/**
* ProbLog Solver - Probabilistic Inference Engine
* NOTE: This is a simplified implementation that doesn't require ProbLog installation
* In production, this would interface with the actual ProbLog Python library
*/
import {
ProbabilisticProgram,
ProbabilisticResult,
BayesianInference,
LearnedProgram,
ParameterLearningRequest
} from '../../types.js';
import { Loggers } from '../../utils/logger.js';
const logger = Loggers.manager;
export class ProbLogSolver {
private pythonPath: string;
constructor() {
this.pythonPath = process.env.PYTHON_PATH || 'python3';
}
/**
* Execute probabilistic query
* SIMPLIFIED: Performs basic exact inference without ProbLog
*/
async executeQuery(
program: ProbabilisticProgram,
query: string,
evidence?: Record<string, boolean>
): Promise<ProbabilisticResult> {
logger.debug('Executing query', { query, evidence });
try {
// Simple exact inference for basic cases
const probability = this.computeProbability(program, query, evidence);
return {
query,
probability,
evidence,
explanation: `Computed probability for ${query}` +
(evidence ? ` given evidence: ${Object.keys(evidence).join(', ')}` : ''),
derivationTrace: this.generateDerivationTrace(program, query, evidence)
};
} catch (error) {
logger.error('Query execution failed', { error });
throw error;
}
}
/**
* Compute probability using simple exact inference
* This is a simplified algorithm - real ProbLog uses more sophisticated methods
*/
private computeProbability(
program: ProbabilisticProgram,
query: string,
evidence?: Record<string, boolean>
): number {
// Find all facts and rules that could derive the query
const queryFact = program.facts.find(f => f.fact === query);
// If it's a direct fact, return its probability
if (queryFact) {
return queryFact.probability;
}
// Check if query can be derived from rules
const applicableRules = program.rules.filter(r => r.head === query);
if (applicableRules.length === 0) {
// Query not found in program - return low probability
return 0.01;
}
// Simple approach: use disjunction of all applicable rules
// P(A or B) = P(A) + P(B) - P(A and B), but we approximate
let totalProb = 0;
for (const rule of applicableRules) {
let ruleProb = rule.probability !== undefined ? rule.probability : 1.0;
// Multiply by probabilities of body atoms
for (const bodyAtom of rule.body) {
// Check if body atom is observed (evidence)
if (evidence && bodyAtom in evidence) {
if (!evidence[bodyAtom]) {
ruleProb = 0; // Rule doesn't apply if evidence contradicts
break;
}
// If evidence supports, continue
continue;
}
// Otherwise, get probability of body atom
const bodyProb = this.computeProbability(program, bodyAtom, evidence);
ruleProb *= bodyProb;
}
totalProb += ruleProb;
}
// Cap at 1.0
return Math.min(totalProb, 1.0);
}
/**
* Perform Bayesian inference
*/
async performInference(
program: ProbabilisticProgram,
queries: string[],
evidence: Record<string, boolean>
): Promise<BayesianInference> {
logger.debug('Performing Bayesian inference', { queries, evidence });
const prior: Record<string, number> = {};
const posterior: Record<string, number> = {};
// Compute priors (without evidence)
for (const query of queries) {
prior[query] = this.computeProbability(program, query, undefined);
}
// Compute posteriors (with evidence)
for (const query of queries) {
posterior[query] = this.computeProbability(program, query, evidence);
}
// Compute likelihood P(evidence)
let likelihood = 1.0;
for (const [fact, value] of Object.entries(evidence)) {
const prob = this.computeProbability(program, fact, undefined);
likelihood *= value ? prob : (1 - prob);
}
return {
prior,
posterior,
likelihood,
evidence,
method: 'exact',
explanation: this.generateInferenceExplanation(prior, posterior, evidence)
};
}
/**
* Learn parameters from data (simplified)
*/
async learnParameters(request: ParameterLearningRequest): Promise<LearnedProgram> {
logger.debug('Learning parameters', {
dataSize: request.trainingData.length,
algorithm: request.algorithm || 'lbfgs'
});
// Simplified parameter learning: frequency counting
const program = { ...request.program };
const parameterChanges: Array<{ parameter: string; before: number; after: number }> = [];
// Count frequencies in training data
const factCounts: Record<string, { total: number; positive: number }> = {};
for (const fact of program.facts) {
factCounts[fact.fact] = { total: 0, positive: 0 };
}
for (const example of request.trainingData) {
for (const fact of program.facts) {
factCounts[fact.fact].total++;
if (example.query === fact.fact && example.observed) {
factCounts[fact.fact].positive++;
}
}
}
// Update probabilities based on frequencies
for (const fact of program.facts) {
const oldProb = fact.probability;
const counts = factCounts[fact.fact];
const newProb = counts.total > 0 ? counts.positive / counts.total : oldProb;
fact.probability = newProb;
parameterChanges.push({
parameter: `P(${fact.fact})`,
before: oldProb,
after: newProb
});
}
// Compute accuracy
let correct = 0;
for (const example of request.trainingData) {
const predicted = this.computeProbability(program, example.query, example.evidence);
const predictedBool = predicted > 0.5;
if (predictedBool === example.observed) {
correct++;
}
}
const accuracy = correct / request.trainingData.length;
return {
program,
accuracy,
convergenceInfo: {
iterations: 1,
finalLoss: 1 - accuracy,
converged: true
},
parameterChanges
};
}
/**
* Generate derivation trace
*/
private generateDerivationTrace(
program: ProbabilisticProgram,
query: string,
evidence?: Record<string, boolean>
): any[] {
const trace: any[] = [];
// Find relevant facts
const fact = program.facts.find(f => f.fact === query);
if (fact) {
trace.push({
stepNumber: 1,
rule: `${fact.probability}::${fact.fact}`,
probability: fact.probability,
dependencies: []
});
}
// Find relevant rules
const rules = program.rules.filter(r => r.head === query);
for (let i = 0; i < rules.length; i++) {
const rule = rules[i];
trace.push({
stepNumber: trace.length + 1,
rule: `${rule.probability !== undefined ? rule.probability + '::' : ''}${rule.head} :- ${rule.body.join(', ')}`,
probability: rule.probability || 1.0,
dependencies: rule.body
});
}
return trace;
}
/**
* Generate inference explanation
*/
private generateInferenceExplanation(
prior: Record<string, number>,
posterior: Record<string, number>,
evidence: Record<string, boolean>
): string {
const lines: string[] = [];
lines.push('Bayesian Inference Results:');
lines.push('');
for (const query of Object.keys(prior)) {
const priorProb = prior[query];
const postProb = posterior[query];
const change = postProb - priorProb;
const percentChange = priorProb > 0 ? ((change / priorProb) * 100).toFixed(1) : 'N/A';
lines.push(`${query}:`);
lines.push(` Prior: ${(priorProb * 100).toFixed(1)}%`);
lines.push(` Posterior: ${(postProb * 100).toFixed(1)}%`);
if (change > 0.001) {
lines.push(` Change: +${(change * 100).toFixed(1)}% (${percentChange}% increase)`);
} else if (change < -0.001) {
lines.push(` Change: ${(change * 100).toFixed(1)}% (${percentChange}% decrease)`);
} else {
lines.push(` Change: negligible`);
}
lines.push('');
}
lines.push(`Evidence observed: ${Object.entries(evidence).map(([k, v]) => `${k}=${v}`).join(', ')}`);
return lines.join('\n');
}
/**
* Check if ProbLog is available (mock)
*/
async isProbLogAvailable(): Promise<boolean> {
// For now, we're using the simplified solver, so always return false
// In a real implementation, this would check for ProbLog installation
return false;
}
}
