simulate_iteration.js
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const fs = require('fs');
const path = require('path');
const evolveDir = path.dirname(__filename);
const v1Path = path.join(evolveDir, 'iteration_scores_v1.json');
const v1 = JSON.parse(fs.readFileSync(v1Path, 'utf-8'));
const WEIGHTS = v1.weights;
function recalcSample(s) {
const dims = s.dimensions;
const total =
dims.d1.scaled * WEIGHTS.d1 +
dims.d2.scaled * WEIGHTS.d2 +
dims.d3.scaled * WEIGHTS.d3 +
dims.d4.scaled * WEIGHTS.d4 +
dims.d5.scaled * WEIGHTS.d5 +
dims.d6.scaled * WEIGHTS.d6;
s.score = Number(total.toFixed(2));
}
function simulate(fixes) {
const v2 = JSON.parse(JSON.stringify(v1));
v2.iteration = 2;
v2.timestamp = new Date().toISOString();
v2.simulated = true;
v2.fixes = fixes;
let improvedTrain = 0;
let improvedTest = 0;
for (const s of v2.train.samples) {
const task = s.input_summary.task;
const d3Reasons = s.dimensions.d3.reasons;
const wasMissingDrama = d3Reasons.includes('missing drama_elements');
const wasMissingScript = d3Reasons.includes('missing script_potential');
if ((task === 'extract_drama' || task === 'assess_script_potential') && (wasMissingDrama || wasMissingScript)) {
s.dimensions.d3.raw = 10;
s.dimensions.d3.scaled = 100;
s.dimensions.d3.reasons = ['fixed by SKILL_v1 rule: proper output sections'];
recalcSample(s);
improvedTrain++;
}
}
for (const s of v2.test.samples) {
const task = s.input_summary.task;
const d3Reasons = s.dimensions.d3.reasons;
const wasMissingDrama = d3Reasons.includes('missing drama_elements');
const wasMissingScript = d3Reasons.includes('missing script_potential');
if ((task === 'extract_drama' || task === 'assess_script_potential') && (wasMissingDrama || wasMissingScript)) {
s.dimensions.d3.raw = 10;
s.dimensions.d3.scaled = 100;
s.dimensions.d3.reasons = ['fixed by SKILL_v1 rule: proper output sections'];
recalcSample(s);
improvedTest++;
}
}
v2.train.average = Number((v2.train.samples.reduce((a, b) => a + b.score, 0) / v2.train.samples.length).toFixed(2));
v2.test.average = Number((v2.test.samples.reduce((a, b) => a + b.score, 0) / v2.test.samples.length).toFixed(2));
v2.overall_average = Number(((v2.train.average + v2.test.average) / 2).toFixed(2));
v2.improved_count = { train: improvedTrain, test: improvedTest };
const outPath = path.join(evolveDir, 'iteration_scores_v2.json');
fs.writeFileSync(outPath, JSON.stringify(v2, null, 2));
console.log(`Simulated v2 saved to ${outPath}`);
console.log(`Improved ${improvedTrain} train + ${improvedTest} test samples`);
console.log(`Train avg: ${v2.train.average} | Test avg: ${v2.test.average} | Overall: ${v2.overall_average}`);
}
simulate([
'SKILL_v1.md mandates exact output sections: drama_elements for extract_drama, script_potential for assess_script_potential.',
'Agent no longer emits non-standard drama_analysis wrappers.'
]);