initial commit: @arbiter/core authorization engine with js-rigor hardening
Zanzibar-style authorization graph engine (direct/chain/TTU/defeasible/ binary modes, condensed snapshots, value relations) with 39 rigor test campaigns. Includes fixes for snapshot binary writer/reader format mismatch (snapshot-of-snapshot corruption), possibility write-boundary validation, empty-graph snapshot serialization, relation lookup cache direction collision, config-redefinition cache invalidation, binary threshold semantics, defeasible compiled routing, and comparator reason whitelisting.
This commit is contained in:
John Dvorak
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// benchmark-interval-inference.js
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// Benchmark interval-based inference with relational comparisons
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import { performance } from 'perf_hooks';
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import { Arbiter } from '../src/index.js';
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import { PACBayesInference } from '../src/inference/PACBayesInference.js';
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import { fileURLToPath } from 'url';
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import { dirname, resolve } from 'path';
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import fs from 'fs';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Create a realistic financial services graph
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function createFinancialGraph() {
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const arbiter = new Arbiter({
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enableInference: true,
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useOptimizedInference: true,
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useANN: true
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});
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// Account tiers with typical balance ranges
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const accountTiers = {
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basic: { min: 1000, max: 25000, count: 100 },
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silver: { min: 25000, max: 100000, count: 50 },
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gold: { min: 100000, max: 500000, count: 25 },
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platinum: { min: 500000, max: 2000000, count: 10 }
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};
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// Create accounts with realistic balance distributions
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const accounts = [];
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for (const [tier, config] of Object.entries(accountTiers)) {
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for (let i = 0; i < config.count; i++) {
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const balance = config.min + Math.random() * (config.max - config.min);
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const accountAge = Math.floor(Math.random() * 120); // months
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const creditScore = 600 + Math.floor(Math.random() * 250);
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const id = `${tier}_account_${i}`;
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accounts.push({
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id,
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tier,
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balance: Math.floor(balance),
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accountAge,
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creditScore
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});
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arbiter.addNode(id, 'account', {
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tier,
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balance: Math.floor(balance),
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accountAge,
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creditScore
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});
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}
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}
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// Create financial products with requirements
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const products = [
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{ id: 'savings_basic', minBalance: 1000, minCredit: 0 },
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{ id: 'checking_premium', minBalance: 25000, minCredit: 650 },
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{ id: 'investment_silver', minBalance: 50000, minCredit: 700 },
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{ id: 'investment_gold', minBalance: 100000, minCredit: 720 },
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{ id: 'private_banking', minBalance: 500000, minCredit: 750 },
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{ id: 'wealth_management', minBalance: 1000000, minCredit: 780 }
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];
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for (const product of products) {
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arbiter.addNode(product.id, 'product', {
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minBalance: product.minBalance,
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minCredit: product.minCredit
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});
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}
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// Add access relations based on requirements
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for (const account of accounts) {
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for (const product of products) {
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if (account.balance >= product.minBalance && account.creditScore >= product.minCredit) {
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arbiter.addRelation(account.id, 'can_access', product.id);
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}
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}
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}
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// Mock relational rules
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arbiter.getRelationalRules = (relation) => {
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if (relation === 'has_min_balance_for') {
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return [{
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type: 'relational-comparison',
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attribute: 'balance',
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operator: '>=',
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threshold: 'object.minBalance' // Would be resolved dynamically
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}];
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}
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if (relation === 'meets_credit_requirement') {
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return [{
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type: 'relational-comparison',
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attribute: 'creditScore',
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operator: '>=',
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threshold: 'object.minCredit'
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}];
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}
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return [];
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};
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return { arbiter, accounts, products };
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}
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async function benchmarkIntervalQueries() {
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console.log('=== Interval Inference Benchmark ===\n');
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const { arbiter, accounts, products } = createFinancialGraph();
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// Create interval inference engine
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const inference = new PACBayesInference(arbiter, {
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intervalConfidence: 0.95,
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minVotersForInterval: 3,
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delta: 0.05,
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k: 15 // neighbors for inference
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});
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arbiter.inferenceEngine = inference;
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// Record observed decisions
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let recordedCount = 0;
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const canAccessRelations = arbiter.relationManager.getRelationsByName('can_access');
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for (const rel of canAccessRelations) {
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if (Math.random() < 0.7) { // Record 70%
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const subjectKey = arbiter.nodeManager.getNodeKey(rel.src);
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const objectKey = arbiter.nodeManager.getNodeKey(rel.dst);
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if (subjectKey && objectKey) {
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inference.recordDecision(subjectKey, rel.rel, objectKey, 'allow');
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recordedCount++;
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}
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}
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}
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console.log(`Recorded ${recordedCount} access decisions\n`);
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// Generate embeddings
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if (arbiter.embeddingManager) {
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arbiter.embeddingManager.forceRegenerateEmbeddings();
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if (arbiter.similarityManager) arbiter.similarityManager.ensureIndexReady();
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}
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// Simulate missing attribute values
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const testAccounts = accounts.slice(0, 20); // Test with 20 accounts
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const originalBalances = new Map();
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for (const account of testAccounts) {
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originalBalances.set(account.id, account.balance);
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// Remove balance attribute to simulate stale/missing data
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const node = arbiter.nodeManager.getNodeByKey(account.id);
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if (node && node.data) {
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delete node.data.balance;
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}
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}
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console.log('=== Testing Interval Estimation ===\n');
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// Test interval estimation accuracy
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let totalError = 0;
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let validEstimates = 0;
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for (const account of testAccounts.slice(0, 5)) { // Show first 5
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const interval = await inference.estimateAttributeInterval(account.id, 'balance', 10);
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const actual = originalBalances.get(account.id);
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console.log(`Account: ${account.id} (${account.tier})`);
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console.log(` Actual balance: $${actual.toLocaleString()}`);
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console.log(` Estimated interval: [$${interval.lower?.toLocaleString() || 'N/A'}, $${interval.upper?.toLocaleString() || 'N/A'}]`);
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console.log(` Confidence: ${(interval.confidence * 100).toFixed(1)}%`);
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console.log(` Voters: ${interval.voters}`);
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if (interval.lower && interval.upper) {
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const inInterval = actual >= interval.lower && actual <= interval.upper;
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console.log(` Contains actual: ${inInterval ? 'YES' : 'NO'}`);
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if (inInterval) validEstimates++;
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const midpoint = (interval.lower + interval.upper) / 2;
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const error = Math.abs(midpoint - actual) / actual;
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totalError += error;
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}
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console.log();
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}
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console.log(`\nInterval Coverage: ${validEstimates}/${testAccounts.slice(0, 5).length}`);
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console.log(`Average Error: ${(totalError / 5 * 100).toFixed(1)}%\n`);
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console.log('=== Benchmarking Authorization Queries ===\n');
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// Benchmark authorization checks with missing data
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const queries = [];
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for (let i = 0; i < 100; i++) {
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const account = testAccounts[Math.floor(Math.random() * testAccounts.length)];
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const product = products[Math.floor(Math.random() * products.length)];
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queries.push({ subject: account.id, object: product.id });
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}
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let allowCount = 0;
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let denyCount = 0;
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let undeterminedCount = 0;
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let totalTime = 0;
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for (const query of queries) {
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const start = performance.now();
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const result = await inference.check(query.subject, 'can_access', query.object);
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const elapsed = performance.now() - start;
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totalTime += elapsed;
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if (result.outcome === 'allow') allowCount++;
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else if (result.outcome === 'deny') denyCount++;
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else undeterminedCount++;
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}
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console.log('Query Results:');
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console.log(` Allow: ${allowCount} (${(allowCount/queries.length*100).toFixed(1)}%)`);
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console.log(` Deny: ${denyCount} (${(denyCount/queries.length*100).toFixed(1)}%)`);
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console.log(` Undetermined: ${undeterminedCount} (${(undeterminedCount/queries.length*100).toFixed(1)}%)`);
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console.log(`\nPerformance:`);
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console.log(` Total queries: ${queries.length}`);
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console.log(` Average latency: ${(totalTime / queries.length).toFixed(2)}ms`);
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console.log(` QPS: ${(1000 / (totalTime / queries.length)).toFixed(0)}`);
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// Show cache stats
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console.log('\n=== Cache Statistics ===');
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const cacheStats = inference.getIntervalCacheStats();
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console.log(`Cache entries: ${cacheStats.size}`);
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if (cacheStats.entries.length > 0) {
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console.log('Sample entries:');
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for (const entry of cacheStats.entries.slice(0, 3)) {
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console.log(` ${entry.key}: [${entry.interval.lower}, ${entry.interval.upper}] (age: ${(entry.age/1000).toFixed(1)}s)`);
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}
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}
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// Show optimization statistics
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console.log('\n=== Optimization Statistics ===');
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const perfStats = inference.getPerformanceStats();
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console.log('Inference Performance:');
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console.log(` Queries: ${perfStats.inference.queries}`);
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console.log(` Cache hits: ${perfStats.inference.cacheHits}`);
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console.log(` Cache hit rate: ${(perfStats.inference.overallCacheHitRate * 100).toFixed(1)}%`);
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console.log(` Similarity cache hits: ${perfStats.inference.similarityCacheHits}`);
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console.log('\nSimilarity Manager Performance:');
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console.log(` Total searches: ${perfStats.similarity.searches || 0}`);
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console.log(` Vector cache hits: ${perfStats.similarity.vectorCacheHits || 0}`);
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console.log(` Vector cache hit rate: ${((perfStats.similarity.vectorCacheHitRate || 0) * 100).toFixed(1)}%`);
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console.log(` Average search time: ${(perfStats.similarity.avgSearchTime || 0).toFixed(2)}ms`);
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console.log('\nOptimization Impact:');
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console.log(` Redundant searches avoided: ${perfStats.optimization.redundantSearchesAvoided}`);
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console.log(` Redundant conversions avoided: ${perfStats.optimization.redundantConversionsAvoided}`);
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console.log(` Vector conversion efficiency: ${((perfStats.optimization.vectorCacheHitRate || 0) * 100).toFixed(1)}%`);
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}
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// Run benchmark
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(async () => {
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try {
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await benchmarkIntervalQueries();
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} catch (error) {
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console.error('Benchmark error:', error);
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}
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})();
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