advanced_hallucination_detection.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
import networkx as nx
import spacy
import wikipedia
import requests
from transformers import (
    AutoModelForSequenceClassification, 
    AutoTokenizer, 
    BertModel, 
    BertTokenizer
)
from sentence_transformers import SentenceTransformer
from typing import List, Dict, Any, Tuple
from sklearn.metrics.pairwise import cosine_similarity

class AdvancedHallucinationDetector:
    def __init__(self):
        # Advanced NLP models
        self.nlp = spacy.load('en_core_web_trf')
        
        # Knowledge Graph and Fact-Checking Components
        self.knowledge_graph = self._build_knowledge_graph()
        self.fact_checker = AdvancedFactChecker()
        
        # Custom Hallucination Detection Transformer
        self.hallucination_model = self._build_custom_hallucination_model()
        
        # Linguistic Analysis Tools
        self.linguistic_analyzer = AdvancedLinguisticAnalyzer()
    
    def _build_knowledge_graph(self) -> nx.DiGraph:
        """
        Build a comprehensive knowledge graph
        
        :return: NetworkX Directed Graph
        """
        G = nx.DiGraph()
        
        # Integrate multiple knowledge sources
        knowledge_sources = [
            self._extract_wikipedia_knowledge,
            self._extract_wikidata_knowledge,
            self._extract_scholarly_knowledge
        ]
        
        for source_extractor in knowledge_sources:
            try:
                source_graph = source_extractor()
                G = nx.compose(G, source_graph)
            except Exception as e:
                print(f"Knowledge extraction error: {e}")
        
        return G
    
    def _extract_wikipedia_knowledge(self) -> nx.DiGraph:
        """
        Extract knowledge from Wikipedia
        
        :return: NetworkX Directed Graph
        """
        G = nx.DiGraph()
        
        # Top categories to explore
        categories = [
            'Science', 'History', 'Geography', 
            'Technology', 'Biology', 'Physics'
        ]
        
        for category in categories:
            try:
                # Get Wikipedia pages for the category
                category_pages = wikipedia.search(category, results=50)
                
                for page_title in category_pages:
                    try:
                        page = wikipedia.page(page_title)
                        
                        # Add nodes and relationships
                        G.add_node(page_title, content=page.summary)
                        
                        # Extract and add relationships
                        for link in page.links[:20]:  # Limit to prevent explosion
                            G.add_edge(page_title, link)
                    
                    except (wikipedia.DisambiguationError, wikipedia.PageError):
                        continue
            
            except Exception as e:
                print(f"Wikipedia extraction error for {category}: {e}")
        
        return G
    
    def _extract_wikidata_knowledge(self) -> nx.DiGraph:
        """
        Extract structured knowledge from Wikidata
        
        :return: NetworkX Directed Graph
        """
        G = nx.DiGraph()
        
        # Wikidata SPARQL endpoint
        sparql_endpoint = "https://query.wikidata.org/sparql"
        
        # Example query for scientific concepts
        query = """
        SELECT ?item ?itemLabel ?description WHERE {
            ?item wdt:P31 wd:Q17444909.  # Scientific concept
            SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
        } LIMIT 1000
        """
        
        try:
            response = requests.get(
                sparql_endpoint, 
                params={'query': query, 'format': 'json'}
            )
            data = response.json()
            
            for item in data['results']['bindings']:
                concept = item['itemLabel']['value']
                description = item.get('description', {}).get('value', '')
                
                G.add_node(concept, description=description)
        
        except Exception as e:
            print(f"Wikidata knowledge extraction error: {e}")
        
        return G
    
    def _extract_scholarly_knowledge(self) -> nx.DiGraph:
        """
        Extract knowledge from scholarly sources
        
        :return: NetworkX Directed Graph
        """
        # Simulate scholarly knowledge extraction
        # In a real-world scenario, this would integrate with academic databases
        G = nx.DiGraph()
        scholarly_domains = [
            'Quantum Physics', 'Machine Learning', 
            'Neuroscience', 'Climate Change'
        ]
        
        for domain in scholarly_domains:
            G.add_node(domain, type='scholarly_domain')
        
        return G
    
    def _build_custom_hallucination_model(self) -> nn.Module:
        """
        Build a custom transformer model for hallucination detection
        
        :return: PyTorch neural network model
        """
        class HallucinationDetectionModel(nn.Module):
            def __init__(self, input_size=768, hidden_size=256):
                super().__init__()
                self.bert = BertModel.from_pretrained('bert-large-uncased')
                
                # Freeze BERT layers
                for param in self.bert.parameters():
                    param.requires_grad = False
                
                # Custom hallucination detection layers
                self.classifier = nn.Sequential(
                    nn.Linear(input_size, hidden_size),
                    nn.ReLU(),
                    nn.Dropout(0.3),
                    nn.Linear(hidden_size, 64),
                    nn.ReLU(),
                    nn.Linear(64, 2),  # Binary classification
                    nn.Softmax(dim=1)
                )
            
            def forward(self, input_ids, attention_mask):
                # Get BERT embeddings
                outputs = self.bert(
                    input_ids=input_ids, 
                    attention_mask=attention_mask
                )
                
                # Use CLS token embedding
                pooled_output = outputs.pooler_output
                
                # Classify
                return self.classifier(pooled_output)
        
        return HallucinationDetectionModel()
    
    def detect_hallucinations(self, text: str) -> Dict[str, Any]:
        """
        Comprehensive hallucination detection
        
        :param text: Input text to analyze
        :return: Detailed hallucination analysis
        """
        # Knowledge graph fact-checking
        kg_fact_check = self._knowledge_graph_fact_check(text)
        
        # Linguistic analysis
        linguistic_analysis = self.linguistic_analyzer.analyze(text)
        
        # Custom transformer hallucination detection
        hallucination_prob = self._custom_transformer_detection(text)
        
        # Combine multiple signals
        hallucination_score = self._combine_hallucination_signals(
            kg_fact_check, 
            linguistic_analysis, 
            hallucination_prob
        )
        
        return {
            'text': text,
            'knowledge_graph_fact_check': kg_fact_check,
            'linguistic_analysis': linguistic_analysis,
            'hallucination_probability': hallucination_score
        }
    
    def _knowledge_graph_fact_check(self, text: str) -> float:
        """
        Fact-check text against knowledge graph
        
        :param text: Input text
        :return: Fact-checking confidence score
        """
        # Extract key entities
        doc = self.nlp(text)
        entities = [ent.text for ent in doc.ents]
        
        # Check entity validity in knowledge graph
        validity_scores = []
        for entity in entities:
            # Check if entity exists in knowledge graph
            if self.knowledge_graph.has_node(entity):
                # Additional checks for relationships and context
                validity = len(list(self.knowledge_graph.neighbors(entity))) / len(self.knowledge_graph)
                validity_scores.append(validity)
        
        return np.mean(validity_scores) if validity_scores else 0.5
    
    def _custom_transformer_detection(self, text: str) -> float:
        """
        Use custom transformer for hallucination detection
        
        :param text: Input text
        :return: Hallucination probability
        """
        # Tokenize input
        tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
        inputs = tokenizer(
            text, 
            return_tensors='pt', 
            truncation=True, 
            max_length=512
        )
        
        # Predict hallucination probability
        with torch.no_grad():
            outputs = self.hallucination_model(
                input_ids=inputs['input_ids'],
                attention_mask=inputs['attention_mask']
            )
            
            # Return probability of hallucination
            return outputs[0][1].item()
    
    def _combine_hallucination_signals(
        self, 
        kg_score: float, 
        linguistic_score: Dict[str, float], 
        transformer_score: float
    ) -> float:
        """
        Combine multiple hallucination detection signals
        
        :return: Final hallucination probability
        """
        # Weighted combination of signals
        signals = [
            (kg_score, 0.3),
            (linguistic_score.get('coherence', 0.5), 0.2),
            (linguistic_score.get('semantic_consistency', 0.5), 0.2),
            (transformer_score, 0.3)
        ]
        
        return np.average(
            [signal for signal, _ in signals], 
            weights=[weight for _, weight in signals]
        )

class AdvancedFactChecker:
    def __init__(self):
        # Initialize fact-checking resources
        self.semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
    
    def cross_reference_sources(self, claim: str) -> float:
        """
        Cross-reference claim across multiple sources
        
        :param claim: Statement to fact-check
        :return: Fact-checking confidence
        """
        # Simulate cross-referencing (would integrate with multiple APIs)
        sources = [
            wikipedia.search(claim),
            # Add more sources like academic databases, news archives
        ]
        
        # Semantic similarity checking
        reference_embeddings = [
            self.semantic_model.encode(source) for source in sources[0]
        ]
        claim_embedding = self.semantic_model.encode(claim)
        
        # Calculate similarity
        similarities = cosine_similarity(
            claim_embedding.reshape(1, -1), 
            reference_embeddings
        )
        
        return np.max(similarities)

class AdvancedLinguisticAnalyzer:
    def __init__(self):
        self.nlp = spacy.load('en_core_web_trf')
    
    def analyze(self, text: str) -> Dict[str, float]:
        """
        Perform advanced linguistic analysis
        
        :param text: Input text
        :return: Linguistic analysis metrics
        """
        doc = self.nlp(text)
        
        return {
            'coherence': self._analyze_coherence(doc),
            'semantic_consistency': self._semantic_consistency(doc),
            'syntactic_complexity': self._syntactic_complexity(doc),
            'entity_consistency': self._entity_consistency(doc)
        }
    
    def _analyze_coherence(self, doc) -> float:
        # Analyze discourse markers and transitions
        discourse_markers = [
            'however', 'therefore', 'consequently', 
            'furthermore', 'moreover'
        ]
        marker_count = sum(1 for token in doc if token.text.lower() in discourse_markers)
        return min(marker_count / len(doc), 1.0)
    
    def _semantic_consistency(self, doc) -> float:
        # Check semantic role consistency
        verb_frames = [token.lemma_ for token in doc if token.pos_ == 'VERB']
        unique_frames = len(set(verb_frames))
        return 1 - min(unique_frames / len(verb_frames), 1.0)
    
    def _syntactic_complexity(self, doc) -> float:
        # Analyze syntactic depth and complexity
        depths = [token.depth for token in doc]
        return np.mean(depths) / len(doc)
    
    def _entity_consistency(self, doc) -> float:
        # Check named entity type consistency
        entity_types = [ent.label_ for ent in doc.ents]
        unique_types = len(set(entity_types))
        return 1 - min(unique_types / len(entity_types), 1.0)

def main():
    # Initialize advanced hallucination detector
    hallucination_detector = AdvancedHallucinationDetector()
    
    # Sample texts for hallucination analysis
    sample_texts = [
        "Quantum computers can solve all mathematical problems instantly.",
        "The human brain is essentially a biological quantum computer.",
        "Climate change is completely unrelated to human industrial activities.",
        "Artificial intelligence will replace all human jobs by 2030."
    ]
    
    # Analyze hallucinations
    results = []
    for text in sample_texts:
        result = hallucination_detector.detect_hallucinations(text)
        results.append(result)
    
    # Display results
    for result in results:
        print(f"Text: {result['text']}")
        print(f"Hallucination Probability: {result['hallucination_probability']}")
        print("---")

if __name__ == "__main__":
    main()