def display(self):
#TODO (Devin): Clean up display to make it prettier.
print("=== Begin Draft State ===")
print("There are {num_picks} picks and {num_bans} bans completed in this draft. \n".format(num_picks=len(self.picks),num_bans=len(self.bans)))
print("Banned Champions: {0}".format(list(map(champion_name_from_id, self.bans))))
print("Picked Champions: {0}".format(list(map(champion_name_from_id, self.picks))))
pos_index = self.get_position_index(0)
enemy_draft_ids = list(map(self.get_champ_id, list(np.where(self.state[:,pos_index])[0])))
print("Enemy Draft: {0}".format(list(map(champion_name_from_id,enemy_draft_ids))))
print("Ally Draft:")
for pos_index in range(2,len(self.state[0,:])): # Iterate through each position columns in state
champ_index = np.where(self.state[:,pos_index])[0] # Find non-zero index
if not champ_index.size: # No pick is found for this position, create a filler string
draft_name = "--"
else:
draft_name = champion_name_from_id(self.get_champ_id(champ_index[0]))
print("Position {p}: {c}".format(p=pos_index-1,c=draft_name))
print("=== End Draft State ===")
for pick_count, exp in enumerate(experiences):
print(" === ")
print(" Match {}, Pick {}".format(count, pick_count))
print(" === ")
state, act, rew, next_state = exp
cid, pos = act
if cid == None:
continue
predicted_q_values = model.predict([state])
predicted_q_values = predicted_q_values[0, :]
submitted_action_id = state.get_action(*act)
data = [(a, *state.format_action(a), predicted_q_values[a])
for a in range(len(predicted_q_values))]
data = [(a, cinfo.champion_name_from_id(cid), pos, Q)
for (a, cid, pos, Q) in data]
df = pd.DataFrame(data,
columns=['act_id', 'cname', 'pos', 'Q(s,a)'])
df.sort_values('Q(s,a)', ascending=False, inplace=True)
df.reset_index(drop=True, inplace=True)
df['rank'] = df.index
df['error'] = abs(df['Q(s,a)'][0] - df['Q(s,a)']) / abs(
df['Q(s,a)'][0])
submitted_row = df[df['act_id'] == submitted_action_id]
print(" Submitted action:")
print(submitted_row)
print("***")
print("Validation matches:")
count = 0
for match in validation_matches:
count += 1
print("Match: {:2} id: {:4} {:6} vs {:6} winner: {:2}".format(
count, match["id"], match["blue_team"], match["red_team"],
match["winner"]))
for team in ["blue", "red"]:
bans = match[team]["bans"]
picks = match[team]["picks"]
pretty_bans = []
pretty_picks = []
for ban in bans:
pretty_bans.append(cinfo.champion_name_from_id(ban[0]))
for pick in picks:
pretty_picks.append(
(cinfo.champion_name_from_id(pick[0]), pick[1]))
print("{} bans:{}".format(team, pretty_bans))
print("{} picks:{}".format(team, pretty_picks))
print("")
print("***")
# Network parameters
state = DraftState(DraftState.BLUE_TEAM, valid_champ_ids)
input_size = state.format_state().shape
output_size = state.num_actions
filter_size = (1024, 1024)
regularization_coeff = 7.5e-5 #1.5e-4
path_to_model = None #"model_predictions/spring_2018/week_3/model_E{}.ckpt".format(30)#None
fig.savefig(fig_name)
# Look at predicted Q values for states in a randomly drawn match
match = random.sample(training_matches, 1)[0]
team = DraftState.RED_TEAM if match["winner"] == 1 else DraftState.BLUE_TEAM
experiences = mp.process_match(match, team)
count = 0
# x labels for q val plots
xticks = []
xtick_locs = []
for a in range(state.num_actions):
cid, pos = state.format_action(a)
if cid not in xticks:
xticks.append(cid)
xtick_locs.append(a)
xtick_labels = [cinfo.champion_name_from_id(cid)[:6] for cid in xticks]
tf.reset_default_graph()
path_to_model = "tmp/model_E{}".format(n_epoch)
model = Model(path_to_model)
for exp in experiences:
state, act, rew, next_state = exp
cid, pos = act
if cid == None:
continue
count += 1
form_act = state.get_action(cid, pos)
pred_act = model.predict_action([state])
pred_act = pred_act[0]
pred_Q = model.predict([state])
pred_Q = pred_Q[0, :]