AdvancedUncertaintyQuantification.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy
import networkx as nx
from typing import List, Dict, Any, Tuple
import transformers
import itertools

class UncertaintyQuantificationModel(nn.Module):
    """
    Comprehensive Probabilistic Reasoning and Uncertainty Modeling System
    """
    def __init__(
        self, 
        input_dim: int = 768, 
        uncertainty_dimensions: int = 256,
        epistemic_layers: int = 4
    ):
        super().__init__()
        
        # Multi-layer Epistemic Uncertainty Encoder
        self.epistemic_encoder = nn.ModuleList([
            nn.Sequential(
                nn.Linear(input_dim if i == 0 else uncertainty_dimensions, uncertainty_dimensions),
                nn.LayerNorm(uncertainty_dimensions),
                nn.ReLU(),
                nn.Dropout(0.3)
            ) for i in range(epistemic_layers)
        ])
        
        # Uncertainty Propagation Attention Mechanism
        self.uncertainty_attention = nn.MultiheadAttention(
            embed_dim=uncertainty_dimensions,
            num_heads=12,
            dropout=0.2
        )
        
        # Epistemic Uncertainty Distribution Estimator
        self.uncertainty_distribution_estimator = nn.Sequential(
            nn.Linear(uncertainty_dimensions, uncertainty_dimensions * 2),
            nn.ReLU(),
            nn.Linear(uncertainty_dimensions * 2, 2 * uncertainty_dimensions)  # Parameters for distribution
        )
        
        # Epistemic Confidence Scoring Network
        self.epistemic_confidence_scorer = nn.Sequential(
            nn.Linear(uncertainty_dimensions, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )
        
        # Uncertainty Boundary Constraint Mechanism
        self.uncertainty_boundary_constraint = nn.Sequential(
            nn.Linear(uncertainty_dimensions, uncertainty_dimensions),
            nn.Tanh(),
            nn.Linear(uncertainty_dimensions, 1),
            nn.Sigmoid()
        )

    def forward(
        self, 
        input_embedding: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Advanced Uncertainty Quantification Processing
        """
        # Multi-layer Epistemic Encoding
        current_embedding = input_embedding
        for encoder_layer in self.epistemic_encoder:
            current_embedding = encoder_layer(current_embedding)
        
        # Uncertainty Propagation Attention
        uncertainty_propagation_output, _ = self.uncertainty_attention(
            current_embedding.unsqueeze(0), 
            current_embedding.unsqueeze(0), 
            current_embedding.unsqueeze(0)
        )
        
        # Uncertainty Distribution Estimation
        distribution_parameters = self.uncertainty_distribution_estimator(
            uncertainty_propagation_output.squeeze()
        )
        
        # Split into mean and variance parameters
        mean_params = distribution_parameters[:distribution_parameters.shape[0]//2]
        variance_params = F.softplus(distribution_parameters[distribution_parameters.shape[0]//2:])
        
        # Epistemic Confidence Scoring
        epistemic_confidence = self.epistemic_confidence_scorer(
            uncertainty_propagation_output.squeeze()
        )
        
        # Uncertainty Boundary Constraint
        uncertainty_boundary_prob = self.uncertainty_boundary_constraint(
            uncertainty_propagation_output.squeeze()
        )
        
        return (
            uncertainty_propagation_output.squeeze(),
            mean_params,
            variance_params,
            epistemic_confidence
        )

class EpistemicUncertaintyKnowledgeGraph:
    """
    Advanced Epistemic Uncertainty Knowledge Representation
    """
    def __init__(self):
        # Create sophisticated uncertainty knowledge graph
        self.uncertainty_graph = nx.DiGraph()
        self._construct_uncertainty_taxonomy()
    
    def _construct_uncertainty_taxonomy(self):
        """
        Create comprehensive uncertainty domain taxonomy
        """
        uncertainty_domains = {
            'EPISTEMIC_UNCERTAINTY': [
                'knowledge_gaps',
                'model_limitations',
                'inferential_ambiguity'
            ],
            'ALEATORIC_UNCERTAINTY': [
                'inherent_randomness',
                'measurement_noise',
                'environmental_variability'
            ],
            'META_UNCERTAINTY': [
                'uncertainty_about_uncertainty',
                'confidence_calibration',
                'epistemic_boundary_exploration'
            ]
        }
        
        # Build graph with complex relationships
        for domain, uncertainty_types in uncertainty_domains.items():
            self.uncertainty_graph.add_node(domain, type='root_domain')
            
            for uncertainty_type in uncertainty_types:
                self.uncertainty_graph.add_node(uncertainty_type, parent_domain=domain)
                self.uncertainty_graph.add_edge(domain, uncertainty_type)
                
                # Create inter-uncertainty relationships
                for other_type in uncertainty_types:
                    if uncertainty_type != other_type:
                        self.uncertainty_graph.add_edge(
                            uncertainty_type, 
                            other_type, 
                            weight=np.random.random(),
                            interaction_type='uncertainty_transfer'
                        )
    
    def analyze_uncertainty_propagation(
        self, 
        start_node: str, 
        end_node: str
    ) -> List[List[str]]:
        """
        Analyze potential uncertainty propagation paths
        """
        try:
            # Find multiple uncertainty propagation paths
            paths = list(nx.all_simple_paths(
                self.uncertainty_graph, 
                source=start_node, 
                target=end_node, 
                cutoff=5
            ))
            return paths
        except nx.NetworkXNoPath:
            return []

class ProbabilisticReasoningConstraintSystem:
    """
    Advanced Probabilistic Reasoning Constraint Mechanism
    """
    def __init__(
        self, 
        uncertainty_quantification_model: UncertaintyQuantificationModel
    ):
        self.uncertainty_model = uncertainty_quantification_model
        self.probabilistic_reasoning_engine = self._create_probabilistic_reasoning_engine()
    
    def _create_probabilistic_reasoning_engine(self):
        """
        Create advanced probabilistic reasoning constraint system
        """
        class ProbabilisticReasoningEngine:
            def evaluate_reasoning_uncertainty(
                self, 
                reasoning_trace: List[str],
                uncertainty_parameters: Tuple[torch.Tensor, torch.Tensor]
            ) -> Dict[str, Any]:
                """
                Comprehensive uncertainty evaluation for reasoning trace
                """
                mean_params, variance_params = uncertainty_parameters
                
                # Compute reasoning trace uncertainty metrics
                uncertainty_metrics = {
                    'trace_entropy': self._compute_reasoning_trace_entropy(reasoning_trace),
                    'epistemic_divergence': self._compute_epistemic_divergence(
                        mean_params, 
                        variance_params
                    ),
                    'reasoning_consistency_score': self._evaluate_reasoning_consistency(
                        reasoning_trace
                    )
                }
                
                return uncertainty_metrics
            
            def _compute_reasoning_trace_entropy(
                self, 
                reasoning_trace: List[str]
            ) -> float:
                """
                Compute entropy of reasoning trace
                """
                # Implement advanced entropy computation
                trace_tokens = [token for step in reasoning_trace for token in step.split()]
                token_dist = {token: trace_tokens.count(token)/len(trace_tokens) for token in set(trace_tokens)}
                entropy = -sum(p * np.log2(p) for p in token_dist.values())
                
                return entropy
            
            def _compute_epistemic_divergence(
                self, 
                mean_params: torch.Tensor, 
                variance_params: torch.Tensor
            ) -> float:
                """
                Compute epistemic divergence based on uncertainty parameters
                """
                # Kullback-Leibler divergence computation
                kl_divergence = 0.5 * torch.sum(
                    torch.log(variance_params) - 
                    torch.log(torch.tensor(1.0)) + 
                    (torch.tensor(1.0) / variance_params) * 
                    (mean_params ** 2)
                )
                
                return kl_divergence.item()
            
            def _evaluate_reasoning_consistency(
                self, 
                reasoning_trace: List[str]
            ) -> float:
                """
                Evaluate consistency of reasoning trace
                """
                # Implement sophisticated reasoning consistency analysis
                consistency_scores = []
                
                for i in range(1, len(reasoning_trace)):
                    prev_step = reasoning_trace[i-1]
                    current_step = reasoning_trace[i]
                    
                    # Compute semantic similarity
                    semantic_similarity = self._compute_semantic_similarity(
                        prev_step, 
                        current_step
                    )
                    
                    consistency_scores.append(semantic_similarity)
                
                return np.mean(consistency_scores)
            
            def _compute_semantic_similarity(
                self, 
                text1: str, 
                text2: str
            ) -> float:
                """
                Compute semantic similarity between two text steps
                """
                # Placeholder for advanced semantic similarity computation
                return np.random.random()
        
        return ProbabilisticReasoningEngine()
    
    def evaluate_probabilistic_reasoning(
        self, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Comprehensive probabilistic reasoning evaluation
        """
        # Embed reasoning trace
        trace_embedding = self._embed_reasoning_trace(reasoning_trace)
        
        # Apply uncertainty quantification model
        uncertainty_embedding, mean_params, variance_params, epistemic_confidence = self.uncertainty_model(
            trace_embedding
        )
        
        # Probabilistic reasoning evaluation
        reasoning_uncertainty = self.probabilistic_reasoning_engine.evaluate_reasoning_uncertainty(
            reasoning_trace,
            (mean_params, variance_params)
        )
        
        return {
            'uncertainty_embedding': uncertainty_embedding.detach().numpy(),
            'mean_parameters': mean_params.detach().numpy(),
            'variance_parameters': variance_params.detach().numpy(),
            'epistemic_confidence': epistemic_confidence.item(),
            'reasoning_uncertainty': reasoning_uncertainty
        }
    
    def _embed_reasoning_trace(
        self, 
        reasoning_trace: List[str]
    ) -> torch.Tensor:
        """
        Generate embedding for reasoning trace
        """
        # Use pre-trained embedding model
        tokenizer = transformers.AutoTokenizer.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )
        embedding_model = transformers.AutoModel.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )
        
        # Concatenate reasoning trace
        trace_text = " ".join(reasoning_trace)
        
        # Tokenize and embed
        inputs = tokenizer(
            trace_text, 
            return_tensors='pt', 
            padding=True, 
            truncation=True
        )
        
        with torch.no_grad():
            outputs = embedding_model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1)
        
        return embedding.squeeze()

# Remaining implementation follows previous patterns...

def main():
    # Initialize uncertainty quantification model
    uncertainty_model = UncertaintyQuantificationModel()
    
    # Create probabilistic reasoning constraint system
    probabilistic_reasoning_system = ProbabilisticReasoningConstraintSystem(
        uncertainty_model
    )
    
    # Sample reasoning trace
    reasoning_trace = [
        "Initial hypothesis about system behavior",
        "Refined conclusion considering probabilistic constraints"
        "Intermediate reasoning step with uncertainty",
    ]
    
    # Evaluate probabilistic reasoning
    evaluation_results = probabilistic_reasoning_system.evaluate_probabilistic_reasoning(
        reasoning_trace
    )
    
    # Visualization and reporting
    import json
    print("Probabilistic Reasoning Evaluation:")
    print(json.dumps(
        {k: str(v) for k, v in evaluation_results.items()}, 
        indent=2
    ))

if __name__ == "__main__":
    main()