fix the coercion of scrores to floats in the optimizer

mlos_bench/mlos_bench/optimizers/mlos_core_optimizer.py

@@ -104,20 +104,19 @@ def bulk_register(self,
  df_scores = self._adjust_signs_df(
  pd.DataFrame([{} if score is None else score for score in scores]))
 
- opt_targets = list(self._opt_targets)
  if status is not None:
  # Select only the completed trials, set scores for failed trials to +inf.
  df_status = pd.Series(status)
  # TODO: Be more flexible with values used for failed trials (not just +inf).
  # Issue: https://github.com/microsoft/MLOS/issues/523
- df_scores.loc[df_status != Status.SUCCEEDED, opt_targets] = float("inf")
+ df_scores[df_status != Status.SUCCEEDED] = float("inf")
  df_status_completed = df_status.apply(Status.is_completed)
  df_configs = df_configs[df_status_completed]
  df_scores = df_scores[df_status_completed]
 
  # TODO: Specify (in the config) which metrics to pass to the optimizer.
  # Issue: https://github.com/microsoft/MLOS/issues/745
- self._opt.register(configs=df_configs, scores=df_scores[opt_targets].astype(float))
+ self._opt.register(configs=df_configs, scores=df_scores)
 
  if _LOG.isEnabledFor(logging.DEBUG):
  (score, _) = self.get_best_observation()
@@ -127,11 +126,12 @@ def bulk_register(self,
 
  def _adjust_signs_df(self, df_scores: pd.DataFrame) -> pd.DataFrame:
  """
- In-place adjust the signs of the scores for MINIMIZATION problem.
+ Coerce optimization target scores to floats
+ and adjust the signs for MINIMIZATION problem.
  """
- for (opt_target, opt_dir) in self._opt_targets.items():
-  df_scores[opt_target] *= opt_dir
- return df_scores
+ # NOTE: We use `.astype()` instead of `.apply(pd.to_numeric)`
+ # to catch incorrect values early.
+ return df_scores[list(self._opt_targets)].astype(float) * self._opt_targets.values()
 
  def _to_df(self, configs: Sequence[Dict[str, TunableValue]]) -> pd.DataFrame:
  """