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"""Advanced Bayesian reasoning for probabilistic analysis.""" | |
import logging | |
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple | |
import json | |
from dataclasses import dataclass, field | |
from enum import Enum | |
from datetime import datetime | |
import numpy as np | |
from collections import defaultdict | |
from .base import ReasoningStrategy, StrategyResult | |
class BayesianHypothesis: | |
"""Bayesian hypothesis with probabilities.""" | |
name: str | |
prior: float | |
likelihood: float | |
posterior: float = 0.0 | |
evidence: List[Dict[str, Any]] = field(default_factory=list) | |
class BayesianStrategy(ReasoningStrategy): | |
"""Advanced Bayesian reasoning that: | |
1. Generates hypotheses | |
2. Calculates prior probabilities | |
3. Updates with evidence | |
4. Computes posteriors | |
5. Provides probabilistic analysis | |
""" | |
def __init__(self, config: Optional[Dict[str, Any]] = None): | |
"""Initialize Bayesian reasoning.""" | |
super().__init__() | |
self.config = config or {} | |
# Configure Bayesian parameters | |
self.prior_weight = self.config.get('prior_weight', 0.3) | |
self.evidence_threshold = self.config.get('evidence_threshold', 0.1) | |
self.min_likelihood = self.config.get('min_likelihood', 0.01) | |
# Initialize hypothesis storage | |
self.hypotheses: List[BayesianHypothesis] = [] | |
async def reason( | |
self, | |
query: str, | |
context: Dict[str, Any] | |
) -> StrategyResult: | |
""" | |
Apply Bayesian reasoning to analyze probabilities and update beliefs. | |
Args: | |
query: The input query to reason about | |
context: Additional context and parameters | |
Returns: | |
StrategyResult containing reasoning results and confidence scores | |
""" | |
try: | |
# Generate initial hypotheses | |
self.hypotheses = await self._generate_hypotheses(query, context) | |
# Calculate prior probabilities | |
priors = await self._calculate_priors(self.hypotheses, context) | |
# Update with evidence | |
posteriors = await self._update_with_evidence(self.hypotheses, priors, context) | |
# Generate analysis | |
analysis = await self._generate_analysis(posteriors, context) | |
# Format results | |
answer = self._format_analysis(analysis) | |
confidence = self._calculate_confidence(posteriors) | |
return StrategyResult( | |
strategy_type="bayesian", | |
success=True, | |
answer=answer, | |
confidence=confidence, | |
reasoning_trace=[{ | |
"step": "bayesian_analysis", | |
"hypotheses": [h.__dict__ for h in self.hypotheses], | |
"priors": priors, | |
"posteriors": posteriors, | |
"analysis": analysis, | |
"timestamp": datetime.now().isoformat() | |
}], | |
metadata={ | |
"num_hypotheses": len(self.hypotheses), | |
"max_posterior": max(posteriors.values()) if posteriors else 0.0, | |
"config": self.config | |
}, | |
performance_metrics={ | |
"prior_weight": self.prior_weight, | |
"evidence_threshold": self.evidence_threshold, | |
"min_likelihood": self.min_likelihood | |
} | |
) | |
except Exception as e: | |
logging.error(f"Bayesian reasoning error: {str(e)}") | |
return StrategyResult( | |
strategy_type="bayesian", | |
success=False, | |
answer=None, | |
confidence=0.0, | |
reasoning_trace=[{ | |
"step": "error", | |
"error": str(e), | |
"timestamp": datetime.now().isoformat() | |
}], | |
metadata={"error": str(e)}, | |
performance_metrics={} | |
) | |
async def _generate_hypotheses( | |
self, | |
query: str, | |
context: Dict[str, Any] | |
) -> List[BayesianHypothesis]: | |
"""Generate plausible hypotheses.""" | |
# Extract key terms for hypothesis generation | |
terms = self._extract_factors(query, set()) | |
# Generate alternative hypotheses | |
alternatives = self._generate_alternative_factors(terms) | |
# Create hypothesis objects | |
hypotheses = [] | |
for name, prior in alternatives.items(): | |
hypotheses.append(BayesianHypothesis( | |
name=name, | |
prior=prior, | |
likelihood=1.0 # Initial likelihood | |
)) | |
return hypotheses | |
async def _calculate_priors( | |
self, | |
hypotheses: List[BayesianHypothesis], | |
context: Dict[str, Any] | |
) -> Dict[str, float]: | |
"""Calculate prior probabilities.""" | |
priors = {} | |
total_prior = sum(h.prior for h in hypotheses) | |
if total_prior > 0: | |
# Normalize priors | |
for h in hypotheses: | |
priors[h.name] = h.prior / total_prior | |
else: | |
# Equal priors if no information | |
prior = 1.0 / len(hypotheses) | |
for h in hypotheses: | |
priors[h.name] = prior | |
return priors | |
async def _update_with_evidence( | |
self, | |
hypotheses: List[BayesianHypothesis], | |
priors: Dict[str, float], | |
context: Dict[str, Any] | |
) -> Dict[str, float]: | |
"""Update probabilities with evidence.""" | |
posteriors = priors.copy() | |
# Get evidence from context | |
evidence = context.get('evidence', []) | |
for e in evidence: | |
# Calculate likelihoods | |
likelihoods = {} | |
total_likelihood = 0.0 | |
for h in hypotheses: | |
likelihood = await self._calculate_likelihood(h, e) | |
likelihoods[h.name] = max(likelihood, self.min_likelihood) | |
total_likelihood += likelihood * posteriors[h.name] | |
# Update posteriors using Bayes' rule | |
if total_likelihood > 0: | |
for h in hypotheses: | |
posteriors[h.name] = ( | |
likelihoods[h.name] * posteriors[h.name] / total_likelihood | |
) | |
return posteriors | |
async def _calculate_likelihood( | |
self, | |
hypothesis: BayesianHypothesis, | |
evidence: Dict[str, Any] | |
) -> float: | |
"""Calculate likelihood of evidence given hypothesis.""" | |
# Simple likelihood calculation | |
# Could be enhanced with more sophisticated methods | |
base_likelihood = 0.5 | |
# Adjust based on evidence strength | |
strength = evidence.get('strength', 0.0) | |
likelihood = base_likelihood * (1 + strength) | |
return min(1.0, max(self.min_likelihood, likelihood)) | |
async def _generate_analysis( | |
self, | |
posteriors: Dict[str, float], | |
context: Dict[str, Any] | |
) -> Dict[str, Any]: | |
"""Generate probabilistic analysis.""" | |
analysis = { | |
'top_hypothesis': max(posteriors.items(), key=lambda x: x[1]), | |
'confidence': self._calculate_confidence(posteriors), | |
'distribution': posteriors, | |
'summary': [] | |
} | |
# Generate summary points | |
for name, prob in sorted(posteriors.items(), key=lambda x: x[1], reverse=True): | |
analysis['summary'].append({ | |
'hypothesis': name, | |
'probability': prob, | |
'strength': 'strong' if prob > 0.7 else 'moderate' if prob > 0.3 else 'weak' | |
}) | |
return analysis | |
def _format_analysis(self, analysis: Dict[str, Any]) -> str: | |
"""Format analysis into readable text.""" | |
top_hyp, top_prob = analysis['top_hypothesis'] | |
text = [ | |
f"Based on Bayesian analysis:", | |
f"- Most likely hypothesis: {top_hyp} (probability: {top_prob:.2f})", | |
"\nProbability distribution:" | |
] | |
for item in analysis['summary']: | |
text.append( | |
f"- {item['hypothesis']}: {item['probability']:.2f} " | |
f"({item['strength']} evidence)" | |
) | |
return "\n".join(text) | |
def _calculate_confidence(self, posteriors: Dict[str, float]) -> float: | |
"""Calculate overall confidence score.""" | |
if not posteriors: | |
return 0.0 | |
# Get top two probabilities | |
probs = sorted(posteriors.values(), reverse=True) | |
top_prob = probs[0] | |
if len(probs) > 1: | |
# Consider the gap between top hypotheses | |
second_prob = probs[1] | |
margin = top_prob - second_prob | |
# Confidence increases with both probability and margin | |
confidence = (top_prob + margin) / 2 | |
else: | |
confidence = top_prob | |
return min(1.0, max(0.0, confidence)) | |
def _extract_factors(self, text: str, terms: Set[str]) -> Set[str]: | |
"""Extract relevant factors from text.""" | |
# Simple word-based extraction | |
# Could be enhanced with NLP techniques | |
words = text.lower().split() | |
return set(words).union(terms) | |
def _generate_alternative_factors(self, terms: Set[str]) -> Dict[str, float]: | |
"""Generate factors for alternative hypothesis.""" | |
# Simple alternative generation | |
# Could be enhanced with domain knowledge | |
alternatives = { | |
'primary': 0.6, | |
'alternative': 0.3, | |
'null': 0.1 | |
} | |
return alternatives | |