Docs/Skills and code/skill script-cohort-analysis

.claude/skills/script-cohort-analysis/scripts/raterFeatures.js

Code46 lines2.2 KBSkill: script-cohort-analysis

// Turn one rater JSON (rubric v3) into numeric features. Shared by analyze.js and perturb.js
// so calibration and cohort analysis measure exactly the same things.
const { toSec, mean } = require('./lib');
const { DIMS, CHECKLIST, ITEM_IDS } = require('./checklist');

function raterFeatures(r, durationSec) {
  const D = durationSec;
  const secs = r.sections.map((s, i) => ({ ...s, idx: i, s: toSec(s.start), e: Math.min(toSec(s.end), D) }));
  const len = (x) => Math.max(0, x.e - x.s);
  const total = secs.reduce((a, x) => a + len(x), 0) || D;
  const pos = Object.fromEntries(secs.map((x) => [x.id, x.idx]));

  const body = secs.slice(1).filter((x) => !['cta', 'sponsor'].includes(x.function));
  const dependent = body.filter((x) => (x.depends_on || []).some((id) => pos[id] !== undefined && pos[id] < x.idx));
  const forward = secs.reduce((a, x) => a
    + (x.references_unintroduced || []).length
    + (x.depends_on || []).filter((id) => pos[id] !== undefined && pos[id] > x.idx).length, 0);

  const weighted = (lo, hi) => {
    let w = 0, s = 0;
    for (const x of secs) {
      const a = Math.max(x.s, lo), b = Math.min(x.e, hi);
      if (b > a) { w += b - a; s += (b - a) * x.interest; }
    }
    return w ? s / w : null;
  };
  const first = weighted(0, D / 2), second = weighted(D / 2, D);

  const f = {
    ext_dependency_density: body.length ? dependent.length / body.length : 0,
    ext_forward_refs: forward,
    ext_filler_ratio: secs.filter((x) => ['filler', 'repetition'].includes(x.function)).reduce((a, x) => a + len(x), 0) / total,
    ext_interest_mean: weighted(0, D),
    ext_interest_trend: first !== null && second !== null ? second - first : 0,
    ext_time_to_promise_sec: toSec(r.promise.timestamp),
    ext_major_loops_per_10min: (r.loops || []).filter((l) => l.size === 'major').length / (D / 600),
    hol_rater_percentile: r.holistic.percentile,
  };
  for (const id of ITEM_IDS) f[`item_${id}`] = r.checks[id].pass ? 1 : 0;
  for (const d of DIMS) f[`chk_${d}`] = mean(CHECKLIST[d].map(([id]) => f[`item_${id}`]));
  f.chk_composite = mean(DIMS.map((d) => f[`chk_${d}`]));
  return f;
}

module.exports = { raterFeatures };

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