def pareto_model():
x0 = -1
x1 = 1
num_samples = 500
memory = 1
# step_sizes = [0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.25, 0.4]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
# step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
step_sizes = [0.05, 0.075, 0.1, 0.125, 0.15, 0.2, 0.3, 0.67]
dim_state = 3
dim_action = len(step_sizes)
env = IntegrationEnv(fun=BrokenPolynomial(),
max_iterations=1000,
initial_step_size=0.075,
step_sizes=step_sizes,
error_tol=0.0005,
memory=memory,
nodes_per_integ=dim_state)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor',
memory=memory), load('scaler_mem1.bin'))
# integrator = IntegratorLinReg(step_sizes, load('linreg_models_estim.bin'))
integrator = Simpson()
# estimator = Estimator(build_estimator_model(dim_state, lr=0.0001, filename='estimator'), load('scaler.bin'),
# threshold=100 * 7.5e-6)
errors = []
steps = []
x1s = []
for i in range(num_samples):
if i % 10 == 0:
print(i)
env.reset()
_, evals, this_x1, err = integrate_env(predictor, integrator, env, x0,
x1)
errors.append(np.mean(err))
steps.append(evals)
x1s.append(this_x1)
print(np.mean(steps))
print(np.mean(errors))
print(np.mean(x1s))
print(np.quantile(errors, 0.9))
np.save('pareto_model_mem1.npy',
np.array([np.mean(errors), np.mean(steps)]))
plt.hist(errors, 25)
plt.show()
def compare():
""" compare predictor to adaptive simpson rule based on an average performance on a sample of functions """
x0 = 0.0
num_samples = 200
error_predictor = 0.0
error_simpson = 0.0
step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
dim_state = 3
dim_action = len(step_sizes)
env = IntegrationEnv(fun=SuperposeSinus(5),
max_iterations=256,
initial_step_size=0.2,
step_sizes=step_sizes,
error_tol=0.0005)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor'), load('scaler.bin'))
for i in range(num_samples):
if i % 10 == 0:
print(i)
x1 = 200.0
env.reset()
integ_pred, evals, x1, _ = integrate_env(predictor, Simpson(), env, x0,
x1)
integ = env.fun.integral(x0, x1)
env.reset(reset_params=False)
# asr = AdaptSimpsConstEvals(env.fun, x0, x1)
# integ_simps = asr(evals)
asr = Simps(env.fun, x0, x1)
integ_simps = asr(num_evals=evals)
error_predictor += abs(integ - integ_pred)
error_simpson += abs(integ - integ_simps)
error_simpson /= num_samples
error_predictor /= num_samples
print('Predictor error: {}'.format(error_predictor))
print('ASR error: {}'.format(error_simpson))
def one_fun_boole():
x0 = 0
x1 = 10
step_sizes = [0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.25, 0.4]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
dim_state = 5
dim_action = len(step_sizes)
memory = 1
env = IntegrationEnv(fun=Sinus(),
max_iterations=256,
initial_step_size=0.1,
step_sizes=step_sizes,
error_tol=0.000001,
memory=memory,
nodes_per_integ=dim_state)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor',
memory=memory), load('scaler_boole_mem1.bin'))
# integrator = IntegratorLinReg(step_sizes, load('linreg_models.bin'), load('scaler.bin'))
integrator = Boole()
_, evals, x1, errors = integrate_env(predictor,
integrator,
env,
x0,
x1,
plot=True)
print('new x1: {}'.format(x1))
print('Predictor error total: {}'.format(np.sum(errors)))
print('Predictor error per step: {}'.format(np.mean(errors)))
print('Predictor evals: {}'.format(evals))
print('')
env.reset(reset_params=False)
booles = BoolesRule(env.fun, x0, x1)
integ_simps, errors = booles(num_evals=evals, stepwise_error=True)
print('Boole error total: {}'.format(np.sum(errors)))
print('Boole error per step: {}'.format(np.mean(errors)))
print('Boole evals: {}'.format(booles.evals))
print('')
booles.plot()
env.reset(reset_params=False)
simps = Simps(env.fun, x0, x1)
integ_simps, errors = simps(num_evals=evals, stepwise_error=True)
print('Simpson error total: {}'.format(np.sum(errors)))
print('Simpson error per step: {}'.format(np.mean(errors)))
print('Simpson evals: {}'.format(simps.evals))
print('')
simps.plot()
env.reset(reset_params=False)
rom = Romberg(env.fun, x0, x1, tol=0.0005, order=3)
integ, errors = rom(0.15, stepwise_errors=True)
print('Romberg error total: {}'.format(np.sum(errors)))
print('Romberg error per step: {}'.format(np.mean(errors)))
print('Romberg evals: {}'.format(rom.evals))
rom.plot()
def one_fun():
x0 = 0.0
x1 = 10.0
step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
dim_state = 3
dim_action = len(step_sizes)
env = IntegrationEnv(fun=Sinus(),
max_iterations=256,
initial_step_size=0.15,
step_sizes=step_sizes,
error_tol=0.0005)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor'), load('scaler.bin'))
integ, evals, x1, _ = integrate_env(predictor,
Simpson(),
env,
x0,
x1,
plot=True)
print('new x1: {}'.format(x1))
print('Predictor error: {}'.format(abs(env.fun.integral(x0, x1) - integ)))
print('Predictor evals: {}'.format(evals))
env.reset(reset_params=False)
asr = AdaptSimpsConstEvals(env.fun, x0, x1)
integ = asr(evals)
print('ASR error: {}'.format(abs(env.fun.integral(x0, x1) - integ)))
print('ASR evals: {}'.format(asr.evals))
asr.plot()
env.reset(reset_params=False)
simps = Simps(env.fun, x0, x1)
integ_simps = simps(num_evals=evals)
print('Simpson error: {}'.format(
abs(env.fun.integral(x0, x1) - integ_simps)))
print('Simpson evals: {}'.format(simps.evals))
simps.plot()
def sample_trainingdata_from_predictor(env: IntegrationEnv,
predictor: Predictor,
integrator: Integrator,
num_episodes: int, threshold: float):
states = []
errors = []
for episode in range(num_episodes):
if episode % 10 == 0:
print(episode)
state = env.reset()
done = False
while not done:
action = predictor(state) # idx of step_size
next_state, reward, done, info = env.iterate(action, integrator)
states.append(next_state[:env.nodes_per_integ])
errors.append(info["exact_error"])
state = next_state
states = np.stack(states)
errors = np.array(errors)
errors = np.reshape(errors, (-1, 1))
states_large = states[np.squeeze(errors > threshold), :]
states_small = states[np.squeeze(errors <= threshold), :]
np.random.shuffle(states_large)
np.random.shuffle(states_small)
length = min(states_large.shape[0], states_small.shape[0])
print('size of data set: {}'.format(2 * length))
states_large = states_large[:length, :]
states_small = states_small[:length, :]
targets = np.zeros(2 * length)
targets[:length] = np.ones(length)
states = np.concatenate((states_large, states_small))
mapIndexPosition = list(zip(states, targets))
np.random.shuffle(mapIndexPosition)
states, targets = zip(*mapIndexPosition)
states = np.stack(states)
targets = np.array(targets)
dump((states, targets), "data_train_estimator")
def main():
gamma = 0.0
num_episodes = 100000
# step_sizes = [0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.25, 0.4]
step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
# step_sizes = [0.05, 0.075, 0.1, 0.125, 0.15, 0.2, 0.3, 0.67]
dim_state = 3 # nodes per integration step
dim_action = len(step_sizes)
memory = 0 # how many integration steps the predictor can look back
# 7.5e-6
env = IntegrationEnv(fun=Sinus(),
max_iterations=256,
initial_step_size=0.075,
error_tol=7.5e-6,
nodes_per_integ=dim_state,
memory=memory,
x0=0,
max_dist=20,
step_size_range=(step_sizes[0], step_sizes[-1]))
# env = IntegrationEnv(fun=Sinus(), max_iterations=128, initial_step_size=0.1, step_sizes=step_sizes,
# error_tol=0.0005, nodes_per_integ=dim_state, memory=memory)
experience = Experience(batch_size=32)
predictor = PredictorQ(
step_sizes=step_sizes,
model=build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename=None,
lr=0.00001,
memory=memory),
scaler=load('model_quad/model_sinus/Simpson/scaler.bin'))
# integrator = IntegratorLinReg(step_sizes, load('linreg_models.bin'), load('scaler.bin'))
# integrator = Boole()
integrator = Simpson()
perf_tracker = PerformanceTracker(env, num_testfuns=1000, x0=-1, x1=1)
# losses = []
# moving_average = []
for episode in range(num_episodes):
state = env.reset()
reward_total = 0
loss_this_episode = 0
steps = 0
done = False
eps = 0.66
if episode < 0:
# eps = 0.01 + (1.0 - 0.01) * math.exp(-0.023 * episode
eps = 0.2 + 0.8 * 2.71828**(
-0.0146068 * episode
) # decrease from 1.0 to approx 0.2 at episode 300
print('episode: {}'.format(episode))
while not done:
# get action from actor
actions = predictor.get_actions(state)
if episode < 0:
action = choose_action(actions, eps, dim_action)
else:
action = choose_action3(actions, eps, dim_action)
step_size = predictor.action_to_stepsize(action)
# execute action
next_state, reward, done, _ = env.iterate(step_size, integrator)
steps += 1
reward_total += reward
# learning
action_next_state = predictor.get_actions(next_state)
target = reward + gamma * np.max(action_next_state)
target_actions = actions.squeeze()
target_actions[action] = target
# print(target)
# print('')
experience.append(state=state, target=target_actions)
if experience.is_full() or done:
states, targets = experience.get_samples()
loss_predictor = predictor.train_on_batch(states, targets)
loss_this_episode += loss_predictor
experience.reset()
state = next_state
print('reward: {}'.format(reward_total))
print('loss_predictor: {}'.format(loss_this_episode))
# losses.append(loss_this_episode)
# if episode % 10 == 0 and len(losses) > 99:
# moving_average.append(np.mean(losses[-100:]))
# plt.plot(moving_average, 'r')
# plt.pause(0.05)
if episode % 100 == 0:
perf_tracker.evaluate_performance(predictor, integrator)
perf_tracker.plot()
perf_tracker.plot_pareto(num_points=7)
# if episode % 250 == 0:
# env.plot(episode=episode, x_min=-1.5, x_max=1.5)
if episode % 10 == 0:
predictor.model.save_weights('predictor')
def compare_romberg():
x0 = 0.0
num_samples = 100
error_predictor = []
error_rom = []
evals_rom = []
evals_predictor = []
step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
dim_state = 3
dim_action = len(step_sizes)
env = IntegrationEnv(fun=Sinus(),
max_iterations=256,
initial_step_size=0.15,
step_sizes=step_sizes,
error_tol=0.0005)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor'), load('scaler.bin'))
for i in range(num_samples):
if i % 10 == 0:
print(i)
x1 = 20.0
# model
env.reset()
_, evals, x1, errors = integrate_env(predictor, Simpson(), env, x0, x1)
error_pred_step = np.mean(errors)
env.reset(reset_params=False)
# romberg
tol = 0.0003
rom = Romberg(env.fun, x0, x1, tol=tol, order=2)
integ_rom, errors = rom(0.15, True)
error_rom_step = np.mean(errors)
evals_rom_step = rom.evals
# for j in range(10):
# rom = Romberg(env.fun, x0, x1, tol=tol, order=2)
# integ_rom, errors = rom(0.15, True)
# error_rom_step = np.mean(errors)
# evals_rom_step = rom.evals
# if error_rom_step < 0.0001:
# tol *= 2.0
# elif error_rom_step > 0.0005:
# tol /= 3.0
# else:
# break
error_predictor.append(error_pred_step)
error_rom.append(error_rom_step)
evals_predictor.append(evals)
evals_rom.append(evals_rom_step)
# error_rom = np.array(error_rom)
# not_converged = np.concatenate((error_rom[error_rom > 0.0005], error_rom[error_rom < 0.0001]))
# if len(not_converged > 0):
# print('romberg did not converge in some cases:')
# print(not_converged)
mean_error_predictor = np.mean(error_predictor)
var_error_predictor = np.var(error_predictor)
mean_error_rom = np.mean(error_rom)
var_error_rom = np.var(error_rom)
mean_evals_predictor = np.mean(evals_predictor)
var_evals_predictor = np.var(evals_predictor)
mean_evals_rom = np.mean(evals_rom)
var_evals_rom = np.var(evals_rom)
print(
'Avg. predictor number of function evaluations per episode: {}'.format(
mean_evals_predictor))
print('Avg. predictor error per step: {}'.format(mean_error_predictor))
print('Avg. romberg number of function evaluations per episode: {}'.format(
mean_evals_rom))
print('Avg. romberg error per step: {}'.format(mean_error_rom))
print('')
print(
'Variance of predictor number of function evaluations per episode: {}'.
format(var_evals_predictor))
print(
'Variance of predictor error per step: {}'.format(var_error_predictor))
print('Variance of rom number of function evaluations per episode: {}'.
format(var_evals_rom))
print('Variance of rom error per step: {}'.format(var_error_rom))
def one_fun():
x0 = -1
x1 = 1
# step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
step_sizes = [0.05, 0.075, 0.1, 0.125, 0.15, 0.2, 0.3, 0.67]
dim_state = 3
dim_action = len(step_sizes)
memory = 1
env = IntegrationEnv(fun=BrokenPolynomial(),
max_iterations=256,
initial_step_size=0.075,
step_sizes=step_sizes,
error_tol=7.5e-6,
memory=memory)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor',
memory=memory), load('scaler_mem1.bin'))
# integrator = IntegratorLinReg(step_sizes, load('linreg_models.bin'), load('scaler.bin'))
integrator = Simpson()
# _, evals, _, errors = integrate_env(predictor, Simpson(), env, x0, x1, plot=True)
# print('new x1: {}'.format(x1))
# print('Predictor error total: {}'.format(np.sum(errors)))
# print('Predictor error per step: {}'.format(np.mean(errors)))
# print('Predictor evals: {}'.format(evals))
# print('')
env.reset(reset_params=False)
_, evals, x1, errors = integrate_env(predictor,
integrator,
env,
x0,
x1,
plot=True)
print('new x1: {}'.format(x1))
print('Predictor error total: {}'.format(np.sum(errors)))
print('Predictor error per step: {}'.format(np.mean(errors)))
print('Predictor evals: {}'.format(evals))
print('')
env.reset(reset_params=False)
asr = AdaptSimpsConstEvals(env.fun, x0, x1)
asr(200)
errors = asr.stepwise_errors
print('ASR error total: {}'.format(np.sum(errors)))
print('ASR error per step: {}'.format(np.mean(errors)))
print('ASR evals: {}'.format(asr.evals))
print('')
asr.plot()
env.reset(reset_params=False)
simps = Simps(env.fun, x0, x1)
integ_simps, errors = simps(num_evals=evals, stepwise_error=True)
# integ_simps, errors = simps(step_size=0.11, stepwise_error=True)
print('Simpson error total: {}'.format(np.sum(errors)))
print('Simpson error per step: {}'.format(np.mean(errors)))
print('Simpson evals: {}'.format(simps.evals))
print('')
simps.plot()
env.reset(reset_params=False)
rom = Romberg(env.fun, x0, x1, tol=0.0005, order=2)
integ, errors = rom(0.15, stepwise_errors=True)
print('Romberg error total: {}'.format(np.sum(errors)))
print('Romberg error per step: {}'.format(np.mean(errors)))
print('Romberg evals: {}'.format(rom.evals))
rom.plot()
def compare_simps_tol():
""" compare model to simpson rule, stepsize for simps is adapted to match tol. """
x0 = 0.0
num_samples = 2000
error_predictor = []
error_simps = []
evals_simps = []
evals_predictor = []
step_sizes = [0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75]
# step_sizes = [0.05, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.3, 0.4]
dim_state = 3
dim_action = len(step_sizes)
env = IntegrationEnv(fun=SuperposeSinus(5),
max_iterations=256,
initial_step_size=0.15,
step_sizes=step_sizes,
error_tol=0.0005)
predictor = PredictorQ(
build_value_model(dim_state=dim_state,
dim_action=dim_action,
filename='predictor'), load('scaler.bin'))
for i in range(num_samples):
if i % 10 == 0:
print(i)
x1 = 20.0
# model
env.reset()
_, evals, x1, errors = integrate_env(predictor, Simpson(), env, x0, x1)
error_pred_step = np.mean(errors)
env.reset(reset_params=False)
# simpson
step_size = 0.177
simp = Simps(env.fun, x0, x1)
_, error_simps_step = simp(step_size=step_size, stepwise_error=True)
error_simps_step = np.mean(error_simps_step)
evals_simps_step = simp.evals
# for j in range(10):
# simp = Simps(env.fun, x0, x1)
# integ_rom, error_simps_step = simp(step_size=step_size, stepwise_error=True)
# error_simps_step /= (simp.evals - 1.0) / 2.0
# evals_simps_step = simp.evals
# if error_simps_step < 0.0001:
# step_size *= 1.5
# elif error_simps_step > 0.0005:
# step_size /= 2.0
# else:
# break
error_predictor.append(error_pred_step)
error_simps.append(error_simps_step)
evals_predictor.append(evals)
evals_simps.append(evals_simps_step)
error_simps = np.array(error_simps)
not_converged = np.concatenate(
(error_simps[error_simps > 0.0005], error_simps[error_simps < 0.0001]))
if len(not_converged > 0):
print('simps did not converge in some cases:')
print(not_converged)
mean_error_predictor = np.mean(error_predictor)
var_error_predictor = np.var(error_predictor)
mean_error_simps = np.mean(error_simps)
var_error_simps = np.var(error_simps)
mean_evals_predictor = np.mean(evals_predictor)
var_evals_predictor = np.var(evals_predictor)
mean_evals_simps = np.mean(evals_simps)
var_evals_simps = np.var(evals_simps)
print(
'Avg. predictor number of function evaluations per episode: {}'.format(
mean_evals_predictor))
print('Avg. predictor error per step: {}'.format(mean_error_predictor))
print('Avg. simpson number of function evaluations per episode: {}'.format(
mean_evals_simps))
print('Avg. simpson error per step: {}'.format(mean_error_simps))
print('')
print(
'Variance of predictor number of function evaluations per episode: {}'.
format(var_evals_predictor))
print(
'Variance of predictor error per step: {}'.format(var_error_predictor))
print('Variance of simpson number of function evaluations per episode: {}'.
format(var_evals_simps))
print('Variance of simpson error per step: {}'.format(var_error_simps))
