def individual()

in jsuarez/extra/figures.py [0:0]

• 77 lines of code
• 24 McCabe index (conditional complexity)

def individual(log, label, npop, logDir='resource/data/exps/', train=True):

   if train:
      split = 'train'
   else:
      split = 'test'

   savedir = osp.join(logDir, label, split)
   if not osp.exists(savedir):
      os.makedirs(savedir)

   if len(log['return']) > 0:
      loglib.dark()
      keys   = reversed('return lifespan value value_loss pg_loss entropy grad_mean grad_std grad_min grad_max'.split())
      colors = Neon.color12()
      fName = 'frag.png'
      for idx, key in enumerate(keys):
         if idx == 0:
            c = colors[idx]
            loglib.plot(log[key], key, (1.0, 0, 0))
         else:
            c = colors[idx]
            loglib.plot(log[key], key, c.norm)
      maxLife = np.max(log['return'])
      loglib.limits(ylims=[0, 50*(1+maxLife//50)])
      loglib.godsword()
      savepath = osp.join(logDir, label, split, fName)
      loglib.save(savepath)
      print(savepath)
      plt.close()

   # Construct population specific code
   pop_mean_keys = ['lifespan{}_mean'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_mean.png')
   gen_plot(log, pop_mean_keys, savefile, train=train)

   # Per population movement probability
   pop_move_keys = ['pop{}_move'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_move.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # Attack probability plots
   pop_move_keys = ['pop{}_range'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_range.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_melee'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_melee.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_mage'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_mage.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # Movement tile entropy
   pop_move_keys = ['pop{}_entropy'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_move_entropy.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # Population attack probabilities when action is selected
   pop_move_keys = ['pop{}_melee_logit'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_melee_logit.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_range_logit'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_range_logit.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_mage_logit'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_mage_logit.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # Sum up all the logits to check if they actually sum to zero
   for i in range(npop):
      logit_sum = np.array(log['pop{}_melee_logit'.format(i)]) + np.array(log['pop{}_range_logit'.format(i)]) + np.array(log['pop{}_mage_logit'.format(i)])
      log['pop{}_sum_logit'.format(i)] = logit_sum

   pop_move_keys = ['pop{}_sum_logit'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_sum_logit.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # Tile exploration statistics
   pop_move_keys = ['pop{}_grass_tiles'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_grass_tiles.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_forest_tiles'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_forest_tiles.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   pop_move_keys = ['pop{}_forest_tiles_depleted'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_forest_depleted.png')
   gen_plot(log, pop_move_keys, savefile, train=train)

   # pop_move_keys = ['pop{}_forest_tiles_other'.format(i) for i in range(npop)]
   # savefile = osp.join(logDir, label, 'pop_forest_tiles_other.png')
   # gen_plot(log, pop_move_keys, savefile, train=train)

   for i in range(npop):
      forest_tiles = np.array(log['pop{}_forest_tiles'.format(i)])
      other_tiles = np.array(log['pop{}_grass_tiles'.format(i)]) + np.array(log['pop{}_forest_tiles_depleted'.format(i)]) + forest_tiles
      forage_percent = forest_tiles / other_tiles
      log['pop{}_forage_success'.format(i)] = forage_percent

   pop_move_keys = ['pop{}_forage_success'.format(i) for i in range(npop)]
   savefile = osp.join(logDir, label, split, 'pop_forage_success.png')
   gen_plot(log, pop_move_keys, savefile, train=train)