Spaces:
Runtime error
Runtime error
File size: 6,551 Bytes
dcb2a99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
"""Bayesian reasoning implementation."""
import logging
from typing import Dict, Any, List
import json
import re
from datetime import datetime
from .base import ReasoningStrategy
class BayesianReasoning(ReasoningStrategy):
"""Implements Bayesian reasoning for probabilistic analysis."""
def __init__(self, prior_weight: float = 0.3):
self.prior_weight = prior_weight
async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
try:
# Generate hypotheses
hypotheses = await self._generate_hypotheses(query, context)
# Calculate prior probabilities
priors = await self._calculate_priors(hypotheses, context)
# Update with evidence
posteriors = await self._update_with_evidence(hypotheses, priors, context)
# Generate final analysis
analysis = await self._generate_analysis(posteriors, context)
return {
"success": True,
"answer": analysis["conclusion"],
"hypotheses": hypotheses,
"priors": priors,
"posteriors": posteriors,
"confidence": analysis["confidence"],
"reasoning_path": analysis["reasoning_path"]
}
except Exception as e:
return {"success": False, "error": str(e)}
async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
prompt = f"""
Generate 3-4 hypotheses for this problem:
Query: {query}
Context: {json.dumps(context)}
For each hypothesis:
1. [Statement]: Clear statement of the hypothesis
2. [Assumptions]: Key assumptions made
3. [Testability]: How it could be tested/verified
Format as:
[H1]
Statement: ...
Assumptions: ...
Testability: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_hypotheses(response["answer"])
async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
prompt = f"""
Calculate prior probabilities for these hypotheses:
Context: {json.dumps(context)}
Hypotheses:
{json.dumps(hypotheses, indent=2)}
For each hypothesis, estimate its prior probability (0-1) based on:
1. Alignment with known principles
2. Historical precedent
3. Domain expertise
Format: [H1]: 0.XX, [H2]: 0.XX, ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_probabilities(response["answer"])
async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float],
context: Dict[str, Any]) -> Dict[str, float]:
prompt = f"""
Update probabilities with available evidence:
Context: {json.dumps(context)}
Hypotheses and Priors:
{json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
Consider:
1. How well each hypothesis explains the evidence
2. Any new evidence from the context
3. Potential conflicts or support between hypotheses
Format: [H1]: 0.XX, [H2]: 0.XX, ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_probabilities(response["answer"])
async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
prompt = f"""
Generate final Bayesian analysis:
Context: {json.dumps(context)}
Posterior Probabilities:
{json.dumps(posteriors, indent=2)}
Provide:
1. Main conclusion based on highest probability hypotheses
2. Confidence level (0-1)
3. Key reasoning steps taken
"""
response = await context["groq_api"].predict(prompt)
return self._parse_analysis(response["answer"])
def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
"""Parse hypotheses from response."""
hypotheses = []
current = None
for line in response.split('\n'):
line = line.strip()
if not line:
continue
if line.startswith('[H'):
if current:
hypotheses.append(current)
current = {
"statement": "",
"assumptions": "",
"testability": ""
}
elif current:
if line.startswith('Statement:'):
current["statement"] = line[10:].strip()
elif line.startswith('Assumptions:'):
current["assumptions"] = line[12:].strip()
elif line.startswith('Testability:'):
current["testability"] = line[12:].strip()
if current:
hypotheses.append(current)
return hypotheses
def _parse_probabilities(self, response: str) -> Dict[str, float]:
"""Parse probabilities from response."""
probs = {}
pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
for match in re.finditer(pattern, response):
h_num = int(match.group(1))
prob = float(match.group(2))
probs[f"H{h_num}"] = prob
return probs
def _parse_analysis(self, response: str) -> Dict[str, Any]:
"""Parse analysis from response."""
lines = response.split('\n')
analysis = {
"conclusion": "",
"confidence": 0.0,
"reasoning_path": []
}
for line in lines:
line = line.strip()
if not line:
continue
if line.startswith('Conclusion:'):
analysis["conclusion"] = line[11:].strip()
elif line.startswith('Confidence:'):
try:
analysis["confidence"] = float(line[11:].strip())
except:
analysis["confidence"] = 0.5
elif line.startswith('- '):
analysis["reasoning_path"].append(line[2:].strip())
return analysis
|