you go follow this framework in responses you generating in this chat:


"""

import random
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from collections import defaultdict

class Solution:
    """Class to represent a potential solution with associated metrics."""
    
    def __init__(self, name: str, features: dict):
        self.name = name
        self.features = features  # Features are stored as key-value pairs.

    def __repr__(self):
        return f"Solution(name={self.name}, features={self.features})"

class DecisionFramework:
    """Framework to perform advanced decision-making through iterative assessment."""

    def __init__(self, problem_description: str):
        self.problem_description = problem_description
        self.solutions = []                # List of potential solutions
        self.criteria_weights = {}         # Weights for each evaluation criterion
        self.evaluations = []              # List of evaluations for solutions
        self.feedback = []                 # User feedback storage
        self.model = None                  # Placeholder for the ML model
        self.vectorizer = CountVectorizer()  # NLP processing for feedback
    
    def define_problem(self):
        """Define the problem statement."""
        print(f"\nProblem Defined: {self.problem_description}")

    def generate_solutions(self, num_solutions: int):
        """Generate potential solutions with random features."""
        print(f"Generating {num_solutions} potential solutions...")
        for i in range(num_solutions):
            solution_name = f"Solution_{i + 1}"
            features = {
                "cost": random.randint(100, 300),
                "effectiveness": random.randint(70, 100),
                "integration": random.randint(60, 90)
            }
            self.solutions.append(Solution(solution_name, features))
        print(f"Generated Solutions: {[str(solution) for solution in self.solutions]}")

    def set_criteria_weights(self, criteria_weights: dict):
        """Set the weights for evaluation criteria."""
        self.criteria_weights = criteria_weights
        print(f"Evaluation Criteria Weights: {self.criteria_weights}")

    def evaluate_solutions(self):
        """Evaluate solutions based on weighted criteria and return evaluations."""
        print("\nEvaluating solutions...")
        self.evaluations.clear()  # Clear previous evaluations
        for solution in self.solutions:
            score = sum(solution.features[feature] * self.criteria_weights.get(feature, 0) for feature in solution.features)
            self.evaluations.append({"solution": solution.name, "score": score})

        print("Evaluated Solutions:")
        for evaluation in self.evaluations:
            print(f" - {evaluation['solution']} with score {evaluation['score']}")
        return self.evaluations

    def select_best_solution(self):
        """Select the best solution based on scores."""
        best_solution = max(self.evaluations, key=lambda x: x['score'])
        print(f"Best Solution Selected: {best_solution['solution']} with score {best_solution['score']}")
        return best_solution['solution']

    def gather_feedback(self):
        """Simulate gathering user feedback."""
        feedback_data = [
            "The selected solution greatly improved efficiency.",
            "More integration with existing tools is required.",
            "Customization options will enhance the user experience."
        ]
        self.feedback.extend(feedback_data)
        print("\nFeedback Collected:")
        for item in self.feedback:
            print(f" - {item}")

    def analyze_feedback(self):
        """Analyze feedback using NLP and clustering to identify common themes."""
        print("\nAnalyzing Feedback...")
        feedback_vectorized = self.vectorizer.fit_transform(self.feedback)
        feedback_matrix = feedback_vectorized.toarray()
        
        # Clustering feedback for theme detection
        cluster_model = KMeans(n_clusters=3)  # Assume 3 thematic clusters
        clusters = cluster_model.fit_predict(feedback_matrix)

        clustered_feedback = defaultdict(list)
        for idx, label in enumerate(clusters):
            clustered_feedback[label].append(self.feedback[idx])

        for cluster, comments in clustered_feedback.items():
            print(f"Cluster {cluster}:")
            for comment in comments:
                print(f" - {comment}")

    def build_ensemble_model(self):
        """Build an ensemble model for future predictions based on evaluations."""
        if len(self.evaluations) == 0:
            print("No evaluations available to train the model.")
            return

        X = np.array([[sol.features['cost'], sol.features['effectiveness'], sol.features['integration']] for sol in self.solutions])
        y = np.array([eval['score'] for eval in self.evaluations])

        # Create and train the ensemble model
        self.model = RandomForestRegressor()
        self.model.fit(X, y)
        print("Ensemble model built and trained on current solutions.")

    def adaptive_learning(self):
        """Update the model based on user interaction and feedback."""
        if self.model is None:
            print("Model is not yet trained; please train the model first.")
            return
        print("Adapting learning based on latest evaluations and feedback...")
        # Example: Retrain model with new feedback-heavy evaluations
        new_X = np.array([[sol.features['cost'], sol.features['effectiveness'], sol.features['integration']] for sol in self.solutions])
        # Placeholder target for demonstration
        new_y = np.array([random.randint(1, 100) for _ in self.solutions])
        self.model.fit(new_X, new_y)  # Example update with new data
        print("Model adaptation completed.")

    def self_evaluate(self):
        """Perform self-analysis of the framework's state."""
        print("\nSelf-Evaluation:")
        if not self.solutions:
            print("No solutions generated.")
        if not self.criteria_weights:
            print("No evaluation criteria specified.")

    def run_full_process(self):
        """Execute the entire decision-making process."""
        self.define_problem()
        num_solutions = random.randint(3, 5)  # Dynamic solution count
        self.generate_solutions(num_solutions)

        self.set_criteria_weights({
            "cost": -1,
            "effectiveness": 2,
            "integration": 1
        })

        self.evaluate_solutions()
        best_solution = self.select_best_solution()
        self.gather_feedback()
        self.analyze_feedback()
        self.build_ensemble_model()  # Building the ensemble
        self.adaptive_learning()  # Learning from interactions
        self.self_evaluate()  # Final self-assessment
        print(f"\nProcess Complete: The best solution selected is '{best_solution}'.")

# Running the Enhanced Complex Decision Framework
if __name__ == "__main__":
    decision_framework = DecisionFramework("Identify and select the optimal decision-making strategy.")
    decision_framework.run_full_process()

""".