bugfix output parameter names with optimization

src/f3dasm/_src/design/domain.py

@@ -84,6 +84,11 @@ def names(self) -> List[str]:
  """Return a list of the names of the parameters"""
  return list(self.keys())
 
+ @property
+ def output_names(self) -> List[str]:
+ """Return a list of the names of the output parameters"""
+ return list(self.output_space.keys())
+
  @property
  def continuous(self) -> Domain:
  """Returns a Domain object containing only the continuous parameters"""
@@ -748,6 +753,7 @@ def _check_output(self, names: List[str]):
  """
  for output_name in names:
  if not self.is_in_output(output_name):
+ print(f"Output {output_name} not in domain. Adding it.")
  self.add_output(output_name, to_disk=False)
 
  def is_in_output(self, output_name: str) -> bool:
@@ -824,7 +830,7 @@ def _domain_factory(domain: Domain | DictConfig | None,
  input_data: pd.DataFrame,
  output_data: pd.DataFrame) -> Domain:
  if isinstance(domain, Domain):
- domain._check_output(output_data.columns)
+ # domain._check_output(output_data.columns)
  return domain
 
  elif isinstance(domain, (Path, str)):

src/f3dasm/_src/experimentdata/experimentdata.py

@@ -125,8 +125,8 @@ def __init__(self,
  job_value = Status.FINISHED
 
  self.domain = _domain_factory(
- domain, self._input_data.to_dataframe(),
- self._output_data.to_dataframe())
+ domain=domain, input_data=self._input_data.to_dataframe(),
+ output_data=self._output_data.to_dataframe())
 
  # Create empty input_data from domain if input_data is empty
  if self._input_data.is_empty():
@@ -139,6 +139,9 @@ def __init__(self,
  if not self._input_data.has_columnnames(self.domain.names):
  self._input_data.set_columnnames(self.domain.names)
 
+ if not self._output_data.has_columnnames(self.domain.output_names):
+ self._output_data.set_columnnames(self.domain.output_names)
+
  # For backwards compatibility; if the output_data has
  # only one column, rename it to 'y'
  if self._output_data.names == [0]:
@@ -1220,13 +1223,19 @@ def _iterate_scipy(self, optimizer: Optimizer,
  # n_data_before_iterate + iterations amount of elements!
  # If x_new is empty, repeat best x0 to fill up total iteration
  if len(self) == n_data_before_iterate:
- repeated_last_element = self.get_n_best_output(
- n_samples=1).to_numpy()[0].ravel()
+ repeated_x, repeated_y = self.get_n_best_output(
+ n_samples=1).to_numpy()
+ # repeated_last_element = self.get_n_best_output(
+ # n_samples=1).to_numpy()[0].ravel()
 
  for repetition in range(iterations):
- self._add_experiments(
- ExperimentSample.from_numpy(repeated_last_element,
- domain=self.domain))
+ # self._add_experiments(
+ # ExperimentSample.from_numpy(repeated_last_element,
+ # domain=self.domain))
+
+ self.add(
+ domain=self.domain, input_data=repeated_x,
+ output_data=repeated_y)
 
  # Repeat last iteration to fill up total iteration
  if len(self) < n_data_before_iterate + iterations:

src/f3dasm/_src/optimization/adapters/scipy_implementations.py

@@ -46,7 +46,8 @@ def run_algorithm(self, iterations: int, data_generator: DataGenerator):
  """
 
  def fun(x):
- sample: ExperimentSample = data_generator._run(x)
+ sample: ExperimentSample = data_generator._run(
+ x, domain=self.domain)
  _, y = sample.to_numpy()
  return float(y)
 
@@ -63,3 +64,5 @@ def fun(x):
  bounds=self.domain.get_bounds(),
  tol=0.0,
  )
+
+ # self.data.evaluate(data_generator=data_generator)