def extract_result(self):
x_min, _ = stochastic_optimization(self.target_model.predict_mean,
self.target_model.bounds,
seed=self.seed)
batch_min = arr2d_to_batch(x_min, self.parameter_names)
outputs = arr2d_to_batch(self.target_model.X, self.parameter_names)
outputs[self.target_name] = self.target_model.Y
return OptimizationResult(x_min=batch_min,
outputs=outputs,
**self._extract_result_kwargs())
def prepare_new_batch(self, batch_index):
"""Prepare values for a new batch.
Parameters
----------
batch_index : int
next batch_index to be submitted
Returns
-------
batch : dict or None
Keys should match to node names in the model. These values will override any
default values or operations in those nodes.
"""
t = self._get_acquisition_index(batch_index)
# Check if we still should take initial points from the prior
if t < 0:
return
# Take the next batch from the acquisition_batch
acquisition = self.state['acquisition']
if len(acquisition) == 0:
acquisition = self.acquisition_method.acquire(self.acq_batch_size, t=t)
batch = arr2d_to_batch(acquisition[:self.batch_size], self.parameter_names)
self.state['acquisition'] = acquisition[self.batch_size:]
return batch
def prepare_new_batch(self, batch_index):
"""Prepare values for a new batch.
Parameters
----------
batch_index : int
next batch_index to be submitted
Returns
-------
batch : dict or None
Keys should match to node names in the model. These values will override any
default values or operations in those nodes.
"""
if self.state['round'] == 0:
# Use the actual prior
return
# Sample from the proposal, condition on actual prior
params = GMDistribution.rvs(*self._gm_params, size=self.batch_size,
prior_logpdf=self._prior.logpdf,
random_state=self._round_random_state)
batch = arr2d_to_batch(params, self.parameter_names)
return batch
def prepare_new_batch(self, batch_index):
if self.state['round'] == 0:
# Use the actual prior
return
# Sample from the proposal
params = GMDistribution.rvs(*self._gm_params, size=self.batch_size,
random_state=self._round_random_state)
batch = arr2d_to_batch(params, self.parameter_names)
return batch
def prepare_new_batch(self, batch_index):
t = self._get_acquisition_index(batch_index)
# Check if we still should take initial points from the prior
if t < 0: return
# Take the next batch from the acquisition_batch
acquisition = self.state['acquisition']
if len(acquisition) == 0:
acquisition = self.acquisition_method.acquire(self.acq_batch_size, t=t)
batch = arr2d_to_batch(acquisition[:self.batch_size], self.parameter_names)
self.state['acquisition'] = acquisition[self.batch_size:]
return batch
def prepare_new_batch(self, batch_index):
"""Prepare values for a new batch.
Parameters
----------
batch_index: int
Returns
-------
batch: dict
"""
t = batch_index - self.n_initial_evidence
if t < 0: # Sample parameter values from the model priors
return
# Acquire parameter values from the acquisition function
acquisition = self.acquisition_method.acquire(self.batch_size, t)
return arr2d_to_batch(acquisition, self.parameter_names)