import random import time from typing import List, Callable, Any, Dict, Tuple import copy class DarwinModeAdvanced: """ Implements the Darwin Pattern for advanced competitive reasoning paths, incorporating multi-objective optimization, ensemble combination, learning from competition history, adaptive competition parameters, specialized reasoning strategies, and performance tracking. """ def __init__( self, generator: Callable[[], Any], evaluator: Callable[[Any], Dict[str, float]], n: int = 5, combination_function: Callable[[List[Any]], Any] = None, learning_function: Callable[[List[Tuple[Any, Dict[str, float]]]], None] = None, competition_criteria: Dict[str, float] = None, strategy_generator: Callable[[], str] = None, # Generates names of reasoning strategies strategy_evaluator: Callable[[str], Dict[str, float]] = None, # Evaluates the strategy itself reflexion_function: Callable[[List[Tuple[Any, Dict[str, float]]]], None] = None, ensemble_size: int = 3 #Number of top solutions to combine for the ensemble ): """ Initializes the DarwinModeAdvanced object. Args: generator: A function that generates a reasoning path. evaluator: A function that evaluates a reasoning path. Should return a dict of scores. n: The number of reasoning paths to generate. combination_function: A function to combine elements from winning paths. learning_function: A function to learn from the competition. competition_criteria: A dictionary of weightings for each competition criterion. Keys should match the keys returned by the evaluator. Defaults to equal weighting if None. strategy_generator: A function that generates the name of a reasoning strategy strategy_evaluator: A function that evaluates a reasoning strategy, returning a dict of scores. reflexion_function: A function to implement reflexion, adjusting the generator based on the competition. ensemble_size: The number of top solutions to combine to create the ensemble. """ self.generator = generator self.evaluator = evaluator self.n = n self.combination_function = combination_function self.learning_function = learning_function self.competition_criteria = competition_criteria if competition_criteria else {"accuracy": 1.0, "completeness": 1.0, "efficiency": 1.0, "novelty": 1.0} self.strategy_generator = strategy_generator self.strategy_evaluator = strategy_evaluator self.reflexion_function = reflexion_function self.ensemble_size = ensemble_size # Normalize competition criteria weights total_weight = sum(self.competition_criteria.values()) self.competition_criteria = {k: v / total_weight for k, v in self.competition_criteria.items()} self.competition_history: List[Tuple[Any, Dict[str, float]]] = [] self.strategy_performance: Dict[str, List[float]] = {} #Track performance of each strategy def run_generation(self) -> Tuple[Any, Dict[str, float]]: """ Runs a single generation of the Darwin Pattern. Returns: A tuple containing the winning reasoning path and its evaluation scores. """ paths = [] evaluations = [] strategies = [] # 1. Generate N reasoning paths, potentially using different strategies for _ in range(self.n): strategy = self.strategy_generator() if self.strategy_generator else "default" strategies.append(strategy) # Optionally apply strategy-specific logic to the generator. This is a placeholder. if strategy != "default": path = self.generator() # Modify the generator based on the strategy else: path = self.generator() paths.append(path) # 2. Evaluate each path for path in paths: evaluation = self.evaluator(path) evaluations.append(evaluation) # 3. Select winners (top 'ensemble_size' paths) winning_paths, winning_evaluations = self.select_winners(paths, evaluations, self.ensemble_size) # 4. Ensemble combination of winning paths ensemble_path = self.combine_ensemble(winning_paths) ensemble_evaluation = self.evaluator(ensemble_path) # 5. Optionally combine best elements if self.combination_function: combined_path = self.combination_function(paths) #Re-evaluate the combined path combined_evaluation = self.evaluator(combined_path) #Choose the best between the ensemble and the combined path ensemble_path, ensemble_evaluation = self.select_winner([ensemble_path, combined_path], [ensemble_evaluation, combined_evaluation]) # 6. Optionally learn from competition if self.learning_function: path_evaluation_pairs = list(zip(paths, evaluations)) self.learning_function(path_evaluation_pairs) #7. Update competition history for path, evaluation in zip(paths, evaluations): self.competition_history.append((path, evaluation)) #8. Track strategy performance for strategy, evaluation in zip(strategies, evaluations): if strategy not in self.strategy_performance: self.strategy_performance[strategy] = [] self.strategy_performance[strategy].append(self.calculate_weighted_score(evaluation)) return ensemble_path, ensemble_evaluation def calculate_weighted_score(self, evaluation: Dict[str, float]) -> float: """ Calculates the weighted score for a given evaluation based on the competition criteria. """ weighted_score = 0 for criterion, weight in self.competition_criteria.items(): if criterion not in evaluation: print(f"Warning: Criterion '{criterion}' not found in evaluation. Using a score of 0.") criterion_score = 0.0 else: criterion_score = evaluation[criterion] weighted_score += weight * criterion_score return weighted_score def select_winners(self, paths: List[Any], evaluations: List[Dict[str, float]], num_winners: int) -> Tuple[List[Any], List[Dict[str, float]]]: """ Selects the winning reasoning paths based on the competition criteria. Args: paths: A list of reasoning paths. evaluations: A list of dictionaries, where each dictionary contains the evaluation scores for a path. num_winners: The number of winners to select. Returns: A tuple containing the winning reasoning paths and their evaluation scores. """ if not paths or not evaluations or len(paths) != len(evaluations): raise ValueError("Paths and evaluations must be non-empty and of equal length.") # Calculate weighted scores for each path weighted_scores = [] for evaluation in evaluations: weighted_scores.append(self.calculate_weighted_score(evaluation)) # Select the paths with the highest weighted scores winners = [] winner_evaluations = [] for _ in range(min(num_winners, len(paths))): winner_index = weighted_scores.index(max(weighted_scores)) winners.append(paths[winner_index]) winner_evaluations.append(evaluations[winner_index]) weighted_scores[winner_index] = float('-inf') # Avoid selecting the same path again return winners, winner_evaluations def select_winner(self, paths: List[Any], evaluations: List[Dict[str, float]]) -> Tuple[Any, Dict[str, float]]: """ Selects the winning reasoning path based on the competition criteria. Args: paths: A list of reasoning paths. evaluations: A list of dictionaries, where each dictionary contains the evaluation scores for a path. Returns: A tuple containing the winning reasoning path and its evaluation scores. """ if not paths or not evaluations or len(paths) != len(evaluations): raise ValueError("Paths and evaluations must be non-empty and of equal length.") # Calculate weighted scores for each path weighted_scores = [] for evaluation in evaluations: weighted_scores.append(self.calculate_weighted_score(evaluation)) # Select the path with the highest weighted score winner_index = weighted_scores.index(max(weighted_scores)) return paths[winner_index], evaluations[winner_index] def combine_ensemble(self, paths: List[Any]) -> Any: """ Combines the winning paths into an ensemble path. This is a placeholder. The specific combination logic will depend on the nature of the reasoning paths. """ if not paths: return None # Example: For numeric reasoning paths, take the average if all(isinstance(path, (int, float)) for path in paths): return sum(paths) / len(paths) # Example: For string reasoning paths, concatenate them if all(isinstance(path, str) for path in paths): return "".join(paths) # Default: Return the first path return paths[0] def adapt_competition_parameters(self): """ Adapts the competition parameters (e.g., competition_criteria) based on the competition history. This is a placeholder; the specific adaptation logic will depend on the problem domain. """ # Example: Increase the weight of a criterion if it consistently leads to better results if not self.competition_history: return # Analyze the competition history to identify criteria that are strongly correlated with success # (e.g., by calculating the correlation between criterion scores and overall weighted scores) # Adjust the weights of the competition criteria accordingly pass def run(self, num_generations: int) -> Tuple[Any, Dict[str, float]]: """ Runs multiple generations of the Darwin Pattern. Args: num_generations: The number of generations to run. Returns: A tuple containing the winning reasoning path from the final generation and its evaluation scores. """ best_path = None best_evaluation = None for i in range(num_generations): print(f"Generation {i+1}/{num_generations}") path, evaluation = self.run_generation() if best_path is None or self.select_winner([best_path, path], [best_evaluation, evaluation])[0] == path: best_path = path best_evaluation = evaluation print(f" Best path this generation: {evaluation}") #Adapt competition parameters after each generation self.adapt_competition_parameters() #Reflexion: Adjust the generator based on the competition if self.reflexion_function: self.reflexion_function(self.competition_history) return best_path, best_evaluation if __name__ == '__main__': # Example Usage (Illustrative) def simple_generator(): """Generates a random number between 1 and 10.""" return random.randint(1, 10) def simple_evaluator(number): """Evaluates how close the number is to 5.""" accuracy = 1 - abs(number - 5) / 5 # Accuracy: closer to 5 is better efficiency = 1 / number # Efficiency: smaller number is better (to some extent) novelty = random.random() #Add a random novelty score return {"accuracy": accuracy, "efficiency": efficiency, "novelty": novelty} def simple_strategy_generator(): """Generates a random reasoning strategy.""" return random.choice(["strategy_A", "strategy_B", "strategy_C"]) def simple_strategy_evaluator(strategy): """Evaluates a reasoning strategy (placeholder).""" #In a real system, this would evaluate the effectiveness of the strategy itself return {"effectiveness": random.random()} def simple_reflexion_function(competition_history): """A dummy reflexion function that prints some info about the history.""" print("\nReflexion:") print(f" Number of past results: {len(competition_history)}") #In a real system, this would analyze the history and adjust the generator's parameters. # Create a DarwinModeAdvanced instance darwin_advanced = DarwinModeAdvanced( generator=simple_generator, evaluator=simple_evaluator, n=5, competition_criteria={"accuracy": 0.6, "efficiency": 0.3, "novelty": 0.1}, strategy_generator=simple_strategy_generator, strategy_evaluator=simple_strategy_evaluator, reflexion_function=simple_reflexion_function, ensemble_size = 2 ) # Run the Darwin Pattern for 3 generations best_number, best_evaluation = darwin_advanced.run(num_generations=3) print(f"\nBest Number: {best_number}") print(f"Best Evaluation: {best_evaluation}") #Print strategy performance print("\nStrategy Performance:") for strategy, scores in darwin_advanced.strategy_performance.items(): print(f" {strategy}: {scores}, Average: {sum(scores)/len(scores) if scores else 0}")