def main():
config_file = open('./config.json')
config = json.load(config_file,
object_hook=lambda d: namedtuple('x', d.keys())
(*d.values()))
param_names = ['voltage', 'flow_rate', 'pressure']
param_range = [(0.0, 5.0), (1.0, 12.0), (10, 100)]
func = RealReaction(num_dim=3,
param_range=param_range,
param_names=param_names,
direction='max',
logger=None)
cell = rnn.StochasticRNNCell(cell=rnn.LSTM,
kwargs={'hidden_size': config.hidden_size},
nlayers=config.num_layers,
reuse=config.reuse)
optimizer = StepOptimizer(cell=cell,
func=func,
ndim=config.num_params,
nsteps=config.num_steps,
ckpt_path=config.save_path,
logger=logger,
constraints=config.constraints)
x_array, y_array = optimizer.run()
def main():
config_file = open('./config.json')
config = json.load(config_file,
object_hook=lambda d: namedtuple('x', d.keys())
(*d.values()))
if config.opt_direction is 'max':
problem_type = 'concave'
else:
problem_type = 'convex'
if config.constraints:
func = ConstraintQuadraticEval(num_dim=config.num_params,
random=config.instrument_error,
ptype=problem_type)
else:
func = QuadraticEval(num_dim=config.num_params,
random=config.instrument_error,
ptype=problem_type)
if config.policy == 'srnn':
cell = rnn.StochasticRNNCell(cell=rnn.LSTM,
kwargs={
'hidden_size': config.hidden_size,
'use_batch_norm_h': config.batch_norm,
'use_batch_norm_x': config.batch_norm,
'use_batch_norm_c': config.batch_norm,
},
nlayers=config.num_layers,
reuse=config.reuse)
if config.policy == 'rnn':
cell = rnn.MultiInputRNNCell(cell=rnn.LSTM,
kwargs={
'hidden_size': config.hidden_size,
'use_batch_norm_h': config.batch_norm,
'use_batch_norm_x': config.batch_norm,
'use_batch_norm_c': config.batch_norm,
},
nlayers=config.num_layers,
reuse=config.reuse)
optimizer = StepOptimizer(cell=cell,
func=func,
ndim=config.num_params,
nsteps=config.num_steps,
ckpt_path=config.save_path,
logger=logger,
constraints=config.constraints)
x_array, y_array = optimizer.run()
# np.savetxt('./scratch/nn_y.csv', y_array, delimiter=',')
# np.save('./scratch/nn_x.npy', y_array)
plt.figure(1)
plt.plot(y_array)
plt.show()
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.plot(x_array[:, 0], x_array[:, 1], x_array[:, 2])
fig2.show()
plt.show()
def main():
config_file = open('./config.json')
config = json.load(config_file,
object_hook=lambda d: namedtuple('x', d.keys())
(*d.values()))
# update number of parameters to all those considred in the data set
param_names = []
param_range = []
for col in state_space:
param_names.append(col)
param_range.append((state_space[col].min(), state_space[col].max()))
func = RealReaction(num_dim=len(param_names),
param_range=param_range,
param_names=param_names,
direction='max',
logger=None)
cell = rnn.StochasticRNNCell(cell=rnn.LSTM,
kwargs={'hidden_size': config.hidden_size},
nlayers=config.num_layers,
reuse=config.reuse)
# Assumes that previous step exists
# e.g. current_step = 2 means that the first step is in place
next_states = []
for baseline_num in range(1, 10):
# print (config.sav)
df = pd.read_csv(
'./ckpt/baseline/baseline_{}/trace.csv'.format(baseline_num))
l = list(df['step'])
current_step = len(l) + 1
save_path_of_previous_step = "./ckpt/baseline/baseline_{}/step{}".format(
baseline_num, current_step - 1)
save_path = './ckpt/baseline/baseline_{}/step{}'.format(
baseline_num, current_step)
if not os.path.exists(save_path):
os.makedirs(save_path)
optimizer = StepOptimizer(cell=cell,
func=func,
ndim=config.num_params,
nsteps=1,
save_path=save_path,
ckpt_path=save_path_of_previous_step,
logger=logger,
constraints=config.constraints)
print(save_path_of_previous_step)
x_array, y_array = optimizer.run(l[-1])
l.append(optimizer.next_idx)
pd.DataFrame(l).to_csv(
'./ckpt/baseline/baseline_{}/trace.csv'.format(baseline_num))
next_states.append(optimizer.next_idx)
pritn(next_states)
def load_model(sess, config, logger):
assert (os.path.exists(config.save_path))
if config.opt_direction == 'max':
problem_type = 'concave'
else:
problem_type = 'convex'
if config.reaction_type == 'quad' and config.constraints == False:
rxn_yeild = reactions.Quadratic(batch_size=config.batch_size,
num_dims=config.num_params,
ptype=problem_type,
random=config.instrument_error)
elif config.reaction_type == 'quad' and config.constraints == True:
rxn_yeild = reactions.ConstraintQuadratic(
batch_size=config.batch_size,
num_dims=config.num_params,
ptype=problem_type,
random=config.instrument_error)
elif config.reaction_type == 'gmm':
rxn_yeild = reactions.GMM(batch_size=config.batch_size,
num_dims=config.num_params,
random=config.instrument_error,
cov=config.norm_cov)
if config.policy == 'srnn':
cell = rnn.StochasticRNNCell(cell=rnn.LSTM,
kwargs={
'hidden_size': config.hidden_size,
'use_batch_norm_h': config.batch_norm,
'use_batch_norm_x': config.batch_norm,
'use_batch_norm_c': config.batch_norm,
},
nlayers=config.num_layers,
reuse=config.reuse)
if config.policy == 'rnn':
cell = rnn.MultiInputRNNCell(cell=rnn.LSTM,
kwargs={
'hidden_size': config.hidden_size,
'use_batch_norm_h': config.batch_norm,
'use_batch_norm_x': config.batch_norm,
'use_batch_norm_c': config.batch_norm,
},
nlayers=config.num_layers,
reuse=config.reuse)
model = Optimizer(cell=cell,
logger=logger,
func=rxn_yeild,
ndim=config.num_params,
batch_size=config.batch_size,
unroll_len=config.unroll_length,
lr=config.learning_rate,
loss_type=config.loss_type,
optimizer=config.optimizer,
trainable_init=config.trainable_init,
direction=config.opt_direction,
constraints=config.constraints,
discount_factor=config.discount_factor)
ckpt = tf.train.get_checkpoint_state(config.save_path)
if ckpt and ckpt.model_checkpoint_path:
logger.info('Reading model parameters from {}.'.format(
ckpt.model_checkpoint_path))
model.saver.restore(sess, ckpt.model_checkpoint_path)
return model