def load(id_, cfg_id, base_cfg_id, return_dict):
return_dict[id_] = (-1, (0, 0))
# base_paths = ['models', 'modelsDec10-14']
base_paths = ['models']
res = []
for base_path in base_paths:
ids_ = [
'../' + base_path + "/%s-*-F-m1",
'../' + base_path + "/%s-*-6-m1"
]
for id__ in ids_:
print(id__ % cfg_id)
res.extend(list(glob.glob(id__ % cfg_id)))
print('####################')
print(id_)
print(res)
stuns = []
for fname in res:
fname = fname[:-3]
# print(fname)
for _ in range(20):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
exp.load_eval(fname, steps=3000, initial_survival_time=3000)
stuns.append(exp.env.n_stuns)
if stuns:
ci = st.t.interval(0.95, len(stuns)-1, loc=np.mean(stuns), scale=st.sem(stuns))
print('avg_stuns:%d' % np.mean(stuns), stuns)
return_dict[id_] = (np.mean(stuns), ci)
else:
return_dict[id_] = (0, (0, 0))
def load(fname, scenario, ret_steps_til_first_kill, ret_fish_pop):
for _ in range(10):
exp = Experiment(BASE_CFG, show_gui=False, dump_cfg=False)
exp.env.select_fish_types(0, scenario, 0)
_, _ = exp.load_eval(fname, steps=MAX_STEPS, initial_survival_time=3000)
ret_steps_til_first_kill.append(exp.env.steps_til_first_kill)
ret_fish_pop.append(len(exp.env.fishes))
def load(id_, cfg_id, base_cfg_id, return_dict):
base_paths = ['../modelsDec21-31', '../modelsJan01-10', '../modelsRest']
res = []
for base_path in base_paths:
ids_ = [
base_path + "/%s-*-F-m1",
base_path + "/%s_sp200-*-F-m1",
base_path + "/%s-*-6-m1",
base_path + "/%s_sp200-*-6-m1"
]
for id__ in ids_:
print(id__ % cfg_id)
res.extend(list(glob.glob(id__ % cfg_id)))
print('####################')
print(id_)
print(res)
failures = 0
total = 0
coop_ratios = []
herding_counter = 0
for fname in res:
fname = fname[:-3]
print(fname)
try:
for _ in range(20):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
Fish.PROCREATE_AFTER_N_STEPS = 500
exp.env.select_fish_types(0, 2, 0)
exp.env.max_fish = 10
Shark.FRICTION = 0.1
Shark.MAX_SPEED_CHANGE = 0.06
fish_pop_hist, _ = exp.load_eval(fname, steps=3000, initial_survival_time=3000)
kpi = fish_pop_hist[-1]
if kpi >= 1 and kpi < 10:
herding_counter += 1
if exp.env.dead_fishes != 0:
coop_ratios.append(exp.env.coop_kills / exp.env.dead_fishes)
else:
failures += 1
print('#### NO dead fishes! ####')
total += 1
except Exception as ex:
print('wtf.', ex)
return_dict['fails'] += 1
if coop_ratios:
ci = st.t.interval(0.95, len(coop_ratios)-1, loc=np.mean(coop_ratios), scale=st.sem(coop_ratios))
print('avg_coop_ratio:%d' % np.mean(coop_ratios))
return_dict[id_] = (np.mean(coop_ratios), ci, failures / float(total), herding_counter / float(total))
else:
return_dict[id_] = (0, (0, 0), 0, 0)
print('Saving', id_)
print(return_dict)
def load(id_, cfg_id, base_cfg_id, return_dict, use_full, shared):
print('Actually starting', id_)
suffix = '-m1'
if shared:
cfg_id = cfg_id.replace('n_net3', 'shared_net3')
base_cfg_id = base_cfg_id.replace('n_net3', 'shared_net3')
suffix = ''
return_dict[id_] = (-1, (0, 0), -1)
# base_paths = ['models', 'modelsDec10-14']
base_paths = ['models']
res = []
for base_path in base_paths:
ids_ = [
'../' + base_path + "/%s-*-F" + suffix,
'../' + base_path + "/%s-*-6" + suffix
]
# print(ids_)
for id__ in ids_:
print(id__ % cfg_id)
res.extend(list(glob.glob(id__ % cfg_id)))
print('Now doing', id_)
dead_fishes = []
coop_ratios = []
failures = []
for fname in res:
if not shared:
fname = fname[:-3]
for _ in range(10):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
exp.load_eval(fname, steps=3000, initial_survival_time=3000)
if exp.env.dead_fishes != 0:
if use_full:
coop_ratios.append(exp.env.full_coop_kills / exp.env.dead_fishes)
else:
coop_ratios.append(exp.env.coop_kills / exp.env.dead_fishes)
dead_fishes.append(exp.env.dead_fishes)
failures.append(0.)
else:
failures.append(1.)
len_ = len(coop_ratios)
coop_ratios_old = coop_ratios[:]
if coop_ratios:
ci = st.t.interval(0.95, len_ - 1, loc=np.mean(coop_ratios), scale=st.sem(coop_ratios_old))
print('avg_coop_ratio:%f' % np.mean(coop_ratios))
ci_fail = st.t.interval(0.95, len(failures)-1, loc=np.mean(failures), scale=st.sem(failures))
print('avg_fail_ratio:%f' % np.mean(failures))
ci_dead = st.t.interval(0.95, len(dead_fishes)-1, loc=np.mean(dead_fishes), scale=st.sem(dead_fishes))
print('avg_dead_ratio:%f' % np.mean(dead_fishes))
return_dict[id_] = (np.mean(coop_ratios), ci, np.mean(failures), ci_fail, np.mean(dead_fishes), ci_dead)
else:
return_dict[id_] = (0, (0, 0), 0, (0, 0), 0, (0, 0))
def load(fname, scenario, ret_dead_fishes, ret_last_pops):
xx = []
for _ in range(100):
exp = Experiment(BASE_CFG, show_gui=False, dump_cfg=False)
exp.env.select_fish_types(0, scenario, 0)
_, _ = exp.load_eval(fname, steps=MAX_STEPS, initial_survival_time=3000)
ret_dead_fishes.append(exp.env.dead_fishes)
ret_last_pops.append(len(exp.env.fishes))
xx.append(exp.env.dead_fishes)
print(fname, xx)
def load(id_, cfg_id, base_cfg_id, return_dict):
base_paths = ['models', 'modelsDec10-14', 'modelsDec15-20']
base_paths = ['models']
res = []
for base_path in base_paths:
ids_ = [
'../' + base_path + "/%s-*-F-m1",
'../' + base_path + "/%s_sp200-*-F-m1",
'../' + base_path + "/%s-*-6-m1",
'../' + base_path + "/%s_sp200-*-6-m1"
]
for id__ in ids_:
print(id__ % cfg_id)
res.extend(list(glob.glob(id__ % cfg_id)))
print('####################')
print(id_)
print(res)
failures = 0
total = 0
coop_ratios = []
for fname in res:
fname = fname[:-3]
print(fname)
try:
for _ in range(20):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
exp.load_eval(fname, steps=3000, initial_survival_time=3000)
if exp.env.dead_fishes != 0:
coop_ratios.append(exp.env.coop_kills /
exp.env.dead_fishes)
else:
failures += 1
print('#### NO dead fishes! ####')
total += 1
except Exception as ex:
print('wtf.', ex)
return_dict['fails'] += 1
if coop_ratios:
ci = st.t.interval(0.95,
len(coop_ratios) - 1,
loc=np.mean(coop_ratios),
scale=st.sem(coop_ratios))
print('avg_coop_ratio:%d' % np.mean(coop_ratios))
return_dict[id_] = (np.mean(coop_ratios), ci, failures / float(total))
else:
return_dict[id_] = (0, (0, 0), 0)
print('Saving', id_)
print(return_dict)
def load(scenario, return_dict, wait):
dead_fishes = []
last_pops = []
tot_rewards = []
for _ in range(100):
exp = Experiment(BASE_CFG, show_gui=False, dump_cfg=False)
exp.env.select_fish_types(0, scenario, 0)
if wait:
rewards = exp.evaluate_static_wait(steps=MAX_STEPS, initial_survival_time=3000)
else:
rewards = exp.evaluate_static(steps=MAX_STEPS, initial_survival_time=3000)
tot_rewards.append(sum(rewards))
dead_fishes.append(exp.env.dead_fishes)
last_pops.append(len(exp.env.fishes))
return_dict[scenario] = (dead_fishes, last_pops)
def load(id_, cfg_id, base_cfg_id, return_dict):
return_dict[id_] = (-1, (0, 0))
# base_paths = ['models', 'modelsDec10-14']
base_paths = ['models']
res = []
for base_path in base_paths:
ids_ = [
'../' + base_path + "/%s-*-F-m1",
'../' + base_path + "/%s-*-6-m1"
]
for id__ in ids_:
print(id__ % cfg_id)
res.extend(list(glob.glob(id__ % cfg_id)))
print('####################')
print(id_)
print(res)
coop_ratios = []
failures = []
for fname in res:
fname = fname[:-3]
# print(fname)
for _ in range(20):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
exp.load_eval(fname, steps=3000, initial_survival_time=3000)
if exp.env.dead_fishes != 0:
print(exp.env.coop_kills, exp.env.dead_fishes)
coop_ratios.append(exp.env.coop_kills / exp.env.dead_fishes)
failures.append(0.)
else:
failures.append(1.)
print('####### NO dead fishes! #######')
# TODO DUBIOUS! Should we maybe add a [0] if there's no dead fishes?
# lets try.
coop_ratios.append(0)
if coop_ratios:
ci = st.t.interval(0.95, len(coop_ratios)-1, loc=np.mean(coop_ratios), scale=st.sem(coop_ratios))
print('avg_coop_ratio:%d' % np.mean(coop_ratios))
ci_fail = st.t.interval(0.95, len(failures)-1, loc=np.mean(failures), scale=st.sem(failures))
print('avg_fail_ratio:%d' % np.mean(failures))
return_dict[id_] = (np.mean(coop_ratios), ci, np.mean(failures), ci_fail)
else:
return_dict[id_] = (0, (0, 0), 0)
def load(id_, cfg_id, return_dict):
# base_cfg_id = 'ma3_obs' # for i10, i5 (sp600)
# base_cfg_id = 'ma3_obs_sp100' # for i2
# base_cfg_id = 'ma3_obs'
base_cfg_id = 'ma3_obs_eval' # sp100
base_paths = ['models']
res = []
two_net = 'two_net' in cfg_id
for base_path in base_paths:
id__ = '../' + base_path + "/%s-*-F"
if two_net:
id__ = '../' + base_path + "/%s-*-F-m1"
res.extend(list(glob.glob(id__ % cfg_id)))
counter_coop = 0.
counter_fail = 0.
counter_greedy = 0.
for fname in res:
if two_net:
fname = fname[:-3]
print('#############################')
print(fname)
for _ in range(3):
exp = Experiment(base_cfg_id, show_gui=False, dump_cfg=False)
fish_pop_hist, _ = exp.load_eval(fname, steps=5000)
if fish_pop_hist[-1] > 0 and fish_pop_hist[-1] < 10:
counter_coop += 1
if fish_pop_hist[-1] == 0:
counter_greedy += 1
if fish_pop_hist[-1] >= 10:
counter_fail += 1
# TODO: For i2 !
# if fish_pop_hist[-1] > 0 and exp.env.dead_fishes > 0:
# counter_coop += 1
# print(exp.env.dead_fishes)
total = counter_coop + counter_greedy + counter_fail
print('c:%d' % counter_coop, 'f:%d' % counter_fail,
'g:%d' % counter_greedy, 't:%d' % total)
print('coop', counter_coop / total)
print('greedy', counter_greedy / total)
print('fail', counter_fail / total)
return_dict[id_] = (counter_coop / total, total)
def load(fname, scenario, ret_dead_fishes, ret_last_pops):
static = fname == 'static'
static_wait = fname == 'static_wait'
temp = []
for _ in range(100):
exp = Experiment(BASE_CFG, show_gui=False, dump_cfg=False)
# exp.env.select_fish_types(0, scenario, 0)
if static_wait:
_ = exp.evaluate_static_wait(steps=MAX_STEPS,
initial_survival_time=3000)
elif static:
_ = exp.evaluate_static(steps=MAX_STEPS,
initial_survival_time=3000)
else:
_, _ = exp.load_eval(fname,
steps=MAX_STEPS,
initial_survival_time=3000)
temp.append(exp.env.dead_fishes)
ret_dead_fishes.append(exp.env.dead_fishes)
ret_last_pops.append(len(exp.env.fishes))
print(fname, temp)
def run():
cfg_id = 'dbg_n_net3_s035_i25_50x50'
Experiment(cfg_id).train()
def new_exp():
return Experiment(conf=None, backend='tiny', read_only=True)