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