def parse_invocations():
out = []
optfeats = get_data('optionalfeatures.json')['optionalfeature']
invocs = [i for i in optfeats if i['featureType'] == 'EI']
for invoc in invocs:
log.info(f"Parsing invocation {invoc['name']}")
text = render(invoc['entries'])
if 'prerequisite' in invoc:
prereqs = []
for prereq in invoc['prerequisite']:
if prereq['type'] == 'prereqPact':
prereqs.append(f"Pact of the {prereq['entry']}")
elif prereq['type'] == 'prereqPatron':
prereqs.append(f"Patron: {prereq['entry']}")
elif prereq['type'] == 'prereqLevel':
prereqs.append(f"Level {prereq['level']}")
elif prereq['type'] == 'prereqSpell':
prereqs.append(f"*{', '.join(prereq['entries'])}* spell")
else:
log.warning(f"Unknown prereq type: {prereq['type']}")
text = f"*Prerequisite: {', '.join(prereqs)}*\n{text}"
inv = {
'name': f"Warlock: Eldritch Invocation: {invoc['name']}",
'text': text,
'srd': invoc['source'] == 'PHB'
}
out.append(inv)
return out
def main(args):
# get utils
datas, changes, code_table, indices_mv = get_data(args.data_path)
num_company, period, num_feature = datas.shape
# parameter
state_shape = (num_company, args.window_size, num_feature)
action_shape = num_company
agent = PGAgent(state_shape,
action_shape,
load_path=None)
# env
env = Kospi200_Env(datas,
changes,
code_table,
indices_mv,
window_size=args.window_size)
# log
total_reward_log = []
# train
for e in tqdm(range(args.num_episode), total=args.num_episode):
state = env.reset()
total_reward = 0
while True:
action = agent.get_action(state)
next_state, rewards, done = env.step(action)
agent.memorize(state, action, rewards)
total_reward += rewards
if done:
agent.learn()
break
env.render(mode='print')
state = next_state
total_reward_log.append(total_reward)
print(f"\n + EPISODE: [{args.num_episode} / {e}] \n"
f" + TOTAL REWARD : {total_reward}")
save_pkl(total_reward_log, os.path.join(args.log_path, f"{num_feature}f"
f"_{args.window_size}w"
f"_reward.pkl"))
agent.model.save_weights(os.path.join(args.checkpoint, f"{num_feature}f"
f"_{args.window_size}w.h5"))
def face_experiment():
'''
applies NMF to the faces dataset and generates an image
'''
config = yaml.safe_load(open('./config/dev.yml'))
experiment_config = yaml.safe_load(open('./experiments/face.yml'))
solvers = experiment_config['solver_list']
config['dataset'] = 'face'
r = experiment_config['r']
config['r'] = r
name = 'face'
X, ground_truth = get_data(config)
#if ground_truth is not -1:
# W = ground_truth[0]
# H = ground_truth[1]
print('Data loaded, rank of X: ', np.linalg.matrix_rank(X))
experiment = Experiment(config, X, experiment_config)
experiment()
images = np.zeros((r, 19, 19))
for solver in experiment.solvers:
W = solver.solution[0]
W /= np.max(W, axis=0)
W = 1 - W
for i in range(r):
images[i, :, :] = np.reshape(W[:, i], (19, 19))
d = 0.05
plt.subplots_adjust(wspace=d, hspace=d)
fig, ax = plt.subplots(4, 4)
fig.set_figheight(8)
fig.set_figwidth(8)
for m in range(4):
for n in range(4):
ax[m, n].imshow(images[4 * m + n, :, :],
cmap='gray',
vmin=0,
vmax=1)
ax[m, n].set_xticks([])
ax[m, n].set_yticks([])
fig.savefig('./experiments/' + name + '/' + solver.name + '.pgf',
bbox_inches='tight')
fig.savefig('./experiments/' + name + '/' + solver.name + '.pdf',
bbox_inches='tight')
return 0
def main(args):
# get utils
datas, changes, code_table, indices_mv = get_data(args.data_path)
num_company, period, num_feature = datas.shape
# parameter
state_shape = (num_company, args.window_size, num_feature)
action_shape = num_company
code_table = list(code_table.items())
agent = PGAgent(state_shape, action_shape, load_path=args.load_path)
total_reward = 0
pos_count = 0
neg_count = 0
for p in range(period - args.window_size + 1):
obs = datas[:, p:p + args.window_size]
obs = np.expand_dims(obs, axis=0)
action = agent.get_action(obs)
one_hot_action = np.zeros_like(action)
prop_action = np.random.choice(range(num_company),
1,
p=action,
replace=False)
for p in prop_action:
one_hot_action[p] = 1
dict_actions = {i: r for i, r in enumerate(action)}
sorted_dict_actions = sorted(dict_actions.items(),
reverse=True,
key=lambda item: item[1])
for i, a in enumerate(sorted_dict_actions[:1]):
print(f"TOP {i + 1} [{code_table[a[0]]}]")
print(f"TOTAL REWARD : {total_reward}")
print(f"POS : {pos_count}")
print(f"NEG : {neg_count}")
def get_names():
return get_data("names.json")['name']
def get_latest_feats():
return get_data("feats.json")['feat']
def get_latest_backgrounds():
return get_data("backgrounds.json")['background']
def get_latest_items():
return get_data("items.json")['item'] + \
get_data("basicitems.json")['basicitem'] + \
get_data("magicvariants.json")['variant']
def get_objects():
return get_data("objects.json")['object']
def get_races_from_web():
return get_data('races.json')['race']
def classic_experiment():
'''
'''
config = yaml.safe_load(open('./config/dev.yml'))
config['dataset'] = 'face'
experiment_config = yaml.safe_load(open('./experiments/classic.yml'))
name = 'classic'
solvers = experiment_config['solver_list']
X, _ = get_data(config)
experiment = Experiment(config, X, experiment_config)
experiment()
fig = plt.figure(figsize=(6, 6))
ax0 = fig.add_subplot(111)
ax0.set_xlabel('iteration')
ax0.set_ylabel('Relative error')
for i, solver in enumerate(experiment.solvers):
x_axis = np.array(solver.output['iteration'])
y_axis = np.array(solver.output['rel_error'])
ax0.plot(x_axis,
y_axis,
color=COLORS[i],
label=solvers[i],
linestyle='--',
markersize=8,
marker='.')
ax0.yaxis.set_major_formatter(FormatStrFormatter('%g'))
ax0.xaxis.set_major_formatter(FormatStrFormatter('%g'))
ax0.get_yaxis().set_tick_params(which='both', direction='in')
ax0.get_xaxis().set_tick_params(which='both', direction='in')
ax0.grid()
ax0.set_ylim([0.1, 0.3])
ax0.legend()
#ax0.set_ylabel(Y_LABELS[feature])
#ax0.set_xscale('log')
#ax0.set_yscale('log')
#s = '_' + str(n) + '_' + str(m) + '_' + str(r)
fig.savefig('./experiments/' + name + '/' + 'graph_iter.pgf',
bbox_inches='tight')
fig.savefig('./experiments/' + name + '/' + 'graph_iter.pdf',
bbox_inches='tight')
fig = plt.figure(figsize=(6, 6))
ax0 = fig.add_subplot(111)
ax0.set_xlabel('time [s]')
ax0.set_ylabel('Relative error')
for i, solver in enumerate(experiment.solvers):
x_axis = np.array(solver.output['time'])
y_axis = np.array(solver.output['rel_error'])
ax0.plot(x_axis,
y_axis,
color=COLORS[i],
label=solvers[i],
linestyle='--',
markersize=8,
marker='.')
ax0.yaxis.set_major_formatter(FormatStrFormatter('%g'))
ax0.xaxis.set_major_formatter(FormatStrFormatter('%g'))
ax0.get_yaxis().set_tick_params(which='both', direction='in')
ax0.get_xaxis().set_tick_params(which='both', direction='in')
ax0.grid()
ax0.set_ylim([0.1, 0.3])
ax0.legend()
#ax0.set_ylabel(Y_LABELS[feature])
#ax0.set_xscale('log')
#ax0.set_yscale('log')
#s = '_' + str(n) + '_' + str(m) + '_' + str(r)
fig.savefig('./experiments/' + name + '/' + 'graph_time.pgf',
bbox_inches='tight')
fig.savefig('./experiments/' + name + '/' + 'graph_time.pdf',
bbox_inches='tight')
