rename to elicit

elk/training/sweep.py

@@ -133,19 +133,19 @@ def execute(self):
  data = replace(
  self.run_template.data, model=model, datasets=train_datasets
  )
- run = replace(self.run_template, data=data, out_dir=out_dir)
+ elicit = replace(self.run_template, data=data, out_dir=out_dir)
  if var_weight is not None and neg_cov_weight is not None:
- assert isinstance(run.net, EigenFitterConfig)
- run.net.var_weight = var_weight
- run.net.neg_cov_weight = neg_cov_weight
+ assert isinstance(elicit.net, EigenFitterConfig)
+ elicit.net.var_weight = var_weight
+ elicit.net.neg_cov_weight = neg_cov_weight
 
  # Add hyperparameter values to output directory if needed
- assert run.out_dir is not None
- run.out_dir /= f"var_weight={var_weight:.2f}"
- run.out_dir /= f"neg_cov_weight={neg_cov_weight:.2f}"
+ assert elicit.out_dir is not None
+ elicit.out_dir /= f"var_weight={var_weight:.2f}"
+ elicit.out_dir /= f"neg_cov_weight={neg_cov_weight:.2f}"
 
  try:
- run.execute()
+ elicit.execute()
  except torch.linalg.LinAlgError as e:
  print(colorize(f"LinAlgError: {e}", "red"))
  continue
@@ -160,10 +160,10 @@ def execute(self):
  if eval_dataset in train_datasets:
  continue
 
- assert run.out_dir is not None
+ assert elicit.out_dir is not None
  # TODO we should fix this so that this isn't needed
 
- eval = run.make_eval(model, eval_dataset)
+ eval = elicit.make_eval(model, eval_dataset)
  eval.execute(highlight_color="green")
 
  if self.visualize: