Complete Migration

agent/hill_climbing.py

@@ -0,0 +1,119 @@
+from typing import List, Iterator, Tuple, Dict
+from .models import EvaluationResult
+from .modules import TweetGeneratorModule, TweetEvaluatorModule
+from .helpers import format_evaluation_for_generator
+
+class HillClimbingOptimizer:
+    """Hill climbing optimizer for tweet improvement."""
+    
+    def __init__(
+        self,
+        generator: TweetGeneratorModule,
+        evaluator: TweetEvaluatorModule,
+        categories: List[str],
+        max_iterations: int = 10,
+        patience: int = 5
+    ):
+        self.generator = generator
+        self.evaluator = evaluator
+        self.categories = categories
+        self.max_iterations = max_iterations
+        self.patience = patience
+    
+    def optimize(self, initial_text: str) -> Iterator[Tuple[str, EvaluationResult, bool, int, Dict[str, str], Dict[str, str]]]:
+        """
+        Optimize tweet using hill climbing algorithm.
+        
+        Yields:
+            Tuple of (current_tweet, evaluation_result, is_improvement, patience_counter, generator_inputs, evaluator_inputs)
+        """
+        # Generate initial tweet
+        generator_inputs = {
+            "input_text": initial_text,
+            "current_tweet": "",
+            "previous_evaluation": ""
+        }
+        current_tweet = self.generator(
+            input_text=initial_text,
+            current_tweet="",
+            previous_evaluation=None
+        )
+        
+        evaluator_inputs = {
+            "original_text": initial_text,
+            "current_best_tweet": "",
+            "tweet_text": current_tweet
+        }
+        current_score = self.evaluator(
+            tweet_text=current_tweet,
+            categories=self.categories,
+            original_text=initial_text,
+            current_best_tweet=""
+        )
+        
+        best_tweet = current_tweet
+        best_score = current_score
+        patience_counter = 0
+        
+        yield (current_tweet, current_score, True, patience_counter, generator_inputs, evaluator_inputs)
+        
+        for iteration in range(1, self.max_iterations):
+            # Generate improved tweet with previous evaluation as feedback
+            try:
+                # Format evaluation for display in generator inputs
+                eval_text = format_evaluation_for_generator(best_score)
+                
+                generator_inputs = {
+                    "input_text": initial_text,
+                    "current_tweet": best_tweet,
+                    "previous_evaluation": eval_text
+                }
+                
+                candidate_tweet = self.generator(
+                    input_text=initial_text,
+                    current_tweet=best_tweet,
+                    previous_evaluation=best_score
+                )
+                
+                # Evaluate candidate
+                evaluator_inputs = {
+                    "original_text": initial_text,
+                    "current_best_tweet": best_tweet,
+                    "tweet_text": candidate_tweet
+                }
+                candidate_score = self.evaluator(
+                    tweet_text=candidate_tweet,
+                    categories=self.categories,
+                    original_text=initial_text,
+                    current_best_tweet=best_tweet
+                )
+                
+                # Check if candidate is better (hill climbing condition)
+                is_improvement = candidate_score > best_score
+                
+                if is_improvement:
+                    best_tweet = candidate_tweet
+                    best_score = candidate_score
+                    patience_counter = 0
+                    yield (candidate_tweet, candidate_score, True, patience_counter, generator_inputs, evaluator_inputs)
+                else:
+                    patience_counter += 1
+                    yield (best_tweet, candidate_score, False, patience_counter, generator_inputs, evaluator_inputs)
+                
+                # Early stopping if no improvement for 'patience' iterations
+                if patience_counter >= self.patience:
+                    break
+                    
+            except Exception as e:
+                # If generation fails, yield current best
+                patience_counter += 1
+                evaluator_inputs = {
+                    "original_text": initial_text,
+                    "current_best_tweet": best_tweet,
+                    "tweet_text": best_tweet
+                }
+                yield (best_tweet, best_score, False, patience_counter, generator_inputs, evaluator_inputs)
+                
+                if patience_counter >= self.patience:
+                    break
+