def test_solver(solver):
history = []
j = 0
seed_width = 20
while True:
solutions = solver.ask()
fitness_list = np.zeros(solver.popsize)
#print(solutions)
# for i in range(solver.popsize):
# fit_func((model,solutions[i]))
seed = np.random.randint(seed_width)
fitness_list = pool.map(fit_func, [(model, solutions[i], seed, 5)
for i in range(solver.popsize)])
solver.tell(fitness_list)
result = solver.result(
) # first element is the best solution, second element is the best fitness
history.append(result[1])
if (j + 1) % 10 == 0:
print("fitness at iteration", (j + 1), result[1])
if args.algo == 'ga':
print("Best:", solver.elite_rewards[0])
print('Seed width', seed_width)
if (j + 1) % 100 == 0:
evluate_func_test(model, result[0])
plt.plot(history)
plt.savefig(args.log_dir + '/loss_plot.png')
plt.close()
if (j + 1) % 1000 == 0:
# save the best result
filename = args.log_dir + '/models/model_parameters_' + str(j + 1)
with open(filename, 'wt') as out:
res = json.dump([np.array(result[0]).round(4).tolist()],
out,
sort_keys=True,
indent=2,
separators=(',', ': '))
# if (j+1) % 40== 0 and args.algo == 'ga':
# #print('----------------------RESET ELITES')
# #new_elite_rewards = []
# new_elite_params = solver.elite_params
# new_elite_rewards = pool.map(fit_func, [(model,solver.elite_params[kk], seed, 5) for kk in range(len(solver.elite_rewards))])
# solver = SimpleGA(NPARAMS, # number of model parameters
# sigma_init=0.5, # initial standard deviation
# popsize=NPOPULATION, # population size
# elite_ratio=0.05, # percentage of the elites
# forget_best=False, # forget the historical best elites
# weight_decay=0.00, # weight decay coefficient
# )
# solver.elite_params = new_elite_params
# solver.elite_rewards = new_elite_rewards
# #print('----------------------RESET ELITES')
if -result[1] <= 0.001:
print("local optimum discovered by solver:\n", result[0])
print("fitness score at this local optimum:", result[1])
# save the best result
filename = args.log_dir + '/models/model_parameters_' + str(j + 1)
with open(filename, 'wt') as out:
res = json.dump([np.array(result[0]).round(4).tolist()],
out,
sort_keys=True,
indent=2,
separators=(',', ': '))
seed_width += 5
new_elite_params = solver.elite_params
new_elite_rewards = pool.map(
fit_func, [(model, solver.elite_params[kk], seed, 5)
for kk in range(len(solver.elite_rewards))])
solver = SimpleGA(
NPARAMS, # number of model parameters
sigma_init=0.5, # initial standard deviation
popsize=NPOPULATION, # population size
elite_ratio=0.05, # percentage of the elites
forget_best=False, # forget the historical best elites
weight_decay=0.00, # weight decay coefficient
)
solver.elite_params = new_elite_params
solver.elite_rewards = new_elite_rewards
#return history, result
j += 1
def test_solver(solver):
history = []
j = 0
seed_width = 100
while True:
solutions = solver.ask()
fitness_list = np.zeros(solver.popsize)
#print(solutions)
# for i in range(solver.popsize):
# fit_func((model,solutions[i]))
seed = 1
fitness_list = pool.map(fit_func, [(model, solutions[i], train_X, 10)
for i in range(solver.popsize)])
if args.algo == 'ga':
# For the elites testing one more time on 30 seed to be sure they are really good
elite_idxs = np.argsort(fitness_list)[::-1][0:solver.elite_popsize]
fitness_list_2 = pool.map(
fit_func, [(model, solutions[elite_idx], train_X, 10)
for elite_idx in elite_idxs])
for kk, elite_idx_ in enumerate(elite_idxs):
fitness_list[elite_idx_] = fitness_list_2[kk]
for kk in range(solver.popsize):
if kk not in elite_idxs:
fitness_list[kk] = -np.inf
# # check how many new unchecked elites sneak in
# new_elites_idxs = np.argsort(fitness_list)[::-1][0:solver.elite_popsize]
# for new_elites_idx in new_elites_idxs:
# if new_elites_idx not in elite_idxs:
# print(new_elites_idx)
solver.tell(fitness_list)
result = solver.result(
) # first element is the best solution, second element is the best fitness
history.append(result[1])
if (j + 1) % 10 == 0:
print("fitness at iteration", (j + 1), result[1])
if args.algo == 'ga':
print("Best:", solver.elite_rewards[0])
if args.algo == 'oes':
print('Best:', solver.curr_best_reward)
print('Seed width', seed_width)
if (j + 1) % 100 == 0:
#evluate_func_test(model, result[0])
plt.plot(history)
plt.savefig(args.log_dir + '/loss_plot.png')
plt.close()
if (j + 1) % 1000 == 0:
# save the best result
filename = args.log_dir + '/models/model_parameters_' + str(j + 1)
with open(filename, 'wt') as out:
res = json.dump([np.array(result[0]).round(4).tolist()],
out,
sort_keys=True,
indent=2,
separators=(',', ': '))
# if (j+1) % 40== 0 and args.algo == 'ga':
# #print('----------------------RESET ELITES')
# #new_elite_rewards = []
# new_elite_params = solver.elite_params
# new_elite_rewards = pool.map(fit_func, [(model,solver.elite_params[kk], seed, 5) for kk in range(len(solver.elite_rewards))])
# solver = SimpleGA(NPARAMS, # number of model parameters
# sigma_init=0.5, # initial standard deviation
# popsize=NPOPULATION, # population size
# elite_ratio=0.05, # percentage of the elites
# forget_best=False, # forget the historical best elites
# weight_decay=0.00, # weight decay coefficient
# )
# solver.elite_params = new_elite_params
# solver.elite_rewards = new_elite_rewards
# #print('----------------------RESET ELITES')
if -result[1] <= 0.001:
print("local optimum discovered by solver:\n", result[0])
print("fitness score at this local optimum:", result[1])
# save the best result
filename = args.log_dir + '/models/model_parameters_' + str(j + 1)
with open(filename, 'wt') as out:
res = json.dump([np.array(result[0]).round(4).tolist()],
out,
sort_keys=True,
indent=2,
separators=(',', ': '))
seed_width += 5
if args.algo == 'ga':
new_elite_params = solver.elite_params
new_elite_rewards = pool.map(
fit_func, [(model, solver.elite_params[kk], train_X, 5)
for kk in range(len(solver.elite_rewards))])
solver = SimpleGA(
NPARAMS, # number of model parameters
sigma_init=0.5, # initial standard deviation
popsize=NPOPULATION, # population size
elite_ratio=0.05, # percentage of the elites
forget_best=False, # forget the historical best elites
weight_decay=0.00, # weight decay coefficient
)
solver.elite_params = new_elite_params
solver.elite_rewards = new_elite_rewards
#return history, result
j += 1