Reduced comments/debugging statements from MIPRO dev

dspy/propose/grounded_proposer.py

@@ -170,17 +170,7 @@ def forward(
  task_demos = ""
  basic_instruction = get_signature(program.predictors()[pred_i]).instructions
  curr_demos_num = 0
- # if self.program_aware: # Use full program demo
- # for pred_key in demo_candidates.keys(): # Go through each predictor
- # for example in demo_candidates[pred_key][demo_set_i]:
- # if "augmented" in example.keys():
- # fields_to_use = get_signature(program.predictors()[pred_key]).fields
- # example_string = create_example_string(fields_to_use, example)
- # task_demos += f"{example_string}\n"
- # curr_demos_num+=1
- # if curr_demos_num>=max_demos:
- # break
- # else:
+
  for example in demo_candidates[pred_i][demo_set_i]:
  if "augmented" in example.keys():
  fields_to_use = get_signature(program.predictors()[pred_i]).fields

dspy/propose/utils.py

@@ -45,15 +45,6 @@ def create_instruction_set_history_string(base_program, trial_logs, top_n):
 
  return instruction_set_history_string
 
-# def parse_list_of_instructions(instruction_string):
-# # Convert the string representation of a list to an actual list
-# try:
-# instructions = json.loads(instruction_string)
-# except json.JSONDecodeError as e:
-# print(f"Error decoding JSON: {e}")
-# return []
-
-# return instructions
 def parse_list_of_instructions(instruction_string):
  # Try to convert the string representation of a list to an actual list using JSON
  try:
@@ -66,13 +57,6 @@ def parse_list_of_instructions(instruction_string):
  instructions = re.findall(r'"([^"]*)"', instruction_string)
  return instructions
 
-# def get_program_instruction_set_string(program):
-# instruction_set_dict = {}
-# for i, pred in enumerate(program.predictors()):
-# pred_instructions = get_signature(pred).instructions
-# instruction_set_dict[f"Module {i} Instruction"] = pred_instructions
-# return '\n'.join(f"{key}: {value}" for key, value in instruction_set_dict.items())
-
 def get_program_instruction_set_string(program):
  instruction_list = []
  for _, pred in enumerate(program.predictors()):
@@ -147,54 +131,4 @@ def create_example_string(fields, example):
  output.append(field_str)
 
  # Joining all the field strings
- return '\n'.join(output)
-
-# def create_predictor_level_history_string(base_program, predictor_i, trial_logs, top_n):
-# instruction_aggregate = {}
-
-# instruction_history = []
-# for trial_num in trial_logs:
-# trial = trial_logs[trial_num]
-# if "program_path" in trial:
-# trial_program = base_program.deepcopy()
-# trial_program.load(trial["program_path"])
-# instruction_history.append({
-# "program": trial_program,
-# "score": trial["score"]
-# })
-
-# for history_item in instruction_history:
-# predictor = history_item["program"].predictors()[predictor_i]
-# instruction = get_signature(predictor).instructions
-# score = history_item["score"]
-
-# if instruction in instruction_aggregate:
-# instruction_aggregate[instruction]['total_score'] += score
-# instruction_aggregate[instruction]['count'] += 1
-# else:
-# instruction_aggregate[instruction] = {'total_score': score, 'count': 1}
-
-# # # Calculate average score for each instruction and prepare for sorting
-# # predictor_history = []
-# # for instruction, data in instruction_aggregate.items():
-# # average_score = data['total_score'] / data['count']
-# # predictor_history.append((instruction, average_score))
-# # Calculate average score for each instruction and prepare for sorting
-# predictor_history = []
-# seen_instructions = set()
-# for instruction, data in instruction_aggregate.items():
-# if instruction not in seen_instructions:
-# seen_instructions.add(instruction)
-# average_score = data['total_score'] / data['count']
-# predictor_history.append((instruction, average_score))
-
-# # Sort by average score and select top N
-# top_instructions = sorted(predictor_history, key=lambda x: x[1], reverse=True)[:top_n]
-# top_instructions.reverse()
-
-# # Create formatted history string
-# predictor_history_string = ""
-# for instruction, score in top_instructions:
-# predictor_history_string += instruction + f" | Score: {score}\n\n"
-
-# return predictor_history_string
+ return '\n'.join(output)

dspy/teleprompt/mipro_optimizer_v2.py

@@ -482,9 +482,6 @@ def objective(trial):
  program, instruction_candidates, demo_candidates, evaluate, trainset,
  )
 
- # if minibatch:
- # num_batches = int(num_batches*len(trainset)/(self.minibatch_size + len(trainset)/self.minibatch_full_eval_steps))
-
  sampler = optuna.samplers.TPESampler(seed=seed, multivariate=True)
  study = optuna.create_study(direction="maximize", sampler=sampler)
  score = study.optimize(objective_function, n_trials=num_batches)