lakehouse/auditor/kb_stats.ts

// kb_stats — on-demand dashboard numbers from the KB scratchpad
// files. Reads data/_auditor/verdicts/*, data/_kb/audit_lessons.jsonl,
// data/_kb/audit_facts.jsonl, data/_kb/audit_discrepancies.jsonl,
// data/_kb/scrum_reviews.jsonl and prints:
//
//   - verdict flip-flop rate (same SHA re-audited, verdict changed?)
//   - consensus discrepancy rate (N runs disagreed on a claim)
//   - confidence distribution from kb_index aggregator
//   - top N recurring entities from audit_facts
//   - fact growth over time
//   - scrum vs inference KB split
//
// Run:  bun run auditor/kb_stats.ts
//       bun run auditor/kb_stats.ts --top 15     # show top 15 entities
//       bun run auditor/kb_stats.ts --json       # machine-readable
//
// This is the "dashboard" without running Grafana. If someone really
// wants a dashboard, wire this output into a static HTML page + cron.

import { readFile, readdir } from "node:fs/promises";
import { join } from "node:path";
import { aggregate } from "./kb_index.ts";

const REPO = "/home/profit/lakehouse";
const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
const AUDIT_LESSONS = `${REPO}/data/_kb/audit_lessons.jsonl`;
const AUDIT_FACTS = `${REPO}/data/_kb/audit_facts.jsonl`;
const AUDIT_DISCREPANCIES = `${REPO}/data/_kb/audit_discrepancies.jsonl`;
const SCRUM_REVIEWS = `${REPO}/data/_kb/scrum_reviews.jsonl`;

interface Args {
  top: number;
  json: boolean;
}

function parseArgs(argv: string[]): Args {
  const a: Args = { top: 10, json: false };
  for (let i = 2; i < argv.length; i++) {
    if (argv[i] === "--top") a.top = Number(argv[++i] ?? 10);
    else if (argv[i] === "--json") a.json = true;
  }
  return a;
}

async function readJsonl<T = any>(path: string): Promise<T[]> {
  try {
    const raw = await readFile(path, "utf8");
    return raw.split("\n").filter(l => l.length > 0).map(l => {
      try { return JSON.parse(l) as T; } catch { return null as any; }
    }).filter(r => r !== null);
  } catch { return []; }
}

async function loadVerdicts(): Promise<Array<{ pr: number; sha: string; overall: string; findings_total: number; findings_block: number; findings_warn: number }>> {
  let files: string[] = [];
  try { files = await readdir(VERDICTS_DIR); } catch { return []; }
  const out = [];
  for (const f of files) {
    if (!f.endsWith(".json")) continue;
    const m = f.match(/^(\d+)-([0-9a-f]+)\.json$/);
    if (!m) continue;
    try {
      const v = JSON.parse(await readFile(join(VERDICTS_DIR, f), "utf8"));
      out.push({
        pr: Number(m[1]),
        sha: m[2],
        overall: String(v.overall),
        findings_total: Number(v.metrics?.findings_total ?? 0),
        findings_block: Number(v.metrics?.findings_block ?? 0),
        findings_warn: Number(v.metrics?.findings_warn ?? 0),
      });
    } catch { /* skip corrupt */ }
  }
  return out;
}

interface Stats {
  audit_count: number;
  verdict_distribution: Record<string, number>;
  // Same PR with multiple SHAs — if verdicts differ, that's drift across
  // the PR's commit history. Not a flip-flop in the classical sense,
  // but worth surfacing (e.g. "PR #8 was block block req req block").
  per_pr_verdict_sequences: Record<number, string[]>;
  // For each PR with ≥ 2 audits, how many distinct verdicts did it
  // produce? 1 = stable; 2+ = some flipping.
  verdict_instability: { pr_count: number; pr_with_multiple_verdicts: number; pr_with_3plus: number };
  consensus: { discrepancy_count: number; tiebreaker_used: number; unresolved: number };
  kb: {
    audit_lessons_rows: number;
    audit_facts_rows: number;
    scrum_reviews_rows: number;
    distinct_finding_signatures: number;
    distinct_entities_across_prs: number;
    entities_in_2plus_prs: number;
    entities_in_5plus_prs: number;
  };
  fact_quality: {
    verifier_verdict_distribution: Record<string, number>;
    facts_dropped_by_verifier_total: number;
    extraction_success_rate: number;
  };
  top_entities: Array<{ name: string; distinct_prs: number; count: number; types: string[] }>;
  kb_by_source: Record<string, number>;
}

async function collect(args: Args): Promise<Stats> {
  const verdicts = await loadVerdicts();
  const lessons = await readJsonl<any>(AUDIT_LESSONS);
  const facts = await readJsonl<any>(AUDIT_FACTS);
  const disc = await readJsonl<any>(AUDIT_DISCREPANCIES);
  const reviews = await readJsonl<any>(SCRUM_REVIEWS);

  // Verdict stability
  const byPr: Record<number, string[]> = {};
  const verdictDist: Record<string, number> = {};
  for (const v of verdicts) {
    (byPr[v.pr] ??= []).push(v.overall);
    verdictDist[v.overall] = (verdictDist[v.overall] ?? 0) + 1;
  }
  let multi = 0, tri = 0;
  for (const [_, seq] of Object.entries(byPr)) {
    const distinct = new Set(seq);
    if (distinct.size >= 2) multi++;
    if (distinct.size >= 3) tri++;
  }

  // Consensus drift
  const consensus = {
    discrepancy_count: disc.length,
    tiebreaker_used: disc.filter(d => String(d.resolution).startsWith("tiebreaker")).length,
    unresolved: disc.filter(d => d.resolution === "unresolved").length,
  };

  // Lesson signatures
  const lessonAgg = await aggregate<any>(AUDIT_LESSONS, {
    keyFn: r => r?.signature,
    scopeFn: r => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
  });

  // Entity aggregation across audit_facts rows
  interface EntAgg { distinct_prs: Set<number>; count: number; types: Set<string>; name: string; sources: Set<string> }
  const entAgg = new Map<string, EntAgg>();
  const sourceCount: Record<string, number> = {};
  let totalVerdictDist: Record<string, number> = { CORRECT: 0, INCORRECT: 0, UNVERIFIABLE: 0, UNCHECKED: 0 };
  let factsDroppedTotal = 0;
  let extractionsWithFacts = 0;

  for (const row of facts) {
    const src = String(row.source ?? "unknown");
    sourceCount[src] = (sourceCount[src] ?? 0) + 1;
    const pr = Number(row.pr_number);
    if (Array.isArray(row.verifier_verdicts)) {
      for (const v of row.verifier_verdicts) {
        totalVerdictDist[v] = (totalVerdictDist[v] ?? 0) + 1;
      }
    }
    factsDroppedTotal += Number(row.facts_dropped_by_verifier ?? 0);
    if ((Array.isArray(row.facts) && row.facts.length > 0) || (Array.isArray(row.entities) && row.entities.length > 0)) {
      extractionsWithFacts++;
    }
    for (const e of Array.isArray(row.entities) ? row.entities : []) {
      const name = String(e?.name ?? "").trim();
      if (name.length < 3) continue;
      const key = name.toLowerCase();
      const agg = entAgg.get(key) ?? { distinct_prs: new Set(), count: 0, types: new Set(), name, sources: new Set() };
      agg.count++;
      if (Number.isFinite(pr) && pr > 0) agg.distinct_prs.add(pr);
      if (e?.type) agg.types.add(String(e.type));
      agg.sources.add(src);
      entAgg.set(key, agg);
    }
  }

  const entitiesIn2Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 2).length;
  const entitiesIn5Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 5).length;
  const topEntities = Array.from(entAgg.values())
    .sort((a, b) => b.distinct_prs.size - a.distinct_prs.size || b.count - a.count)
    .slice(0, args.top)
    .map(a => ({
      name: a.name,
      distinct_prs: a.distinct_prs.size,
      count: a.count,
      types: Array.from(a.types),
    }));

  const stats: Stats = {
    audit_count: verdicts.length,
    verdict_distribution: verdictDist,
    per_pr_verdict_sequences: byPr,
    verdict_instability: {
      pr_count: Object.keys(byPr).length,
      pr_with_multiple_verdicts: multi,
      pr_with_3plus: tri,
    },
    consensus,
    kb: {
      audit_lessons_rows: lessons.length,
      audit_facts_rows: facts.length,
      scrum_reviews_rows: reviews.length,
      distinct_finding_signatures: lessonAgg.size,
      distinct_entities_across_prs: entAgg.size,
      entities_in_2plus_prs: entitiesIn2Plus,
      entities_in_5plus_prs: entitiesIn5Plus,
    },
    fact_quality: {
      verifier_verdict_distribution: totalVerdictDist,
      facts_dropped_by_verifier_total: factsDroppedTotal,
      extraction_success_rate: facts.length > 0 ? extractionsWithFacts / facts.length : 0,
    },
    top_entities: topEntities,
    kb_by_source: sourceCount,
  };
  return stats;
}

function renderHuman(s: Stats): string {
  const lines: string[] = [];
  lines.push("═══ KB STATS ═══");
  lines.push("");
  lines.push(`Audits: ${s.audit_count} total across ${s.verdict_instability.pr_count} distinct PRs`);
  lines.push(`Verdicts: ${Object.entries(s.verdict_distribution).map(([k, v]) => `${k}=${v}`).join("  ")}`);
  const multiplePct = s.verdict_instability.pr_count > 0
    ? Math.round(100 * s.verdict_instability.pr_with_multiple_verdicts / s.verdict_instability.pr_count)
    : 0;
  lines.push(`Verdict instability: ${s.verdict_instability.pr_with_multiple_verdicts}/${s.verdict_instability.pr_count} PRs had 2+ distinct verdicts (${multiplePct}%) — 3+ distinct: ${s.verdict_instability.pr_with_3plus}`);
  lines.push("");
  lines.push("─── Consensus ───");
  lines.push(`  discrepancies logged: ${s.consensus.discrepancy_count}`);
  lines.push(`  tiebreaker used: ${s.consensus.tiebreaker_used}`);
  lines.push(`  unresolved: ${s.consensus.unresolved}`);
  const dRate = s.audit_count > 0 ? (100 * s.consensus.discrepancy_count / s.audit_count).toFixed(1) : "0";
  lines.push(`  discrepancy rate: ${dRate}% of audits`);
  lines.push("");
  lines.push("─── KB size ───");
  lines.push(`  audit_lessons.jsonl:     ${s.kb.audit_lessons_rows} rows, ${s.kb.distinct_finding_signatures} distinct signatures`);
  lines.push(`  audit_facts.jsonl:       ${s.kb.audit_facts_rows} rows, ${s.kb.distinct_entities_across_prs} distinct entities`);
  lines.push(`  scrum_reviews.jsonl:     ${s.kb.scrum_reviews_rows} rows`);
  lines.push(`  entities in 2+ PRs:      ${s.kb.entities_in_2plus_prs}`);
  lines.push(`  entities in 5+ PRs:      ${s.kb.entities_in_5plus_prs} ← strong cross-cutting signal`);
  lines.push("");
  lines.push("─── Fact quality ───");
  const v = s.fact_quality.verifier_verdict_distribution;
  lines.push(`  verifier verdicts:  CORRECT=${v.CORRECT ?? 0}  UNVERIFIABLE=${v.UNVERIFIABLE ?? 0}  UNCHECKED=${v.UNCHECKED ?? 0}  INCORRECT=${v.INCORRECT ?? 0}`);
  lines.push(`  facts dropped by verifier: ${s.fact_quality.facts_dropped_by_verifier_total}`);
  lines.push(`  extraction success rate: ${(s.fact_quality.extraction_success_rate * 100).toFixed(1)}%`);
  lines.push("");
  lines.push("─── KB sources ───");
  for (const [src, n] of Object.entries(s.kb_by_source)) {
    lines.push(`  ${src}: ${n}`);
  }
  lines.push("");
  lines.push(`─── Top ${s.top_entities.length} recurring entities ───`);
  for (const e of s.top_entities) {
    lines.push(`  [${e.distinct_prs} PRs × ${e.count} obs]  ${e.name}  (${e.types.join(",")})`);
  }
  return lines.join("\n");
}

async function main() {
  const args = parseArgs(process.argv);
  const stats = await collect(args);
  if (args.json) {
    console.log(JSON.stringify(stats, (_, v) => v instanceof Set ? Array.from(v) : v, 2));
  } else {
    console.log(renderHuman(stats));
  }
}

main().catch(e => { console.error("[kb_stats] fatal:", e); process.exit(1); });