// benchmark-interval-inference.js
// Benchmark interval-based inference with relational comparisons

import { performance } from 'perf_hooks';
import { Arbiter } from '../src/index.js';
import { PACBayesInference } from '../src/inference/PACBayesInference.js';
import { fileURLToPath } from 'url';
import { dirname, resolve } from 'path';
import fs from 'fs';

const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);

// Create a realistic financial services graph
function createFinancialGraph() {
  const arbiter = new Arbiter({
    enableInference: true,
    useOptimizedInference: true,
    useANN: true
  });

  // Account tiers with typical balance ranges
  const accountTiers = {
    basic: { min: 1000, max: 25000, count: 100 },
    silver: { min: 25000, max: 100000, count: 50 },
    gold: { min: 100000, max: 500000, count: 25 },
    platinum: { min: 500000, max: 2000000, count: 10 }
  };

  // Create accounts with realistic balance distributions
  const accounts = [];
  for (const [tier, config] of Object.entries(accountTiers)) {
    for (let i = 0; i < config.count; i++) {
      const balance = config.min + Math.random() * (config.max - config.min);
      const accountAge = Math.floor(Math.random() * 120); // months
      const creditScore = 600 + Math.floor(Math.random() * 250);
      
      const id = `${tier}_account_${i}`;
      accounts.push({
        id,
        tier,
        balance: Math.floor(balance),
        accountAge,
        creditScore
      });
      
      arbiter.addNode(id, 'account', {
        tier,
        balance: Math.floor(balance),
        accountAge,
        creditScore
      });
    }
  }

  // Create financial products with requirements
  const products = [
    { id: 'savings_basic', minBalance: 1000, minCredit: 0 },
    { id: 'checking_premium', minBalance: 25000, minCredit: 650 },
    { id: 'investment_silver', minBalance: 50000, minCredit: 700 },
    { id: 'investment_gold', minBalance: 100000, minCredit: 720 },
    { id: 'private_banking', minBalance: 500000, minCredit: 750 },
    { id: 'wealth_management', minBalance: 1000000, minCredit: 780 }
  ];

  for (const product of products) {
    arbiter.addNode(product.id, 'product', {
      minBalance: product.minBalance,
      minCredit: product.minCredit
    });
  }

  // Add access relations based on requirements
  for (const account of accounts) {
    for (const product of products) {
      if (account.balance >= product.minBalance && account.creditScore >= product.minCredit) {
        arbiter.addRelation(account.id, 'can_access', product.id);
      }
    }
  }

  // Mock relational rules
  arbiter.getRelationalRules = (relation) => {
    if (relation === 'has_min_balance_for') {
      return [{
        type: 'relational-comparison',
        attribute: 'balance',
        operator: '>=',
        threshold: 'object.minBalance' // Would be resolved dynamically
      }];
    }
    if (relation === 'meets_credit_requirement') {
      return [{
        type: 'relational-comparison',
        attribute: 'creditScore',
        operator: '>=',
        threshold: 'object.minCredit'
      }];
    }
    return [];
  };

  return { arbiter, accounts, products };
}

async function benchmarkIntervalQueries() {
  console.log('=== Interval Inference Benchmark ===\n');
  
  const { arbiter, accounts, products } = createFinancialGraph();
  
  // Create interval inference engine
  const inference = new PACBayesInference(arbiter, {
    intervalConfidence: 0.95,
    minVotersForInterval: 3,
    delta: 0.05,
    k: 15 // neighbors for inference
  });
  
  arbiter.inferenceEngine = inference;

  // Record observed decisions
  let recordedCount = 0;
  const canAccessRelations = arbiter.relationManager.getRelationsByName('can_access');
  for (const rel of canAccessRelations) {
    if (Math.random() < 0.7) { // Record 70%
      const subjectKey = arbiter.nodeManager.getNodeKey(rel.src);
      const objectKey = arbiter.nodeManager.getNodeKey(rel.dst);
      if (subjectKey && objectKey) {
        inference.recordDecision(subjectKey, rel.rel, objectKey, 'allow');
        recordedCount++;
      }
    }
  }
  console.log(`Recorded ${recordedCount} access decisions\n`);

  // Generate embeddings
  if (arbiter.embeddingManager) {
    arbiter.embeddingManager.forceRegenerateEmbeddings();
    if (arbiter.similarityManager) arbiter.similarityManager.ensureIndexReady();
  }

  // Simulate missing attribute values
  const testAccounts = accounts.slice(0, 20); // Test with 20 accounts
  const originalBalances = new Map();
  
  for (const account of testAccounts) {
    originalBalances.set(account.id, account.balance);
    // Remove balance attribute to simulate stale/missing data
    const node = arbiter.nodeManager.getNodeByKey(account.id);
    if (node && node.data) {
      delete node.data.balance;
    }
  }

  console.log('=== Testing Interval Estimation ===\n');
  
  // Test interval estimation accuracy
  let totalError = 0;
  let validEstimates = 0;
  
  for (const account of testAccounts.slice(0, 5)) { // Show first 5
    const interval = await inference.estimateAttributeInterval(account.id, 'balance', 10);
    const actual = originalBalances.get(account.id);
    
    console.log(`Account: ${account.id} (${account.tier})`);
    console.log(`  Actual balance: $${actual.toLocaleString()}`);
    console.log(`  Estimated interval: [$${interval.lower?.toLocaleString() || 'N/A'}, $${interval.upper?.toLocaleString() || 'N/A'}]`);
    console.log(`  Confidence: ${(interval.confidence * 100).toFixed(1)}%`);
    console.log(`  Voters: ${interval.voters}`);
    
    if (interval.lower && interval.upper) {
      const inInterval = actual >= interval.lower && actual <= interval.upper;
      console.log(`  Contains actual: ${inInterval ? 'YES' : 'NO'}`);
      if (inInterval) validEstimates++;
      
      const midpoint = (interval.lower + interval.upper) / 2;
      const error = Math.abs(midpoint - actual) / actual;
      totalError += error;
    }
    console.log();
  }

  console.log(`\nInterval Coverage: ${validEstimates}/${testAccounts.slice(0, 5).length}`);
  console.log(`Average Error: ${(totalError / 5 * 100).toFixed(1)}%\n`);

  console.log('=== Benchmarking Authorization Queries ===\n');
  
  // Benchmark authorization checks with missing data
  const queries = [];
  for (let i = 0; i < 100; i++) {
    const account = testAccounts[Math.floor(Math.random() * testAccounts.length)];
    const product = products[Math.floor(Math.random() * products.length)];
    queries.push({ subject: account.id, object: product.id });
  }

  let allowCount = 0;
  let denyCount = 0;
  let undeterminedCount = 0;
  let totalTime = 0;
  
  for (const query of queries) {
    const start = performance.now();
    const result = await inference.check(query.subject, 'can_access', query.object);
    const elapsed = performance.now() - start;
    totalTime += elapsed;
    
    if (result.outcome === 'allow') allowCount++;
    else if (result.outcome === 'deny') denyCount++;
    else undeterminedCount++;
  }

  console.log('Query Results:');
  console.log(`  Allow: ${allowCount} (${(allowCount/queries.length*100).toFixed(1)}%)`);
  console.log(`  Deny: ${denyCount} (${(denyCount/queries.length*100).toFixed(1)}%)`);
  console.log(`  Undetermined: ${undeterminedCount} (${(undeterminedCount/queries.length*100).toFixed(1)}%)`);
  console.log(`\nPerformance:`);
  console.log(`  Total queries: ${queries.length}`);
  console.log(`  Average latency: ${(totalTime / queries.length).toFixed(2)}ms`);
  console.log(`  QPS: ${(1000 / (totalTime / queries.length)).toFixed(0)}`);

  // Show cache stats
  console.log('\n=== Cache Statistics ===');
  const cacheStats = inference.getIntervalCacheStats();
  console.log(`Cache entries: ${cacheStats.size}`);
  if (cacheStats.entries.length > 0) {
    console.log('Sample entries:');
    for (const entry of cacheStats.entries.slice(0, 3)) {
      console.log(`  ${entry.key}: [${entry.interval.lower}, ${entry.interval.upper}] (age: ${(entry.age/1000).toFixed(1)}s)`);
    }
  }

  // Show optimization statistics
  console.log('\n=== Optimization Statistics ===');
  const perfStats = inference.getPerformanceStats();
  console.log('Inference Performance:');
  console.log(`  Queries: ${perfStats.inference.queries}`);
  console.log(`  Cache hits: ${perfStats.inference.cacheHits}`);
  console.log(`  Cache hit rate: ${(perfStats.inference.overallCacheHitRate * 100).toFixed(1)}%`);
  console.log(`  Similarity cache hits: ${perfStats.inference.similarityCacheHits}`);
  
  console.log('\nSimilarity Manager Performance:');
  console.log(`  Total searches: ${perfStats.similarity.searches || 0}`);
  console.log(`  Vector cache hits: ${perfStats.similarity.vectorCacheHits || 0}`);
  console.log(`  Vector cache hit rate: ${((perfStats.similarity.vectorCacheHitRate || 0) * 100).toFixed(1)}%`);
  console.log(`  Average search time: ${(perfStats.similarity.avgSearchTime || 0).toFixed(2)}ms`);
  
  console.log('\nOptimization Impact:');
  console.log(`  Redundant searches avoided: ${perfStats.optimization.redundantSearchesAvoided}`);
  console.log(`  Redundant conversions avoided: ${perfStats.optimization.redundantConversionsAvoided}`);
  console.log(`  Vector conversion efficiency: ${((perfStats.optimization.vectorCacheHitRate || 0) * 100).toFixed(1)}%`);
}

// Run benchmark
(async () => {
  try {
    await benchmarkIntervalQueries();
  } catch (error) {
    console.error('Benchmark error:', error);
  }
})();