def mems_exp(cfg):
log.info("============= Configuration =============")
log.info(f"Config:\n{cfg.pretty()}")
log.info("=========================================")
search_space = gen_search_space(cfg.problem)
outcome_con = gen_outcome_constraints(cfg.problem)
exp = SimpleExperiment(
name=cfg.problem.name,
search_space=SearchSpace(search_space),
evaluation_function=jumper,
objective_name="Energy_(uJ)",
minimize=False,
outcome_constraints=outcome_con,
)
optimization_config = OptimizationConfig(objective=Objective(
metric=MEMsMetric(name="Energy_(uJ)"),
minimize=False,
), )
class MyRunner(Runner):
def run(self, trial):
return {"name": str(trial.index)}
exp.runner = MyRunner()
exp.optimization_config = optimization_config
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.plot.contour import plot_contour
print(f"Running {cfg.bo.random} Sobol initialization trials...")
sobol = Models.SOBOL(exp.search_space)
num_search = cfg.bo.random
for i in range(num_search):
exp.new_trial(generator_run=sobol.gen(1))
exp.trials[len(exp.trials) - 1].run()
# data = exp.fetch_data()
num_opt = cfg.bo.optimized
for i in range(num_opt):
if (i % 5) == 0 and cfg.plot_during:
plot = plot_contour(
model=gpei,
param_x="N",
param_y="L",
metric_name="Energy_(uJ)",
)
data = plot[0]['data']
lay = plot[0]['layout']
render(plot)
print(f"Running GP+EI optimization trial {i + 1}/{num_opt}...")
# Reinitialize GP+EI model at each step with updated data.
batch = exp.new_trial(generator_run=gpei.gen(1))
gpei = Models.BOTORCH(experiment=exp, data=exp.eval())
plot_learn = plot_learning(exp, cfg)
# go.Figure(plot_learn).show()
save_fig([plot_learn], "optimize")
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.plot.contour import plot_contour
plot = plot_contour(model=gpei,
param_x="N",
param_y="L",
metric_name="Energy_(uJ)",
lower_is_better=cfg.metric.minimize)
save_fig(plot, dir=f"N_L_Energy")
# render(plot)
plot = plot_contour(model=gpei,
param_x="N",
param_y="w",
metric_name="Energy_(uJ)",
lower_is_better=cfg.metric.minimize)
# render(plot)
save_fig(plot, dir=f"N_w_Energy")
plot = plot_contour(model=gpei,
param_x="w",
param_y="L",
metric_name="Energy_(uJ)",
lower_is_better=cfg.metric.minimize)
save_fig(plot, dir=f"w_L_Energy")
class optimization():
def __init__(self):
self.max_worker = multiprocessing.cpu_count()
if self.max_worker > 1:
self.max_worker -= 5
#self.max_worker = 1
self.best_obj_so_far = float('-inf')
self.exp = None #define an experiment for ax
self.best_parameters_from_experiment = None
self.global_iteration_count = 0
def evaluate_parameter(self,parameter, weight=None):
total_returns = []
q = multiprocessing.Queue(maxsize = self.max_worker)
for iteration_index in range(0,C.ROUNDS_OF_WORKER):
p_list = []
for worker in range(0,self.max_worker):
try:
p = multiprocessing.Process(target = self.get_one_sample_score,\
args = (q,parameter))
p.start()
p_list.append(p)
except:
pass
for j in range(len(p_list)):
res = q.get()
total_returns.append(res[0])
self.global_iteration_count += 1
if np.mean(total_returns)>self.best_obj_so_far:
self.best_obj_so_far = np.mean(total_returns)
self.best_parameters_from_experiment = parameter
print('the best objective so far is',self.best_obj_so_far)
print('the associated best parameters are',parameter)
print('the current time is',datetime.datetime.now())
cwd = os.getcwd()
parameter_file = 'best_parameter_from_direct_experiment.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters_from_experiment))
print('this is global iteration',self.global_iteration_count)
print('the objective for ths iteration is',np.mean(total_returns))
print('the current time is',datetime.datetime.now())
print(' ')
optimization_objective = (np.mean(total_returns) - np.std(total_returns) * 0.5, np.std(total_returns))
return {'adj_return': optimization_objective}
def get_one_sample_score(self,q,free_parameters):
free_para1 = free_parameters['cov_para_1']
free_para2 = free_parameters['cov_para_2']
free_para3 = free_parameters['cov_para_3']
free_para4 = free_parameters['cov_para_4']
free_para5 = free_parameters['cov_para_5']
free_para6 = free_parameters['cov_para_6']
free_para7 = free_parameters['cov_para_7']
free_para8 = free_parameters['cov_para_8']
free_para9 = free_parameters['cov_para_9']
free_para10 = free_parameters['cov_para_10']
diag_1 = 1
diag_2 = np.sqrt(1-free_para1**2)
if (1-free_para2**2-free_para3**2)<0:
diag_3 = np.sqrt(np.abs(1-free_para2**2-free_para3**2))
else:
diag_3 = np.sqrt(1-free_para2**2-free_para3**2)
if (1-free_para4**2-free_para5**2-free_para6**2)<0:
diag_4 = np.sqrt(np.abs(1-free_para4**2-free_para5**2-free_para6**2))
else:
diag_4 = np.sqrt(1-free_para4**2-free_para5**2-free_para6**2)
if (1-free_para7**2-free_para8**2-free_para9**2-free_para10**2)<0:
diag_5 = np.sqrt(np.abs(1-free_para7**2-free_para8**2-free_para9**2-free_para10**2))
else:
diag_5 = np.sqrt(1-free_para7**2-free_para8**2-free_para9**2-free_para10**2)
lower_triangular_matrix = np.asarray([[diag_1,0,0,0,0],
[free_para1,diag_2,0,0,0],
[free_para2,free_para3,diag_3,0,0],
[free_para4,free_para5,free_para6,diag_4,0],
[free_para7,free_para8,free_para9,free_para10,diag_5]])
cov_matrix = lower_triangular_matrix@lower_triangular_matrix.transpose()
#now, define the marginal distribution of the gaussian copula
univerates = [{'loc': 0,
'scale': 1,
'a': free_parameters['beta_1a'],
'b': free_parameters['beta_1b'],
'type': 'copulas.univariate.beta.BetaUnivariate'},
{'loc': 0,
'scale': 1,
'a': free_parameters['beta_2a'],
'b': free_parameters['beta_2b'],
'type': 'copulas.univariate.beta.BetaUnivariate'},
{'loc': 0,
'scale': 1,
'a': free_parameters['beta_3a'],
'b': free_parameters['beta_3b'],
'type': 'copulas.univariate.beta.BetaUnivariate'},
{'loc': 0,
'scale': 1,
'a': free_parameters['beta_4a'],
'b': free_parameters['beta_4b'],
'type': 'copulas.univariate.beta.BetaUnivariate'},
{'loc': 0,
'scale': 1,
'a': free_parameters['beta_5a'],
'b': free_parameters['beta_5b'],
'type': 'copulas.univariate.beta.BetaUnivariate'}]
#now, we construct the gaussian copula
copula_parameters = {}
copula_parameters['covariance'] = cov_matrix
copula_parameters['univariates'] = univerates
copula_parameters['type'] = 'copulas.multivariate.gaussian.GaussianMultivariate'
copula_parameters['columns'] = [0,1,2,3,4]
new_dist = Multivariate.from_dict(copula_parameters)
#other parameters needed for transforming the features
lambda_expon_1 = free_parameters['lambda_expon_1']
lambda_expon_2 = free_parameters['lambda_expon_2']
lambda_expon_3 = free_parameters['lambda_expon_3']
lambda_expon_4 = free_parameters['lambda_expon_4']
lambda_expons = [lambda_expon_1,lambda_expon_2,lambda_expon_3,lambda_expon_4]
#now, we begin to simulate trading
#first, initialize the observation
locol_env = trading_vix.trading_vix()
this_trajectory_reward = []
has_at_least_sell = False
null_objective = True
current_feature = locol_env.reset()
for time_index in range(0,200):
#compute an action given current observation
transformed_features = []
for feature_index in range(len(lambda_expons)):
transformation = expon.cdf(current_feature[feature_index,0],scale = 1.0/lambda_expons[feature_index])
min_transformation = 0.1
transformation = min_transformation*np.exp(np.log(1.0/min_transformation)*transformation)
transformed_features.append(transformation)
transformed_features = np.asarray(transformed_features)
transformed_features = np.reshape(transformed_features,(1,-1))
#holding_position = expit(current_feature[-1,:][0])
holding_position = current_feature[-1,:][0]
if holding_position<0:
print('holding is less than 0, there is some problem and the holding position is',holding_position)
if holding_position>1:
print('holding is greater than 1, there is some problem and the holding position is',holding_position)
min_transformed_holding = 0.1
transformed_holding = min_transformed_holding*np.exp(np.log(1.0/min_transformed_holding)*holding_position)
transformed_holding = np.reshape(transformed_holding,(1,1))
data_point_for_df = np.concatenate((transformed_features,transformed_holding),axis = 1)
assert data_point_for_df.shape[1] == 5
data_point_for_copula = pd.DataFrame(data_point_for_df)
action = new_dist.cdf(data_point_for_copula)
#print('action in optimization trading vix is',action)
#apply the action to the environment
current_feature, reward, has_at_least_sell = locol_env.step(action)
if has_at_least_sell and null_objective:
#print('sold at least once')
null_objective = False
#record reward
this_trajectory_reward.append(reward)
#final time step,get the long term reward
reward = locol_env.final()
this_trajectory_reward.append(reward)
if null_objective:
objective = -1e9
else:
objective = np.sum(this_trajectory_reward)
q.put([objective])
def init_search_space(self):
search_space = SearchSpace(
parameters=[
RangeParameter(name="cov_para_1", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_2", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_3", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_4", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_5", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_6", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_7", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_8", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_9", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_10", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="beta_1a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_1b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_2a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_2b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_3a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_3b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_4a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_4b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_5a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_5b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="lambda_expon_1", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
RangeParameter(name="lambda_expon_2", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
RangeParameter(name="lambda_expon_3", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
RangeParameter(name="lambda_expon_4", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
]
)
self.exp = SimpleExperiment(
name="0",
search_space=search_space,
evaluation_function=self.evaluate_parameter,
objective_name="adj_return"
)
def initialization_trials(self):
sobol = Models.SOBOL(self.exp.search_space)
for i in range(C.INITIALIZATION_TRIALS):
self.exp.new_trial(generator_run=sobol.gen(1))
def optimization_trials(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
print(device)
for i in range(C.OPTIMIZATION_TRIALS):
print('Running GP+EI optimization trial', i+1)
# Reinitialize GP+EI model at each step with updated data.
#gpei = Models.GPEI(experiment=self.exp, data=self.exp.eval(), dtype=torch.float32, device=device)
gpei = Models.GPEI(experiment=self.exp, data=self.exp.eval())
batch = self.exp.new_trial(generator_run=gpei.gen(1))
def get_best_parameters(self):
_, trial = list(self.exp.trials.items())[-1]
print(trial)
trial.generator_run
gr = trial.generator_run
best_arm, best_arm_predictions = gr.best_arm_predictions
best_parameters = best_arm.parameters
self.best_parameters = best_parameters
print('the best parameters are',best_parameters)
def store_parameters(self):
cwd = os.getcwd()
#cwd = os.path.join(cwd, 'data_folder')
parameter_file = 'best_parameter_from_multi_armed_bandit.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters))
cwd = os.getcwd()
parameter_file = 'best_parameter_from_direct_experiment.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters_from_experiment))
def run_optimization(self):
self.init_search_space()
print('Begin initialization trials')
self.initialization_trials()
self.optimization_trials()
self.get_best_parameters()
self.store_parameters()
# #### Run the optimization loop
#
# We're ready to run the Bayesian Optimization loop.
for i in range(5):
print(f"Running optimization batch {i+1}/5...")
# user added:
# get_botorch is a self-contained macro-framework that does everything for you:
# 1) It fits the GP model to the new data
# 2) If optimizes the acquisition function and provides the next candidate
# 3) It evaluates such candidate (next point)
# 4) It returns the best suggestion (best guess)
model = get_botorch(
experiment=exp,
data=exp.eval(),
search_space=exp.search_space,
model_constructor=_get_and_fit_simple_custom_gp,
)
batch = exp.new_trial(generator_run=model.gen(1))
# user TODO: See https://ax.dev/api/_modules/ax/modelbridge/factory.html#get_botorch
# 1) Pass a custom acquisition function constructor
# 2) Maybe change the optimizer
# 3)
print(batch)
print("Done!")
def mems_exp(cfg):
log.info("============= Configuration =============")
log.info(f"Config:\n{cfg.pretty()}")
log.info("=========================================")
raise NotImplementedError("TODO load experimental data and verify likelihood of differnet points")
search_space = gen_search_space(cfg.problem)
outcome_con = gen_outcome_constraints(cfg.problem)
exp = SimpleExperiment(
name=cfg.problem.name,
search_space=SearchSpace(search_space),
evaluation_function=jumper,
objective_name="Energy_(uJ)",
minimize=False,
outcome_constraints=outcome_con,
)
optimization_config = OptimizationConfig(
objective=Objective(
metric=MEMsMetric(name="Energy_(uJ)"),
minimize=False,
),
)
class MyRunner(Runner):
def run(self, trial):
return {"name": str(trial.index)}
exp.runner = MyRunner()
exp.optimization_config = optimization_config
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.plot.contour import plot_contour
print(f"Running {cfg.bo.random} Sobol initialization trials...")
sobol = Models.SOBOL(exp.search_space)
num_search = cfg.bo.random
for i in range(num_search):
exp.new_trial(generator_run=sobol.gen(1))
exp.trials[len(exp.trials) - 1].run()
# data = exp.fetch_data()
num_opt = cfg.bo.optimized
for i in range(num_opt):
if (i % 5) == 0 and cfg.plot_during:
plot = plot_contour(model=gpei,
param_x="N",
param_y="L",
metric_name="Energy_(uJ)", )
data = plot[0]['data']
lay = plot[0]['layout']
render(plot)
print(f"Running GP+EI optimization trial {i + 1}/{num_opt}...")
# Reinitialize GP+EI model at each step with updated data.
batch = exp.new_trial(generator_run=gpei.gen(1))
gpei = Models.BOTORCH(experiment=exp, data=exp.eval())
gpei = Models.BOTORCH(experiment=exp, data=exp.eval())
from ax.models.torch.botorch_defaults import predict_from_model
import torch
X = torch.Tensor([[2, 7e-4, 1e-4], [1, 5e-4, 1e-4]]).double()
mean, cov = predict_from_model(gpei.model.model, X)
# X(Tensor) – n x d parameters
ll = log_likelihood(X, mean, cov)
plot_ll(ll)
class optimization():
def __init__(self):
self.max_worker = multiprocessing.cpu_count()
if self.max_worker > 1:
self.max_worker -= 5
#self.max_worker = 1
self.best_obj_so_far = float('-inf')
self.exp = None #define an experiment for ax
self.best_parameters_from_experiment = None
self.global_iteration_count = 0
def evaluate_parameter(self,parameter, weight=None):
total_returns = []
q = multiprocessing.Queue(maxsize = self.max_worker)
for iteration_index in range(0,C.ROUNDS_OF_WORKER):
p_list = []
for worker in range(0,self.max_worker):
try:
p = multiprocessing.Process(target = self.get_one_sample_score,\
args = (q,parameter))
p.start()
p_list.append(p)
except:
pass
for j in range(len(p_list)):
res = q.get()
total_returns.append(res[0])
self.global_iteration_count += 1
if np.mean(total_returns)>self.best_obj_so_far:
self.best_obj_so_far = np.mean(total_returns)
self.best_parameters_from_experiment = parameter
print('the best objective so far is',self.best_obj_so_far)
print('the associated best parameters are',parameter)
print('the current time is',datetime.datetime.now())
cwd = os.getcwd()
parameter_file = 'best_parameter_from_direct_experiment.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters_from_experiment))
print('this is global iteration',self.global_iteration_count)
print('the objective for ths iteration is',np.mean(total_returns))
print('the current time is',datetime.datetime.now())
print(' ')
optimization_objective = (np.mean(total_returns) - np.std(total_returns) * 0.5, np.std(total_returns))
return {'adj_return': optimization_objective}
def get_one_sample_score(self,q,free_parameters):
#the size of each gaussian copula cannot be too large
#otherwise, the CDF will almost always be 0.
#the max dimension of the copula is 3
#so, we will use a hierarchical structure of gaussian copulas
#with each of having dimension of less than 3.
cov_input = [free_parameters['cov_para_1'],free_parameters['cov_para_2'],free_parameters['cov_para_3']]
marginal_input = [free_parameters['beta_1a'],free_parameters['beta_1b'],
free_parameters['beta_2a'],free_parameters['beta_2b'],
free_parameters['beta_3a'],free_parameters['beta_3b']]
dist_1 = utils.construct_a_copula(cov_input,marginal_input)
cov_input = [free_parameters['cov_para_4']]
marginal_input = [free_parameters['beta_4a'],free_parameters['beta_4b'],
free_parameters['beta_5a'],free_parameters['beta_5b']]
dist_2 = utils.construct_a_copula(cov_input,marginal_input)
#other parameters needed for transforming the features
lambda_expon_1 = free_parameters['lambda_expon_1']
lambda_expon_2 = free_parameters['lambda_expon_2']
lambda_expon_3 = free_parameters['lambda_expon_3']
lambda_expons = [lambda_expon_1,lambda_expon_2,lambda_expon_3]
#now, we begin to simulate trading
#first, initialize the observation
locol_env = trading_vix_objective_2.trading_vix()
this_trajectory_reward = []
current_feature = locol_env.reset()
for time_index in range(0,200):
#compute an action given current observation
transformed_features = []
for feature_index in range(len(lambda_expons)):
transformation = expon.cdf(current_feature[feature_index,0],scale = 1.0/lambda_expons[feature_index])
transformed_features.append(transformation)
transformed_features = np.asarray(transformed_features)
transformed_features = np.reshape(transformed_features,(1,-1))
data_point_for_copula_1 = pd.DataFrame(transformed_features)
copula_1_output = dist_1.cdf(data_point_for_copula_1)
data_point_for_copula_2 = np.asarray([copula_1_output,current_feature[-1,:][0]])
data_point_for_copula_2 = np.reshape(data_point_for_copula_2,(1,-1))
data_point_for_copula_2 = pd.DataFrame(data_point_for_copula_2)
copula_2_output = dist_2.cdf(data_point_for_copula_2)
#print('action is',copula_1_output)
#apply the action to the environment
current_feature, reward = locol_env.step(copula_1_output)
#record reward
this_trajectory_reward.append(reward)
#final time step,get the long term reward
reward = locol_env.final()
this_trajectory_reward.append(reward)
#print('the sum of the reward is',np.sum(this_trajectory_reward))
objective = np.sum(this_trajectory_reward)
if objective == 0:
objective = -1e9
q.put([objective])
def init_search_space(self):
search_space = SearchSpace(
parameters=[
RangeParameter(name="cov_para_1", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_2", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_3", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="cov_para_4", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
# RangeParameter(name="cov_para_5", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
# RangeParameter(name="cov_para_6", parameter_type=ParameterType.FLOAT, lower=-0.9, upper=0.9),
RangeParameter(name="beta_1a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_1b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_2a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_2b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_3a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_3b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_4a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_4b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_5a", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="beta_5b", parameter_type=ParameterType.FLOAT, lower=0, upper=20),
RangeParameter(name="lambda_expon_1", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
RangeParameter(name="lambda_expon_2", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
RangeParameter(name="lambda_expon_3", parameter_type=ParameterType.FLOAT, lower=0.001, upper=0.1),
]
)
self.exp = SimpleExperiment(
name="0",
search_space=search_space,
evaluation_function=self.evaluate_parameter,
objective_name="adj_return"
)
def initialization_trials(self):
sobol = Models.SOBOL(self.exp.search_space)
for i in range(C.INITIALIZATION_TRIALS):
self.exp.new_trial(generator_run=sobol.gen(1))
def optimization_trials(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
print(device)
for i in range(C.OPTIMIZATION_TRIALS):
print('Running GP+EI optimization trial', i+1)
# Reinitialize GP+EI model at each step with updated data.
#gpei = Models.GPEI(experiment=self.exp, data=self.exp.eval(), dtype=torch.float32, device=device)
gpei = Models.GPEI(experiment=self.exp, data=self.exp.eval())
batch = self.exp.new_trial(generator_run=gpei.gen(1))
def get_best_parameters(self):
_, trial = list(self.exp.trials.items())[-1]
print(trial)
trial.generator_run
gr = trial.generator_run
best_arm, best_arm_predictions = gr.best_arm_predictions
best_parameters = best_arm.parameters
self.best_parameters = best_parameters
print('the best parameters are',best_parameters)
def store_parameters(self):
cwd = os.getcwd()
#cwd = os.path.join(cwd, 'data_folder')
parameter_file = 'index_copula_parameter.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters))
cwd = os.getcwd()
parameter_file = 'best_parameter_from_direct_experiment.json'
cwd = os.path.join(cwd,parameter_file)
with open(cwd, 'w') as statusFile:
statusFile.write(jsonpickle.encode(self.best_parameters_from_experiment))
def run_optimization(self):
self.init_search_space()
print('Begin initialization trials')
self.initialization_trials()
self.optimization_trials()
self.get_best_parameters()
self.store_parameters()
parameter_type=ParameterType.FLOAT),
RangeParameter(name="leafes", lower=2, upper=1000,
parameter_type=ParameterType.INT)]
)
experiment = SimpleExperiment(
name = f"weather_lbgm_{dt.datetime.today().strftime('%d-%m-%Y')}",
search_space = search_space,
evaluation_function = mdl.obj_fun,
)
sobol = Models.SOBOL(experiment.search_space)
for i in range(n_random_trials):
experiment.new_trial(generator_run=sobol.gen(1))
best_arm = None
for i in range(n_searches):
gpei = Models.GPEI(experiment=experiment, data=experiment.eval())
generator_run = gpei.gen(1)
best_arm, _ = generator_run.best_arm_predictions
experiment.new_trial(generator_run=generator_run)
best_para_ax = best_arm.parameters
n_oos = 0
params['num_boost_round'] = 200
mdl = Model(data_mat, lags, n_oos, n_val, prediction_range, target_vars_inds, params)
mdl.fit(best_para_ax)
X_pred = mdl.standing_forecast()
def pid(cfg):
env_name = cfg.env.params.name
env = gym.make(env_name)
env.reset()
full_rewards = []
exp_cfg = cfg.experiment
from learn.utils.plotly import hv_characterization
hv_characterization()
def compare_control(env, cfg, save=True):
import torch
from learn.control.pid import PidPolicy
controllers = []
labels = []
metrics = []
# PID baselines
# /Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/sweeps/2020-04-14/11-12-02
# from learn.simulate_sac import *
# Rotation policy
sac_policy1 = torch.load(
'/Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/outputs/2020-03-24/18-32-26/trial_70000.dat'
)
controllers.append(sac_policy1['policy'])
labels.append("SAC - Rotation")
metrics.append(0)
# Living reward policy
sac_policy2 = torch.load(
'/Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/outputs/2020-03-24/18-31-45/trial_35000.dat'
)
controllers.append(sac_policy2['policy'])
labels.append("SAC - Living")
metrics.append(1)
# Square cost policy
# sac_policy2 = torch.load(
# '/Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/sweeps/2020-03-25/20-30-47/metric.name=Square,robot=iono_sim/26/trial_40000.dat')
controllers.append(sac_policy2['policy'])
labels.append("SAC - Square")
metrics.append(2)
# un-Optimized PID parameters
pid_params = [[2531.917, 61.358, 33.762], [2531.917, 61.358, 33.762]]
pid = PidPolicy(cfg)
pid.set_params(pid_params)
controllers.append(pid)
labels.append("PID - temp")
metrics.append(0)
controllers.append(pid)
labels.append("PID - temp")
metrics.append(1)
# Optimized PID parameters
pid_params = [[2531.917, 61.358, 3333.762],
[2531.917, 61.358, 3333.762]]
pid = PidPolicy(cfg)
pid.set_params(pid_params)
controllers.append(pid)
labels.append("PID - improved")
metrics.append(2)
from learn.control.mpc import MPController
cfg.policy.mode = 'mpc'
# dynam_model = torch.load(
# '/Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/outputs/2020-03-25/10-45-17/trial_1.dat')
dynam_model = torch.load(
'/Users/nato/Documents/Berkeley/Research/Codebases/dynamics-learn/sweeps/2020-03-25/20-30-57/metric.name=Rotation,robot=iono_sim/14/trial_9.dat'
)
mpc = MPController(env, dynam_model['model'], cfg)
controllers.append(mpc)
labels.append("MPC - 1")
metrics.append(0)
controllers.append(mpc)
labels.append("MPC - 2")
metrics.append(1)
controllers.append(mpc)
labels.append("MPC - 3")
metrics.append(2)
import plotly.graph_objects as go
import plotly
colors = [
'#1f77b4', # muted blue
'#ff7f0e', # safety orange
'#2ca02c', # cooked asparagus green
'#d62728', # brick red
'#9467bd', # muted purple
'#8c564b', # chestnut brown
'#e377c2', # raspberry yogurt pink
'#7f7f7f', # middle gray
'#bcbd22', # curry yellow-green
'#17becf' # blue-teal
]
markers = [
"cross",
"circle-open-dot",
"x-open-dot",
"triangle-up-open-dot",
"y-down-open",
"diamond-open-dot",
"hourglass",
"hash",
"star",
"square",
]
m1 = living_reward
m2 = rotation_mat
m3 = squ_cost
eval_metrics = [m1, m2, m3]
metric_names = ["Living", "Rotation", "Square"]
fig = plotly.subplots.make_subplots(
rows=3,
cols=2,
# subplot_titles=["Living", "Rotation", "Square"],
subplot_titles=[
"Pitch",
"Roll",
" ",
" ",
" ",
" ",
],
vertical_spacing=0.03,
horizontal_spacing=0.03,
shared_xaxes=True,
) # go.Figure()
fig_mpc = go.Figure()
fig_sac = go.Figure()
pry = [1, 0, 2]
# state0 = 2*env.reset()
# state0 = env.reset()
state0 = np.array([0, np.deg2rad(15), 0, 0, 0, 0])
for i, (con, lab, m) in enumerate(zip(controllers, labels, metrics)):
print(f"Evaluating controller type {lab}")
_ = env.reset()
env.set_state(np.concatenate((np.zeros(6), state0)))
state = state0
states = []
actions = []
rews = []
done = False
# for t in range(cfg.experiment.r_len + 1):
for t in range(500):
if done:
break
if "SAC" in lab:
with torch.no_grad():
with eval_mode(con):
action = con.select_action(state)
if i < 2:
action = np.array([65535, 65535, 65535, 65535
]) * (action + 1) / 2
else:
action = np.array([3000, 3000, 3000, 3000
]) * (action + 1) / 2
else:
action = con.get_action(state, metric=eval_metrics[m])
states.append(state)
actions.append(action)
state, rew, done, _ = env.step(action)
done = done
states = np.stack(states)
actions = np.stack(actions)
pitch = np.degrees(states[:, pry[0]])
roll = np.degrees(states[:, pry[1]])
# deal with markers
num_mark = np.zeros(len(pitch))
mark_every = 50
m_size = 32
start = np.random.randint(0, int(len(pitch) / 10))
num_mark[start::mark_every] = m_size
if "SAC" in lab:
fig_sac.add_trace(
go.Scatter(
y=pitch,
name=metric_names[m], # legendgroup=lab[:3],
# showlegend=(True if (i % 3 == 0) else False),
line=dict(color=colors[m], width=4),
cliponaxis=False,
mode='lines+markers',
marker=dict(color=colors[m],
symbol=markers[-m],
size=num_mark.tolist())))
elif "MPC" in lab:
fig_mpc.add_trace(
go.Scatter(
y=pitch,
name=metric_names[m], # legendgroup=lab[:3],
# showlegend=(True if (i % 3 == 0) else False),
line=dict(color=colors[m], width=4),
cliponaxis=False,
mode='lines+markers',
marker=dict(color=colors[m],
symbol=markers[-m],
size=num_mark.tolist())))
fig.add_trace(
go.Scatter(
y=pitch,
name=lab[:3] + str(m),
legendgroup=lab[:3],
showlegend=(True if (i % 3 == 0) else False),
line=dict(color=colors[int(i / 3)],
width=2), # mode='lines+markers',
# marker=dict(color=colors[i], symbol=markers[i], size=16)
),
row=m + 1,
col=1)
fig.add_trace(
go.Scatter(
y=roll,
name=lab[:3] + str(m),
legendgroup=lab[:3],
showlegend=(False),
line=dict(color=colors[int(i / 3)],
width=2), # mode='lines+markers',
# marker=dict(color=colors[i], symbol=markers[i], size=16)
),
row=m + 1,
col=2)
fig.update_layout(
title='Comparison of Controllers and Reward Functions',
font=dict(family="Times New Roman, Times, serif",
size=24,
color="black"),
legend_orientation="h",
legend=dict(
x=.6,
y=0.07,
bgcolor='rgba(205, 223, 212, .4)',
bordercolor="Black",
),
# xaxis_title='Timestep',
# yaxis_title='Angle (Degrees)',
plot_bgcolor='white',
width=1600,
height=1000,
# xaxis=dict(
# showline=True,
# showgrid=False,
# showticklabels=True, ),
# yaxis=dict(
# showline=True,
# showgrid=False,
# showticklabels=True, ),
)
fig_sac.update_layout( # title='Comparison of SAC Policies',
font=dict(family="Times New Roman, Times, serif",
size=32,
color="black"),
legend_orientation="h",
legend=dict(
x=.35,
y=0.1,
bgcolor='rgba(205, 223, 212, .4)',
bordercolor="Black",
),
# xaxis_title='Timestep',
# yaxis_title='Angle (Degrees)',
showlegend=False,
plot_bgcolor='white',
width=1600,
height=800,
margin=dict(t=5, r=5),
)
fig_mpc.update_layout( # title='Comparison of MPC Policies',
font=dict(family="Times New Roman, Times, serif",
size=32,
color="black"),
legend_orientation="h",
showlegend=False,
legend=dict(
x=.35,
y=0.1,
bgcolor='rgba(205, 223, 212, .4)',
bordercolor="Black",
# ncol= 2,
),
# xaxis_title='Timestep',
# yaxis_title='Angle (Degrees)',
plot_bgcolor='white',
width=1600,
height=800,
margin=dict(t=5, r=5),
)
reg_color = 'rgba(255,60,60,.15)'
fig_sac.add_trace(
go.Scatter(x=[0, 500],
y=[5, 5],
name='Living Region',
legendgroup='Living Region',
fill='tozeroy',
mode='lines',
fillcolor=reg_color,
line=dict(width=0.0,
color=reg_color))) # fill down to xaxis
fig_sac.add_trace(
go.Scatter(x=[0, 500],
y=[-5, -5],
showlegend=False,
legendgroup='Living Region',
fill='tozeroy',
mode='lines',
fillcolor=reg_color,
line=dict(width=0.0,
color=reg_color))) # fill down to xaxis
fig_mpc.add_trace(
go.Scatter(x=[0, 500],
y=[5, 5],
name='Living Region',
legendgroup='Living Region',
fill='tozeroy',
mode='lines',
fillcolor=reg_color,
line=dict(width=0.0,
color=reg_color))) # fill down to xaxis
fig_mpc.add_trace(
go.Scatter(x=[0, 500],
y=[-5, -5],
showlegend=False,
legendgroup='Living Region',
fill='tozeroy',
mode='lines',
fillcolor=reg_color,
line=dict(width=0.0,
color=reg_color))) # fill down to xaxis
# SOLO
rang_ind = [-20, 20]
fig_sac.update_xaxes(
title_text="Timestep",
range=[0, 500],
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig_sac.update_yaxes(
title_text="Pitch (degrees)",
range=rang_ind,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig_sac.show()
fig_sac.write_image(os.getcwd() + "/compare_sac.pdf")
fig_mpc.update_xaxes(
title_text="Timestep",
range=[0, 500],
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig_mpc.update_yaxes(
title_text="Pitch (degrees)",
range=rang_ind,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig_mpc.show()
fig_mpc.write_image(os.getcwd() + "/compare_mpc.pdf")
# COMPARISON
fig.update_xaxes(
title_text="Timestep",
row=3,
col=1,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_xaxes(
row=2,
col=1,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_xaxes(
row=1,
col=1,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_xaxes(
title_text="Timestep",
row=3,
col=2,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_xaxes(
row=2,
col=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_xaxes(
row=1,
col=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
# fig.update_xaxes(title_text="xaxis 1 title", row=1, col=1)
# fig.update_yaxes(title_text="Roll (Degrees)", row=1, col=1)
rang = [-30, 30]
nticks = 6
fig.update_yaxes(
title_text="Living Rew.",
range=rang,
row=1,
col=1,
nticks=nticks,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_yaxes(
title_text="Rotation Rew.",
range=rang,
row=2,
col=1,
nticks=nticks,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_yaxes(
title_text="Square Cost",
range=rang,
row=3,
col=1,
nticks=nticks,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_yaxes(
range=rang,
row=1,
col=2,
nticks=nticks,
showticklabels=False,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_yaxes(
range=rang,
row=2,
col=2,
nticks=nticks,
showticklabels=False,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
fig.update_yaxes(
range=rang,
row=3,
col=2,
nticks=nticks,
showticklabels=False,
ticks="inside",
tickwidth=2,
zeroline=True,
zerolinecolor='rgba(0,0,0,.5)',
zerolinewidth=1,
)
print(f"Plotting {len(labels)} control responses")
# save = False
# if save:
# fig.write_image(os.getcwd() + "compare.png")
# else:
# fig.show()
#
# return fig
# compare_control(env, cfg, save=True)
# quit()
plot_results(logx=False, save=True, mpc=False)
quit()
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # Evalutation Function # # # # # # # # # # # # # # # # # # # #
def bo_rollout_wrapper(params, weights=None): # env, controller, exp_cfg):
pid_1 = [params["pitch-p"], params["pitch-i"], params["pitch-d"]]
# pid_1 = [params["roll-p"], params["roll-i"],
# params["roll-d"]] # [params["pitch-p"], params["pitch-i"], params["pitch-d"]]
pid_2 = [params["roll-p"], params["roll-i"], params["roll-d"]]
print(
f"Optimizing Parameters {np.round(pid_1, 3)},{np.round(pid_2, 3)}")
pid_params = [[pid_1[0], pid_1[1], pid_1[2]],
[pid_2[0], pid_2[1], pid_2[2]]]
# pid_params = [[1000, 0, 0], [1000, 0, 0]]
pid = PidPolicy(cfg)
pid.set_params(pid_params)
cum_cost = []
r = 0
fncs = [squ_cost, living_reward, rotation_mat]
mult_rewards = [[] for _ in range(len(fncs))]
while r < cfg.experiment.repeat:
pid.reset()
states, actions, rews, sim_error = rollout(env, pid, exp_cfg)
# plot_rollout(states, actions, pry=[1, 0, 2])
rewards_full = get_rewards(states, actions, fncs=fncs)
for i, vec in enumerate(rewards_full):
mult_rewards[i].append(vec)
# if sim_error:
# print("Repeating strange simulation")
# continue
# if len(rews) < 400:
# cum_cost.append(-(cfg.experiment.r_len - len(rews)) / cfg.experiment.r_len)
# else:
rollout_cost = np.sum(rews) / cfg.experiment.r_len # / len(rews)
# if rollout_cost > max_cost:
# max_cost = rollout_cost
# rollout_cost += get_reward_euler(states[-1], actions[-1])
cum_cost.append(rollout_cost)
r += 1
std = np.std(cum_cost)
cum_cost = np.mean(cum_cost)
# print(f"Cum. Cost {cum_cost / max_cost}")
# print(f"- Mean Cum. Cost / Rew: {cum_cost}, std dev: {std}")
eval = {
"Square": (np.mean(rewards_full[0]), np.std(rewards_full[0])),
"Living": (np.mean(rewards_full[1]), np.std(rewards_full[1])),
"Rotation": (np.mean(rewards_full[2]), np.std(rewards_full[2]))
}
for n, (key, value) in enumerate(eval.items()):
if n == 0:
print(f"- Square {np.round(value, 4)}")
elif n == 1:
print(f"- Living {np.round(value, 4)}")
else:
print(f"- Rotn {np.round(value, 4)}")
return eval
# return cum_cost.reshape(1, 1), std
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
from ax import (
ComparisonOp,
ParameterType,
RangeParameter,
SearchSpace,
SimpleExperiment,
OutcomeConstraint,
)
exp = SimpleExperiment(
name="PID Control Robot",
search_space=SearchSpace([
RangeParameter(
name=f"roll-p",
parameter_type=ParameterType.FLOAT,
lower=1.0,
upper=10000.0,
log_scale=True,
),
# FixedParameter(name="roll-i", value=0.0, parameter_type=ParameterType.FLOAT),
RangeParameter(
name=f"roll-i",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=1000.0,
log_scale=False,
),
RangeParameter(
name=f"roll-d",
parameter_type=ParameterType.FLOAT,
lower=.1,
upper=5000.0,
log_scale=True,
),
RangeParameter(
name=f"pitch-p",
parameter_type=ParameterType.FLOAT,
lower=1.0,
upper=10000.0,
log_scale=True,
),
RangeParameter(
name=f"pitch-d",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=1000.0,
log_scale=False,
),
RangeParameter(
name=f"pitch-i",
parameter_type=ParameterType.FLOAT,
lower=.1,
upper=5000.0,
log_scale=True,
),
# FixedParameter(name="pitch-i", value=0.0, parameter_type=ParameterType.FLOAT),
]),
evaluation_function=bo_rollout_wrapper,
objective_name=cfg.metric.name,
minimize=cfg.metric.minimize,
outcome_constraints=[],
)
from ax.storage.metric_registry import register_metric
from ax.storage.runner_registry import register_runner
class GenericMetric(Metric):
def fetch_trial_data(self, trial):
records = []
for arm_name, arm in trial.arms_by_name.items():
params = arm.parameters
mean, sem = bo_rollout_wrapper(params)
records.append({
"arm_name": arm_name,
"metric_name": self.name,
"mean": mean,
"sem": sem,
"trial_index": trial.index,
})
return Data(df=pd.DataFrame.from_records(records))
class MyRunner(Runner):
def run(self, trial):
return {"name": str(trial.index)}
optimization_config = OptimizationConfig(objective=Objective(
metric=GenericMetric(name=cfg.metric.name),
minimize=cfg.metric.minimize,
), )
register_metric(GenericMetric)
register_runner(MyRunner)
exp.runner = MyRunner()
exp.optimization_config = optimization_config
log.info(f"Running experiment, metric name {cfg.metric.name}")
log.info(f"Running Sobol initialization trials...")
sobol = Models.SOBOL(exp.search_space)
num_search = cfg.bo.random
for i in range(num_search):
exp.new_trial(generator_run=sobol.gen(1))
exp.trials[len(exp.trials) - 1].run()
import plotly.graph_objects as go
gpei = Models.BOTORCH(experiment=exp, data=exp.eval())
objectives = ["Living", "Square", "Rotation"]
def plot_all(model, objectives, name="", rend=False):
for o in objectives:
plot = plot_contour(
model=model,
param_x="roll-p",
param_y="roll-d",
metric_name=o,
)
plot[0]['layout']['title'] = o
data = plot[0]['data']
lay = plot[0]['layout']
for i, d in enumerate(data):
if i > 1:
d['cliponaxis'] = False
fig = {
"data": data,
"layout": lay,
}
go.Figure(fig).write_image(name + o + ".png")
if rend: render(plot)
plot_all(gpei, objectives, name="Random fit-")
num_opt = cfg.bo.optimized
for i in range(num_opt):
log.info(f"Running GP+EI optimization trial {i + 1}/{num_opt}...")
# Reinitialize GP+EI model at each step with updated data.
batch = exp.new_trial(generator_run=gpei.gen(1))
gpei = Models.BOTORCH(experiment=exp, data=exp.eval())
if ((i + 1) % 10) == 0:
plot_all(gpei,
objectives,
name=f"optimizing {str(i + 1)}-",
rend=False)
from ax.plot.exp_utils import exp_to_df
best_arm, _ = gpei.gen(1).best_arm_predictions
best_parameters = best_arm.parameters
log.info(f"Best parameters {best_parameters}")
experiment_log = {
"Exp": exp_to_df(exp=exp),
"Cfg": cfg,
"Best_param": best_parameters,
}
log.info("Printing Parameters")
log.info(exp_to_df(exp=exp))
save_log(cfg, exp, experiment_log)
fig_learn = plot_learning(exp, cfg)
fig_learn.write_image("learning" + ".png")
fig_learn.show()
plot_all(gpei, objectives, name=f"FINAL -", rend=True)
def optimize(self):
SOBOL_TRIALS = 75
gpei_list = [50, 30, 10, 0]
parameters = None
for gpei_trials in gpei_list:
try:
search_space = SearchSpace(parameters=[
ChoiceParameter(name='risk_function',
values=['polynomial', 'exponential'],
parameter_type=ParameterType.STRING),
RangeParameter(name='alpha_poly',
lower=1.0,
upper=5.0,
parameter_type=ParameterType.FLOAT),
RangeParameter(name='alpha_exp',
lower=0.0,
upper=1.0,
parameter_type=ParameterType.FLOAT),
RangeParameter(name='exp_threshold',
lower=1.0,
upper=10.0,
parameter_type=ParameterType.FLOAT)
])
experiment = SimpleExperiment(
name='risk_function_parametrisation',
search_space=search_space,
evaluation_function=self.evaluate_parameterization,
objective_name='par10',
minimize=True)
sobol = Models.SOBOL(experiment.search_space)
for _ in range(SOBOL_TRIALS):
experiment.new_trial(generator_run=sobol.gen(1))
best_arm = None
for _ in range(gpei_trials):
gpei = Models.GPEI(experiment=experiment,
data=experiment.eval())
generator_run = gpei.gen(1)
best_arm, _ = generator_run.best_arm_predictions
experiment.new_trial(generator_run=generator_run)
parameters = best_arm.parameters
break
except:
print('GPEI Optimization failed')
if gpei_trials == 0:
# choose expectation if optimization failed for all gpei_trial values
# exp thresholds are dummy variables
parameters = {
'risk_function': 'polynomial',
'alpha_poly': 1.0,
'alpha_exp': 1.0,
'exp_threshold': 1.0
}
else:
continue
return self.resolve_risk_function(parameters['risk_function'],
parameters['alpha_poly'],
parameters['alpha_exp'],
parameters['exp_threshold'])
search_space=transformer_search_space,
evaluation_function=transformer.trainer,
objective_name='f1',
)
# Run the optimization and fit a GP on all data
sobol = modelbridge.get_sobol(search_space=exp.search_space)
print(f"\nRunning Sobol initialization trials...\n{'='*40}\n")
for _ in range(init_trials):
exp.new_trial(generator_run=sobol.gen(1))
for i in range(opt_trials):
print(
f"\nRunning GP+EI optimization trial {i+1}/{opt_trials}...\n{'='*40}\n"
)
gpei = modelbridge.get_GPEI(experiment=exp, data=exp.eval())
exp.new_trial(generator_run=gpei.gen(1))
# save every 5 trials
if (i + 1) % 5 == 0:
output_dir = os.path.join(
'Ax_output', dset,
datetime.datetime.now().strftime('%m%d-%H%M%S'))
os.makedirs(output_dir)
# Save all experiment parameters
df = exp.eval().df
df.to_csv(os.path.join(output_dir, 'exp_eval.csv'), index=False)
# Save best parameter
best_arm_name = df.arm_name[df['mean'] == df['mean'].max()].values[0]
