def import_bo():
"""
Test if edbo is installed and the main method can
be instantiated
"""
from edbo.bro import BO
bo = BO()
return len(bo.obj.domain) == 0
def BO_pred(acq_func, plot=False, return_='pred', append=False, init='external'):
# Experiment index
X = np.linspace(0,1,1000)
exindex = pd.DataFrame([[x, f(x)] for x in X], columns=['x', 'f(x)'])
training_points = [50, 300, 500, 900]
# Instatiate BO class
bo = BO(exindex=exindex,
domain=exindex.drop('f(x)', axis=1),
results=exindex.iloc[training_points],
acquisition_function=acq_func,
init_method=init,
lengthscale_prior=[GammaPrior(1.2,1.1), 0.2],
noise_prior=None,
batch_size=random.sample([1,2,3,4,5,6,7,8,9,10],1)[0],
fast_comp=True,
gpu=True)
bo.run(append=append)
# Check prediction
if return_ == 'pred':
try:
bo.model.predict(to_torch(bo.obj.domain)) # torch.tensor
bo.model.predict(bo.obj.domain.values) # numpy.array
bo.model.predict(list(bo.obj.domain.values)) # list
bo.model.predict(exindex.drop('f(x)', axis=1)) # pandas.DataFrame
except:
return False
pred = bo.model.predict(bo.obj.domain.iloc[[32]])
pred = bo.obj.scaler.unstandardize(pred)
return (pred[0] - 1.33) < 0.1
# Check predictive postrior variance
elif return_ == 'var':
try:
bo.model.predict(to_torch(bo.obj.domain)) # torch.tensor
bo.model.predict(bo.obj.domain.values) # numpy.array
bo.model.predict(list(bo.obj.domain.values)) # list
bo.model.predict(exindex.drop('f(x)', axis=1)) # pandas.DataFrame
except:
return False
var = bo.model.variance(bo.obj.domain.iloc[[32]])
return (var[0] - 0.04) < 0.1
# Make sure sampling works with tensors, arrays, lists, and DataFrames
elif return_ == 'sample':
try:
bo.model.sample_posterior(to_torch(bo.obj.domain)) # torch.tensor
bo.model.sample_posterior(bo.obj.domain.values) # numpy.array
bo.model.sample_posterior(list(bo.obj.domain.values)) # list
bo.model.sample_posterior(exindex.drop('f(x)', axis=1)) # pandas.DataFrame
return True
except:
return False
# Plot model
elif return_ == 'plot':
next_points = bo.obj.get_results(bo.proposed_experiments)
mean = bo.obj.scaler.unstandardize(bo.model.predict(bo.obj.domain))
std = np.sqrt(bo.model.variance(bo.obj.domain)) * bo.obj.scaler.std * 2
samples = bo.obj.scaler.unstandardize(bo.model.sample_posterior(bo.obj.domain, batch_size=3))
plt.figure(1, figsize=(6,6))
# Model mean and standard deviation
plt.subplot(211)
plt.plot(X, exindex['f(x)'], color='black')
plt.plot(X, mean, label='GP')
plt.fill_between(X, mean-std, mean+std, alpha=0.4)
# Known results and next selected point
plt.scatter(bo.obj.results_input()['x'], bo.obj.results_input()['f(x)'], color='black', label='known')
plt.scatter(next_points['x'],next_points['f(x)'], color='red', label='next_experiments')
plt.ylabel('f(x)')
# Samples
plt.subplot(212)
for sample in samples:
plt.plot(X, torch_to_numpy(sample, gpu=True))
plt.xlabel('x')
plt.ylabel('Posterior Samples')
plt.show()
return True
elif return_ == 'simulate':
if init != 'external':
bo.init_seq.batch_size = random.sample([2,3,4,5,6,7,8,9,10],1)[0]
bo.simulate(iterations=5)
bo.plot_convergence()
bo.model.regression()
return True
def BO_pred(acq_func,
plot=False,
return_='pred',
append=False,
init='external',
fast_comp=True):
# Experiment index
X = np.linspace(0, 1, 1000)
exindex = pd.DataFrame([[x, f(x)] for x in X], columns=['x', 'f(x)'])
training_points = [50, 300, 500, 900]
# Instatiate BO class
bo = BO(exindex=exindex,
domain=exindex.drop('f(x)', axis=1),
results=exindex.iloc[training_points],
acquisition_function=acq_func,
init_method=init,
lengthscale_prior=[GammaPrior(1.2, 1.1), 0.2],
noise_prior=None,
batch_size=random.sample([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 1)[0],
fast_comp=fast_comp)
bo.run(append=append)
# Check prediction
if return_ == 'pred':
try:
bo.model.predict(to_torch(bo.obj.domain)) # torch.tensor
bo.model.predict(bo.obj.domain.values) # numpy.array
bo.model.predict(list(bo.obj.domain.values)) # list
bo.model.predict(exindex.drop('f(x)', axis=1)) # pandas.DataFrame
except:
return False
pred = bo.model.predict(bo.obj.domain.iloc[[32]])
pred = bo.obj.scaler.unstandardize(pred)
return (pred[0] - 1.33) < 0.1
# Check predictive postrior variance
elif return_ == 'var':
try:
bo.model.predict(to_torch(bo.obj.domain)) # torch.tensor
bo.model.predict(bo.obj.domain.values) # numpy.array
bo.model.predict(list(bo.obj.domain.values)) # list
bo.model.predict(exindex.drop('f(x)', axis=1)) # pandas.DataFrame
except:
return False
var = bo.model.variance(bo.obj.domain.iloc[[32]])
return (var[0] - 0.04) < 0.1
# Make sure sampling works with tensors, arrays, lists, and DataFrames
elif return_ == 'sample':
try:
bo.model.sample_posterior(to_torch(bo.obj.domain)) # torch.tensor
bo.model.sample_posterior(bo.obj.domain.values) # numpy.array
bo.model.sample_posterior(list(bo.obj.domain.values)) # list
bo.model.sample_posterior(exindex.drop('f(x)',
axis=1)) # pandas.DataFrame
return True
except:
return False
elif return_ == 'simulate':
if init != 'external':
bo.init_seq.batch_size = random.sample(
[2, 3, 4, 5, 6, 7, 8, 9, 10], 1)[0]
bo.simulate(iterations=5)
return True
elif return_ == 'none':
return True