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ZhouzoneZhouzone
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feat: publish refined seevomap home
1 parent 47110a2 commit f9ae59f

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Lines changed: 2169 additions & 203 deletions

‎.github/workflows/deploy-pages.yml‎

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node-version: 20
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cache: npm
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- name: Validate Pages bootstrap token
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env:
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PAGES_TOKEN: ${{ secrets.PAGES_TOKEN }}
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run: |
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if [ -z "${PAGES_TOKEN}" ]; then
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echo "Missing required secret: PAGES_TOKEN" >&2
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echo "Create a fine-grained or classic PAT with access to this repo and Pages, then add it as a repository secret named PAGES_TOKEN." >&2
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exit 1
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fi
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- name: Configure Pages
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uses: actions/configure-pages@v5
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with:
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token: ${{ secrets.PAGES_TOKEN }}
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enablement: true
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- name: Install dependencies
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run: npm ci

‎.github/workflows/preview-pages.yml‎

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node-version: 20
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cache: npm
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- name: Validate Pages bootstrap token
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env:
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PAGES_TOKEN: ${{ secrets.PAGES_TOKEN }}
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run: |
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if [ -z "${PAGES_TOKEN}" ]; then
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echo "Missing required secret: PAGES_TOKEN" >&2
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exit 1
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fi
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- name: Configure Pages
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uses: actions/configure-pages@v5
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with:
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token: ${{ secrets.PAGES_TOKEN }}
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enablement: true
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- name: Build preview site bundle
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run: |

‎scripts/graph-aesthetic-metrics.ts‎

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import * as fs from "node:fs";
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import * as path from "node:path";
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import { layoutSemanticNodes } from "../src/utils/graphSemanticLayout";
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import { getNobelReferences } from "../src/utils/nobelReferences";
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import { getPublicDomainLabel } from "../src/utils/publicDomains";
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import type { MapData } from "../src/utils/types";
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function mean(values: number[]): number {
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return values.reduce((sum, value) => sum + value, 0) / Math.max(values.length, 1);
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}
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function range(values: number[]): { min: number; max: number; span: number } {
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const min = Math.min(...values);
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const max = Math.max(...values);
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return { min, max, span: max - min };
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}
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const mapPath = path.resolve(process.cwd(), "dist/map.json");
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const map = JSON.parse(fs.readFileSync(mapPath, "utf8")) as MapData;
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const semanticNodes = layoutSemanticNodes(map.nodes);
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const nobelReferences = getNobelReferences();
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const canopyOrder = [
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"Earth & Space",
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"Economics",
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"Engineering",
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"Chemistry",
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"Life Science",
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"Medicine",
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"Mathematics",
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"Physics",
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];
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const canopyMeans = canopyOrder.map((label) => {
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const items = semanticNodes.filter((item) => getPublicDomainLabel(item.node.domain) === label);
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return {
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label,
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meanX: mean(items.map((item) => item.x)),
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meanY: mean(items.map((item) => item.y)),
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};
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});
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const upperDomainClouds = canopyOrder.map((label) => {
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const items = semanticNodes.filter((item) => getPublicDomainLabel(item.node.domain) === label);
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const xs = items.map((item) => item.x);
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const ys = items.map((item) => item.y);
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const xRange = range(xs);
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const yRange = range(ys);
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const area = xRange.span * yRange.span;
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return {
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label,
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meanX: mean(xs),
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meanY: mean(ys),
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spreadX: xRange.span,
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spreadY: yRange.span,
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aspectRatio: xRange.span / Math.max(yRange.span, 1e-6),
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area,
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};
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});
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const canopyGaps = canopyMeans.slice(1).map((item, index) => item.meanX - canopyMeans[index].meanX);
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const canopyYGaps = canopyMeans.slice(1).map((item, index) => item.meanY - canopyMeans[index].meanY);
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const canopyWidth =
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(canopyMeans[canopyMeans.length - 1]?.meanX ?? 0) - (canopyMeans[0]?.meanX ?? 0);
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const canopyCenterX =
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((canopyMeans[0]?.meanX ?? 0) + (canopyMeans[canopyMeans.length - 1]?.meanX ?? 0)) / 2;
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const outerCanopyMeanY = mean([
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canopyMeans[0]?.meanY ?? 0,
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canopyMeans[1]?.meanY ?? 0,
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canopyMeans[6]?.meanY ?? 0,
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canopyMeans[7]?.meanY ?? 0,
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]);
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const centerCanopyMeanY = mean([
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canopyMeans[3]?.meanY ?? 0,
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canopyMeans[4]?.meanY ?? 0,
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]);
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const shoulderCanopyMeanY = mean([
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canopyMeans[2]?.meanY ?? 0,
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canopyMeans[5]?.meanY ?? 0,
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]);
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const pretraining = semanticNodes.filter(
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(item) => item.node.label === "Minimize val_loss for GPT-2 124M on FineWeb (1h budget)",
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);
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const posttraining = semanticNodes.filter(
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(item) => item.node.label === "Maximize math reasoning accuracy via GRPO on Qwen2.5-Math-1.5B",
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);
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const preX = range(pretraining.map((item) => item.x));
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const preY = range(pretraining.map((item) => item.y));
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const postX = range(posttraining.map((item) => item.x));
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const postY = range(posttraining.map((item) => item.y));
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const modelCompression = semanticNodes.filter((item) => item.node.domain === "model_compression");
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const overlapX = Math.min(preX.max, postX.max) - Math.max(preX.min, postX.min);
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const overlapY = Math.min(preY.max, postY.max) - Math.max(preY.min, postY.min);
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const unionWidth = Math.max(preX.max, postX.max) - Math.min(preX.min, postX.min);
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const unionHeight = Math.max(preY.max, postY.max) - Math.min(preY.min, postY.min);
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const unionAspectRatio = unionWidth / Math.max(unionHeight, 1e-6);
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const informationMeanY = mean([
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mean(pretraining.map((item) => item.y)),
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mean(posttraining.map((item) => item.y)),
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]);
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const informationMeanX = mean([
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mean(pretraining.map((item) => item.x)),
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mean(posttraining.map((item) => item.x)),
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]);
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const nobelPeripheryCount = nobelReferences.filter(
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(reference) =>
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reference.x < 0.31 || reference.x > 0.67 || reference.y < 0.28 || reference.y > 0.66,
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).length;
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const nobelInnerCount = nobelReferences.filter(
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(reference) =>
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reference.x > 0.39 && reference.x < 0.61 && reference.y > 0.29 && reference.y < 0.6,
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).length;
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console.log(
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JSON.stringify(
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{
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canopy: {
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means: canopyMeans.map((item) => ({
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...item,
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meanX: Number(item.meanX.toFixed(3)),
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meanY: Number(item.meanY.toFixed(3)),
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})),
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clouds: upperDomainClouds.map((item) => ({
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label: item.label,
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meanX: Number(item.meanX.toFixed(3)),
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meanY: Number(item.meanY.toFixed(3)),
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spreadX: Number(item.spreadX.toFixed(3)),
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spreadY: Number(item.spreadY.toFixed(3)),
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aspectRatio: Number(item.aspectRatio.toFixed(3)),
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area: Number(item.area.toFixed(4)),
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})),
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gaps: canopyGaps.map((gap) => Number(gap.toFixed(3))),
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yGaps: canopyYGaps.map((gap) => Number(gap.toFixed(3))),
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maxGap: Number(Math.max(...canopyGaps).toFixed(3)),
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minGap: Number(Math.min(...canopyGaps).toFixed(3)),
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width: Number(canopyWidth.toFixed(3)),
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centerX: Number(canopyCenterX.toFixed(3)),
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maxAbsYGap: Number(Math.max(...canopyYGaps.map((gap) => Math.abs(gap))).toFixed(3)),
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outerMeanY: Number(outerCanopyMeanY.toFixed(3)),
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centerMeanY: Number(centerCanopyMeanY.toFixed(3)),
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shoulderMeanY: Number(shoulderCanopyMeanY.toFixed(3)),
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},
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informationCloud: {
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meanX: Number(informationMeanX.toFixed(3)),
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meanY: Number(informationMeanY.toFixed(3)),
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centerOffsetFromCanopy: Number(Math.abs(informationMeanX - canopyCenterX).toFixed(3)),
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gapToShoulder: Number((informationMeanY - shoulderCanopyMeanY).toFixed(3)),
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pretrainingMeanX: Number(mean(pretraining.map((item) => item.x)).toFixed(3)),
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pretrainingMeanY: Number(mean(pretraining.map((item) => item.y)).toFixed(3)),
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pretrainingSpreadX: Number(preX.span.toFixed(3)),
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pretrainingSpreadY: Number(preY.span.toFixed(3)),
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posttrainingMeanX: Number(mean(posttraining.map((item) => item.x)).toFixed(3)),
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posttrainingMeanY: Number(mean(posttraining.map((item) => item.y)).toFixed(3)),
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posttrainingSpreadX: Number(postX.span.toFixed(3)),
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posttrainingSpreadY: Number(postY.span.toFixed(3)),
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centroidGapX: Number(Math.abs(mean(pretraining.map((item) => item.x)) - mean(posttraining.map((item) => item.x))).toFixed(3)),
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centroidGapY: Number(Math.abs(mean(pretraining.map((item) => item.y)) - mean(posttraining.map((item) => item.y))).toFixed(3)),
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overlapX: Number(overlapX.toFixed(3)),
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overlapY: Number(overlapY.toFixed(3)),
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unionWidth: Number(unionWidth.toFixed(3)),
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unionHeight: Number(unionHeight.toFixed(3)),
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unionAspectRatio: Number(unionAspectRatio.toFixed(3)),
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},
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bridges: {
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modelCompressionMeanX: Number(mean(modelCompression.map((item) => item.x)).toFixed(3)),
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modelCompressionMeanY: Number(mean(modelCompression.map((item) => item.y)).toFixed(3)),
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},
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nobel: {
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count: nobelReferences.length,
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peripheryCount: nobelPeripheryCount,
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peripheryRatio: Number((nobelPeripheryCount / Math.max(nobelReferences.length, 1)).toFixed(3)),
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innerCount: nobelInnerCount,
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},
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},
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null,
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2,
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),
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);

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