def ex5_2():
minor = 2
version = "5_2"
case = get_case6ww2()
expts = 1
in_cloud = False
roleouts = 300
episodes = 1 # samples per learning step
# results = run_experiments(expts, get_re_experiment, case, roleouts,
# episodes, in_cloud, minor)
# save_results(results, "RothErev", version)
#
#
# results = run_experiments(expts, get_q_experiment, case, roleouts,
# episodes, in_cloud, minor)
# save_results(results, "Q", version)
#
#
roleouts, episodes = 100, 5
results = run_experiments(expts, get_reinforce_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "REINFORCE", version)
#
#
roleouts, episodes = 50, 10
results = run_experiments(expts, get_enac_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "ENAC", version)
def ex6_1():
version = "6_1"
case = get_case24_ieee_rts3()
in_cloud = False
years = 1
roleouts = 364 * years
episodes = 1 # number of days per learning step
t0 = time()
# rewards, results = run_years(get_passive_experiment, case, roleouts,
# episodes, in_cloud)
# save_results(results, "passive", version)
# save_rewards(rewards, "passive", version)
t_passive = time()
# rewards, results = run_years(get_re_experiment, case, roleouts,
# episodes, in_cloud)
# save_results(results, "StatefulRE", version)
# save_rewards(rewards, "StatefulRE", version)
t_re = time()
rewards, results = run_years(get_q_experiment, case, roleouts,
episodes, in_cloud)
save_results(results, "Q", version)
save_rewards(rewards, "Q", version)
t_q = time()
roleouts = 52 * years
episodes = 7 # number of days per learning step
# rewards, results = run_years(get_reinforce_experiment, case, roleouts,
# episodes,in_cloud)
# save_results(results, "REINFORCE", version)
# save_rewards(rewards, "REINFORCE", version)
t_reinforce = time()
# rewards, results = run_years(get_enac_experiment, case, roleouts,
# episodes,in_cloud)
# save_results(results, "ENAC", version)
# save_rewards(rewards, "ENAC", version)
t_enac = time()
print "Roth-Erev completed in %.2fs." % (t_re - t_passive)
print "Q-learning completed in %.2fs." % (t_q - t_re)
print "REINFORCE completed in %.2fs." % (t_reinforce - t_q)
print "ENAC completed in %.2fs." % (t_enac - t_reinforce)
print "Experiment 6.1 completed in %.2fs." % (time() - t0)
def ex6_1():
version = "6_1"
case = get_case24_ieee_rts3()
in_cloud = False
years = 1
roleouts = 364 * years
episodes = 1 # number of days per learning step
t0 = time()
# rewards, results = run_years(get_passive_experiment, case, roleouts,
# episodes, in_cloud)
# save_results(results, "passive", version)
# save_rewards(rewards, "passive", version)
t_passive = time()
# rewards, results = run_years(get_re_experiment, case, roleouts,
# episodes, in_cloud)
# save_results(results, "StatefulRE", version)
# save_rewards(rewards, "StatefulRE", version)
t_re = time()
rewards, results = run_years(get_q_experiment, case, roleouts, episodes,
in_cloud)
save_results(results, "Q", version)
save_rewards(rewards, "Q", version)
t_q = time()
roleouts = 52 * years
episodes = 7 # number of days per learning step
# rewards, results = run_years(get_reinforce_experiment, case, roleouts,
# episodes,in_cloud)
# save_results(results, "REINFORCE", version)
# save_rewards(rewards, "REINFORCE", version)
t_reinforce = time()
# rewards, results = run_years(get_enac_experiment, case, roleouts,
# episodes,in_cloud)
# save_results(results, "ENAC", version)
# save_rewards(rewards, "ENAC", version)
t_enac = time()
print "Roth-Erev completed in %.2fs." % (t_re - t_passive)
print "Q-learning completed in %.2fs." % (t_q - t_re)
print "REINFORCE completed in %.2fs." % (t_reinforce - t_q)
print "ENAC completed in %.2fs." % (t_enac - t_reinforce)
print "Experiment 6.1 completed in %.2fs." % (time() - t0)
def ex5_4():
minor = 4
version = "5_4"
case = get_case6ww()
expts = 10
in_cloud = False
roleouts = 30
episodes = 5 # samples per learning step
results = run_experiments(expts, get_reinforce_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "REINFORCE", version)
roleouts = 30
episodes = 5 # samples per learning step
results = run_experiments(expts, get_enac_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "ENAC", version)
def ex5_1():
minor = 1
version = "5_1"
case = get_case6ww()
expts = 8
in_cloud = False
# roleouts = 300
# episodes = 1 # samples per learning step
#
# results = run_experiments(expts, get_re_experiment, case, roleouts,
# episodes, in_cloud, minor)
# save_results(results, "RothErev", version)
#
#
# results = run_experiments(expts, get_q_experiment, case, roleouts,
# episodes, in_cloud, minor)
# save_results(results, "Q", version)
roleouts = 30
episodes = 5 # samples per learning step
results = run_experiments(expts, get_reinforce_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "REINFORCE", version)
roleouts = 30
episodes = 5 # samples per learning step
results = run_experiments(expts, get_enac_experiment, case, roleouts,
episodes, in_cloud, minor)
save_results(results, "ENAC", version)
import argparse
from pathlib import Path
from common import save_results
parser = argparse.ArgumentParser(description="Saves the result files of a experiment to a zip file")
parser.add_argument("directory", help="Directory in which the cleaned datasets are")
parser.add_argument("filename", help="Filename to save")
args = parser.parse_args()
path = Path(args.directory)
if not path.is_dir():
print("The selected path is not a directory")
exit(1)
save_results(path.resolve(), args.filename)
input_shape=(options.step_size, x.shape[1]),
return_sequences=True))
model.add(
Dense(units=y.shape[1], kernel_initializer='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
start_time = time.time() # -------------------------------------------------┐
result = model.fit(
x_train,
y_train, # │
validation_data=(x_test,
y_test), # train_time │
callbacks=[earlyStopping, modelCheckpoint], # │
verbose=1,
epochs=300) # │
train_time = time.time() - start_time # -------------------------------------┘
model.summary()
print('train_time = {}s'.format(train_time))
# write files
print('===== write files =====')
model = load_model(options.model_path)
common.save_results(ANN_NAME,
options=options,
model=model,
validation_data=(x_test, y_test),
fit_result=result,
train_time=train_time)
def calculate(sett):
wep_type = sett['weapon']
use_personal = sett['use_personal']
use_primed = sett['use_primed']
use_corrupted = sett['use_corrupted']
use_faction = sett['use_faction']
sortby = sett['sortby']
num_of_top_results = sett['num_of_top_results']
point_limit = sett['point_limit']
builder_start = datetime.now()
wep = get_item(wep_type)
mod_types = get_list_from_dict(wep.type, weapon_type_dependencies)
pers_mods = {}
if use_personal:
personal_mods = sett['personal_mods']
for m_t in mod_types:
if m_t not in personal_mods:
continue
pers_mods.update(personal_mods[m_t])
mods_data = get_mods({'name': [name for name in pers_mods]})
else:
mods_data = get_mods({'type': mod_types})
mods_data = dict((name, mod) for name, mod in mods_data.iteritems() if (use_primed or not mod.primed) and (use_corrupted or not mod.corrupted) and (use_faction or not mod.faction))
if sett['use_mods_smart']:
from common import stat_dependencies
wanted_stats = get_list_from_dict(sortby, stat_dependencies)
mods_data = dict((name, mod) for name, mod in mods_data.iteritems() if set(mod.s) & set(wanted_stats))
if use_personal:
all_mods = dict((name, [mod.get_ranked(rank) for rank in pers_mods[name]]) for name, mod in mods_data.iteritems())
else:
all_mods = dict((name, [mod.get_ranked(-1)]) for name, mod in mods_data.iteritems())
incompatible_mods = get_incompatible({'type': mod_types})
for inc_mod in incompatible_mods:
if inc_mod[1] not in all_mods:
continue
if inc_mod[0] not in all_mods:
all_mods[inc_mod[0]] = []
all_mods[inc_mod[0]] += all_mods[inc_mod[1]]
del all_mods[inc_mod[1]]
num_of_mods = range(sett['num_of_mods_min'], sett['num_of_mods_max'] + 1)
custom_polarities = sett['custom_polarities']
if wep_type in custom_polarities:
weap_pols = custom_polarities[wep_type]
wep.set_polarities(weap_pols)
else:
weap_pols = wep.polarities
print 'polarities:', weap_pols
print 'mods:', all_mods
all_stats = []
mod_dict = all_mods
mod_classes = sorted(mod_cls for mod_cls in mod_dict)
poss_mods = tuple(all_mods[name] for name in mod_classes)
for tmp_num_of_mods in num_of_mods:
start = datetime.now()
all_possible = 0
mod_sets = []
for mod_comb in combinations(poss_mods, tmp_num_of_mods):
for prod in product(*mod_comb):
mod_sets.append(prod)
all_possible += 1
end = datetime.now()
if point_limit is not None:
mod_sets = [mods for mods in mod_sets if effective_drain(mods, weap_pols) <= point_limit]
end2 = datetime.now()
m_s_len = len(mod_sets)
print '%d mod combinations to try: %d (out of %d, eligible: %.2f%%, took: %s, took2: %s)' % (tmp_num_of_mods, m_s_len, all_possible, 0 if all_possible == 0 else 100.0 * float(m_s_len) / all_possible, end - start, end2 - start)
# continue
for i, mods in enumerate(mod_sets):
if i % 100000 == 0 and i != 0:
print 'i:', i
all_stats.sort(key=lambda s: -s['stats'][sortby])
all_stats = all_stats[:num_of_top_results]
stats = wep.mod_with(mods, sett['limit_fr_of_non_auto'])
all_stats.append({'stats': stats, 'mods': mods, 'base_stats': wep.s})
all_stats.sort(key=lambda s: -s['stats'][sortby])
all_stats = all_stats[:num_of_top_results]
save_results(all_stats, wep.s, wep, use_personal, use_primed, use_corrupted, use_faction, sortby)
print '%s | %s | %s | %s | %s | sorted by %s | av. pt. %s' % (wep.name, 'personal' if use_personal else 'best', 'primed' if use_primed else 'no primed', 'corrupted' if use_corrupted else 'no corrupted', 'faction' if use_faction else 'no faction', sortby, point_limit)
print '############ BEST:'
if len(all_stats) > 0:
pretty_print_stats(all_stats[0])
print 'used mods:', all_stats[0]['mods']
else:
print 'none :('
builder_end = datetime.now()
print '### builder took: %s' % (builder_end - builder_start)
