def tcp_test(num):
count_failed = 0
count_success = 0
steps = []
counts = []
for i in range(num):
print(i)
controller = cppCellModel.controller_constructor(50, 50, 100, 350)
counts.append(cppCellModel.HCellCount())
for i in range(35):
#print("Before", cppCellModel.HCellCount(), cppCellModel.CCellCount())
cppCellModel.irradiate(controller, 2)
##print("After", cppCellModel.HCellCount(), cppCellModel.CCellCount())
cppCellModel.go(controller, 24)
if cppCellModel.CCellCount() == 0:
steps.append(i + 1)
break
count = cppCellModel.CCellCount()
if count > 10:
count_failed += 1
elif count == 0:
count_success += 1
counts[-1] /= cppCellModel.HCellCount()
cppCellModel.delete_controller(controller)
print("Percentage of full recovs :", (100 * count_success) / num)
print("Percentage of almost recovs :", (100 * (num - count_failed)) / num)
print("Average dose in successes :", 2 * sum(steps) / len(steps))
print(sum(counts) / len(counts))
def adjust_reward(self, dose, ccell_killed, hcells_lost):
if self.special_reward and self.inTerminalState():
if self.end_type == "L" or self.end_type == "T":
return -1
else:
if self.reward == 'dose':
return -dose / 200 + 0.5 - (
self.init_hcell_count -
cppCellModel.HCellCount()) / 3000
else:
return 0.5 - (
self.init_hcell_count - cppCellModel.HCellCount()
) / 3000 #(cppCellModel.HCellCount() / self.init_hcell_count) - 0.5 - (2 * hcells_lost/2500)
else:
if self.reward == 'dose' or self.reward == 'oar':
return -dose / 200 + (ccell_killed - 5 * hcells_lost) / 100000
elif self.reward == 'killed':
return (ccell_killed - 5 * hcells_lost) / 100000
def act(self, action):
dose = 1 + action / 2 if self.action_type == 'DQN' else action[
0] * 4 + 1
rest = 24 if self.action_type == 'DQN' else int(
round(action[1] * 60 + 12))
if self.dose_map is not None:
tumor_radius = cppCellModel.tumor_radius(self.controller_capsule)
pre_hcell = cppCellModel.HCellCount()
pre_ccell = cppCellModel.CCellCount()
self.total_dose += dose
self.num_doses += 1 if dose > 0 else 0
cppCellModel.irradiate(self.controller_capsule, dose)
self.radiation_h_killed += (pre_hcell - cppCellModel.HCellCount())
if self.dataset is not None:
self.dataset[0].append(
cppCellModel.controllerTick(self.controller_capsule) - 350)
self.dataset[1].append((pre_ccell, cppCellModel.CCellCount()))
self.dataset[2].append(dose)
if self.dose_map is not None:
self.add_radiation(
dose, tumor_radius,
cppCellModel.get_center_x(self.controller_capsule),
cppCellModel.get_center_y(self.controller_capsule))
self.dose_maps.append(
(cppCellModel.controllerTick(self.controller_capsule) - 350,
np.copy(self.dose_map)))
self.tumor_images.append(
(cppCellModel.controllerTick(self.controller_capsule) - 350,
cppCellModel.observeDensity(self.controller_capsule)))
p_hcell = cppCellModel.HCellCount()
p_ccell = cppCellModel.CCellCount()
cppCellModel.go(self.controller_capsule, rest)
post_hcell = cppCellModel.HCellCount()
post_ccell = cppCellModel.CCellCount()
reward = self.adjust_reward(dose, pre_ccell - post_ccell,
pre_hcell - min(post_hcell, p_hcell))
if self.verbose:
print("Radiation dose :", dose, "Gy ", "remaining :", post_ccell,
"time =", rest, "reward=", reward)
return reward
def inTerminalState(self):
if cppCellModel.CCellCount() <= 0:
if self.verbose:
print("No more cancer")
self.end_type = 'W'
return True
elif cppCellModel.HCellCount() < 10:
if self.verbose:
print("Cancer wins")
self.end_type = "L"
return True
elif cppCellModel.controllerTick(self.controller_capsule) > 1550:
if self.verbose:
print("Time out!")
self.end_type = "T"
return True
else:
return False
def reset(self, mode):
cppCellModel.delete_controller(self.controller_capsule)
self.controller_capsule = cppCellModel.controller_constructor(
50, 50, 100, 350)
self.init_hcell_count = cppCellModel.HCellCount()
if mode == -1:
self.verbose = False
else:
self.verbose = True
self.total_dose = 0
self.num_doses = 0
self.radiation_h_killed = 0
if self.dose_map is not None:
self.dose_maps.append(
(cppCellModel.controllerTick(self.controller_capsule) - 350,
np.copy(self.dose_map)))
self.tumor_images.append(
(cppCellModel.controllerTick(self.controller_capsule) - 350,
cppCellModel.observeDensity(self.controller_capsule)))
return self.observe()
def observe(self):
if self.obs_type == 'scalars':
return [
cppCellModel.controllerTick(self.controller_capsule) / 2000,
cppCellModel.HCellCount() / 100000,
cppCellModel.CCellCount() / 50000
]
else:
if self.obs_type == 'densities':
cells = (np.array(cppCellModel.observeDensity(
self.controller_capsule),
dtype=np.float32)) / 100.0
else:
cells = (np.array(
cppCellModel.observeSegmentation(self.controller_capsule),
dtype=np.float32) + 1.0) / 2.0 # Obs from 0 to 1
if self.resize:
cells = cv2.resize(cells,
dsize=(25, 25),
interpolation=cv2.INTER_CUBIC)
return [cells]
def __init__(self, obs_type, resize, reward, action_type, special_reward):
"""Constructor of the environment
Parameters:
obs_type : Type of observations provided to the agent ('head' for segmentation or 'types' for weighted sums)
resize : True if the observations should be resized to 25 * 25 arrays
reward : Type of reward function used ('dose' to minimize the total dose, 'killed' to maximize damage to cancer
cells while miniizing damage to healthy tissue and 'oar' to minimize damage to the Organ At Risk
action_type : 'DQN' means that we have a discrete action domain and 'DDPG' means that it is continuous
special_reward : True if the agent should receive a special reward at the end of the episode.
"""
self.controller_capsule = cppCellModel.controller_constructor(
50, 50, 100, 350)
self.init_hcell_count = cppCellModel.HCellCount()
self.obs_type = obs_type
self.resize = resize
self.reward = reward
self.action_type = action_type
self.special_reward = special_reward
self.dose_map = None
self.dataset = None
def surviving_fraction(self):
return cppCellModel.HCellCount() / self.init_hcell_count