import numpy as np import pandas as pd import random class SelfImprovingValidationSystem: """ Implementation of Patent 9: Self-Improving Validation System. This system automatically adjusts validation thresholds based on outcomes, uses reinforcement learning for continuous improvement, and tracks performance metrics. """ def __init__(self, initial_thresholds, metrics=['accuracy', 'precision', 'recall', 'f1_score'], learning_rate=0.1, discount_factor=0.9, exploration_rate=0.1): """ Initializes the validation system. Args: initial_thresholds (dict): A dictionary of initial thresholds for different validation criteria. Example: {'score_a': 0.7, 'score_b': 0.8} metrics (list): List of performance metrics to track. learning_rate (float): Learning rate for the reinforcement learning algorithm. discount_factor (float): Discount factor for the reinforcement learning algorithm. exploration_rate (float): Exploration rate for the reinforcement learning algorithm (epsilon-greedy). """ self.thresholds = initial_thresholds self.metrics = metrics self.history = [] # Store historical performance and thresholds self.learning_rate = learning_rate self.discount_factor = discount_factor self.exploration_rate = exploration_rate self.q_table = {} # Q-table for reinforcement learning self.state = self._get_state() def _get_state(self): """ Defines the state of the system based on current thresholds. This could be more sophisticated, using recent performance, etc. Returns: tuple: A tuple representing the current state of the system based on thresholds. """ return tuple(sorted(self.thresholds.items())) # Sort to ensure consistent state representation def validate(self, data): """ Validates a data point against the current thresholds. Args: data (dict): A dictionary containing the data to validate. Must have keys corresponding to the thresholds. Example: {'score_a': 0.75, 'score_b': 0.85} Returns: bool: True if the data point passes validation, False otherwise. """ for criterion, threshold in self.thresholds.items(): if data[criterion] < threshold: return False return True def evaluate(self, data, ground_truth): """ Evaluates the performance of the validation system on a set of data and calculates performance metrics. Args: data (list): A list of data points (dictionaries) to validate. ground_truth (list): A list of ground truth labels (True/False) corresponding to each data point. Returns: dict: A dictionary of performance metrics. """ predictions = [self.validate(d) for d in data] tp = sum(1 for p, gt in zip(predictions, ground_truth) if p and gt) tn = sum(1 for p, gt in zip(predictions, ground_truth) if not p and not gt) fp = sum(1 for p, gt in zip(predictions, ground_truth) if p and not gt) fn = sum(1 for p, gt in zip(predictions, ground_truth) if not p and gt) results = {} if 'accuracy' in self.metrics: results['accuracy'] = (tp + tn) / len(data) if len(data) > 0 else 0 if 'precision' in self.metrics: results['precision'] = tp / (tp + fp) if (tp + fp) > 0 else 0 if 'recall' in self.metrics: results['recall'] = tp / (tp + fn) if (tp + fn) > 0 else 0 if 'f1_score' in self.metrics: precision = results.get('precision', tp / (tp + fp) if (tp + fp) > 0 else 0) recall = results.get('recall', tp / (tp + fn) if (tp + fn) > 0 else 0) results['f1_score'] = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 return results def adjust_thresholds(self, reward): """ Adjusts the thresholds based on the reward received from the environment using a reinforcement learning approach (Q-learning). Args: reward (float): The reward received from the environment. """ state = self.state action = self._choose_action() new_state = self._apply_action(action) # Update Q-table if (state, action) not in self.q_table: self.q_table[(state, action)] = 0.0 best_next_action = self._choose_best_action(new_state) if (new_state, best_next_action) not in self.q_table: self.q_table[(new_state, best_next_action)] = 0.0 old_value = self.q_table.get((state, action), 0.0) next_max = self.q_table.get((new_state, best_next_action), 0.0) new_value = (1 - self.learning_rate) * old_value + self.learning_rate * (reward + self.discount_factor * next_max) self.q_table[(state, action)] = new_value self.state = new_state # Update the current state def _choose_action(self): """ Chooses an action using an epsilon-greedy approach. Returns: tuple: A tuple representing the action to take. The tuple contains the criterion to adjust and the adjustment amount. """ if random.random() < self.exploration_rate: # Explore: Choose a random action criterion = random.choice(list(self.thresholds.keys())) adjustment = random.choice([-0.05, 0.05]) # Adjust threshold by +/- 0.05 return (criterion, adjustment) else: # Exploit: Choose the best action based on the Q-table return self._choose_best_action(self.state) def _choose_best_action(self, state): """ Chooses the best action based on the Q-table for a given state. Args: state (tuple): The current state of the system. Returns: tuple: The best action to take. """ best_action = None best_value = float('-inf') for criterion in self.thresholds.keys(): for adjustment in [-0.05, 0.05]: action = (criterion, adjustment) value = self.q_table.get((state, action), 0.0) # Default Q-value is 0.0 if value > best_value: best_value = value best_action = action if best_action is None: #If no actions have been tried, return a random action criterion = random.choice(list(self.thresholds.keys())) adjustment = random.choice([-0.05, 0.05]) return (criterion, adjustment) return best_action def _apply_action(self, action): """ Applies an action to the system by adjusting a threshold. Args: action (tuple): The action to apply. Contains the criterion and the adjustment amount. Returns: tuple: The new state of the system after applying the action. """ criterion, adjustment = action self.thresholds[criterion] += adjustment self.thresholds[criterion] = max(0.0, min(1.0, self.thresholds[criterion])) # Keep thresholds within [0, 1] return self._get_state() def train(self, data, ground_truth, epochs=100): """ Trains the validation system using reinforcement learning. Args: data (list): A list of data points (dictionaries) to validate. ground_truth (list): A list of ground truth labels (True/False) corresponding to each data point. epochs (int): The number of training epochs. """ for epoch in range(epochs): # Evaluate performance results = self.evaluate(data, ground_truth) # Define a reward function (example: F1-score) reward = results.get('f1_score', 0.0) # Store history self.history.append({'epoch': epoch, 'thresholds': self.thresholds.copy(), 'metrics': results}) # Adjust thresholds based on reward self.adjust_thresholds(reward) print(f"Epoch {epoch + 1}/{epochs}, F1-score: {reward:.4f}, Thresholds: {self.thresholds}") def get_improvement_analytics(self): """ Provides improvement analytics based on the training history. Returns: pandas.DataFrame: A DataFrame containing the training history. """ return pd.DataFrame(self.history) # Example Usage: if __name__ == '__main__': # Sample data data = [ {'score_a': 0.75, 'score_b': 0.85}, {'score_a': 0.60, 'score_b': 0.70}, {'score_a': 0.90, 'score_b': 0.95}, {'score_a': 0.55, 'score_b': 0.65}, {'score_a': 0.80, 'score_b': 0.90}, ] ground_truth = [True, False, True, False, True] # Initialize the validation system initial_thresholds = {'score_a': 0.7, 'score_b': 0.8} system = SelfImprovingValidationSystem(initial_thresholds) # Train the system system.train(data, ground_truth, epochs=50) # Get improvement analytics analytics = system.get_improvement_analytics() print("\nImprovement Analytics:") print(analytics) #Example of using the system after training new_data = {'score_a': 0.82, 'score_b': 0.88} is_valid = system.validate(new_data) print(f"\nNew data validation result: {is_valid}")