--' ) legend = ax . legend ( loc = 'upper right' ) plt . tight_layout ( ) plt . savefig ( '%s/val_seen_error.png' % ( PLOT_DIR ) ) plt . close ( fig )