From 616919fda715f93732e21a37030da15f82fe0394 Mon Sep 17 00:00:00 2001 From: rNLKJA <62606765+rNLKJA@users.noreply.github.com> Date: Tue, 6 Oct 2026 00:58:18 +1030 Subject: [PATCH 01/18] feat(stats): add statistics helpers verified against scipy and R Wilson and Newcombe intervals, seeded percentile bootstrap (plain, stratified, paired and vector-valued), Wilcoxon signed-rank (exact and tie-corrected normal approximation) with rank-biserial effect size, Bradley-Terry by MM with an Elo conversion, type-7 quantiles and high-precision normal functions. Unit tests pin reference values from scipy 1.17, statsmodels and R. Co-Authored-By: Claude Opus 5.5 --- web/src/lib/stats/bootstrap.ts | 152 +++++++++++++++++ web/src/lib/stats/bradley-terry.ts | 107 ++++++++++++ web/src/lib/stats/descriptive.ts | 73 ++++++++ web/src/lib/stats/format.ts | 37 ++++ web/src/lib/stats/normal.ts | 116 +++++++++++++ web/src/lib/stats/proportion.ts | 71 ++++++++ web/src/lib/stats/stats.test.ts | 260 +++++++++++++++++++++++++++++ web/src/lib/stats/wilcoxon.ts | 108 ++++++++++++ 8 files changed, 924 insertions(+) create mode 100644 web/src/lib/stats/bootstrap.ts create mode 100644 web/src/lib/stats/bradley-terry.ts create mode 100644 web/src/lib/stats/descriptive.ts create mode 100644 web/src/lib/stats/format.ts create mode 100644 web/src/lib/stats/normal.ts create mode 100644 web/src/lib/stats/proportion.ts create mode 100644 web/src/lib/stats/stats.test.ts create mode 100644 web/src/lib/stats/wilcoxon.ts diff --git a/web/src/lib/stats/bootstrap.ts b/web/src/lib/stats/bootstrap.ts new file mode 100644 index 0000000..d2982ed --- /dev/null +++ b/web/src/lib/stats/bootstrap.ts @@ -0,0 +1,152 @@ +/** + * Seeded percentile bootstrap. Every interval records its seed and number of + * resamples so a result on the site can be reproduced exactly. + */ +import { createRng } from "@/lib/rng"; +import { mean, quantileSorted, sd } from "./descriptive"; + +export interface BootstrapOptions { + /** Number of resamples (default 2000). */ + reps?: number; + /** Confidence level (default 0.95). */ + level?: number; + /** Seed for the resampling RNG. */ + seed: number; +} + +export interface BootstrapCI { + /** The statistic on the original sample. */ + estimate: number; + lower: number; + upper: number; + level: number; + reps: number; + seed: number; +} + +/** Percentile interval of a bootstrap distribution (type-7 quantiles). */ +export function percentileInterval( + replicates: readonly number[], + level: number, +): { lower: number; upper: number } { + const finite = replicates.filter(Number.isFinite).sort((a, b) => a - b); + if (finite.length === 0) return { lower: NaN, upper: NaN }; + const alpha = (1 - level) / 2; + return { lower: quantileSorted(finite, alpha), upper: quantileSorted(finite, 1 - alpha) }; +} + +/** Bootstrap a statistic of one sample by resampling its elements with replacement. */ +export function bootstrap( + data: readonly T[], + statistic: (sample: readonly T[]) => number, + { reps = 2000, level = 0.95, seed }: BootstrapOptions, +): BootstrapCI { + const estimate = statistic(data); + const n = data.length; + if (n === 0) return { estimate, lower: NaN, upper: NaN, level, reps, seed }; + const rng = createRng(seed); + const sample = new Array(n); + const replicates = new Array(reps); + for (let b = 0; b < reps; b++) { + for (let i = 0; i < n; i++) sample[i] = data[Math.floor(rng() * n)]; + replicates[b] = statistic(sample); + } + return { estimate, ...percentileInterval(replicates, level), level, reps, seed }; +} + +/** + * Stratified bootstrap: resample within each stratum independently, keeping + * stratum sizes fixed (e.g. games within each pairing of a round robin). + */ +export function stratifiedBootstrap( + strata: readonly (readonly T[])[], + statistic: (strata: readonly (readonly T[])[]) => number, + { reps = 2000, level = 0.95, seed }: BootstrapOptions, +): BootstrapCI { + const estimate = statistic(strata); + const rng = createRng(seed); + const samples = strata.map((s) => new Array(s.length)); + const replicates = new Array(reps); + for (let b = 0; b < reps; b++) { + strata.forEach((s, k) => { + const out = samples[k]; + for (let i = 0; i < s.length; i++) out[i] = s[Math.floor(rng() * s.length)]; + }); + replicates[b] = statistic(samples); + } + return { estimate, ...percentileInterval(replicates, level), level, reps, seed }; +} + +/** Bootstrap CI for a mean. */ +export const bootstrapMean = (xs: readonly number[], opts: BootstrapOptions) => + bootstrap(xs, mean, opts); + +export interface PairedDifference { + n: number; + meanX: number; + meanY: number; + /** Mean of the paired differences x - y, with a paired bootstrap CI. */ + meanDiff: BootstrapCI; + /** Cohen's d_z: mean difference / SD of the differences. */ + dz: number; +} + +/** + * Paired comparison of two measurements on the same units: resamples the + * units (pairs), never the two arms independently. + */ +export function pairedMeanDifference( + x: readonly number[], + y: readonly number[], + opts: BootstrapOptions, +): PairedDifference { + if (x.length !== y.length) throw new RangeError("paired samples must have equal length"); + const d = x.map((xi, i) => xi - y[i]); + const s = sd(d); + return { + n: d.length, + meanX: mean(x), + meanY: mean(y), + meanDiff: bootstrapMean(d, opts), + dz: s > 0 ? mean(d) / s : 0, + }; +} + +/** + * Stratified bootstrap of a vector-valued statistic (e.g. every player's + * strength from one refit), with a percentile interval per component. + */ +export function stratifiedBootstrapVector( + strata: readonly (readonly T[])[], + statistic: (strata: readonly (readonly T[])[]) => number[], + { reps = 2000, level = 0.95, seed }: BootstrapOptions, +): { + estimate: number[]; + lower: number[]; + upper: number[]; + level: number; + reps: number; + seed: number; +} { + const estimate = statistic(strata); + const rng = createRng(seed); + const samples = strata.map((s) => new Array(s.length)); + const replicates: number[][] = estimate.map(() => new Array(reps)); + for (let b = 0; b < reps; b++) { + strata.forEach((s, k) => { + const out = samples[k]; + for (let i = 0; i < s.length; i++) out[i] = s[Math.floor(rng() * s.length)]; + }); + const value = statistic(samples); + value.forEach((v, c) => (replicates[c][b] = v)); + } + const intervals = replicates.map((r) => percentileInterval(r, level)); + return { + estimate, + lower: intervals.map((i) => i.lower), + upper: intervals.map((i) => i.upper), + level, + reps, + seed, + }; +} diff --git a/web/src/lib/stats/bradley-terry.ts b/web/src/lib/stats/bradley-terry.ts new file mode 100644 index 0000000..3c853e3 --- /dev/null +++ b/web/src/lib/stats/bradley-terry.ts @@ -0,0 +1,107 @@ +/** + * Bradley-Terry strengths from pairwise results, fitted by the + * minorisation-maximisation (MM) algorithm of Hunter (2004). + * + * P(i beats j) = p_i / (p_i + p_j). Draws count as half a win to each side. + * Strengths are returned on the log scale (theta_i = ln p_i, mean zero) and on + * the Elo scale (400 · log10 p_i), optionally anchored to a reference player. + * + * `prior` adds that many virtual drawn games to every pairing that was played + * (a light shrinkage towards equal strength). Without it, an agent that wins + * or loses every game has an infinite maximum-likelihood strength. + */ + +export interface PairOutcome { + /** Index of the first player. */ + i: number; + /** Index of the second player. */ + j: number; + /** Points scored by i against j (wins + draws / 2). */ + scoreI: number; + /** Points scored by j against i. */ + scoreJ: number; +} + +export interface BradleyTerryOptions { + /** Virtual drawn games added to each played pairing (default 1, i.e. +0.5 points each side). */ + prior?: number; + maxIter?: number; + tol?: number; +} + +export interface BradleyTerryFit { + /** ln p_i, centred so the mean is zero. */ + theta: number[]; + iterations: number; + converged: boolean; +} + +export function fitBradleyTerry( + k: number, + outcomes: readonly PairOutcome[], + { prior = 1, maxIter = 10_000, tol = 1e-10 }: BradleyTerryOptions = {}, +): BradleyTerryFit { + // Aggregate into a symmetric matrix of points and games. + const points = Array.from({ length: k }, () => new Float64Array(k)); + const games = Array.from({ length: k }, () => new Float64Array(k)); + for (const { i, j, scoreI, scoreJ } of outcomes) { + if (i === j) continue; + points[i][j] += scoreI; + points[j][i] += scoreJ; + games[i][j] += scoreI + scoreJ; + games[j][i] += scoreI + scoreJ; + } + if (prior > 0) { + for (let i = 0; i < k; i++) { + for (let j = 0; j < k; j++) { + if (i !== j && games[i][j] > 0) { + points[i][j] += prior / 2; + games[i][j] += prior; + } + } + } + } + + const wins = points.map((row) => row.reduce((s, v) => s + v, 0)); + let p = new Float64Array(k).fill(1); + let iterations = 0; + let converged = false; + for (; iterations < maxIter; iterations++) { + const next = new Float64Array(k); + for (let i = 0; i < k; i++) { + let denom = 0; + for (let j = 0; j < k; j++) { + if (i !== j && games[i][j] > 0) denom += games[i][j] / (p[i] + p[j]); + } + next[i] = denom > 0 ? wins[i] / denom : p[i]; + } + // Normalise to geometric mean 1 so the fit is identifiable. + const logMean = next.reduce((s, v) => s + Math.log(v), 0) / k; + const scale = Math.exp(logMean); + let change = 0; + for (let i = 0; i < k; i++) { + next[i] /= scale; + change = Math.max(change, Math.abs(Math.log(next[i]) - Math.log(p[i]))); + } + p = next; + if (change < tol) { + converged = true; + iterations++; + break; + } + } + const theta = Array.from(p, (v) => Math.log(v)); + const centre = theta.reduce((s, v) => s + v, 0) / k; + return { theta: theta.map((t) => t - centre), iterations, converged }; +} + +/** Convert natural-log strengths to Elo points (400 per factor of 10 in odds). */ +export const ELO_PER_NAT = 400 / Math.LN10; + +export function toElo(theta: readonly number[], anchor?: number): number[] { + const offset = anchor === undefined ? 0 : theta[anchor]; + return theta.map((t) => (t - offset) * ELO_PER_NAT); +} + +/** Expected score of a player rated `a` against one rated `b` on the Elo scale. */ +export const eloExpected = (a: number, b: number) => 1 / (1 + 10 ** ((b - a) / 400)); diff --git a/web/src/lib/stats/descriptive.ts b/web/src/lib/stats/descriptive.ts new file mode 100644 index 0000000..fb95825 --- /dev/null +++ b/web/src/lib/stats/descriptive.ts @@ -0,0 +1,73 @@ +/** Descriptive statistics. Every function treats its input as read-only. */ + +export function sum(xs: readonly number[]): number { + let s = 0; + for (const x of xs) s += x; + return s; +} + +export function mean(xs: readonly number[]): number { + if (xs.length === 0) return NaN; + return sum(xs) / xs.length; +} + +/** Sample variance (ddof = 1 by default, like R's var and numpy's var(ddof=1)). */ +export function variance(xs: readonly number[], ddof = 1): number { + if (xs.length - ddof <= 0) return NaN; + const m = mean(xs); + let ss = 0; + for (const x of xs) ss += (x - m) ** 2; + return ss / (xs.length - ddof); +} + +export const sd = (xs: readonly number[], ddof = 1) => Math.sqrt(variance(xs, ddof)); + +/** + * Quantile with linear interpolation between order statistics: R's default + * type 7 and numpy's default "linear" method. + */ +export function quantile(xs: readonly number[], p: number): number { + if (xs.length === 0) return NaN; + const sorted = [...xs].sort((a, b) => a - b); + return quantileSorted(sorted, p); +} + +/** As `quantile`, for input that is already sorted ascending. */ +export function quantileSorted(sorted: readonly number[], p: number): number { + const n = sorted.length; + if (n === 0) return NaN; + if (p <= 0) return sorted[0]; + if (p >= 1) return sorted[n - 1]; + const h = (n - 1) * p; + const lo = Math.floor(h); + const hi = Math.min(lo + 1, n - 1); + return sorted[lo] + (h - lo) * (sorted[hi] - sorted[lo]); +} + +export const median = (xs: readonly number[]) => quantile(xs, 0.5); + +/** Geometric mean of strictly positive values. */ +export function geometricMean(xs: readonly number[]): number { + if (xs.length === 0) return NaN; + return Math.exp(mean(xs.map(Math.log))); +} + +/** + * Average ranks (1-based) with ties sharing the mean of the ranks they span, + * plus the sizes of each tie group (used for variance corrections). + */ +export function rankWithTies(xs: readonly number[]): { ranks: number[]; tieSizes: number[] } { + const order = xs.map((x, i) => [x, i] as const).sort((a, b) => a[0] - b[0]); + const ranks = new Array(xs.length); + const tieSizes: number[] = []; + let i = 0; + while (i < order.length) { + let j = i; + while (j + 1 < order.length && order[j + 1][0] === order[i][0]) j++; + const avg = (i + j) / 2 + 1; + for (let k = i; k <= j; k++) ranks[order[k][1]] = avg; + tieSizes.push(j - i + 1); + i = j + 1; + } + return { ranks, tieSizes }; +} diff --git a/web/src/lib/stats/format.ts b/web/src/lib/stats/format.ts new file mode 100644 index 0000000..bf17090 --- /dev/null +++ b/web/src/lib/stats/format.ts @@ -0,0 +1,37 @@ +/** Consistent number formatting for estimates and intervals (en-AU). */ + +export function formatPct(p: number, digits = 1): string { + if (!Number.isFinite(p)) return "–"; + return `${(p * 100).toFixed(digits)}%`; +} + +/** "[83.4%, 93.8%]" */ +export function formatPctInterval(lower: number, upper: number, digits = 1): string { + return `[${formatPct(lower, digits)}, ${formatPct(upper, digits)}]`; +} + +export function formatNumber(x: number, digits = 1): string { + if (!Number.isFinite(x)) return x > 0 ? "+∞" : x < 0 ? "−∞" : "–"; + const s = x.toLocaleString("en-AU", { + minimumFractionDigits: digits, + maximumFractionDigits: digits, + }); + return s.replace(/^-/, "−"); +} + +export function formatSigned(x: number, digits = 1): string { + const s = formatNumber(x, digits); + return x > 0 ? `+${s}` : s; +} + +/** "[−12.3, 4.5]" */ +export function formatInterval(lower: number, upper: number, digits = 1): string { + return `[${formatNumber(lower, digits)}, ${formatNumber(upper, digits)}]`; +} + +/** p-values: "< 0.001" or three significant decimals. */ +export function formatP(p: number): string { + if (!Number.isFinite(p)) return "–"; + if (p < 0.001) return "< 0.001"; + return p.toFixed(3); +} diff --git a/web/src/lib/stats/normal.ts b/web/src/lib/stats/normal.ts new file mode 100644 index 0000000..5056db3 --- /dev/null +++ b/web/src/lib/stats/normal.ts @@ -0,0 +1,116 @@ +/** + * Standard normal distribution helpers, accurate to near double precision. + * + * erf / erfc use a positive-term series for small arguments and a continued + * fraction (modified Lentz) for large ones, so there is no catastrophic + * cancellation in the tails that Wilcoxon p-values live in. + */ + +const SQRT_PI = Math.sqrt(Math.PI); + +/** erf(x) for 0 <= x < 2 via erf(x) = 2/sqrt(pi) e^{-x^2} sum 2^n x^{2n+1} / (2n+1)!! */ +function erfSeries(x: number): number { + const x2 = x * x; + let term = x; + let sum = x; + for (let n = 1; n < 200; n++) { + term *= (2 * x2) / (2 * n + 1); + sum += term; + if (term < sum * 1e-17) break; + } + return (2 / SQRT_PI) * Math.exp(-x2) * sum; +} + +/** erfc(x) for x >= 2 via its continued fraction. */ +function erfcContinuedFraction(x: number): number { + // erfc(x) = e^{-x^2}/sqrt(pi) * 1/(x + (1/2)/(x + 1/(x + (3/2)/(x + 2/(x + ...))))) + const tiny = 1e-300; + let f = x; + if (f === 0) f = tiny; + let c = f; + let d = 0; + for (let i = 1; i < 5000; i++) { + const a = i / 2; + d = x + a * d; + d = d === 0 ? tiny : d; + c = x + a / c; + c = c === 0 ? tiny : c; + d = 1 / d; + const delta = c * d; + f *= delta; + if (Math.abs(delta - 1) < 1e-16) break; + } + return Math.exp(-x * x) / SQRT_PI / f; +} + +export function erfc(x: number): number { + if (Number.isNaN(x)) return NaN; + if (x < 0) return 2 - erfc(-x); + if (x < 2) return 1 - erfSeries(x); + return erfcContinuedFraction(x); +} + +export function erf(x: number): number { + return 1 - erfc(x); +} + +/** P(Z <= z) for a standard normal Z. */ +export function normalCdf(z: number): number { + return 0.5 * erfc(-z / Math.SQRT2); +} + +/** P(Z > z), computed directly so small upper tails keep their precision. */ +export function normalSf(z: number): number { + return 0.5 * erfc(z / Math.SQRT2); +} + +/** + * Inverse standard normal CDF. Acklam's rational approximation (relative + * error about 1e-9) refined with one Halley step against normalCdf. + */ +export function normalQuantile(p: number): number { + if (!(p > 0 && p < 1)) { + if (p === 0) return -Infinity; + if (p === 1) return Infinity; + return NaN; + } + const a = [ + -3.969683028665376e1, 2.209460984245205e2, -2.759285104469687e2, 1.38357751867269e2, + -3.066479806614716e1, 2.506628277459239, + ]; + const b = [ + -5.447609879822406e1, 1.615858368580409e2, -1.556989798598866e2, 6.680131188771972e1, + -1.328068155288572e1, + ]; + const c = [ + -7.784894002430293e-3, -3.223964580411365e-1, -2.400758277161838, -2.549732539343734, + 4.374664141464968, 2.938163982698783, + ]; + const d = [7.784695709041462e-3, 3.224671290700398e-1, 2.445134137142996, 3.754408661907416]; + const lo = 0.02425; + let x: number; + if (p < lo) { + const q = Math.sqrt(-2 * Math.log(p)); + x = + (((((c[0] * q + c[1]) * q + c[2]) * q + c[3]) * q + c[4]) * q + c[5]) / + ((((d[0] * q + d[1]) * q + d[2]) * q + d[3]) * q + 1); + } else if (p <= 1 - lo) { + const q = p - 0.5; + const r = q * q; + x = + ((((((a[0] * r + a[1]) * r + a[2]) * r + a[3]) * r + a[4]) * r + a[5]) * q) / + (((((b[0] * r + b[1]) * r + b[2]) * r + b[3]) * r + b[4]) * r + 1); + } else { + const q = Math.sqrt(-2 * Math.log(1 - p)); + x = + -(((((c[0] * q + c[1]) * q + c[2]) * q + c[3]) * q + c[4]) * q + c[5]) / + ((((d[0] * q + d[1]) * q + d[2]) * q + d[3]) * q + 1); + } + // One Halley refinement step. + const e = normalCdf(x) - p; + const u = e * Math.sqrt(2 * Math.PI) * Math.exp((x * x) / 2); + return x - u / (1 + (x * u) / 2); +} + +/** Two-sided critical value for a confidence level, e.g. 0.95 → 1.959964. */ +export const zCritical = (level: number) => normalQuantile(1 - (1 - level) / 2); diff --git a/web/src/lib/stats/proportion.ts b/web/src/lib/stats/proportion.ts new file mode 100644 index 0000000..4032cc5 --- /dev/null +++ b/web/src/lib/stats/proportion.ts @@ -0,0 +1,71 @@ +/** + * Confidence intervals for proportions. + * + * Wilson's score interval is used everywhere a win rate, illegal-move rate or + * agreement rate is shown: unlike the Wald interval it behaves at 0% and 100% + * and keeps close to nominal coverage for the small samples a browser can run. + */ +import { zCritical } from "./normal"; + +export interface ProportionCI { + successes: number; + n: number; + /** Point estimate successes / n (NaN when n = 0). */ + p: number; + lower: number; + upper: number; + level: number; +} + +export function wilson(successes: number, n: number, level = 0.95): ProportionCI { + if (!Number.isFinite(successes) || !Number.isFinite(n) || successes < 0 || successes > n) { + throw new RangeError(`invalid proportion ${successes}/${n}`); + } + if (n === 0) return { successes, n, p: NaN, lower: 0, upper: 1, level }; + const z = zCritical(level); + const p = successes / n; + const z2 = z * z; + const denom = 1 + z2 / n; + const centre = (p + z2 / (2 * n)) / denom; + const half = (z * Math.sqrt((p * (1 - p)) / n + z2 / (4 * n * n))) / denom; + return { + successes, + n, + p, + // Clamp exact endpoints so 0/n and n/n report exactly 0 and 1. + lower: successes === 0 ? 0 : Math.max(0, centre - half), + upper: successes === n ? 1 : Math.min(1, centre + half), + level, + }; +} + +export interface DifferenceCI { + /** p1 - p2 */ + estimate: number; + lower: number; + upper: number; + level: number; +} + +/** + * Newcombe's hybrid score interval (method 10) for the difference of two + * independent proportions, built from the two Wilson intervals. + */ +export function newcombeDifference( + x1: number, + n1: number, + x2: number, + n2: number, + level = 0.95, +): DifferenceCI { + const a = wilson(x1, n1, level); + const b = wilson(x2, n2, level); + const d = a.p - b.p; + const lower = d - Math.sqrt((a.p - a.lower) ** 2 + (b.upper - b.p) ** 2); + const upper = d + Math.sqrt((a.upper - a.p) ** 2 + (b.p - b.lower) ** 2); + return { estimate: d, lower, upper, level }; +} + +/** Cohen's h effect size for two proportions. */ +export const cohensH = (p1: number, p2: number) => + 2 * Math.asin(Math.sqrt(p1)) - 2 * Math.asin(Math.sqrt(p2)); diff --git a/web/src/lib/stats/stats.test.ts b/web/src/lib/stats/stats.test.ts new file mode 100644 index 0000000..3b632bf --- /dev/null +++ b/web/src/lib/stats/stats.test.ts @@ -0,0 +1,260 @@ +/** + * Reference values were computed with scipy 1.17.1 / statsmodels (uv, Python + * 3.12) and cross-checked with R 4.x (prop.test(correct = FALSE), + * wilcox.test, quantile(type = 7)). See "Verifying the statistics" in the + * README. + */ +import { describe, expect, it } from "vitest"; + +import { bootstrap, bootstrapMean, pairedMeanDifference, stratifiedBootstrap } from "./bootstrap"; +import { type PairOutcome, eloExpected, fitBradleyTerry, toElo } from "./bradley-terry"; +import { geometricMean, mean, median, quantile, rankWithTies, sd, variance } from "./descriptive"; +import { formatP, formatPct, formatPctInterval, formatSigned } from "./format"; +import { erf, normalCdf, normalQuantile, normalSf, zCritical } from "./normal"; +import { cohensH, newcombeDifference, wilson } from "./proportion"; +import { wilcoxonSignedRank } from "./wilcoxon"; + +describe("normal distribution", () => { + it("matches scipy.stats.norm", () => { + expect(normalCdf(1.2345)).toBeCloseTo(0.8914916766373298, 14); + expect(normalQuantile(0.975)).toBeCloseTo(1.959963984540054, 12); + expect(normalQuantile(0.001)).toBeCloseTo(-3.090232306167813, 11); + expect(normalSf(3.5) / 0.00023262907903552502).toBeCloseTo(1, 12); + expect(zCritical(0.95)).toBeCloseTo(1.959963984540054, 12); + }); + + it("is symmetric and handles the extremes", () => { + expect(normalCdf(0)).toBe(0.5); + expect(normalCdf(-2) + normalCdf(2)).toBeCloseTo(1, 15); + expect(erf(0.5)).toBeCloseTo(0.5204998778130465, 15); + expect(normalQuantile(0)).toBe(-Infinity); + expect(normalQuantile(1)).toBe(Infinity); + expect(normalQuantile(2)).toBeNaN(); + for (const p of [1e-8, 0.01, 0.3, 0.5, 0.77, 0.999999]) { + expect(normalCdf(normalQuantile(p)) / p).toBeCloseTo(1, 10); + } + }); +}); + +describe("descriptive statistics", () => { + const a = [3.1, 0.2, 5.5, 2.2, 9.9, 4.4, 1.0]; + + it("matches numpy (ddof = 1) and R type-7 quantiles", () => { + expect(variance(a)).toBeCloseTo(10.716190476190476, 12); + expect(sd(a)).toBeCloseTo(3.2735592977965857, 12); + const expected = [0.32, 1.6, 3.1, 7.26, 9.24]; + [0.025, 0.25, 0.5, 0.9, 0.975].forEach((p, i) => + expect(quantile(a, p)).toBeCloseTo(expected[i], 12), + ); + expect(median([4, 1, 3, 2])).toBe(2.5); + expect(mean([])).toBeNaN(); + expect(geometricMean([1, 100])).toBeCloseTo(10, 12); + }); + + it("does not mutate its input", () => { + const copy = [...a]; + quantile(a, 0.3); + expect(a).toEqual(copy); + }); + + it("assigns average ranks to ties", () => { + const { ranks, tieSizes } = rankWithTies([10, 20, 10, 30, 20, 20]); + expect(ranks).toEqual([1.5, 4, 1.5, 6, 4, 4]); + expect(tieSizes.sort()).toEqual([1, 2, 3]); + }); +}); + +describe("Wilson score interval", () => { + // statsmodels proportion_confint(method="wilson"); R prop.test(correct = FALSE) + it.each([ + [144, 160, 0.8437374796961266, 0.9375055641666694], + [0, 10, 0.0, 0.27753279986288926], + [10, 10, 0.7224672001371106, 1.0], + [7, 20, 0.18119182410108203, 0.5671457233147638], + [1, 4, 0.0455872608097006, 0.6993581574175982], + [17, 20, 0.639581135259243, 0.9476312541037833], + [50, 100, 0.4038315303659956, 0.5961684696340044], + ])("%i / %i", (x, n, lo, hi) => { + const ci = wilson(x, n); + expect(ci.p).toBeCloseTo(x / n, 15); + expect(ci.lower).toBeCloseTo(lo, 12); + expect(ci.upper).toBeCloseTo(hi, 12); + }); + + it("supports other confidence levels", () => { + const ci = wilson(7, 20, 0.9); + expect(ci.lower).toBeCloseTo(0.202260040056761, 12); + expect(ci.upper).toBeCloseTo(0.5334873111515284, 12); + }); + + it("is uninformative with no data and rejects impossible counts", () => { + expect(wilson(0, 0)).toMatchObject({ lower: 0, upper: 1 }); + expect(() => wilson(5, 4)).toThrow(RangeError); + expect(() => wilson(-1, 4)).toThrow(RangeError); + }); + + it("Newcombe difference interval matches statsmodels (method newcomb)", () => { + const cases: [number, number, number, number, number, number][] = [ + [144, 160, 30, 40, 0.02810942807812558, 0.3065002035431229], + [17, 20, 15, 20, -0.15171072320577428, 0.3395035190745355], + [0, 10, 5, 10, -0.7634069094874361, -0.11736746745022203], + ]; + for (const [x1, n1, x2, n2, lo, hi] of cases) { + const d = newcombeDifference(x1, n1, x2, n2); + expect(d.estimate).toBeCloseTo(x1 / n1 - x2 / n2, 15); + expect(d.lower).toBeCloseTo(lo, 12); + expect(d.upper).toBeCloseTo(hi, 12); + } + expect(cohensH(0.5, 0.5)).toBe(0); + expect(cohensH(0.9, 0.5)).toBeCloseTo(0.9272952180016122, 12); + }); +}); + +describe("Wilcoxon signed-rank test", () => { + it("normal approximation with a zero and tied ranks (scipy, correction off/on)", () => { + const x = [125, 115, 130, 140, 140, 115, 140, 125, 140, 135]; + const y = [110, 122, 125, 120, 140, 124, 123, 137, 135, 145]; + const r = wilcoxonSignedRank(x, y); + expect(r.method).toBe("approx"); + expect(r.zeros).toBe(1); + expect(r.n).toBe(9); + expect(r.statistic).toBe(18); + expect(r.pValue).toBeCloseTo(0.5936305914425295, 12); + const c = wilcoxonSignedRank(x, y, { continuityCorrection: true }); + expect(c.pValue).toBeCloseTo(0.6352893188352069, 12); + }); + + it("exact null distribution without ties (scipy method='exact', R exact = TRUE)", () => { + const d = [1.5, -0.3, 2.2, 3.1, -1.7, 0.9, 4.0, 2.6, -0.2, 1.1, 0.4, 5.3]; + const r = wilcoxonSignedRank(d); + expect(r.method).toBe("exact"); + expect(r.statistic).toBe(10); + expect(r.wPlus).toBe(68); // R's V + expect(r.pValue).toBeCloseTo(0.02099609375, 14); + const approx = wilcoxonSignedRank(d, undefined, { method: "approx" }); + expect(approx.pValue).toBeCloseTo(0.0229090993543566, 12); + expect(r.rankBiserial).toBeCloseTo((68 - 10) / 78, 14); + }); + + it("drops zeros and corrects for ties (scipy; R wilcox.test V = 95.5)", () => { + const d = [3, -1, 4, 4, 0, 2, -2, 5, 3, 0, 1, 6, -1, 2, 2, 7]; + const r = wilcoxonSignedRank(d); + expect(r.method).toBe("approx"); + expect(r.statistic).toBe(9.5); + expect(r.wPlus).toBe(95.5); + expect(r.pValue).toBeCloseTo(0.00672649504011445, 12); + expect(() => wilcoxonSignedRank(d, undefined, { method: "exact" })).toThrow(RangeError); + }); + + it("handles all-zero differences", () => { + expect(wilcoxonSignedRank([1, 2], [1, 2])).toMatchObject({ n: 0, zeros: 2, pValue: 1 }); + }); +}); + +describe("bootstrap", () => { + const xs = Array.from({ length: 40 }, (_, i) => (i * 37) % 23); + + it("is reproducible for a seed and records how it was made", () => { + const a = bootstrapMean(xs, { seed: 7, reps: 500 }); + const b = bootstrapMean(xs, { seed: 7, reps: 500 }); + expect(a).toEqual(b); + expect(a).toMatchObject({ seed: 7, reps: 500, level: 0.95 }); + expect(a.estimate).toBeCloseTo(mean(xs), 12); + expect(a.lower).toBeLessThan(a.estimate); + expect(a.upper).toBeGreaterThan(a.estimate); + }); + + it("is close to the normal-theory interval for a mean", () => { + const ci = bootstrapMean(xs, { seed: 1, reps: 4000 }); + const se = sd(xs) / Math.sqrt(xs.length); + expect(ci.upper - ci.lower).toBeGreaterThan(2 * 1.96 * se * 0.85); + expect(ci.upper - ci.lower).toBeLessThan(2 * 1.96 * se * 1.15); + }); + + it("collapses on a constant sample", () => { + const ci = bootstrap([5, 5, 5], mean, { seed: 3, reps: 100 }); + expect([ci.lower, ci.estimate, ci.upper]).toEqual([5, 5, 5]); + }); + + it("stratified resampling keeps stratum sizes", () => { + const strata = [ + [1, 1, 1], + [0, 0, 0, 0, 0], + ]; + const ci = stratifiedBootstrap(strata, (s) => s[0].length * 10 + s[1].length, { + seed: 1, + reps: 50, + }); + expect([ci.lower, ci.upper]).toEqual([35, 35]); + }); + + it("paired difference resamples pairs", () => { + const x = [10, 12, 9, 15, 11, 14, 13, 10]; + const y = x.map((v, i) => v - 2 - (i % 2)); + const r = pairedMeanDifference(x, y, { seed: 5, reps: 1000 }); + expect(r.meanDiff.estimate).toBeCloseTo(2.5, 12); + expect(r.meanDiff.lower).toBeGreaterThanOrEqual(2); + expect(r.meanDiff.upper).toBeLessThanOrEqual(3); + expect(r.dz).toBeCloseTo(2.5 / sd(x.map((v, i) => v - y[i])), 12); + expect(() => pairedMeanDifference([1], [1, 2], { seed: 1 })).toThrow(RangeError); + }); +}); + +describe("Bradley-Terry", () => { + // wins[i][j] = wins of i over j; reference: maximum likelihood with scipy BFGS. + const W = [ + [0, 7, 9, 10], + [3, 0, 6, 9], + [1, 4, 0, 6], + [0, 1, 4, 0], + ]; + const outcomes: PairOutcome[] = []; + for (let i = 0; i < 4; i++) + for (let j = i + 1; j < 4; j++) outcomes.push({ i, j, scoreI: W[i][j], scoreJ: W[j][i] }); + + it("matches the maximum-likelihood fit (no prior)", () => { + const fit = fitBradleyTerry(4, outcomes, { prior: 0 }); + expect(fit.converged).toBe(true); + const expected = [ + 1.539924527762305, 0.37009020841656426, -0.5322860297988234, -1.3777287063800459, + ]; + fit.theta.forEach((t, i) => expect(t).toBeCloseTo(expected[i], 6)); + }); + + it("matches the fit with one virtual draw per pairing", () => { + const fit = fitBradleyTerry(4, outcomes, { prior: 1 }); + const expected = [ + 1.2976598516423081, 0.32010753473623765, -0.4507020061425011, -1.167065380236045, + ]; + fit.theta.forEach((t, i) => expect(t).toBeCloseTo(expected[i], 6)); + }); + + it("stays finite when a player wins every game, thanks to the prior", () => { + const sweep = [ + { i: 0, j: 1, scoreI: 10, scoreJ: 0 }, + { i: 1, j: 2, scoreI: 6, scoreJ: 4 }, + { i: 0, j: 2, scoreI: 10, scoreJ: 0 }, + ]; + const fit = fitBradleyTerry(3, sweep); + expect(fit.theta.every(Number.isFinite)).toBe(true); + expect(fit.theta[0]).toBeGreaterThan(fit.theta[1]); + }); + + it("converts to an anchored Elo scale", () => { + const elo = toElo([Math.LN10, 0], 1); + expect(elo[0]).toBeCloseTo(400, 12); + expect(elo[1]).toBe(0); + expect(eloExpected(400, 0)).toBeCloseTo(10 / 11, 12); + }); +}); + +describe("formatting", () => { + it("formats estimates the way the site shows them", () => { + expect(formatPct(0.9)).toBe("90.0%"); + expect(formatPctInterval(0.8437, 0.9375)).toBe("[84.4%, 93.8%]"); + expect(formatSigned(-3.21)).toBe("−3.2"); + expect(formatSigned(3.21)).toBe("+3.2"); + expect(formatP(0.0004)).toBe("< 0.001"); + expect(formatP(0.0421)).toBe("0.042"); + }); +}); diff --git a/web/src/lib/stats/wilcoxon.ts b/web/src/lib/stats/wilcoxon.ts new file mode 100644 index 0000000..3892166 --- /dev/null +++ b/web/src/lib/stats/wilcoxon.ts @@ -0,0 +1,108 @@ +/** + * Wilcoxon signed-rank test for paired samples, matching + * scipy.stats.wilcoxon(zero_method="wilcox") and R's wilcox.test(paired = TRUE). + * + * - zero differences are dropped ("wilcox" zero method); + * - tied |differences| get average ranks and a variance correction; + * - "exact" uses the null distribution of W+ (only valid without ties); + * - "approx" is the normal approximation, optionally with a continuity + * correction (off by default, like scipy). + */ +import { rankWithTies } from "./descriptive"; +import { normalSf } from "./normal"; + +export type WilcoxonMethod = "auto" | "exact" | "approx"; + +export interface WilcoxonOptions { + method?: WilcoxonMethod; + continuityCorrection?: boolean; +} + +export interface WilcoxonResult { + /** Pairs with a non-zero difference (the effective sample size). */ + n: number; + /** Pairs dropped because the difference was zero. */ + zeros: number; + /** Sum of ranks of positive differences (R's V statistic). */ + wPlus: number; + wMinus: number; + /** min(W+, W-), scipy's two-sided statistic. */ + statistic: number; + /** Normal-approximation z (null for the exact method). */ + z: number | null; + /** Two-sided p-value. */ + pValue: number; + method: "exact" | "approx"; + /** Matched-pairs rank-biserial correlation (W+ - W-) / (W+ + W-), in [-1, 1]. */ + rankBiserial: number; +} + +/** Number of subsets of {1..n} with each rank sum, i.e. the null distribution of W+ × 2^n. */ +function signedRankCounts(n: number): number[] { + const max = (n * (n + 1)) / 2; + const counts = new Array(max + 1).fill(0); + counts[0] = 1; + for (let k = 1; k <= n; k++) { + for (let s = max; s >= k; s--) counts[s] += counts[s - k]; + } + return counts; +} + +export function wilcoxonSignedRank( + x: readonly number[], + y?: readonly number[], + { method = "auto", continuityCorrection = false }: WilcoxonOptions = {}, +): WilcoxonResult { + if (y && y.length !== x.length) throw new RangeError("paired samples must have equal length"); + const diffs = y ? x.map((xi, i) => xi - y[i]) : [...x]; + const nonZero = diffs.filter((d) => d !== 0); + const zeros = diffs.length - nonZero.length; + const n = nonZero.length; + if (n === 0) { + return { + n, + zeros, + wPlus: 0, + wMinus: 0, + statistic: 0, + z: null, + pValue: 1, + method: "approx", + rankBiserial: 0, + }; + } + + const { ranks, tieSizes } = rankWithTies(nonZero.map(Math.abs)); + let wPlus = 0; + let wMinus = 0; + nonZero.forEach((d, i) => { + if (d > 0) wPlus += ranks[i]; + else wMinus += ranks[i]; + }); + const statistic = Math.min(wPlus, wMinus); + const hasTies = tieSizes.some((t) => t > 1); + const rankBiserial = (wPlus - wMinus) / (wPlus + wMinus); + + const useExact = method === "exact" || (method === "auto" && n <= 50 && !hasTies && zeros === 0); + + if (useExact) { + if (hasTies) throw new RangeError("the exact Wilcoxon test requires untied differences"); + const counts = signedRankCounts(n); + const total = 2 ** n; + // W+ is an integer without ties. Two-sided p = 2 · P(W+ <= min) (symmetric null). + let tail = 0; + for (let s = 0; s <= statistic; s++) tail += counts[s]; + const pValue = Math.min(1, (2 * tail) / total); + return { n, zeros, wPlus, wMinus, statistic, z: null, pValue, method: "exact", rankBiserial }; + } + + const meanW = (n * (n + 1)) / 4; + let varW = (n * (n + 1) * (2 * n + 1)) / 24; + for (const t of tieSizes) varW -= (t * t * t - t) / 48; + const se = Math.sqrt(varW); + let numerator = statistic - meanW; + if (continuityCorrection) numerator += numerator < 0 ? 0.5 : numerator > 0 ? -0.5 : 0; + const z = se > 0 ? numerator / se : 0; + const pValue = Math.min(1, 2 * normalSf(Math.abs(z))); + return { n, zeros, wPlus, wMinus, statistic, z, pValue, method: "approx", rankBiserial }; +} From 0e00601b45221064783f711e80a4221af0d14cee Mon Sep 17 00:00:00 2001 From: rNLKJA <62606765+rNLKJA@users.noreply.github.com> Date: Tue, 6 Oct 2026 00:59:11 +1030 Subject: [PATCH 02/18] feat(eval): add tournament harness, paired A* study and alpha-beta study - /tournament: seeded round robin of the original agent against variants that change one knob (fixed depth 2/3, greedy one-ply) and the random baseline, colour-swapped pairs, run in a pool of Web Workers. Wilson CIs, Bradley-Terry Elo with stratified bootstrap CIs, first-move effect, move times, illegal moves, CSV export. - Precomputed 1,200-game reference tournament (TypeScript port) and a cross-check that replays the feasible pairings from the unchanged Python (scripts/crosscheck_tournament.py); the 144/160 benchmark is restated with its Wilson interval. - Alpha-beta efficiency study: nodes with vs without pruning on 120 positions, plus a clearly labelled textbook variant. Finding: the original's inverted beta update leaves 92-100% of the tree searched. The agent itself is unchanged. - /astar: paired Manhattan vs Euclidean study with paired bootstrap CI, Wilcoxon signed-rank test, effect sizes and a BFS optimality check, live in a worker and as a 980-board reference. - Agent port gains opt-in fixedDepth and prune options; defaults keep parity with the original (all parity tests unchanged and passing). Co-Authored-By: Claude Opus 5.5 --- scripts/crosscheck_tournament.py | 151 ++ web/package.json | 11 +- web/pnpm-lock.yaml | 1237 ++++++++++++++++- web/scripts/generate-reference-studies.ts | 109 ++ web/src/app/astar/page.tsx | 60 +- .../app/data/alpha-beta-study.csv/route.ts | 21 + .../app/data/astar-paired-study.csv/route.ts | 11 + .../reference-tournament-games.csv/route.ts | 10 + .../reference-tournament-summary.csv/route.ts | 15 + web/src/app/tournament/page.tsx | 470 +++++++ web/src/components/astar/heuristic-study.tsx | 137 +- .../components/astar/paired-study-summary.tsx | 131 ++ web/src/components/layout/page-header.tsx | 66 + web/src/components/stats/interval.tsx | 144 ++ web/src/components/stats/scroll-table.tsx | 27 + .../tournament/alpha-beta-table.tsx | 98 ++ .../tournament/reference-tournament.tsx | 38 + .../tournament/tournament-results.tsx | 214 +++ .../tournament/tournament-runner.tsx | 268 ++++ web/src/components/ui/checkbox.tsx | 25 + web/src/components/ui/input.tsx | 18 + web/src/hooks/use-astar-benchmark.ts | 31 +- web/src/hooks/use-tournament-pool.ts | 111 ++ web/src/lib/agent/minimax.ts | 11 +- web/src/lib/agent/player.ts | 9 +- web/src/lib/analysis/alpha-beta.ts | 256 ++++ web/src/lib/analysis/analysis.test.ts | 168 +++ web/src/lib/analysis/astar-paired.ts | 241 ++++ web/src/lib/csv.ts | 28 + web/src/lib/data/alpha-beta-study.json | 1 + web/src/lib/data/astar-paired-study.json | 1 + web/src/lib/data/load.ts | 41 + web/src/lib/data/python-crosscheck.json | 1 + web/src/lib/data/reference-config.ts | 28 + web/src/lib/data/reference-tournament.json | 1 + web/src/lib/download.ts | 12 + web/src/lib/rng.ts | 24 + web/src/lib/tournament/agents.ts | 113 ++ web/src/lib/tournament/analyse.ts | 300 ++++ web/src/lib/tournament/crosscheck.ts | 67 + web/src/lib/tournament/play.ts | 146 ++ web/src/lib/tournament/schedule.ts | 64 + web/src/lib/tournament/tournament.test.ts | 289 ++++ web/src/workers/astar-benchmark.worker.ts | 9 +- web/src/workers/tournament.worker.ts | 34 + 45 files changed, 5197 insertions(+), 50 deletions(-) create mode 100644 scripts/crosscheck_tournament.py create mode 100644 web/scripts/generate-reference-studies.ts create mode 100644 web/src/app/data/alpha-beta-study.csv/route.ts create mode 100644 web/src/app/data/astar-paired-study.csv/route.ts create mode 100644 web/src/app/data/reference-tournament-games.csv/route.ts create mode 100644 web/src/app/data/reference-tournament-summary.csv/route.ts create mode 100644 web/src/app/tournament/page.tsx create mode 100644 web/src/components/astar/paired-study-summary.tsx create mode 100644 web/src/components/layout/page-header.tsx create mode 100644 web/src/components/stats/interval.tsx create mode 100644 web/src/components/stats/scroll-table.tsx create mode 100644 web/src/components/tournament/alpha-beta-table.tsx create mode 100644 web/src/components/tournament/reference-tournament.tsx create mode 100644 web/src/components/tournament/tournament-results.tsx create mode 100644 web/src/components/tournament/tournament-runner.tsx create mode 100644 web/src/components/ui/checkbox.tsx create mode 100644 web/src/components/ui/input.tsx create mode 100644 web/src/hooks/use-tournament-pool.ts create mode 100644 web/src/lib/analysis/alpha-beta.ts create mode 100644 web/src/lib/analysis/analysis.test.ts create mode 100644 web/src/lib/analysis/astar-paired.ts create mode 100644 web/src/lib/csv.ts create mode 100644 web/src/lib/data/alpha-beta-study.json create mode 100644 web/src/lib/data/astar-paired-study.json create mode 100644 web/src/lib/data/load.ts create mode 100644 web/src/lib/data/python-crosscheck.json create mode 100644 web/src/lib/data/reference-config.ts create mode 100644 web/src/lib/data/reference-tournament.json create mode 100644 web/src/lib/download.ts create mode 100644 web/src/lib/tournament/agents.ts create mode 100644 web/src/lib/tournament/analyse.ts create mode 100644 web/src/lib/tournament/crosscheck.ts create mode 100644 web/src/lib/tournament/play.ts create mode 100644 web/src/lib/tournament/schedule.ts create mode 100644 web/src/lib/tournament/tournament.test.ts create mode 100644 web/src/workers/tournament.worker.ts diff --git a/scripts/crosscheck_tournament.py b/scripts/crosscheck_tournament.py new file mode 100644 index 0000000..cb38bb0 --- /dev/null +++ b/scripts/crosscheck_tournament.py @@ -0,0 +1,151 @@ +# /// script +# requires-python = ">=3.12,<3.13" +# dependencies = ["numpy>=1.26", "scipy>=1.11"] +# /// +""" +Cross-check the tournament harness against the ORIGINAL Python agents. + +The reference tournament on /tournament is played by the parity-tested +TypeScript port, because depth-3 search in the original Python (deepcopy of +the board at every node) is far too slow for 1,200 games. This script replays +the subset that IS feasible in Python, with the same protocol: + + * agents built from the original code with exactly one knob changed: + - minimax-dynamic: _4399.Player, unchanged; + - minimax-d2: dynamic_depth_allocation patched to return 2; + - greedy: dynamic_depth_allocation patched to return 1 and + action(enforceGamePlayer=False) (no opening book); + - random: random_play_agent, unchanged; + * every pairing plays colour-swapped pairs of games, both games of a pair + seeded identically (Python's global `random`); + * the subject's referee `Game` validates every action. + +The RNG streams differ from the TypeScript run (Python's Mersenne Twister vs +mulberry32), so individual games differ; the comparison is between win +rates, with intervals, on the web page. + +Like benchmark_agent.py it re-executes itself with PYTHONHASHSEED=0 so the +set iteration order inside get_valid_actions is reproducible. + + uv run scripts/crosscheck_tournament.py +""" + +from __future__ import annotations + +import json +import logging +import os +import platform +import random +import sys +import time +from itertools import combinations +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +PART_B = ROOT / "coursework" / "Project Part B" / "code" +SKELETON = ROOT / "coursework" / "Project Part B" / "skeleton-code-B" +OUT = ROOT / "web" / "src" / "lib" / "data" / "python-crosscheck.json" + +AGENTS = ["minimax-dynamic", "minimax-d2", "greedy", "random"] +SIZES = [4, 5] +ROUNDS = 20 # colour-swapped pairs per pairing per size -> 40 games +SEED = 2022 + + +def pin_hash_seed() -> None: + if os.environ.get("PYTHONHASHSEED") != "0": + os.execve( + sys.executable, + [sys.executable, *sys.argv], + {**os.environ, "PYTHONHASHSEED": "0"}, + ) + + +def main() -> None: + pin_hash_seed() + os.chdir(PART_B) # utility/evaluation.py opens ./utility/weights.json + sys.path.insert(0, str(PART_B)) + sys.path.insert(1, str(SKELETON)) + + import _4399.player as player_module # noqa: E402 + from _4399.player import Player as MinimaxPlayer # noqa: E402 + from random_play_agent.player import Player as RandomPlayer # noqa: E402 + from referee.game import Game # noqa: E402 + + logging.getLogger().setLevel(logging.WARNING) + original_depth = player_module.dynamic_depth_allocation + + class Variant(MinimaxPlayer): + """The original player with the depth (and opening book) overridden per call.""" + + depth: int | None = None + book: bool = True + + def action(self): # type: ignore[override] + if self.depth is not None: + player_module.dynamic_depth_allocation = lambda board, d=self.depth: d + try: + return super().action(enforceGamePlayer=self.book) + finally: + player_module.dynamic_depth_allocation = original_depth + + def make(agent: str, colour: str, n: int): + if agent == "random": + return RandomPlayer(colour, n) + p = Variant(colour, n) + if agent == "minimax-d2": + p.depth = 2 + elif agent == "greedy": + p.depth = 1 + p.book = False + return p + + def play(n: int, red: str, blue: str, seed: int) -> dict: + random.seed(seed) + game = Game(n) + players = [make(red, "red", n), make(blue, "blue", n)] + while not game.over(): + colour = "red" if game.nturns % 2 == 0 else "blue" + action = players[game.nturns % 2].action() + sanitised = game.update(colour, action) + for p in players: + p.turn(colour, sanitised) + winner = None + if game.result == "winner: red": + winner = "red" + elif game.result == "winner: blue": + winner = "blue" + return {"n": n, "red": red, "blue": blue, "seed": seed, "winner": winner, "turns": game.nturns} + + games = [] + started = time.perf_counter() + for n in SIZES: + for a, b in combinations(AGENTS, 2): + t0 = time.perf_counter() + for r in range(ROUNDS): + seed = SEED * 1_000_003 + n * 10_007 + AGENTS.index(a) * 101 + AGENTS.index(b) * 11 + r + games.append(play(n, a, b, seed)) + games.append(play(n, b, a, seed)) + print(f"n={n} {a} vs {b}: {time.perf_counter() - t0:.1f}s", flush=True) + + OUT.parent.mkdir(parents=True, exist_ok=True) + payload = { + "meta": { + "generatedBy": "scripts/crosscheck_tournament.py", + "implementation": "original Python (coursework/Project Part B), variants by patching one knob", + "python": platform.python_version(), + "pythonHashSeed": os.environ.get("PYTHONHASHSEED"), + "config": {"agents": AGENTS, "sizes": SIZES, "rounds": ROUNDS, "seed": SEED}, + "seconds": round(time.perf_counter() - started, 1), + }, + "games": games, + } + with open(OUT, "w") as f: + json.dump(payload, f) + f.write("\n") + print(f"wrote {OUT.relative_to(ROOT)}") + + +if __name__ == "__main__": + main() diff --git a/web/package.json b/web/package.json index a5e7359..1f63433 100644 --- a/web/package.json +++ b/web/package.json @@ -10,9 +10,12 @@ "typecheck": "tsc --noEmit", "test": "vitest run", "format": "prettier --write .", - "format:check": "prettier --check ." + "format:check": "prettier --check .", + "gen:reference": "tsx scripts/generate-reference-studies.ts", + 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is-unicode-supported: 1.3.0 + longest-streak@3.1.0: {} + loose-envify@1.4.0: dependencies: js-tokens: 4.0.0 @@ -7157,8 +7883,165 @@ snapshots: dependencies: '@jridgewell/sourcemap-codec': 1.6.0 + markdown-table@3.0.4: {} + math-intrinsics@1.1.0: {} + mdast-util-find-and-replace@3.0.3: + dependencies: + '@types/mdast': 4.0.4 + escape-string-regexp: 5.0.0 + unist-util-is: 6.0.1 + unist-util-visit-parents: 6.0.2 + + mdast-util-from-markdown@2.1.0: + dependencies: + '@types/mdast': 4.0.4 + '@types/unist': 3.0.3 + decode-named-character-reference: 1.3.0 + devlop: 1.1.0 + mdast-util-to-string: 4.0.0 + micromark: 4.0.3 + micromark-util-decode-numeric-character-reference: 2.0.2 + micromark-util-decode-string: 2.0.1 + micromark-util-normalize-identifier: 2.0.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + unist-util-stringify-position: 4.0.0 + transitivePeerDependencies: + - supports-color + + mdast-util-gfm-autolink-literal@2.0.1: + dependencies: + '@types/mdast': 4.0.4 + ccount: 2.0.1 + devlop: 1.1.0 + mdast-util-find-and-replace: 3.0.3 + micromark-util-character: 2.1.1 + + mdast-util-gfm-footnote@2.1.0: + dependencies: + '@types/mdast': 4.0.4 + devlop: 1.1.0 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + micromark-util-normalize-identifier: 2.0.1 + transitivePeerDependencies: + - supports-color + + mdast-util-gfm-strikethrough@2.0.1: + dependencies: + '@types/mdast': 4.0.4 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-gfm-table@2.0.0: + dependencies: + '@types/mdast': 4.0.4 + devlop: 1.1.0 + markdown-table: 3.0.4 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-gfm-task-list-item@2.0.0: + dependencies: + '@types/mdast': 4.0.4 + devlop: 1.1.0 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-gfm@3.1.0: + dependencies: + mdast-util-from-markdown: 2.1.0 + mdast-util-gfm-autolink-literal: 2.0.1 + mdast-util-gfm-footnote: 2.1.0 + mdast-util-gfm-strikethrough: 2.0.1 + mdast-util-gfm-table: 2.0.0 + mdast-util-gfm-task-list-item: 2.0.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-mdx-expression@2.0.1: + dependencies: + '@types/estree-jsx': 1.0.5 + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + devlop: 1.1.0 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-mdx-jsx@3.2.0: + dependencies: + '@types/estree-jsx': 1.0.5 + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + '@types/unist': 3.0.3 + ccount: 2.0.1 + devlop: 1.1.0 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + parse-entities: 4.0.2 + stringify-entities: 4.0.4 + unist-util-stringify-position: 4.0.0 + vfile-message: 4.0.3 + transitivePeerDependencies: + - supports-color + + mdast-util-mdxjs-esm@2.0.1: + dependencies: + '@types/estree-jsx': 1.0.5 + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + devlop: 1.1.0 + mdast-util-from-markdown: 2.1.0 + mdast-util-to-markdown: 2.2.0 + transitivePeerDependencies: + - supports-color + + mdast-util-phrasing@4.1.0: + dependencies: + '@types/mdast': 4.0.4 + unist-util-is: 6.0.1 + + mdast-util-to-hast@13.2.1: + dependencies: + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + '@ungap/structured-clone': 1.4.0 + devlop: 1.1.0 + micromark-util-sanitize-uri: 2.0.1 + trim-lines: 3.0.1 + unist-util-position: 5.0.0 + unist-util-visit: 5.1.0 + vfile: 6.0.3 + + mdast-util-to-markdown@2.2.0: + dependencies: + '@types/mdast': 4.0.4 + '@types/unist': 3.0.3 + longest-streak: 3.1.0 + mdast-util-phrasing: 4.1.0 + mdast-util-to-string: 4.0.0 + micromark-util-character: 2.1.1 + micromark-util-classify-character: 2.0.1 + micromark-util-decode-string: 2.0.1 + micromark-util-html-tag-name: 2.0.1 + unist-util-visit: 5.1.0 + zwitch: 2.0.4 + + mdast-util-to-string@4.0.0: + dependencies: + '@types/mdast': 4.0.4 + media-typer@1.1.1: {} merge-descriptors@2.0.0: {} @@ -7167,6 +8050,203 @@ snapshots: merge2@1.4.1: {} + micromark-core-commonmark@2.0.4: + dependencies: + decode-named-character-reference: 1.3.0 + devlop: 1.1.0 + micromark-factory-destination: 2.0.1 + micromark-factory-label: 2.0.1 + micromark-factory-space: 2.1.0 + micromark-factory-title: 2.0.1 + micromark-factory-whitespace: 2.0.1 + micromark-util-character: 2.1.1 + micromark-util-chunked: 2.0.1 + micromark-util-classify-character: 2.0.1 + micromark-util-edit-map: 1.0.0 + micromark-util-html-tag-name: 2.0.1 + micromark-util-normalize-identifier: 2.0.1 + micromark-util-resolve-all: 2.0.1 + micromark-util-subtokenize: 2.1.0 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm-autolink-literal@2.1.0: + dependencies: + micromark-util-character: 2.1.1 + micromark-util-sanitize-uri: 2.0.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm-footnote@2.1.0: + dependencies: + devlop: 1.1.0 + micromark-core-commonmark: 2.0.4 + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-normalize-identifier: 2.0.1 + micromark-util-sanitize-uri: 2.0.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm-strikethrough@2.1.0: + dependencies: + devlop: 1.1.0 + micromark-util-chunked: 2.0.1 + micromark-util-classify-character: 2.0.1 + micromark-util-resolve-all: 2.0.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm-table@2.1.2: + dependencies: + devlop: 1.1.0 + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm-tagfilter@2.0.0: + dependencies: + micromark-util-types: 2.0.3 + + micromark-extension-gfm-task-list-item@2.1.0: + dependencies: + devlop: 1.1.0 + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-extension-gfm@3.0.0: + dependencies: + micromark-extension-gfm-autolink-literal: 2.1.0 + micromark-extension-gfm-footnote: 2.1.0 + micromark-extension-gfm-strikethrough: 2.1.0 + micromark-extension-gfm-table: 2.1.2 + micromark-extension-gfm-tagfilter: 2.0.0 + micromark-extension-gfm-task-list-item: 2.1.0 + micromark-util-combine-extensions: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-factory-destination@2.0.1: + dependencies: + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-factory-label@2.0.1: + dependencies: + devlop: 1.1.0 + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-factory-space@2.1.0: + dependencies: + micromark-util-character: 2.1.1 + micromark-util-types: 2.0.3 + + micromark-factory-title@2.0.1: + dependencies: + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-factory-whitespace@2.0.1: + dependencies: + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-util-character@2.1.1: + dependencies: + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-util-chunked@2.0.1: + dependencies: + micromark-util-symbol: 2.0.1 + + micromark-util-classify-character@2.0.1: + dependencies: + micromark-util-character: 2.1.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-util-combine-extensions@2.0.1: + dependencies: + micromark-util-chunked: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-util-decode-numeric-character-reference@2.0.2: + dependencies: + micromark-util-symbol: 2.0.1 + + micromark-util-decode-string@2.0.1: + dependencies: + decode-named-character-reference: 1.3.0 + micromark-util-character: 2.1.1 + micromark-util-decode-numeric-character-reference: 2.0.2 + micromark-util-symbol: 2.0.1 + + micromark-util-edit-map@1.0.0: + dependencies: + micromark-util-types: 2.0.3 + + micromark-util-encode@2.0.1: {} + + micromark-util-html-tag-name@2.0.1: {} + + micromark-util-normalize-identifier@2.0.1: + dependencies: + micromark-util-symbol: 2.0.1 + + micromark-util-resolve-all@2.0.1: + dependencies: + micromark-util-types: 2.0.3 + + micromark-util-sanitize-uri@2.0.1: + dependencies: + micromark-util-character: 2.1.1 + micromark-util-encode: 2.0.1 + micromark-util-symbol: 2.0.1 + + micromark-util-subtokenize@2.1.0: + dependencies: + devlop: 1.1.0 + micromark-util-chunked: 2.0.1 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + + micromark-util-symbol@2.0.1: {} + + micromark-util-types@2.0.3: {} + + micromark@4.0.3: + dependencies: + '@types/debug': 4.1.13 + debug: 4.4.3 + decode-named-character-reference: 1.3.0 + devlop: 1.1.0 + micromark-core-commonmark: 2.0.4 + micromark-factory-space: 2.1.0 + micromark-util-character: 2.1.1 + micromark-util-chunked: 2.0.1 + micromark-util-combine-extensions: 2.0.1 + micromark-util-decode-numeric-character-reference: 2.0.2 + micromark-util-edit-map: 1.0.0 + micromark-util-encode: 2.0.1 + micromark-util-normalize-identifier: 2.0.1 + micromark-util-resolve-all: 2.0.1 + micromark-util-sanitize-uri: 2.0.1 + micromark-util-subtokenize: 2.1.0 + micromark-util-symbol: 2.0.1 + micromark-util-types: 2.0.3 + transitivePeerDependencies: + - supports-color + micromatch@4.0.8: dependencies: braces: 3.0.3 @@ -7381,6 +8461,16 @@ snapshots: dependencies: callsites: 3.1.0 + parse-entities@4.0.2: + dependencies: + '@types/unist': 2.0.11 + character-entities-legacy: 3.0.0 + character-reference-invalid: 2.0.1 + decode-named-character-reference: 1.3.0 + is-alphanumerical: 2.0.1 + is-decimal: 2.0.1 + is-hexadecimal: 2.0.1 + parse-json@5.2.0: dependencies: '@babel/code-frame': 7.29.7 @@ -7462,6 +8552,8 @@ snapshots: object-assign: 4.1.1 react-is: 16.13.1 + property-information@7.2.0: {} + proxy-addr@2.0.8: dependencies: forwarded: 0.2.0 @@ -7555,6 +8647,24 @@ snapshots: react-is@16.13.1: {} + react-markdown@10.1.0(@types/react@19.3.0)(react@19.2.8): + dependencies: + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + '@types/react': 19.3.0 + devlop: 1.1.0 + hast-util-to-jsx-runtime: 2.3.6 + html-url-attributes: 3.0.1 + mdast-util-to-hast: 13.2.1 + react: 19.2.8 + remark-parse: 11.0.0 + remark-rehype: 11.1.2 + unified: 11.0.5 + unist-util-visit: 5.1.0 + vfile: 6.0.3 + transitivePeerDependencies: + - supports-color + react-remove-scroll-bar@2.3.8(@types/react@19.3.0)(react@19.2.8): dependencies: react: 19.2.8 @@ -7612,6 +8722,40 @@ snapshots: gopd: 1.2.0 set-function-name: 2.0.2 + remark-gfm@4.0.1: + dependencies: + '@types/mdast': 4.0.4 + mdast-util-gfm: 3.1.0 + micromark-extension-gfm: 3.0.0 + remark-parse: 11.0.0 + remark-stringify: 11.0.0 + unified: 11.0.5 + transitivePeerDependencies: + - supports-color + + remark-parse@11.0.0: + dependencies: + '@types/mdast': 4.0.4 + mdast-util-from-markdown: 2.1.0 + micromark-util-types: 2.0.3 + unified: 11.0.5 + transitivePeerDependencies: + - supports-color + + remark-rehype@11.1.2: + dependencies: + '@types/hast': 3.0.5 + '@types/mdast': 4.0.4 + mdast-util-to-hast: 13.2.1 + unified: 11.0.5 + vfile: 6.0.3 + + remark-stringify@11.0.0: + dependencies: + '@types/mdast': 4.0.4 + mdast-util-to-markdown: 2.2.0 + unified: 11.0.5 + require-from-string@2.0.2: {} resolve-from@4.0.0: {} @@ -7874,8 +9018,15 @@ snapshots: source-map@0.6.1: {} + space-separated-tokens@2.0.2: {} + stable-hash@0.0.5: {} + standardwebhooks@1.1.1: + dependencies: + '@stablelib/base64': 1.0.1 + fast-sha256: 1.3.0 + statuses@2.0.2: {} std-env@4.3.0: {} @@ -7944,6 +9095,11 @@ snapshots: define-properties: 1.2.1 es-object-atoms: 1.1.2 + stringify-entities@4.0.4: + dependencies: + character-entities-html4: 2.1.0 + character-entities-legacy: 3.0.0 + stringify-object@5.0.0: dependencies: get-own-enumerable-keys: 1.0.0 @@ -7966,6 +9122,14 @@ snapshots: strip-json-comments@3.1.1: {} + style-to-js@1.1.21: + dependencies: + style-to-object: 1.0.14 + + style-to-object@1.0.14: + dependencies: + inline-style-parser: 0.2.7 + styled-jsx@5.1.6(@babel/core@7.29.7)(react@19.2.8): dependencies: client-only: 0.0.1 @@ -8002,6 +9166,12 @@ snapshots: toidentifier@1.0.1: {} + trim-lines@3.0.1: {} + + trough@2.2.0: {} + + ts-algebra@2.0.0: {} + ts-api-utils@2.5.0(typescript@5.9.3): dependencies: typescript: 5.9.3 @@ -8026,6 +9196,12 @@ snapshots: tslib@2.8.1: {} + tsx@4.23.15: + dependencies: + esbuild: 0.28.2 + optionalDependencies: + fsevents: 2.3.3 + tw-animate-css@1.4.0: {} type-check@0.4.0: @@ -8098,6 +9274,39 @@ snapshots: unicorn-magic@0.3.0: {} + unified@11.0.5: + dependencies: + '@types/unist': 3.0.3 + bail: 2.0.2 + devlop: 1.1.0 + extend: 3.0.2 + is-plain-obj: 4.1.0 + trough: 2.2.0 + vfile: 6.0.3 + + unist-util-is@6.0.1: + dependencies: + '@types/unist': 3.0.3 + + unist-util-position@5.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-stringify-position@4.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-visit-parents@6.0.2: + dependencies: + '@types/unist': 3.0.3 + unist-util-is: 6.0.1 + + unist-util-visit@5.1.0: + dependencies: + '@types/unist': 3.0.3 + unist-util-is: 6.0.1 + unist-util-visit-parents: 6.0.2 + universalify@2.0.1: {} unpipe@1.0.0: {} @@ -8160,7 +9369,17 @@ snapshots: vary@1.1.2: {} - vite@8.3.2(@types/node@22.20.5)(jiti@2.7.0): + vfile-message@4.0.3: + dependencies: + '@types/unist': 3.0.3 + unist-util-stringify-position: 4.0.0 + + vfile@6.0.3: + dependencies: + '@types/unist': 3.0.3 + vfile-message: 4.0.3 + + vite@8.3.2(@types/node@22.20.5)(esbuild@0.28.2)(jiti@2.7.0)(tsx@4.23.15): dependencies: lightningcss: 1.33.0 picomatch: 4.0.7 @@ -8169,13 +9388,15 @@ snapshots: tinyglobby: 0.2.17 optionalDependencies: '@types/node': 22.20.5 + esbuild: 0.28.2 fsevents: 2.3.3 jiti: 2.7.0 + tsx: 4.23.15 - vitest@5.0.3(@types/node@22.20.5)(vite@8.3.2(@types/node@22.20.5)(jiti@2.7.0)): + vitest@5.0.3(@types/node@22.20.5)(vite@8.3.2(@types/node@22.20.5)(esbuild@0.28.2)(jiti@2.7.0)(tsx@4.23.15)): dependencies: '@types/chai': 5.2.3 - '@vitest/mocker': 5.0.3(vite@8.3.2(@types/node@22.20.5)(jiti@2.7.0)) + '@vitest/mocker': 5.0.3(vite@8.3.2(@types/node@22.20.5)(esbuild@0.28.2)(jiti@2.7.0)(tsx@4.23.15)) chai: 6.3.0 es-module-lexer: 2.3.2 expect-type: 1.4.0 @@ -8186,7 +9407,7 @@ snapshots: tinybench: 6.2.0 tinyexec: 1.3.1 tinyglobby: 0.2.17 - vite: 8.3.2(@types/node@22.20.5)(jiti@2.7.0) + vite: 8.3.2(@types/node@22.20.5)(esbuild@0.28.2)(jiti@2.7.0)(tsx@4.23.15) why-is-node-running: 3.2.1 optionalDependencies: '@types/node': 22.20.5 @@ -8274,3 +9495,5 @@ snapshots: zod@3.25.76: {} zod@4.6.5: {} + + zwitch@2.0.4: {} diff --git a/web/scripts/generate-reference-studies.ts b/web/scripts/generate-reference-studies.ts new file mode 100644 index 0000000..c347ccd --- /dev/null +++ b/web/scripts/generate-reference-studies.ts @@ -0,0 +1,109 @@ +/** + * Precompute the reference studies shown on /tournament and /astar, using the + * parity-tested TypeScript port (the original Python is too slow for depth-3 + * search at this scale; scripts/crosscheck_tournament.py re-runs the feasible + * subset with the original code). + * + * cd web && pnpm gen:reference # all three studies + * cd web && pnpm gen:reference tournament # just one + * + * Output (deterministic for the seeds below, apart from wall-clock timings): + * src/lib/data/reference-tournament.json + * src/lib/data/astar-paired-study.json + * src/lib/data/alpha-beta-study.json + */ +import { writeFileSync } from "node:fs"; +import os from "node:os"; +import path from "node:path"; + +import { measurePruning, samplePositions, summarisePruning } from "../src/lib/analysis/alpha-beta"; +import { runPairedStudy, summarisePairedStudy } from "../src/lib/analysis/astar-paired"; +import { AGENT_IDS } from "../src/lib/tournament/agents"; +import { summariseTournament } from "../src/lib/tournament/analyse"; +import { playTournamentGame } from "../src/lib/tournament/play"; +import { buildSchedule } from "../src/lib/tournament/schedule"; +import { + ALPHA_BETA_CONFIG, + ASTAR_STUDY_CONFIG, + REFERENCE_TOURNAMENT_CONFIG, +} from "../src/lib/data/reference-config"; + +const DATA = path.join(import.meta.dirname, "..", "src", "lib", "data"); +const only = process.argv[2]; + +const meta = () => ({ + generatedBy: "web/scripts/generate-reference-studies.ts", + generatedAt: new Date().toISOString().slice(0, 10), + node: process.version, + cpu: os.cpus()[0]?.model ?? "unknown", + implementation: "TypeScript port (parity-tested against the original Python)", +}); + +function write(name: string, payload: unknown) { + const file = path.join(DATA, name); + writeFileSync(file, `${JSON.stringify(payload)}\n`); + console.log(`wrote ${path.relative(process.cwd(), file)}`); +} + +if (!only || only === "tournament") { + const config = REFERENCE_TOURNAMENT_CONFIG; + const schedule = buildSchedule({ ...config, agents: [...AGENT_IDS] }); + console.log(`tournament: ${schedule.length} games`); + const started = Date.now(); + const games = schedule.map((spec, i) => { + const g = playTournamentGame(spec); + if ((i + 1) % 100 === 0) + console.log( + ` ${i + 1}/${schedule.length} (${((Date.now() - started) / 1000).toFixed(0)} s)`, + ); + return { + ...g, + red: { + ...g.red, + totalMs: Number(g.red.totalMs.toFixed(3)), + maxMs: Number(g.red.maxMs.toFixed(3)), + }, + blue: { + ...g.blue, + totalMs: Number(g.blue.totalMs.toFixed(3)), + maxMs: Number(g.blue.maxMs.toFixed(3)), + }, + }; + }); + write("reference-tournament.json", { meta: { ...meta(), config }, games }); + const s = summariseTournament(games, { reps: 1000, seed: config.seed }); + for (const a of s.standings) { + console.log(` deep searches ${a.id}: ${(a.deepSearchShare * 100).toFixed(1)}%`); + console.log( + `${a.id.padEnd(16)} elo ${a.elo.estimate.toFixed(0)} [${a.elo.lower.toFixed(0)}, ${a.elo.upper.toFixed(0)}] win ${(a.winRate.p * 100).toFixed(1)}% [${(a.winRate.lower * 100).toFixed(1)}, ${(a.winRate.upper * 100).toFixed(1)}] ${a.moveMs.estimate.toFixed(2)} ms/move`, + ); + } + for (const p of s.pairs) { + console.log( + `${p.a} vs ${p.b}: ${p.aWins}-${p.bWins}-${p.draws} (${(p.aWinRate.p * 100).toFixed(1)}% [${(p.aWinRate.lower * 100).toFixed(1)}, ${(p.aWinRate.upper * 100).toFixed(1)}])`, + ); + } + console.log(`red wins ${s.colour.redWins}/${s.colour.games}`); +} + +if (!only || only === "astar") { + const rows = runPairedStudy(ASTAR_STUDY_CONFIG); + write("astar-paired-study.json", { meta: { ...meta(), config: ASTAR_STUDY_CONFIG }, rows }); + const s = summarisePairedStudy(rows, { seed: ASTAR_STUDY_CONFIG.seed }); + console.log(JSON.stringify(s, null, 2)); +} + +if (!only || only === "alphabeta") { + const positions = samplePositions(ALPHA_BETA_CONFIG); + const samples = positions.flatMap((p) => + ALPHA_BETA_CONFIG.depths.flatMap((depth) => + (["shuffled", "canonical"] as const).map((order) => measurePruning(p, depth, order)), + ), + ); + write("alpha-beta-study.json", { meta: { ...meta(), config: ALPHA_BETA_CONFIG }, samples }); + for (const g of summarisePruning(samples, { seed: ALPHA_BETA_CONFIG.seed })) { + console.log( + `n=${g.n} d=${g.depth} ${g.order}: ratio ${g.meanRatio.estimate.toFixed(3)} [${g.meanRatio.lower.toFixed(3)}, ${g.meanRatio.upper.toFixed(3)}] full ${g.meanFull.toFixed(0)} pruned ${g.meanPruned.toFixed(0)} match ${g.valueMatches}/${g.positions} | textbook ${g.textbookRatio.estimate.toFixed(3)} [${g.textbookRatio.lower.toFixed(3)}, ${g.textbookRatio.upper.toFixed(3)}] nodes ${g.meanTextbook.toFixed(0)} match ${g.textbookValueMatches}`, + ); + } +} diff --git a/web/src/app/astar/page.tsx b/web/src/app/astar/page.tsx index e2c8820..ca2046a 100644 --- a/web/src/app/astar/page.tsx +++ b/web/src/app/astar/page.tsx @@ -1,19 +1,77 @@ +import { Download } from "lucide-react"; import type { Metadata } from "next"; import { AstarLab } from "@/components/astar/astar-lab"; import { HeuristicStudy } from "@/components/astar/heuristic-study"; +import { PairedStudySummaryView } from "@/components/astar/paired-study-summary"; +import { Finding, Section } from "@/components/layout/page-header"; +import { Button } from "@/components/ui/button"; +import { summarisePairedStudy } from "@/lib/analysis/astar-paired"; +import { loadAstarStudy } from "@/lib/data/load"; +import { ASTAR_STUDY_CONFIG } from "@/lib/data/reference-config"; +import { formatPct, formatSigned } from "@/lib/stats/format"; export const metadata: Metadata = { title: "A* Lab", description: - "Edit a Cachex board, run the original Part A A* search step by step, and compare Manhattan and Euclidean heuristics.", + "Edit a Cachex board, run the original Part A A* search step by step, and compare Manhattan and Euclidean heuristics in a paired study with intervals and an optimality check.", }; export default function AstarPage() { + const { rows, meta } = loadAstarStudy(); + const summary = summarisePairedStudy(rows, { seed: ASTAR_STUDY_CONFIG.seed }); + const e = summary.expansions; + const o = summary.optimality; + const cfg = ASTAR_STUDY_CONFIG; return (
+
+ {cfg.boardsPerDimension} random boards for every size from {cfg.dimensions[0]} ×{" "} + {cfg.dimensions[0]} to {cfg.dimensions.at(-1)} × {cfg.dimensions.at(-1)} from the + notebook's generator (seed {cfg.seed}), each searched with both heuristics and + solved by breadth-first search. Generated by{" "} + {meta.generatedBy} with the A* port that + reproduces the original paths and node counts exactly. +

+ } + > +
    + + The report's direction holds, and is now measured. Manhattan + expanded {formatSigned(e.meanDiff.estimate)} nodes per board on average (95% CI{" "} + {formatSigned(e.meanDiff.lower)} to {formatSigned(e.meanDiff.upper)}); fewer on{" "} + {e.manhattanFewer} boards, more on {e.euclideanFewer}. + + + But it finds a shortest path less often. Manhattan returned an + optimal path on {formatPct(o.manhattan.p)} of solvable boards (95% CI{" "} + {formatPct(o.manhattan.lower)} to {formatPct(o.manhattan.upper)}), Euclidean on{" "} + {formatPct(o.euclidean.p)} ({formatPct(o.euclidean.lower)} to{" "} + {formatPct(o.euclidean.upper)}). + + + Why: neither heuristic is admissible here. On this hex grid a step + like (1, −1) costs 1, but Manhattan scores it 2 and Euclidean √2. Overestimating makes + the search greedier: fewer expansions, longer paths. The two sample inputs happen to + be solved optimally (8 and 13 cells, confirmed by BFS). + +
+ + +
diff --git a/web/src/app/data/alpha-beta-study.csv/route.ts b/web/src/app/data/alpha-beta-study.csv/route.ts new file mode 100644 index 0000000..f84ddb1 --- /dev/null +++ b/web/src/app/data/alpha-beta-study.csv/route.ts @@ -0,0 +1,21 @@ +import { toCsv } from "@/lib/csv"; +import { loadAlphaBetaStudy } from "@/lib/data/load"; + +export const dynamic = "force-static"; + +export function GET() { + const rows = loadAlphaBetaStudy().samples.map((s) => ({ + board_n: s.n, + depth: s.depth, + move_order: s.order, + empty_cells: s.empty, + nodes_full_minimax: s.nodesFull, + nodes_original_alphabeta: s.nodesPruned, + ratio_original: Number(s.ratio.toFixed(6)), + original_value_matches: s.valueMatches, + nodes_textbook_alphabeta: s.nodesTextbook, + ratio_textbook: Number(s.ratioTextbook.toFixed(6)), + textbook_value_matches: s.textbookValueMatches, + })); + return new Response(toCsv(rows), { headers: { "Content-Type": "text/csv; charset=utf-8" } }); +} diff --git a/web/src/app/data/astar-paired-study.csv/route.ts b/web/src/app/data/astar-paired-study.csv/route.ts new file mode 100644 index 0000000..e8ca68c --- /dev/null +++ b/web/src/app/data/astar-paired-study.csv/route.ts @@ -0,0 +1,11 @@ +import { pairedStudyCsvRows } from "@/lib/analysis/astar-paired"; +import { toCsv } from "@/lib/csv"; +import { loadAstarStudy } from "@/lib/data/load"; + +export const dynamic = "force-static"; + +export function GET() { + return new Response(toCsv(pairedStudyCsvRows(loadAstarStudy().rows)), { + headers: { "Content-Type": "text/csv; charset=utf-8" }, + }); +} diff --git a/web/src/app/data/reference-tournament-games.csv/route.ts b/web/src/app/data/reference-tournament-games.csv/route.ts new file mode 100644 index 0000000..3f1a033 --- /dev/null +++ b/web/src/app/data/reference-tournament-games.csv/route.ts @@ -0,0 +1,10 @@ +import { loadReferenceTournament } from "@/lib/data/load"; +import { gamesCsv } from "@/lib/tournament/analyse"; + +export const dynamic = "force-static"; + +export function GET() { + return new Response(gamesCsv(loadReferenceTournament().games), { + headers: { "Content-Type": "text/csv; charset=utf-8" }, + }); +} diff --git a/web/src/app/data/reference-tournament-summary.csv/route.ts b/web/src/app/data/reference-tournament-summary.csv/route.ts new file mode 100644 index 0000000..0b69b2c --- /dev/null +++ b/web/src/app/data/reference-tournament-summary.csv/route.ts @@ -0,0 +1,15 @@ +import { loadReferenceTournament } from "@/lib/data/load"; +import { REFERENCE_TOURNAMENT_CONFIG } from "@/lib/data/reference-config"; +import { summariseTournament, summaryCsv } from "@/lib/tournament/analyse"; + +export const dynamic = "force-static"; + +export function GET() { + const summary = summariseTournament(loadReferenceTournament().games, { + reps: 1000, + seed: REFERENCE_TOURNAMENT_CONFIG.seed, + }); + return new Response(summaryCsv(summary), { + headers: { "Content-Type": "text/csv; charset=utf-8" }, + }); +} diff --git a/web/src/app/tournament/page.tsx b/web/src/app/tournament/page.tsx new file mode 100644 index 0000000..439b197 --- /dev/null +++ b/web/src/app/tournament/page.tsx @@ -0,0 +1,470 @@ +import { ArrowRight, Download } from "lucide-react"; +import type { Metadata } from "next"; +import Link from "next/link"; + +import { Finding, PageHeader, Section } from "@/components/layout/page-header"; +import { EstimateCI, IntervalAxis, IntervalBar } from "@/components/stats/interval"; +import { AlphaBetaTable } from "@/components/tournament/alpha-beta-table"; +import { ReferenceTournament } from "@/components/tournament/reference-tournament"; +import { TournamentRunner } from "@/components/tournament/tournament-runner"; +import { Button } from "@/components/ui/button"; +import { ScrollTable } from "@/components/stats/scroll-table"; +import { summarisePruning } from "@/lib/analysis/alpha-beta"; +import benchmark from "@/lib/data/agent-benchmark.json"; +import { loadAlphaBetaStudy, loadPythonCrosscheck, loadReferenceTournament } from "@/lib/data/load"; +import { ALPHA_BETA_CONFIG, REFERENCE_TOURNAMENT_CONFIG } from "@/lib/data/reference-config"; +import { formatPct, formatSigned } from "@/lib/stats/format"; +import { wilson } from "@/lib/stats/proportion"; +import { AGENTS } from "@/lib/tournament/agents"; +import { summariseTournament } from "@/lib/tournament/analyse"; +import { compareImplementations } from "@/lib/tournament/crosscheck"; + +export const metadata: Metadata = { + title: "Tournament", + description: + "A seeded, colour-swapped round robin between the original minimax agent and controlled variants, with Wilson and bootstrap intervals, Bradley-Terry strengths and an alpha-beta efficiency study.", +}; + +const WORDS = ["no", "one", "two", "three", "four", "five", "six", "seven", "eight"]; + +const pctCI = (lo: number, hi: number) => `[${(lo * 100).toFixed(1)}, ${(hi * 100).toFixed(1)}]`; + +export default function TournamentPage() { + // Original benchmark (Python), restated with intervals. + const totals = benchmark.results.reduce( + (a, r) => ({ wins: a.wins + r.wins, games: a.games + r.games }), + { wins: 0, games: 0 }, + ); + const overall = wilson(totals.wins, totals.games); + const bySize = [...new Set(benchmark.results.map((r) => r.n))].map((n) => { + const rows = benchmark.results.filter((r) => r.n === n); + const wins = rows.reduce((s, r) => s + r.wins, 0); + const games = rows.reduce((s, r) => s + r.games, 0); + return { n, ci: wilson(wins, games) }; + }); + + // Reference tournament (TypeScript port). + const ref = loadReferenceTournament(); + const cfg = REFERENCE_TOURNAMENT_CONFIG; + const summaries = [ + { + key: "all", + label: "All sizes", + summary: summariseTournament(ref.games, { reps: 1000, seed: cfg.seed }), + }, + ...cfg.sizes.map((n) => ({ + key: String(n), + label: `${n} × ${n}`, + summary: summariseTournament(ref.games, { reps: 1000, seed: cfg.seed, sizes: [n] }), + })), + ]; + const all = summaries[0].summary; + const pair = (a: string, b: string) => all.pairs.find((p) => p.a === a && p.b === b)!; + const dynVsGreedy = pair("minimax-dynamic", "greedy"); + const dynVsD3 = pair("minimax-dynamic", "minimax-d3"); + const dynVsRandom = pair("minimax-dynamic", "random"); + const standing = (id: string) => all.standings.find((s) => s.id === id)!; + const dyn = standing("minimax-dynamic"); + const d3 = standing("minimax-d3"); + const deepSearches = ref.games.reduce( + (acc, g) => { + for (const s of [g.red, g.blue]) { + if (s.agent !== "minimax-dynamic") continue; + acc.searches += s.searches; + acc.deep += s.deepSearches; + } + return acc; + }, + { searches: 0, deep: 0 }, + ); + const hardware = `${ref.meta.cpu ?? "unknown CPU"}, Node ${ref.meta.node ?? "?"}`; + + // Cross-check against the original Python. + const py = loadPythonCrosscheck(); + const cross = compareImplementations(ref.games, py.games); + + // Alpha-beta efficiency. + const ab = loadAlphaBetaStudy(); + const groups = summarisePruning(ab.samples, { seed: ALPHA_BETA_CONFIG.seed }); + const shuffled = groups.filter((g) => g.order === "shuffled"); + const canonical = groups.filter((g) => g.order === "canonical"); + const worst = shuffled.reduce((a, b) => (b.meanRatio.estimate < a.meanRatio.estimate ? b : a)); + const deepest = shuffled.find((g) => g.n === 6 && g.depth === 3)!; + const allMatch = ab.samples.every((s) => s.valueMatches && s.textbookValueMatches); + + return ( +
+
+ +

+ A seeded round robin between the original{" "} + _4399 agent and variants that each change + exactly one knob, with colour-swapped pairs to cancel the first-move advantage. Every + number comes with its sample size and a 95% interval. +

+

+ Jump to:{" "} + + original benchmark + {" "} + ·{" "} + + reference round robin + {" "} + ·{" "} + + Python cross-check + {" "} + ·{" "} + + run your own + {" "} + ·{" "} + + alpha-beta efficiency + +

+ + +
+ The headline result from the original Python ({benchmark.meta.generatedBy}, Python{" "} + {benchmark.meta.python}): the agent beat the subject's random agent in{" "} + {totals.wins} of {totals.games} games on boards 4 to 7, 20 seeded games per size and + colour. The count is unchanged; the interval is new. +

+ } + > +
+
+
+ {formatPct(overall.p)} +
+

+ {totals.wins} of {totals.games} games won against the random agent +

+
+ +

+ Wilson 95% CI{" "} + + {formatPct(overall.lower)} to {formatPct(overall.upper)} + +

+
+
+ + By board size (both colours pooled, 40 games each). Small samples give wide + intervals. + + } + > + + + + Board + + + Interval + `${t * 100}%`} + /> + + + Win rate (95% CI) + + + + + {bySize.map(({ n, ci }) => ( + + + {n} × {n} + + + + + + + + + ))} + + +
+
+ +
+

+ Every pairing played {cfg.rounds * 2} games per board size ({cfg.rounds} seeds, each + played twice with colours swapped) on{" "} + {cfg.sizes.map((n) => `${n}\u00a0×\u00a0${n}`).join(", ")}. Tournament seed{" "} + {cfg.seed}. Played by the TypeScript port, which reproduces the original Python move + for move in deterministic mode; the original is too slow for depth-3 search at this + scale, so a feasible subset was re-run in Python below. +

+ + } + > +
    + + Depth matters; dynamic depth barely runs. Fixed depth 3 beat the + original in {dynVsD3.bWins} of {dynVsD3.games} games ( + {formatPct(1 - dynVsD3.aWinRate.p)}, 95% CI {formatPct(1 - dynVsD3.aWinRate.upper)} to{" "} + {formatPct(1 - dynVsD3.aWinRate.lower)}). The original searched deeper than one ply on + only {deepSearches.deep} of {deepSearches.searches.toLocaleString("en-AU")} searched + moves ({formatPct(deepSearches.deep / deepSearches.searches)}): its thresholds only + deepen once fewer than 15% of cells are empty, and most games end before that. + + + The original is indistinguishable from greedy one-ply. Head to head + it won {dynVsGreedy.aWins} and lost {dynVsGreedy.bWins} ( + {formatPct(dynVsGreedy.aWinRate.p)}, 95% CI {formatPct(dynVsGreedy.aWinRate.lower)} to{" "} + {formatPct(dynVsGreedy.aWinRate.upper)}), and their Elo intervals overlap almost + entirely. The opening book and the late deepening add no detectable strength here. + + + Against random it reproduces the original benchmark.{" "} + {dynVsRandom.aWins}/{dynVsRandom.games} wins ({formatPct(dynVsRandom.aWinRate.p)}, 95% + CI {formatPct(dynVsRandom.aWinRate.lower)} to {formatPct(dynVsRandom.aWinRate.upper)}) + on boards 4 to 6, consistent with the 90% the Python run recorded on boards 4 to 7. + + + Strength costs time. Depth 3 is{" "} + {Math.round(d3.elo.estimate - dyn.elo.estimate)} Elo above the original but spends + about {Math.round(d3.moveMs.estimate / dyn.moveMs.estimate)}× as long per move, partly + because of the pruning bug described{" "} + + below + + . No first-move advantage was detectable: Red won {formatPct(all.colour.redWinRate.p)}{" "} + (95% CI {formatPct(all.colour.redWinRate.lower)} to{" "} + {formatPct(all.colour.redWinRate.upper)}). + +
+ + +
+ +
+ scripts/crosscheck_tournament.py rebuilt + the same variants from the unchanged Python (patching only{" "} + dynamic_depth_allocation and the + opening-book flag) and replayed the pairings that are feasible in Python on boards{" "} + {cross[0]?.sizes.join(" and ")}, with the same colour-swapped protocol. Random streams + differ between the languages, so games differ; the question is whether win rates + agree. With {cross[0]?.python.n} games per pairing this can only rule out large + discrepancies. +

+ } + > + + Win rate of the first-named agent. Difference = Python − TypeScript, with + Newcombe's 95% interval; an interval that contains 0 is consistent with no + difference. + + } + > + + + + Pairing + + + Original Python + + + TypeScript port + + + Difference interval + `${t > 0 ? "+" : ""}${Math.round(t * 100)}`} + /> + + + Difference (pp) + + + + + {cross.map((r) => ( + + + {AGENTS[r.a].short} vs{" "} + {AGENTS[r.b].short} + + + + + + + + + + + + + + + ))} + + +
+ +
+ Same harness, same statistics, run on your machine in Web Workers. Pick a seed to make + the run reproducible, then export the games and the summary as CSV. +

+ } + > + +
+ +
+

+ {ab.samples.length / (ALPHA_BETA_CONFIG.depths.length * 2)} mid-game positions ( + {ALPHA_BETA_CONFIG.perSize} per board size, from seeded greedy-vs-random games, seed{" "} + {ALPHA_BETA_CONFIG.seed}) were searched three ways at the same depth and move order: + plain minimax (no pruning), the original alpha-beta as submitted, and a textbook + alpha-beta. The share of the full tree each one visits is the pruning ratio; lower + is better. +

+

+ The original prunes very little (at best it still visits{" "} + {formatPct(worst.meanRatio.estimate)} of the tree). The cause is one line in{" "} + minimax.py: the minimising branch updates + beta with{" "} + if beta <= min_score: beta = min_score + , which can only raise beta, so the window never narrows there. The answer is still + right ({allMatch ? "all" : "not all"} {ab.samples.length} searches returned the same + root value as plain minimax), just slow. With{" "} + beta = min(beta, min_score) the same + search on 6 × 6 at depth 3 would visit {formatPct(deepest.textbookRatio.estimate)}{" "} + of the tree instead of {formatPct(deepest.meanRatio.estimate)}. The agent on this + site is left as submitted. +

+ + } + > + +
+ + Canonical (r, q) move order + +
+ +
+
+
+ + +
+
+
+
+ ); +} diff --git a/web/src/components/astar/heuristic-study.tsx b/web/src/components/astar/heuristic-study.tsx index 68510ad..8baefc9 100644 --- a/web/src/components/astar/heuristic-study.tsx +++ b/web/src/components/astar/heuristic-study.tsx @@ -1,12 +1,21 @@ "use client"; -import { FlaskConical, Loader2 } from "lucide-react"; +import { Download, FlaskConical, Loader2 } from "lucide-react"; +import { useMemo, useState } from "react"; +import { Segmented } from "@/components/play/primitives"; import { Button } from "@/components/ui/button"; import { useAstarBenchmark } from "@/hooks/use-astar-benchmark"; import { useElementWidth } from "@/hooks/use-element-width"; -import type { BenchmarkRow } from "@/lib/astar/random-board"; +import { + type PairedStudyRow, + pairedStudyCsvRows, + summarisePairedStudy, +} from "@/lib/analysis/astar-paired"; +import { toCsv } from "@/lib/csv"; +import { downloadText } from "@/lib/download"; import { randomSeed } from "@/lib/rng"; +import { PairedStudySummaryView } from "./paired-study-summary"; // The notebook looped `for i in range(100)`, skipping i < 2: every size from 2 to 99. const DIMENSIONS = Array.from({ length: 98 }, (_, i) => i + 2); @@ -15,6 +24,8 @@ const LAST = DIMENSIONS.at(-1)!; export function HeuristicStudy() { const { state, run } = useAstarBenchmark(); + const [boards, setBoards] = useState("1"); + const perSize = Number(boards); return (
@@ -30,21 +41,47 @@ export function HeuristicStudy() { Our report compared node expansions on random boards from the notebook's generator (random barriers, random start and goal) and found the two heuristics similar on small boards, with Manhattan expanding slightly fewer nodes as boards grew. Re-run the - experiment here: one random board for every size from {FIRST} × {FIRST} to {LAST} ×{" "} - {LAST}, counted the way the notebook counted them. + experiment here: random boards for every size from {FIRST} × {FIRST} to {LAST} × {LAST}, + counted the way the notebook counted them. One board per size is the notebook's + protocol; more boards per size give tighter intervals.

+

+ The comparison is now paired: both + heuristics search the same boards, so the analysis works on the per-board difference + (paired bootstrap interval, Wilcoxon signed-rank test, effect sizes). Each board is also + solved by breadth-first search to check whether A* returned a shortest path. +

+ +
+ + Boards per size + + +
-
{state.status === "idle" && (
- Press “Run the study” to generate {DIMENSIONS.length} random boards in a Web - Worker. + Press “Run the study” to generate {DIMENSIONS.length * perSize} random + boards in a Web Worker.
)} {state.status === "running" && ( @@ -52,8 +89,10 @@ export function HeuristicStudy() { className="text-muted-foreground flex h-56 items-center justify-center gap-2 rounded-2xl border border-dashed px-4 text-center text-sm" role="status" > - Running {DIMENSIONS.length * 2}{" "} - searches ({DIMENSIONS.length} boards × 2 heuristics)… + Running{" "} + {DIMENSIONS.length * state.boardsPerDimension * 2} searches ( + {DIMENSIONS.length * state.boardsPerDimension} boards × 2 heuristics) plus a + breadth-first check on each board…
)} {state.status === "error" && ( @@ -65,22 +104,73 @@ export function HeuristicStudy() {

)} {state.status === "done" && ( - + )}
); } -function StudyChart({ +function LiveResults({ rows, seed, + boardsPerDimension, + elapsedMs, +}: { + rows: PairedStudyRow[]; + seed: number; + boardsPerDimension: number; + elapsedMs: number; +}) { + const summary = useMemo(() => summarisePairedStudy(rows, { reps: 2000, seed }), [rows, seed]); + return ( +
+ + + +
+ ); +} + +function StudyChart({ + rows: boardRows, + seed, elapsedMs, + boardsPerDimension, }: { - rows: BenchmarkRow[]; + rows: PairedStudyRow[]; seed: number; elapsedMs: number; + boardsPerDimension: number; }) { + // One point per board size: the mean over that size's boards. + const rows = useMemo(() => { + const byDim = new Map(); + for (const r of boardRows) byDim.set(r.dimension, [...(byDim.get(r.dimension) ?? []), r]); + return [...byDim.entries()].map(([dimension, list]) => ({ + dimension, + manhattan: list.reduce((s, r) => s + r.manhattan.expansions, 0) / list.length, + euclidean: list.reduce((s, r) => s + r.euclidean.expansions, 0) / list.length, + })); + }, [boardRows]); // Draw at the container's real width so the axis labels stay legible. const [boxRef, measured] = useElementWidth(760); const W = Math.max(280, measured); @@ -94,11 +184,11 @@ function StudyChart({ const line = (key: "manhattan" | "euclidean") => rows.map((r) => `${x(r.dimension).toFixed(1)},${y(r[key]).toFixed(1)}`).join(" "); - const mWins = rows.filter((r) => r.manhattan < r.euclidean).length; - const eWins = rows.filter((r) => r.euclidean < r.manhattan).length; - const ties = rows.length - mWins - eWins; - const sumM = rows.reduce((s, r) => s + r.manhattan, 0); - const sumE = rows.reduce((s, r) => s + r.euclidean, 0); + const mWins = boardRows.filter((r) => r.manhattan.expansions < r.euclidean.expansions).length; + const eWins = boardRows.filter((r) => r.euclidean.expansions < r.manhattan.expansions).length; + const ties = boardRows.length - mWins - eWins; + const sumM = boardRows.reduce((s, r) => s + r.manhattan.expansions, 0); + const sumE = boardRows.reduce((s, r) => s + r.euclidean.expansions, 0); const yTicks = [0, 0.25, 0.5, 0.75, 1].map((f) => Math.round(maxY * f)); const tickStep = W < 480 ? 40 : 20; const xTicks = rows @@ -113,10 +203,10 @@ function StudyChart({ return (
- + {yTicks.map((t) => ( @@ -198,6 +288,7 @@ function StudyChart({ ))} Metric: the notebook's expansion counter (nodes added to the queue after the start) + {boardsPerDimension > 1 ? `, averaged over ${boardsPerDimension} boards per size` : ""} seed {seed} · {Math.round(elapsedMs)} ms in a Web Worker diff --git a/web/src/components/astar/paired-study-summary.tsx b/web/src/components/astar/paired-study-summary.tsx new file mode 100644 index 0000000..f61d8b3 --- /dev/null +++ b/web/src/components/astar/paired-study-summary.tsx @@ -0,0 +1,131 @@ +/** + * Paired Manhattan vs Euclidean results. Pure (no hooks): used for the + * precomputed reference study on the server and for live runs on the client. + */ +import { EstimateCI, IntervalAxis, IntervalBar } from "@/components/stats/interval"; +import { ScrollTable } from "@/components/stats/scroll-table"; +import type { PairedStudySummary } from "@/lib/analysis/astar-paired"; +import { formatNumber, formatP, formatPct, formatSigned } from "@/lib/stats/format"; + +const pctCI = (lo: number, hi: number) => `[${(lo * 100).toFixed(1)}, ${(hi * 100).toFixed(1)}]`; + +export function PairedStudySummaryView({ summary }: { summary: PairedStudySummary }) { + const e = summary.expansions; + const o = summary.optimality; + const w = e.wilcoxon; + const rows = [ + { label: "Manhattan", ci: o.manhattan, excess: o.manhattanExcess }, + { label: "Euclidean", ci: o.euclidean, excess: o.euclideanExcess }, + { label: "Both the same length", ci: o.agreement, excess: null }, + ]; + return ( +
+
+

Node expansions (paired, {summary.boards} boards)

+
+ + + + +
+

+ Manhattan expanded fewer nodes on {e.manhattanFewer} boards, Euclidean on{" "} + {e.euclideanFewer}, with {e.ties} ties. Negative differences favour Manhattan. +

+
+ +
+

+ Did A* find a shortest path? ({summary.boardsWithPath} boards with a path) +

+ Compared with a breadth-first search on the same board. Wilson 95% intervals. + } + > + + + + Heuristic + + + Interval + `${t * 100}%`} /> + + + Rate (95% CI) + + + Extra cells + + + + + {rows.map((r) => ( + + + {r.label} + + {r.excess === null ? "agreement rate" : "shortest path found"} + + + + + + + + + + {r.excess === null ? "" : `+${formatNumber(r.excess, 2)}`} + + + ))} + + +

+ Extra cells: mean path length beyond the shortest, over boards with a path (largest seen:{" "} + {o.maxExcess}). Neither heuristic is admissible on this grid: both overestimate the true + distance for {formatPct(summary.overestimation.manhattan)} of start/goal pairs on an empty{" "} + {summary.overestimation.dimension} × {summary.overestimation.dimension} board. +

+
+
+ ); +} + +function Stat({ label, value, hint }: { label: string; value: string; hint?: string }) { + return ( +
+
{label}
+
{value}
+ {hint &&
{hint}
} +
+ ); +} diff --git a/web/src/components/layout/page-header.tsx b/web/src/components/layout/page-header.tsx new file mode 100644 index 0000000..24b3b95 --- /dev/null +++ b/web/src/components/layout/page-header.tsx @@ -0,0 +1,66 @@ +import type { ReactNode } from "react"; + +import { cn } from "@/lib/utils"; + +export function PageHeader({ + eyebrow, + title, + children, +}: { + eyebrow: string; + title: string; + children?: ReactNode; +}) { + return ( +
+

+ {eyebrow} +

+

{title}

+ {children &&
{children}
} +
+ ); +} + +export function Section({ + id, + eyebrow, + title, + intro, + children, + className, +}: { + id: string; + eyebrow?: string; + title: string; + intro?: ReactNode; + children: ReactNode; + className?: string; +}) { + return ( +
+
+ {eyebrow && ( +

+ {eyebrow} +

+ )} +

+ {title} +

+ {intro &&
{intro}
} +
+
{children}
+
+ ); +} + +export function Finding({ children }: { children: ReactNode }) { + return ( +
  • {children}
    • + ); +} diff --git a/web/src/components/stats/interval.tsx b/web/src/components/stats/interval.tsx new file mode 100644 index 0000000..163aaa8 --- /dev/null +++ b/web/src/components/stats/interval.tsx @@ -0,0 +1,144 @@ +/** + * Forest-plot style interval marks for tables: a 2px interval line with end + * caps and an 8px point estimate wearing a surface ring, on a hairline track + * with an optional reference line (e.g. 50% or 0). Exact numbers always sit + * in an adjacent cell, so the mark never carries information on its own. + */ +import { cn } from "@/lib/utils"; + +const clamp = (x: number) => Math.min(100, Math.max(0, x)); + +export function IntervalBar({ + estimate, + lower, + upper, + min, + max, + reference, + label, + className, +}: { + estimate: number; + lower: number; + upper: number; + min: number; + max: number; + reference?: number; + /** Accessible description, also shown as a tooltip. */ + label: string; + className?: string; +}) { + const pct = (x: number) => clamp(((x - min) / (max - min || 1)) * 100); + const lo = pct(Math.min(lower, upper)); + const hi = pct(Math.max(lower, upper)); + const valid = [estimate, lower, upper].every(Number.isFinite); + return ( +
    +
    + {reference !== undefined && ( +
    + )} + {valid && ( + <> +
    +
    +
    +
    + + )} +
    + ); +} + +/** Axis labels for a column of IntervalBars (put it in the header cell). */ +export function IntervalAxis({ + min, + max, + ticks, + format, +}: { + min: number; + max: number; + ticks: number[]; + format: (x: number) => string; +}) { + return ( +
    + {ticks.map((t, i) => { + const left = clamp(((t - min) / (max - min || 1)) * 100); + const align = + i === 0 + ? "translate-x-0" + : i === ticks.length - 1 + ? "-translate-x-full" + : "-translate-x-1/2"; + return ( + + {format(t)} + + ); + })} +
    + ); +} + +/** "76.0% [72.0, 79.6]" style estimate with its interval, interval de-emphasised. */ +export function EstimateCI({ + estimate, + interval, + className, +}: { + estimate: string; + interval: string; + className?: string; +}) { + return ( + + {estimate} {interval} + + ); +} + +/** Nice round ticks spanning [min, max]. */ +export function niceTicks(min: number, max: number, count = 4): number[] { + const span = max - min || 1; + const raw = span / count; + const mag = 10 ** Math.floor(Math.log10(raw)); + const step = [1, 2, 2.5, 5, 10].map((m) => m * mag).find((s) => span / s <= count) ?? 10 * mag; + const out: number[] = []; + for (let t = Math.ceil(min / step) * step; t <= max + 1e-9; t += step) + out.push(Number(t.toFixed(10))); + return out; +} diff --git a/web/src/components/stats/scroll-table.tsx b/web/src/components/stats/scroll-table.tsx new file mode 100644 index 0000000..f65940b --- /dev/null +++ b/web/src/components/stats/scroll-table.tsx @@ -0,0 +1,27 @@ +import type { ReactNode } from "react"; + +import { cn } from "@/lib/utils"; + +/** + * A table that scrolls sideways on narrow screens, with its caption kept + * outside the scroll area so it always wraps to the viewport. + */ +export function ScrollTable({ + caption, + className, + children, +}: { + caption?: ReactNode; + /** Classes for the (e.g. a min-width). */ + className?: string; + children: ReactNode; +}) { + return ( +
    + {caption &&
    {caption}
    } +
    +
    {children}
    +
    +
    + ); +} diff --git a/web/src/components/tournament/alpha-beta-table.tsx b/web/src/components/tournament/alpha-beta-table.tsx new file mode 100644 index 0000000..6320845 --- /dev/null +++ b/web/src/components/tournament/alpha-beta-table.tsx @@ -0,0 +1,98 @@ +/** + * Alpha-beta efficiency table: share of the full minimax tree each search + * visits, per board size and depth, with bootstrap intervals over positions. + */ +import { EstimateCI, IntervalAxis, IntervalBar } from "@/components/stats/interval"; +import { ScrollTable } from "@/components/stats/scroll-table"; +import type { PruningGroup } from "@/lib/analysis/alpha-beta"; +import { formatNumber, formatPct } from "@/lib/stats/format"; + +const ci = (lo: number, hi: number) => `[${(lo * 100).toFixed(1)}, ${(hi * 100).toFixed(1)}]`; + +export function AlphaBetaTable({ groups, caption }: { groups: PruningGroup[]; caption: string }) { + return ( + {caption}}> + + + + Board · depth + + + Full tree (nodes) + + + Share of tree visited + `${t * 100}%`} /> + + + Original: share visited + + + Textbook: share visited + + + Same value + + + + + {groups.map((g) => ( + + + {g.n}×{g.n} · d{g.depth} + + {g.positions} positions + + + + {formatNumber(g.meanFull, 0)} + + + + + + + + + {formatNumber(g.meanPruned, 0)} nodes + + + + + + {formatNumber(g.meanTextbook, 0)} nodes + + + + {g.valueMatches}/{g.positions} · {g.textbookValueMatches}/{g.positions} + + + ))} + + + ); +} diff --git a/web/src/components/tournament/reference-tournament.tsx b/web/src/components/tournament/reference-tournament.tsx new file mode 100644 index 0000000..da9d8b3 --- /dev/null +++ b/web/src/components/tournament/reference-tournament.tsx @@ -0,0 +1,38 @@ +"use client"; + +import { useState } from "react"; + +import { Segmented } from "@/components/play/primitives"; +import type { TournamentSummary } from "@/lib/tournament/analyse"; +import { Leaderboard, PairwiseTable, SummaryStats } from "./tournament-results"; + +/** Switches between precomputed summaries (all sizes, or one board size). */ +export function ReferenceTournament({ + summaries, + hardware, +}: { + summaries: { key: string; label: string; summary: TournamentSummary }[]; + /** Where the move times were measured, e.g. "Apple M4, Node v26". */ + hardware: string; +}) { + const [key, setKey] = useState(summaries[0].key); + const current = summaries.find((s) => s.key === key) ?? summaries[0]; + return ( +
    +
    + ({ value: s.key, label: s.label }))} + /> +
    + + + +
    + ); +} diff --git a/web/src/components/tournament/tournament-results.tsx b/web/src/components/tournament/tournament-results.tsx new file mode 100644 index 0000000..9a5caf4 --- /dev/null +++ b/web/src/components/tournament/tournament-results.tsx @@ -0,0 +1,214 @@ +/** + * Results tables shared by the precomputed reference tournament and the + * in-browser runner. Pure presentational components (no hooks), so they render + * on the server for the reference and on the client for live runs. + */ +import { EstimateCI, IntervalAxis, IntervalBar, niceTicks } from "@/components/stats/interval"; +import { ScrollTable } from "@/components/stats/scroll-table"; +import { AGENTS } from "@/lib/tournament/agents"; +import type { TournamentSummary } from "@/lib/tournament/analyse"; +import { formatNumber, formatPct } from "@/lib/stats/format"; + +const pctCI = (lo: number, hi: number) => `[${(lo * 100).toFixed(1)}, ${(hi * 100).toFixed(1)}]`; + +export function Leaderboard({ summary, caption }: { summary: TournamentSummary; caption: string }) { + const finite = summary.standings + .flatMap((s) => [s.elo.lower, s.elo.upper]) + .filter(Number.isFinite); + const lo = Math.min(0, ...finite); + const hi = Math.max(100, ...finite); + const pad = (hi - lo) * 0.06; + const min = lo - pad; + const max = hi + pad; + return ( + {caption}}> + + + + Agent + + + Strength interval + String(t)} + /> + + + Elo vs random (95% CI) + + + Win rate (95% CI) + + + W–D–L + + + Illegal + + + ms / move (95% CI) + + + + + {summary.standings.map((s) => { + const anchor = s.id === summary.anchor; + return ( + + + {AGENTS[s.id].label} + {AGENTS[s.id].knob} + + + {anchor ? ( + reference (0) + ) : ( + + )} + + + {anchor ? ( + 0 + ) : ( + + )} + + + + + + {s.wins}–{s.draws}–{s.losses} + + {s.illegalMoves} + + + + + ); + })} + + + ); +} + +export function PairwiseTable({ summary }: { summary: TournamentSummary }) { + return ( + + Head to head. Win rate of the first-named agent; the vertical line marks 50%. Each pairing + played equal games as Red and as Blue. + + } + > + + + + Pairing + + + W–L (D) + + + Win rate interval + `${t * 100}%`} /> + + + Win rate (95% CI) + + + As Red / as Blue + + + + + {summary.pairs.map((p) => ( + + + {AGENTS[p.a].short} vs{" "} + {AGENTS[p.b].short} + + + {p.aWins}–{p.bWins} + {p.draws > 0 && ({p.draws})} + + + + + + + + + {p.aAsRed.wins}/{p.aAsRed.games} · {p.aAsBlue.wins}/{p.aAsBlue.games} + + + ))} + + + ); +} + +export function SummaryStats({ summary }: { summary: TournamentSummary }) { + const c = summary.colour; + return ( +
    + + + +
    + ); +} + +function Stat({ label, value, hint }: { label: string; value: string; hint?: string }) { + return ( +
    +
    {label}
    +
    {value}
    + {hint &&
    {hint}
    } +
    + ); +} diff --git a/web/src/components/tournament/tournament-runner.tsx b/web/src/components/tournament/tournament-runner.tsx new file mode 100644 index 0000000..f9c5957 --- /dev/null +++ b/web/src/components/tournament/tournament-runner.tsx @@ -0,0 +1,268 @@ +"use client"; + +import { Download, Loader2, Play, Shuffle, Square } from "lucide-react"; +import { useMemo, useState } from "react"; + +import { Field, Panel } from "@/components/play/primitives"; +import { Button } from "@/components/ui/button"; +import { Checkbox } from "@/components/ui/checkbox"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { useTournamentPool } from "@/hooks/use-tournament-pool"; +import { downloadText } from "@/lib/download"; +import { randomSeed } from "@/lib/rng"; +import { AGENT_IDS, AGENTS, type AgentId } from "@/lib/tournament/agents"; +import { gamesCsv, summariseTournament, summaryCsv } from "@/lib/tournament/analyse"; +import type { GameRecord } from "@/lib/tournament/play"; +import { buildSchedule } from "@/lib/tournament/schedule"; +import { Leaderboard, PairwiseTable, SummaryStats } from "./tournament-results"; + +const SIZES = [3, 4, 5, 6, 7]; +const ROUNDS = [1, 2, 5, 10, 20]; + +type Status = + | { kind: "idle" } + | { kind: "running"; total: number; started: number } + | { kind: "done"; elapsedMs: number; cancelled: boolean } + | { kind: "error"; message: string }; + +export function TournamentRunner() { + const [agents, setAgents] = useState([ + "minimax-dynamic", + "minimax-d2", + "greedy", + "random", + ]); + const [sizes, setSizes] = useState([4, 5]); + const [rounds, setRounds] = useState(5); + const [seed, setSeed] = useState(4399); + const [seedText, setSeedText] = useState("4399"); + const [records, setRecords] = useState([]); + const [status, setStatus] = useState({ kind: "idle" }); + const [ranSeed, setRanSeed] = useState(seed); + const pool = useTournamentPool(); + + const running = status.kind === "running"; + const games = + agents.length >= 2 && sizes.length + ? ((agents.length * (agents.length - 1)) / 2) * sizes.length * rounds * 2 + : 0; + const slow = agents.includes("minimax-d3") && sizes.some((n) => n >= 6); + const tooBig = agents.some((a) => sizes.some((n) => n > AGENTS[a].maxLiveN)); + + const toggle = (list: T[], item: T) => + list.includes(item) ? list.filter((x) => x !== item) : [...list, item]; + + const start = async () => { + const schedule = buildSchedule({ agents, sizes, rounds, seed }); + setRecords([]); + setRanSeed(seed); + const started = performance.now(); + setStatus({ kind: "running", total: schedule.length, started }); + const collected: GameRecord[] = []; + try { + const outcome = await pool.run(schedule, (r) => { + collected.push(r); + setRecords([...collected]); + }); + setStatus({ + kind: "done", + elapsedMs: performance.now() - started, + cancelled: outcome === "cancelled", + }); + } catch (err) { + setStatus({ kind: "error", message: err instanceof Error ? err.message : String(err) }); + } + }; + + const summary = useMemo( + () => + status.kind === "done" && records.length > 0 + ? summariseTournament(records, { reps: 500, seed: ranSeed }) + : null, + [records, ranSeed, status.kind], + ); + + const stamp = `seed-${ranSeed}`; + + return ( +
    + +
    +
    + + Agents + + {AGENT_IDS.map((id) => ( +
    + setAgents((a) => toggle(a, id))} + className="mt-0.5" + /> + +
    + ))} +
    + +
    + + Board sizes + +
    + {SIZES.map((n) => ( + + ))} +
    +
    + +
    + + Colour-swapped pairs per pairing and size + +
    + {ROUNDS.map((r) => ( + + ))} +
    +
    + + +
    + { + setSeedText(e.target.value); + const v = Number.parseInt(e.target.value, 10); + if (Number.isFinite(v) && v >= 0) setSeed(v); + }} + /> + +
    +     + +

    + {games.toLocaleString("en-AU")} games, played in parallel Web Workers. + {slow && " Depth 3 on 6 × 6 and above takes a few seconds per game."} + {tooBig && " Some agents are slow at the larger sizes selected."} +

    + + {running ? ( + + ) : ( + + )} +
    +     + +
    + {status.kind === "idle" && ( +
    + Choose agents and board sizes, then run. Every game is reproducible from the seed; each + pairing plays the same seed twice with colours swapped. +
    + )} + {status.kind === "running" && ( +
    +
    + Played {records.length} of {status.total}{" "} + games… +
    +
    +
    +
    +
    + )} + {status.kind === "error" && ( +

    + {status.message} +

    + )} + {summary && status.kind === "done" && ( + <> +
    +

    + {status.cancelled ? "Stopped early: " : ""} + {records.length} games in {(status.elapsedMs / 1000).toFixed(1)} s · seed {ranSeed} +

    + + +
    + + + + + )} +
    +
    + ); +} diff --git a/web/src/components/ui/checkbox.tsx b/web/src/components/ui/checkbox.tsx new file mode 100644 index 0000000..4f33bf0 --- /dev/null +++ b/web/src/components/ui/checkbox.tsx @@ -0,0 +1,25 @@ +"use client" + +import * as React from "react" +import { cn } from "cn" +import { CheckIcon } from "lucide-react" +import { Checkbox as CheckboxPrimitive } from "radix-ui" + +function Checkbox({ className, ...props }: React.ComponentProps) { + return ( + + + + + + ) +} + +export { Checkbox } diff --git a/web/src/components/ui/input.tsx b/web/src/components/ui/input.tsx new file mode 100644 index 0000000..2a7d204 --- /dev/null +++ b/web/src/components/ui/input.tsx @@ -0,0 +1,18 @@ +import * as React from "react" +import { cn } from "cn" + +function Input({ className, type, ...props }: React.ComponentProps<"input">) { + return ( + + ) +} + +export { Input } diff --git a/web/src/hooks/use-astar-benchmark.ts b/web/src/hooks/use-astar-benchmark.ts index 3992385..d40017a 100644 --- a/web/src/hooks/use-astar-benchmark.ts +++ b/web/src/hooks/use-astar-benchmark.ts @@ -2,16 +2,22 @@ import { useCallback, useEffect, useRef, useState } from "react"; -import { type BenchmarkRow, runHeuristicBenchmark } from "@/lib/astar/random-board"; +import { type PairedStudyRow, runPairedStudy } from "@/lib/analysis/astar-paired"; import type { BenchmarkResponse } from "@/workers/astar-benchmark.worker"; type State = | { status: "idle" } - | { status: "running"; seed: number } - | { status: "done"; seed: number; rows: BenchmarkRow[]; elapsedMs: number } + | { status: "running"; seed: number; boardsPerDimension: number } + | { + status: "done"; + seed: number; + boardsPerDimension: number; + rows: PairedStudyRow[]; + elapsedMs: number; + } | { status: "error"; message: string }; -/** Runs the heuristic study in a Web Worker (main-thread fallback). */ +/** Runs the paired heuristic study in a Web Worker (main-thread fallback). */ export function useAstarBenchmark() { const [state, setState] = useState({ status: "idle" }); const workerRef = useRef(null); @@ -31,6 +37,7 @@ export function useAstarBenchmark() { ? { status: "done", seed: prev.status === "running" ? prev.seed : 0, + boardsPerDimension: prev.status === "running" ? prev.boardsPerDimension : 1, rows: res.rows, elapsedMs: res.elapsedMs, } @@ -46,18 +53,24 @@ export function useAstarBenchmark() { return () => worker?.terminate(); }, []); - const run = useCallback((dimensions: number[], seed: number) => { + const run = useCallback((dimensions: number[], boardsPerDimension: number, seed: number) => { const id = ++latest.current; - setState({ status: "running", seed }); + setState({ status: "running", seed, boardsPerDimension }); const worker = workerRef.current; if (worker) { - worker.postMessage({ id, dimensions, seed }); + worker.postMessage({ id, dimensions, boardsPerDimension, seed }); } else { setTimeout(() => { const started = performance.now(); - const rows = runHeuristicBenchmark(dimensions, seed); + const rows = runPairedStudy({ dimensions, boardsPerDimension, seed }); if (id === latest.current) - setState({ status: "done", seed, rows, elapsedMs: performance.now() - started }); + setState({ + status: "done", + seed, + boardsPerDimension, + rows, + elapsedMs: performance.now() - started, + }); }, 0); } }, []); diff --git a/web/src/hooks/use-tournament-pool.ts b/web/src/hooks/use-tournament-pool.ts new file mode 100644 index 0000000..c9caccb --- /dev/null +++ b/web/src/hooks/use-tournament-pool.ts @@ -0,0 +1,111 @@ +"use client"; + +import { useCallback, useEffect, useRef } from "react"; + +import { type GameRecord, playTournamentGame } from "@/lib/tournament/play"; +import type { GameSpec } from "@/lib/tournament/schedule"; +import type { TournamentJobResult } from "@/workers/tournament.worker"; + +/** Number of parallel workers: leave one core for the page, cap at 4. */ +export function poolSize(): number { + const cores = typeof navigator !== "undefined" ? navigator.hardwareConcurrency || 2 : 2; + return Math.max(1, Math.min(4, cores - 1)); +} + +/** + * Runs a list of games across a small pool of Web Workers, reporting each + * record as it finishes. Falls back to the main thread (yielding between + * games) when workers are unavailable. Cancelling terminates the workers. + */ +export function useTournamentPool() { + const workers = useRef([]); + const cancelled = useRef(false); + + const stop = useCallback(() => { + cancelled.current = true; + for (const w of workers.current) w.terminate(); + workers.current = []; + }, []); + + useEffect(() => stop, [stop]); + + const run = useCallback( + (specs: GameSpec[], onRecord: (record: GameRecord) => void): Promise<"done" | "cancelled"> => { + stop(); + cancelled.current = false; + let next = 0; + let finished = 0; + + return new Promise((resolve, reject) => { + const finishIfDone = () => { + if (finished === specs.length) { + stop(); + cancelled.current = false; + resolve("done"); + } + }; + if (specs.length === 0) return resolve("done"); + + let pool: Worker[] = []; + try { + pool = Array.from( + { length: Math.min(poolSize(), specs.length) }, + () => + new Worker(new URL("../workers/tournament.worker.ts", import.meta.url), { + type: "module", + }), + ); + } catch { + pool = []; + } + + if (pool.length === 0) { + // Main-thread fallback. + const step = () => { + if (cancelled.current) return resolve("cancelled"); + if (next >= specs.length) return resolve("done"); + onRecord(playTournamentGame(specs[next++])); + setTimeout(step, 0); + }; + return step(); + } + + workers.current = pool; + const dispatch = (w: Worker) => { + if (next >= specs.length) return; + const id = next++; + w.postMessage({ id, spec: specs[id] }); + }; + for (const w of pool) { + w.onmessage = (event: MessageEvent) => { + if (cancelled.current) return; + const res = event.data; + if (!res.ok) { + stop(); + reject(new Error(res.error)); + return; + } + finished++; + onRecord(res.record); + dispatch(w); + finishIfDone(); + }; + w.onerror = () => { + stop(); + reject(new Error("A tournament worker crashed.")); + }; + dispatch(w); + } + const poll = setInterval(() => { + if (cancelled.current) { + clearInterval(poll); + resolve("cancelled"); + } else if (finished === specs.length) clearInterval(poll); + }, 200); + }); + }, + [stop], + ); + + return { run, stop }; +} diff --git a/web/src/lib/agent/minimax.ts b/web/src/lib/agent/minimax.ts index ee99493..8f6cb9e 100644 --- a/web/src/lib/agent/minimax.ts +++ b/web/src/lib/agent/minimax.ts @@ -17,6 +17,11 @@ export interface SearchContext { */ order: MoveOrder; bias: BiasFn; + /** + * Alpha-beta cut-offs (the original always prunes). Turning this off gives + * plain minimax with the same move order, for the pruning-efficiency study. + */ + prune: boolean; /** Number of nodes visited (for the UI). */ nodes: number; /** Root-level (action, score) pairs, recorded when requested. */ @@ -26,9 +31,11 @@ export interface SearchContext { export const createContext = ( order: MoveOrder = "canonical", bias: BiasFn = noBias, + prune = true, ): SearchContext => ({ order, bias, + prune, nodes: 0, }); @@ -109,7 +116,7 @@ export function minimax( maxAction = action; } if (maxScore >= alpha) alpha = maxScore; - if (alpha >= beta) break; + if (ctx.prune && alpha >= beta) break; } return [maxScore, maxAction]; } @@ -126,7 +133,7 @@ export function minimax( minAction = action; } if (beta <= minScore) beta = minScore; - if (alpha >= beta) break; + if (ctx.prune && alpha >= beta) break; } return [minScore, minAction]; } diff --git a/web/src/lib/agent/player.ts b/web/src/lib/agent/player.ts index 860752c..50fc57d 100644 --- a/web/src/lib/agent/player.ts +++ b/web/src/lib/agent/player.ts @@ -78,6 +78,11 @@ export interface AgentOptions { bias?: BiasFn; /** Use the opening book on turns 1 and 2 (the original's default). */ enforceGamePlay?: boolean; + /** + * Search to this depth instead of `dynamic_depth_allocation` (used by the + * tournament's fixed-depth and greedy variants; the original never sets it). + */ + fixedDepth?: number; } /** Choose the agent's action for `colour`, given the full action history. */ @@ -96,7 +101,7 @@ export function agentAction( colour: Colour, options: AgentOptions = {}, ): AgentDecision { - const { order = "canonical", bias = noBias, enforceGamePlay = true } = options; + const { order = "canonical", bias = noBias, enforceGamePlay = true, fixedDepth } = options; if (board.turn <= 2 && enforceGamePlay) { const { action, rule } = enforcedGamestartPlay(board.n, colour, board); @@ -110,7 +115,7 @@ export function agentAction( if (gameEnd(tmp)) return { action, explanation: { kind: "instant-win" } }; } - const depth = dynamicDepthAllocation(board); + const depth = fixedDepth ?? dynamicDepthAllocation(board); const ctx = createContext(order, bias); ctx.rootScores = []; const [score, action] = minimax(board, depth, -Infinity, Infinity, colour === "red", ctx, true); diff --git a/web/src/lib/analysis/alpha-beta.ts b/web/src/lib/analysis/alpha-beta.ts new file mode 100644 index 0000000..1c1fcfc --- /dev/null +++ b/web/src/lib/analysis/alpha-beta.ts @@ -0,0 +1,256 @@ +/** + * How much work does alpha-beta pruning save the `_4399` search? + * + * For a sample of mid-game positions, the ported minimax runs twice at the + * same depth from the same position: once as written (with cut-offs) and once + * with pruning switched off. Plain minimax visits every node, so its count does + * not depend on move order; the pruned count does, so it is measured with both + * the agent's shuffled order and the canonical (r, q) order. The evaluation's + * random tie-break bias is switched off so both runs score positions + * identically, which lets the study check that pruning never changes the + * value of the root. + * + * Finding: the original's minimising branch updates beta with + * `if beta <= min_score: beta = min_score`, which only ever raises beta, so + * the window never tightens there and most cut-offs are lost. The result is + * still correct (a looser window never changes the minimax value), just slow. + * The study therefore also measures a textbook alpha-beta (beta = + * min(beta, score)) with identical move generation and evaluation, to show + * what the one-line fix would buy. The agent itself is left as submitted. + */ +import { AgentBoard } from "@/lib/agent/agent-board"; +import { noBias } from "@/lib/agent/evaluation"; +import { evaluate } from "@/lib/agent/evaluation"; +import { + type SearchContext, + createContext, + gameEnd, + getValidActions, + minimax, +} from "@/lib/agent/minimax"; +import type { Action, Colour } from "@/lib/cachex/types"; +import { createRng, deriveSeed, randInt } from "@/lib/rng"; +import { type BootstrapCI, bootstrap, bootstrapMean } from "@/lib/stats/bootstrap"; +import { geometricMean, median } from "@/lib/stats/descriptive"; +import { tournamentMove } from "@/lib/tournament/agents"; +import { playTournamentGame } from "@/lib/tournament/play"; + +export type SearchOrder = "shuffled" | "canonical"; + +export interface PruningPosition { + n: number; + actions: Action[]; + /** Cells still empty. */ + empty: number; + toMove: Colour; + seed: number; +} + +export interface PruningSample { + n: number; + depth: number; + order: SearchOrder; + empty: number; + nodesPruned: number; + nodesFull: number; + /** nodesPruned / nodesFull: the share of the full tree alpha-beta still visits. */ + ratio: number; + /** Did pruning return the same root value as full minimax? */ + valueMatches: boolean; + /** Nodes visited by textbook alpha-beta (corrected beta update), same order. */ + nodesTextbook: number; + ratioTextbook: number; + textbookValueMatches: boolean; +} + +/** + * Mid-game positions from seeded greedy-vs-random games, taken after the + * opening (turn 3 onwards) and before the game ends. + */ +export function samplePositions({ + sizes, + perSize, + seed, +}: { + sizes: readonly number[]; + perSize: number; + seed: number; +}): PruningPosition[] { + const out: PruningPosition[] = []; + for (const n of sizes) { + let attempt = 0; + let found = 0; + while (found < perSize && attempt < perSize * 20) { + const gameSeed = deriveSeed(seed, n, attempt++); + const actions = replayActions(n, gameSeed); + if (actions.length < 4) continue; + const rng = createRng(deriveSeed(gameSeed, 1)); + const cut = randInt(rng, 2, actions.length - 1); + const board = AgentBoard.fromActions(n, actions.slice(0, cut)); + out.push({ + n, + actions: actions.slice(0, cut), + empty: board.emptyCount(), + toMove: cut % 2 === 0 ? "red" : "blue", + seed: gameSeed, + }); + found++; + } + } + return out; +} + +/** The actions of one seeded greedy (Red) vs random (Blue) game. */ +function replayActions(n: number, seed: number): Action[] { + const actions: Action[] = []; + playTournamentGame( + { id: "sample", n, red: "greedy", blue: "random", seed, round: 0 }, + { + now: () => 0, + move: (id, size, history, colour, moveSeed) => { + const { action } = tournamentMove(id, size, history, colour, createRng(moveSeed)); + actions.push(action); + return action; + }, + }, + ); + return actions; +} + +/** + * Textbook alpha-beta: identical to the port except that the minimising + * branch lowers beta (beta = min(beta, score)). Study-only; never used to play. + */ +export function textbookAlphaBeta( + board: AgentBoard, + depth: number, + alpha: number, + beta: number, + maximizingPlayer: boolean, + ctx: SearchContext, +): number { + ctx.nodes += 1; + const player: Colour = maximizingPlayer ? "red" : "blue"; + if (depth === 0 || gameEnd(board)) { + if (board.winner === null) return evaluate(board, ctx.bias); + return board.winner === "red" ? Infinity : -Infinity; + } + const actions = getValidActions(board, ctx.order); + if (maximizingPlayer) { + let best = -Infinity; + for (const action of actions) { + const child = board.clone(); + child.update(player, action); + best = Math.max(best, textbookAlphaBeta(child, depth - 1, alpha, beta, false, ctx)); + alpha = Math.max(alpha, best); + if (alpha >= beta) break; + } + return best; + } + let best = Infinity; + for (const action of actions) { + const child = board.clone(); + child.update(player, action); + best = Math.min(best, textbookAlphaBeta(child, depth - 1, alpha, beta, true, ctx)); + beta = Math.min(beta, best); + if (alpha >= beta) break; + } + return best; +} + +export function measurePruning( + position: PruningPosition, + depth: number, + order: SearchOrder, +): PruningSample { + const board = AgentBoard.fromActions(position.n, position.actions); + const maximizing = position.toMove === "red"; + const context = (prune: boolean) => + createContext( + order === "canonical" ? "canonical" : createRng(deriveSeed(position.seed, depth)), + noBias, + prune, + ); + const search = (prune: boolean) => { + const ctx = context(prune); + const [score] = minimax(board.clone(), depth, -Infinity, Infinity, maximizing, ctx); + return { score, nodes: ctx.nodes }; + }; + const pruned = search(true); + const full = search(false); + const tctx = context(true); + const textbook = textbookAlphaBeta(board.clone(), depth, -Infinity, Infinity, maximizing, tctx); + return { + n: position.n, + depth, + order, + empty: position.empty, + nodesPruned: pruned.nodes, + nodesFull: full.nodes, + ratio: pruned.nodes / full.nodes, + valueMatches: pruned.score === full.score, + nodesTextbook: tctx.nodes, + ratioTextbook: tctx.nodes / full.nodes, + textbookValueMatches: textbook === full.score, + }; +} + +export interface PruningGroup { + n: number; + depth: number; + order: SearchOrder; + positions: number; + meanFull: number; + meanPruned: number; + /** Mean per-position ratio with a bootstrap CI over positions. */ + meanRatio: BootstrapCI; + medianRatio: number; + geoMeanRatio: number; + /** Σ pruned / Σ full, with a bootstrap CI over positions. */ + pooledRatio: BootstrapCI; + valueMatches: number; + meanTextbook: number; + /** Mean per-position ratio for textbook alpha-beta. */ + textbookRatio: BootstrapCI; + textbookValueMatches: number; +} + +export function summarisePruning( + samples: readonly PruningSample[], + { reps = 2000, seed = 30024 }: { reps?: number; seed?: number } = {}, +): PruningGroup[] { + const groups = new Map(); + for (const s of samples) { + const key = `${s.n}|${s.depth}|${s.order}`; + groups.set(key, [...(groups.get(key) ?? []), s]); + } + return [...groups.values()] + .map((list, k) => { + const ratios = list.map((s) => s.ratio); + return { + n: list[0].n, + depth: list[0].depth, + order: list[0].order, + positions: list.length, + meanFull: list.reduce((s, x) => s + x.nodesFull, 0) / list.length, + meanPruned: list.reduce((s, x) => s + x.nodesPruned, 0) / list.length, + meanRatio: bootstrapMean(ratios, { reps, seed: seed + k }), + medianRatio: median(ratios), + geoMeanRatio: geometricMean(ratios), + pooledRatio: bootstrap( + list, + (xs) => + xs.reduce((s, x) => s + x.nodesPruned, 0) / xs.reduce((s, x) => s + x.nodesFull, 0), + { reps, seed: seed + 1000 + k }, + ), + valueMatches: list.filter((s) => s.valueMatches).length, + meanTextbook: list.reduce((s, x) => s + x.nodesTextbook, 0) / list.length, + textbookRatio: bootstrapMean( + list.map((s) => s.ratioTextbook), + { reps, seed: seed + 2000 + k }, + ), + textbookValueMatches: list.filter((s) => s.textbookValueMatches).length, + }; + }) + .sort((a, b) => a.order.localeCompare(b.order) || a.n - b.n || a.depth - b.depth); +} diff --git a/web/src/lib/analysis/analysis.test.ts b/web/src/lib/analysis/analysis.test.ts new file mode 100644 index 0000000..29d3053 --- /dev/null +++ b/web/src/lib/analysis/analysis.test.ts @@ -0,0 +1,168 @@ +import { describe, expect, it } from "vitest"; + +import { astar } from "@/lib/astar/astar"; +import { PRESETS } from "@/lib/astar/presets"; +import { parsePartAInput } from "@/lib/astar/input"; +import { runHeuristicBenchmark } from "@/lib/astar/random-board"; +import { measurePruning, samplePositions, summarisePruning } from "./alpha-beta"; +import { + bfsPathCells, + hexDistance, + overestimationRate, + pairedStudyCsvRows, + runPairedStudy, + summarisePairedStudy, +} from "./astar-paired"; + +describe("hex distance and BFS ground truth", () => { + it("measures distance in the six HEX_STEPS directions", () => { + expect(hexDistance([0, 0], [1, -1])).toBe(1); + expect(hexDistance([0, 0], [1, 1])).toBe(2); + expect(hexDistance([2, 2], [0, 4])).toBe(2); + expect(hexDistance([0, 0], [3, 0])).toBe(3); + }); + + it("both heuristics overestimate exactly when dr and dq have opposite signs", () => { + // (1, -1) is one step away, but Manhattan says 2 and Euclidean √2. + const rates = overestimationRate(5); + expect(rates.manhattan).toBeCloseTo(rates.euclidean, 12); + // opposite-sign pairs on a 5×5 board: Σ over dr,dq ≠ 0 with opposite signs + let count = 0; + for (let a = 0; a < 25; a++) + for (let b = 0; b < 25; b++) { + const dr = Math.floor(b / 5) - Math.floor(a / 5); + const dq = (b % 5) - (a % 5); + if (dr * dq < 0) count++; + } + expect(rates.manhattan).toBeCloseTo(count / (25 * 24), 12); + }); + + it("BFS agrees with A* on the sample inputs (which are shortest paths)", () => { + for (const preset of PRESETS) { + const board = parsePartAInput(JSON.stringify(preset.input)); + const bfs = bfsPathCells(board); + const m = astar(board, "manhattan"); + if (preset.originalPath === null) { + expect(bfs).toBeNull(); + expect(m.path).toEqual([]); + } else { + expect(bfs).not.toBeNull(); + expect(m.path.length).toBeGreaterThanOrEqual(bfs!); + } + } + const s1 = bfsPathCells(parsePartAInput(JSON.stringify(PRESETS[0].input))); + const s2 = bfsPathCells(parsePartAInput(JSON.stringify(PRESETS[1].input))); + expect([s1, s2]).toEqual([8, 13]); + }); +}); + +describe("paired heuristic study", () => { + it("reproduces the existing one-board-per-size benchmark exactly", () => { + const dims = [3, 8, 15, 30]; + const rows = runPairedStudy({ dimensions: dims, boardsPerDimension: 1, seed: 17 }); + const old = runHeuristicBenchmark(dims, 17); + rows.forEach((r, i) => { + expect(r.manhattan.expansions).toBe(old[i].manhattan); + expect(r.euclidean.expansions).toBe(old[i].euclidean); + expect(r.manhattan.cells > 0).toBe(old[i].pathFound); + }); + }); + + const rows = runPairedStudy({ + dimensions: Array.from({ length: 20 }, (_, i) => i + 4), + boardsPerDimension: 3, + seed: 5, + }); + const summary = summarisePairedStudy(rows, { reps: 500, seed: 1 }); + + it("never finds a path shorter than BFS, and finds one exactly when BFS does", () => { + for (const r of rows) { + if (r.optimalCells === null) { + expect(r.manhattan.cells).toBe(0); + expect(r.euclidean.cells).toBe(0); + } else { + expect(r.manhattan.cells).toBeGreaterThanOrEqual(r.optimalCells); + expect(r.euclidean.cells).toBeGreaterThanOrEqual(r.optimalCells); + } + } + }); + + it("summarises paired differences consistently", () => { + const e = summary.expansions; + expect(summary.boards).toBe(60); + expect(e.manhattanFewer + e.euclideanFewer + e.ties).toBe(60); + expect(e.meanDiff.estimate).toBeCloseTo(e.meanManhattan - e.meanEuclidean, 9); + expect(e.meanDiff.lower).toBeLessThanOrEqual(e.meanDiff.estimate); + expect(e.meanDiff.upper).toBeGreaterThanOrEqual(e.meanDiff.estimate); + expect(e.wilcoxon.n + e.wilcoxon.zeros).toBe(60); + expect(e.wilcoxon.pValue).toBeGreaterThan(0); + expect(e.wilcoxon.pValue).toBeLessThanOrEqual(1); + const o = summary.optimality; + expect(o.manhattan.n).toBe(summary.boardsWithPath); + expect(o.manhattanExcess).toBeGreaterThanOrEqual(0); + expect(pairedStudyCsvRows(rows)).toHaveLength(60); + }); +}); + +describe("alpha-beta efficiency", () => { + const positions = samplePositions({ sizes: [4, 5], perSize: 6, seed: 3 }); + + it("samples reproducible mid-game positions", () => { + expect(positions).toHaveLength(12); + expect(samplePositions({ sizes: [4, 5], perSize: 6, seed: 3 })).toEqual(positions); + for (const p of positions) { + expect(p.actions.length).toBeGreaterThanOrEqual(2); + expect(p.toMove).toBe(p.actions.length % 2 === 0 ? "red" : "blue"); + } + }); + + it("pruning visits no more nodes and never changes the root value", () => { + for (const p of positions) { + for (const depth of [2, 3]) { + for (const order of ["canonical", "shuffled"] as const) { + const s = measurePruning(p, depth, order); + expect(s.valueMatches).toBe(true); + expect(s.textbookValueMatches).toBe(true); + expect(s.nodesPruned).toBeLessThanOrEqual(s.nodesFull); + expect(s.nodesTextbook).toBeLessThanOrEqual(s.nodesFull); + expect(s.ratio).toBeGreaterThan(0); + } + } + } + }); + + it("the original's inverted beta update costs pruning (textbook prunes at least as much here)", () => { + let original = 0; + let textbook = 0; + for (const p of positions) { + const s = measurePruning(p, 3, "canonical"); + original += s.nodesPruned; + textbook += s.nodesTextbook; + } + expect(textbook).toBeLessThan(original); + }); + + it("full minimax visits 1 + b + b(b−1) nodes at depth 2 when nothing ends early", () => { + const p = positions[0]; + const s = measurePruning(p, 2, "canonical"); + const b = p.empty; + // A depth-2 subtree can end early only if a move wins, which is rare this early. + expect(s.nodesFull).toBeLessThanOrEqual(1 + b + b * (b - 1)); + }); + + it("summarises by board size, depth and order", () => { + const samples = positions.flatMap((p) => [ + measurePruning(p, 2, "canonical"), + measurePruning(p, 2, "shuffled"), + ]); + const groups = summarisePruning(samples, { reps: 200, seed: 1 }); + expect(groups).toHaveLength(4); + for (const g of groups) { + expect(g.positions).toBe(6); + expect(g.valueMatches).toBe(6); + expect(g.meanRatio.estimate).toBeGreaterThan(0); + expect(g.meanRatio.estimate).toBeLessThanOrEqual(1); + expect(g.pooledRatio.lower).toBeLessThanOrEqual(g.pooledRatio.estimate); + } + }); +}); diff --git a/web/src/lib/analysis/astar-paired.ts b/web/src/lib/analysis/astar-paired.ts new file mode 100644 index 0000000..16b17d5 --- /dev/null +++ b/web/src/lib/analysis/astar-paired.ts @@ -0,0 +1,241 @@ +/** + * Paired Manhattan vs Euclidean study for the Part A A* search. + * + * Both heuristics run on the *same* random boards (the notebook's generator), + * so the comparison is paired: the unit of analysis is a board, and the + * quantity of interest is the per-board difference in node expansions. + * + * It also checks optimality. On this hex grid (axial coordinates, the six + * steps in HEX_STEPS) the true distance between two cells is + * max(|dr|, |dq|, |dr + dq|). Manhattan |dr| + |dq| and Euclidean + * sqrt(dr² + dq²) both exceed it whenever dr and dq have opposite signs, so + * neither heuristic is admissible and A* may return a longer path than + * necessary. A breadth-first search gives the true shortest path to compare + * against. + */ +import { type AStarBoard, astar } from "@/lib/astar/astar"; +import { notebookExpansions, randomNotebookBoard } from "@/lib/astar/random-board"; +import { HEX_STEPS } from "@/lib/cachex/board"; +import type { Coord } from "@/lib/cachex/types"; +import { createRng } from "@/lib/rng"; +import { type BootstrapCI, pairedMeanDifference } from "@/lib/stats/bootstrap"; +import { median } from "@/lib/stats/descriptive"; +import { type ProportionCI, wilson } from "@/lib/stats/proportion"; +import { type WilcoxonResult, wilcoxonSignedRank } from "@/lib/stats/wilcoxon"; + +/** True shortest-path distance (in steps) between two cells on an empty board. */ +export function hexDistance(a: Coord, b: Coord): number { + const dr = b[0] - a[0]; + const dq = b[1] - a[1]; + return Math.max(Math.abs(dr), Math.abs(dq), Math.abs(dr + dq)); +} + +/** + * Shortest path length in cells (start and goal included, like the A* output) + * when every occupied tile blocks, or null when the goal is unreachable. + */ +export function bfsPathCells(board: AStarBoard): number | null { + const { n, cells, start, goal } = board; + const id = (r: number, q: number) => r * n + q; + if (cells[id(start[0], start[1])] !== null) return null; + const dist = new Int32Array(n * n).fill(-1); + const queue = new Int32Array(n * n); + let head = 0; + let tail = 0; + const s = id(start[0], start[1]); + dist[s] = 0; + queue[tail++] = s; + const g = id(goal[0], goal[1]); + while (head < tail) { + const cur = queue[head++]; + if (cur === g) return dist[cur] + 1; + const r = Math.floor(cur / n); + const q = cur % n; + for (const [dr, dq] of HEX_STEPS) { + const nr = r + dr; + const nq = q + dq; + if (nr < 0 || nq < 0 || nr >= n || nq >= n) continue; + const ni = id(nr, nq); + if (dist[ni] !== -1 || cells[ni] !== null) continue; + dist[ni] = dist[cur] + 1; + queue[tail++] = ni; + } + } + return null; +} + +export interface HeuristicRun { + /** The notebook's expansion counter (queue insertions after the start). */ + expansions: number; + /** Priority-queue pops. */ + pops: number; + /** Path length in cells, or 0 when no path was found. */ + cells: number; +} + +export interface PairedStudyRow { + dimension: number; + /** Index of the board within its dimension. */ + board: number; + /** BFS shortest path in cells, or null when start and goal are disconnected. */ + optimalCells: number | null; + manhattan: HeuristicRun; + euclidean: HeuristicRun; +} + +export interface PairedStudyConfig { + dimensions: readonly number[]; + boardsPerDimension: number; + seed: number; +} + +const run = (board: AStarBoard, heuristic: "manhattan" | "euclidean"): HeuristicRun => { + const r = astar(board, heuristic); + return { expansions: notebookExpansions(r), pops: r.pops, cells: r.path.length }; +}; + +/** + * Generate boards in dimension order from one seeded stream. With one board + * per dimension this reproduces `runHeuristicBenchmark` board for board. + */ +export function runPairedStudy({ + dimensions, + boardsPerDimension, + seed, +}: PairedStudyConfig): PairedStudyRow[] { + const rng = createRng(seed); + const rows: PairedStudyRow[] = []; + for (const dimension of dimensions) { + for (let b = 0; b < boardsPerDimension; b++) { + const board = randomNotebookBoard(dimension, rng); + rows.push({ + dimension, + board: b, + optimalCells: bfsPathCells(board), + manhattan: run(board, "manhattan"), + euclidean: run(board, "euclidean"), + }); + } + } + return rows; +} + +export interface PairedStudySummary { + boards: number; + boardsWithPath: number; + expansions: { + meanManhattan: number; + meanEuclidean: number; + /** Mean of (Manhattan − Euclidean) per board, with a paired bootstrap CI. */ + meanDiff: BootstrapCI; + medianDiff: number; + /** Cohen's d_z of the paired differences. */ + dz: number; + wilcoxon: WilcoxonResult; + manhattanFewer: number; + euclideanFewer: number; + ties: number; + }; + optimality: { + /** Boards (with a path) where A* returned a shortest path. */ + manhattan: ProportionCI; + euclidean: ProportionCI; + /** Boards where both heuristics returned paths of the same length. */ + agreement: ProportionCI; + /** Mean extra cells over the shortest path, across boards with a path. */ + manhattanExcess: number; + euclideanExcess: number; + /** Largest extra length seen. */ + maxExcess: number; + }; + /** Proportion of ordered cell pairs on an empty board where each heuristic overestimates. */ + overestimation: { dimension: number; manhattan: number; euclidean: number }; +} + +/** Share of ordered (start, goal) pairs on an empty d × d board where h > true distance. */ +export function overestimationRate(dimension: number): { manhattan: number; euclidean: number } { + let pairs = 0; + let m = 0; + let e = 0; + for (let a = 0; a < dimension * dimension; a++) { + for (let b = 0; b < dimension * dimension; b++) { + if (a === b) continue; + const s: Coord = [Math.floor(a / dimension), a % dimension]; + const g: Coord = [Math.floor(b / dimension), b % dimension]; + const d = hexDistance(s, g); + const dr = Math.abs(g[0] - s[0]); + const dq = Math.abs(g[1] - s[1]); + pairs++; + if (dr + dq > d) m++; + if (Math.sqrt(dr * dr + dq * dq) > d + 1e-12) e++; + } + } + return { manhattan: m / pairs, euclidean: e / pairs }; +} + +export function summarisePairedStudy( + rows: readonly PairedStudyRow[], + { reps = 2000, seed = 2022 }: { reps?: number; seed?: number } = {}, +): PairedStudySummary { + const m = rows.map((r) => r.manhattan.expansions); + const e = rows.map((r) => r.euclidean.expansions); + const paired = pairedMeanDifference(m, e, { reps, seed }); + const diffs = m.map((x, i) => x - e[i]); + const withPath = rows.filter((r) => r.optimalCells !== null); + const optimal = (key: "manhattan" | "euclidean") => + withPath.filter((r) => r[key].cells === r.optimalCells).length; + const excess = (key: "manhattan" | "euclidean") => + withPath.length + ? withPath.reduce((s, r) => s + (r[key].cells - r.optimalCells!), 0) / withPath.length + : 0; + const maxExcess = withPath.reduce( + (mx, r) => + Math.max(mx, r.manhattan.cells - r.optimalCells!, r.euclidean.cells - r.optimalCells!), + 0, + ); + const reference = 10; + return { + boards: rows.length, + boardsWithPath: withPath.length, + expansions: { + meanManhattan: paired.meanX, + meanEuclidean: paired.meanY, + meanDiff: paired.meanDiff, + medianDiff: median(diffs), + dz: paired.dz, + wilcoxon: wilcoxonSignedRank(m, e), + manhattanFewer: diffs.filter((d) => d < 0).length, + euclideanFewer: diffs.filter((d) => d > 0).length, + ties: diffs.filter((d) => d === 0).length, + }, + optimality: { + manhattan: wilson(optimal("manhattan"), withPath.length), + euclidean: wilson(optimal("euclidean"), withPath.length), + agreement: wilson( + withPath.filter((r) => r.manhattan.cells === r.euclidean.cells).length, + withPath.length, + ), + manhattanExcess: excess("manhattan"), + euclideanExcess: excess("euclidean"), + maxExcess, + }, + overestimation: { dimension: reference, ...overestimationRate(reference) }, + }; +} + +/** One row per board, for CSV export. */ +export function pairedStudyCsvRows(rows: readonly PairedStudyRow[]) { + return rows.map((r) => ({ + dimension: r.dimension, + board: r.board, + path_exists: r.optimalCells !== null, + shortest_path_cells: r.optimalCells, + manhattan_expansions: r.manhattan.expansions, + euclidean_expansions: r.euclidean.expansions, + diff_m_minus_e: r.manhattan.expansions - r.euclidean.expansions, + manhattan_pops: r.manhattan.pops, + euclidean_pops: r.euclidean.pops, + manhattan_path_cells: r.manhattan.cells, + euclidean_path_cells: r.euclidean.cells, + })); +} diff --git a/web/src/lib/csv.ts b/web/src/lib/csv.ts new file mode 100644 index 0000000..18a15fc --- /dev/null +++ b/web/src/lib/csv.ts @@ -0,0 +1,28 @@ +/** + * Minimal RFC 4180 CSV writer. String cells that start with a formula + * character are prefixed with an apostrophe so spreadsheet apps do not + * evaluate text (for example LLM output) as a formula. + */ +export type CsvValue = string | number | boolean | null | undefined; + +const FORMULA_START = /^[=+\-@\t\r]/; + +export function csvCell(value: CsvValue): string { + if (value === null || value === undefined) return ""; + if (typeof value === "number") return Number.isFinite(value) ? String(value) : ""; + if (typeof value === "boolean") return value ? "true" : "false"; + let s = value; + if (FORMULA_START.test(s)) s = `'${s}`; + return /[",\n\r]/.test(s) ? `"${s.replace(/"/g, '""')}"` : s; +} + +export function toCsv>( + rows: readonly T[], + columns: readonly (keyof T & string)[] = rows.length + ? (Object.keys(rows[0]) as (keyof T & string)[]) + : [], +): string { + const lines = [columns.map(csvCell).join(",")]; + for (const row of rows) lines.push(columns.map((c) => csvCell(row[c])).join(",")); + return `${lines.join("\r\n")}\r\n`; +} diff --git a/web/src/lib/data/alpha-beta-study.json b/web/src/lib/data/alpha-beta-study.json new file mode 100644 index 0000000..9215841 --- /dev/null +++ b/web/src/lib/data/alpha-beta-study.json @@ -0,0 +1 @@ +{"meta":{"generatedBy":"web/scripts/generate-reference-studies.ts","generatedAt":"2026-10-05","node":"v26.10.0","cpu":"Apple M4","implementation":"TypeScript port (parity-tested against the original 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diff --git a/web/src/lib/data/load.ts b/web/src/lib/data/load.ts new file mode 100644 index 0000000..05b4be9 --- /dev/null +++ b/web/src/lib/data/load.ts @@ -0,0 +1,41 @@ +/** + * Build-time loaders for the generated study data. Read with fs (not + * `import`) so tsc does not infer types for large JSON files; only used by + * Server Components and route handlers, which run at build time. + */ +import { readFileSync } from "node:fs"; +import path from "node:path"; + +import type { PruningSample } from "@/lib/analysis/alpha-beta"; +import type { PairedStudyRow } from "@/lib/analysis/astar-paired"; +import type { PythonGame } from "@/lib/tournament/crosscheck"; +import type { GameRecord } from "@/lib/tournament/play"; + +export interface StudyMeta { + generatedBy: string; + generatedAt?: string; + node?: string; + python?: string; + cpu?: string; + implementation: string; + config: Record; + seconds?: number; +} + +const DATA_DIR = path.join(process.cwd(), "src", "lib", "data"); + +function read(name: string): T { + return JSON.parse(readFileSync(path.join(DATA_DIR, name), "utf8")) as T; +} + +export const loadReferenceTournament = () => + read<{ meta: StudyMeta; games: GameRecord[] }>("reference-tournament.json"); + +export const loadAstarStudy = () => + read<{ meta: StudyMeta; rows: PairedStudyRow[] }>("astar-paired-study.json"); + +export const loadAlphaBetaStudy = () => + read<{ meta: StudyMeta; samples: PruningSample[] }>("alpha-beta-study.json"); + +export const loadPythonCrosscheck = () => + read<{ meta: StudyMeta; games: PythonGame[] }>("python-crosscheck.json"); diff --git a/web/src/lib/data/python-crosscheck.json b/web/src/lib/data/python-crosscheck.json new file mode 100644 index 0000000..e24e295 --- /dev/null +++ b/web/src/lib/data/python-crosscheck.json @@ -0,0 +1 @@ +{"meta": {"generatedBy": "scripts/crosscheck_tournament.py", "implementation": "original Python (coursework/Project Part B), variants by patching one knob", "python": "3.12.7", "pythonHashSeed": "0", "config": {"agents": ["minimax-dynamic", "minimax-d2", "greedy", "random"], "sizes": [4, 5], "rounds": 20, "seed": 2022}, "seconds": 212.7}, "games": [{"n": 4, "red": "minimax-dynamic", 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{"n": 5, "red": "random", "blue": "greedy", "seed": 2022056355, "winner": "blue", "turns": 18}]} diff --git a/web/src/lib/data/reference-config.ts b/web/src/lib/data/reference-config.ts new file mode 100644 index 0000000..41b8b16 --- /dev/null +++ b/web/src/lib/data/reference-config.ts @@ -0,0 +1,28 @@ +/** + * Configurations of the precomputed reference studies (see + * web/scripts/generate-reference-studies.ts). Kept in one place so the pages + * can state exactly how each result was produced. + */ +import type { AgentId } from "@/lib/tournament/agents"; + +export const REFERENCE_TOURNAMENT_CONFIG = { + agents: ["minimax-dynamic", "minimax-d2", "minimax-d3", "greedy", "random"] as AgentId[], + sizes: [4, 5, 6], + /** Colour-swapped pairs per pairing per size → 40 games per pairing per size. */ + rounds: 20, + seed: 2022, +}; + +export const ASTAR_STUDY_CONFIG = { + /** The notebook's range: every board size from 2 to 99. */ + dimensions: Array.from({ length: 98 }, (_, i) => i + 2), + boardsPerDimension: 10, + seed: 2022, +}; + +export const ALPHA_BETA_CONFIG = { + sizes: [4, 5, 6], + perSize: 40, + depths: [2, 3], + seed: 2022, +}; diff --git a/web/src/lib/data/reference-tournament.json b/web/src/lib/data/reference-tournament.json new file mode 100644 index 0000000..600801e --- /dev/null +++ b/web/src/lib/data/reference-tournament.json @@ -0,0 +1 @@ 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diff --git a/web/src/lib/download.ts b/web/src/lib/download.ts new file mode 100644 index 0000000..51d7859 --- /dev/null +++ b/web/src/lib/download.ts @@ -0,0 +1,12 @@ +/** Save text as a file from the browser (CSV / JSON exports). */ +export function downloadText(filename: string, text: string, type = "text/csv;charset=utf-8") { + const blob = new Blob([text], { type }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + document.body.appendChild(a); + a.click(); + a.remove(); + setTimeout(() => URL.revokeObjectURL(url), 1000); +} diff --git a/web/src/lib/rng.ts b/web/src/lib/rng.ts index 23be1e7..2e33496 100644 --- a/web/src/lib/rng.ts +++ b/web/src/lib/rng.ts @@ -45,3 +45,27 @@ export function sample(rng: Rng, items: readonly T[], k: number): T[] { export function randomSeed(): number { return Math.floor(Math.random() * 2 ** 31); } + +/** + * Derive a 32-bit seed from a parent seed and any number of integer labels + * (board size, pairing, round...). Uses the murmur3 finaliser so nearby + * inputs give unrelated seeds; the same inputs always give the same seed. + */ +export function deriveSeed(...parts: number[]): number { + let h = 0x9e3779b9; + for (const part of parts) { + let k = Math.imul(part | 0, 0xcc9e2d51); + k = (k << 15) | (k >>> 17); + k = Math.imul(k, 0x1b873593); + h ^= k; + h = (h << 13) | (h >>> 19); + h = (Math.imul(h, 5) + 0xe6546b64) | 0; + } + h ^= parts.length; + h ^= h >>> 16; + h = Math.imul(h, 0x85ebca6b); + h ^= h >>> 13; + h = Math.imul(h, 0xc2b2ae35); + h ^= h >>> 16; + return h >>> 0; +} diff --git a/web/src/lib/tournament/agents.ts b/web/src/lib/tournament/agents.ts new file mode 100644 index 0000000..40b4ba1 --- /dev/null +++ b/web/src/lib/tournament/agents.ts @@ -0,0 +1,113 @@ +/** + * The agents in the round-robin harness. Every minimax variant is the ported + * `_4399` agent with exactly one knob changed, so a difference in results can + * be attributed to that knob: + * + * - minimax-dynamic: the original as submitted (dynamic depth 1 to 4); + * - minimax-d2 / minimax-d3: the same agent with the depth fixed; + * - greedy: no opening book and depth fixed at 1 (the instant-win check is + * kept), i.e. one-ply lookahead on the hand-tuned evaluation; + * - random: the subject's random baseline. + * + * The same variants are built from the original Python in + * scripts/crosscheck_tournament.py by patching `dynamic_depth_allocation` + * and passing `enforceGamePlayer=False`. + */ +import { randomBias } from "@/lib/agent/evaluation"; +import { type AgentDecision, type AgentOptions, chooseAgentAction } from "@/lib/agent/player"; +import { chooseRandomAction } from "@/lib/agent/random-player"; +import type { Action, Colour } from "@/lib/cachex/types"; +import type { Rng } from "@/lib/rng"; + +export const AGENT_IDS = [ + "minimax-dynamic", + "minimax-d2", + "minimax-d3", + "greedy", + "random", +] as const; + +export type AgentId = (typeof AGENT_IDS)[number]; + +export interface TournamentAgent { + id: AgentId; + label: string; + short: string; + description: string; + /** What changes relative to the original agent. */ + knob: string; + /** Largest board the in-browser harness offers this agent on (search cost). */ + maxLiveN: number; + options: AgentOptions | null; +} + +export const AGENTS: Record = { + "minimax-dynamic": { + id: "minimax-dynamic", + label: "Minimax, dynamic depth (original)", + short: "Original", + description: + "The _4399 agent as submitted: opening book, instant-win check, then alpha-beta search whose depth grows from 1 to 4 as the board fills.", + knob: "none (as submitted)", + maxLiveN: 8, + options: {}, + }, + "minimax-d2": { + id: "minimax-d2", + label: "Minimax, fixed depth 2", + short: "Depth 2", + description: "The same agent, searching two plies on every move.", + knob: "depth fixed at 2", + maxLiveN: 7, + options: { fixedDepth: 2 }, + }, + "minimax-d3": { + id: "minimax-d3", + label: "Minimax, fixed depth 3", + short: "Depth 3", + description: "The same agent, searching three plies on every move. Slow above 5 × 5.", + knob: "depth fixed at 3", + maxLiveN: 6, + options: { fixedDepth: 3 }, + }, + greedy: { + id: "greedy", + label: "Greedy one-ply", + short: "Greedy", + description: + "No opening book: takes an immediate win if there is one, otherwise the move with the best evaluation one ply ahead.", + knob: "depth fixed at 1, no opening book", + maxLiveN: 8, + options: { fixedDepth: 1, enforceGamePlay: false }, + }, + random: { + id: "random", + label: "Random baseline", + short: "Random", + description: + "The subject's random agent: steals with probability 1/2 as Blue's first move, otherwise a uniformly random empty cell.", + knob: "not a minimax agent", + maxLiveN: 10, + options: null, + }, +}; + +export const isAgentId = (x: unknown): x is AgentId => + typeof x === "string" && (AGENT_IDS as readonly string[]).includes(x); + +/** Ask an agent for its move given the full action history. */ +export function tournamentMove( + id: AgentId, + n: number, + history: readonly Action[], + colour: Colour, + rng: Rng, +): AgentDecision { + const agent = AGENTS[id]; + if (agent.options === null) return chooseRandomAction(n, history, rng); + return chooseAgentAction(n, history, colour, { + ...agent.options, + order: rng, + bias: randomBias(rng), + }); +} diff --git a/web/src/lib/tournament/analyse.ts b/web/src/lib/tournament/analyse.ts new file mode 100644 index 0000000..6697abf --- /dev/null +++ b/web/src/lib/tournament/analyse.ts @@ -0,0 +1,300 @@ +/** + * Turns game records into the numbers the tournament page reports: + * win rates with Wilson intervals, Bradley-Terry strengths on the Elo scale + * with stratified bootstrap intervals, the first-move (colour) effect and + * mean move times with bootstrap intervals. + */ +import { type CsvValue, toCsv } from "@/lib/csv"; +import { type BootstrapCI, bootstrapMean, stratifiedBootstrapVector } from "@/lib/stats/bootstrap"; +import { type PairOutcome, fitBradleyTerry, toElo } from "@/lib/stats/bradley-terry"; +import { type ProportionCI, wilson } from "@/lib/stats/proportion"; +import { AGENTS, type AgentId } from "./agents"; +import { type GameRecord, winningAgent } from "./play"; +import { canonicalAgents, pairKey } from "./schedule"; + +export interface Interval { + estimate: number; + lower: number; + upper: number; +} + +export interface AgentStanding { + id: AgentId; + games: number; + wins: number; + losses: number; + draws: number; + /** Games this agent lost by an illegal move. */ + forfeits: number; + illegalMoves: number; + moves: number; + winRate: ProportionCI; + /** Bradley-Terry strength on the Elo scale (anchored, see summary.anchor). */ + elo: Interval; + /** Mean time per move (ms), averaged per game, with a bootstrap CI over games. */ + moveMs: BootstrapCI; + /** Mean search nodes per move. */ + nodesPerMove: number; + /** Share of searched moves that looked deeper than one ply. */ + deepSearchShare: number; +} + +export interface PairStanding { + a: AgentId; + b: AgentId; + games: number; + aWins: number; + bWins: number; + draws: number; + /** Win rate of `a` against `b` (draws count as not-wins). */ + aWinRate: ProportionCI; + /** Wins for `a` when `a` played Red, and games as Red. */ + aAsRed: { games: number; wins: number }; + aAsBlue: { games: number; wins: number }; +} + +export interface TournamentSummary { + games: number; + sizes: number[]; + agents: AgentId[]; + standings: AgentStanding[]; + pairs: PairStanding[]; + colour: { + games: number; + redWins: number; + blueWins: number; + draws: number; + redWinRate: ProportionCI; + }; + meanTurns: number; + /** Agent whose Elo is fixed at 0 (random when present). */ + anchor: AgentId | null; + bootstrap: { reps: number; seed: number; prior: number }; +} + +export interface SummariseOptions { + reps?: number; + seed?: number; + /** Virtual drawn games per pairing in the Bradley-Terry fit. */ + prior?: number; + sizes?: number[]; +} + +/** Points to each side of one game: 1 / 0, or 0.5 each for a draw. */ +function points(g: GameRecord): { red: number; blue: number } { + if (g.winner === null) return { red: 0.5, blue: 0.5 }; + return g.winner === "red" ? { red: 1, blue: 0 } : { red: 0, blue: 1 }; +} + +function outcomesFor(records: readonly GameRecord[], index: Map): PairOutcome[] { + return records.map((g) => { + const p = points(g); + return { + i: index.get(g.red.agent)!, + j: index.get(g.blue.agent)!, + scoreI: p.red, + scoreJ: p.blue, + }; + }); +} + +export function summariseTournament( + allRecords: readonly GameRecord[], + { reps = 1000, seed = 4399, prior = 1, sizes }: SummariseOptions = {}, +): TournamentSummary { + const records = sizes ? allRecords.filter((g) => sizes.includes(g.n)) : [...allRecords]; + const agents = canonicalAgents([...new Set(records.flatMap((g) => [g.red.agent, g.blue.agent]))]); + const index = new Map(agents.map((id, i) => [id, i])); + const anchor: AgentId | null = agents.includes("random") ? "random" : null; + const anchorIndex = anchor === null ? undefined : index.get(anchor); + + // Bradley-Terry with a bootstrap stratified by pairing and board size. + const strataMap = new Map(); + for (const g of records) { + const key = `${g.n}|${pairKey(g.red.agent, g.blue.agent)}`; + const list = strataMap.get(key) ?? []; + list.push(g); + strataMap.set(key, list); + } + const strata = [...strataMap.values()]; + const eloOf = (s: readonly (readonly GameRecord[])[]) => + toElo( + fitBradleyTerry(agents.length, outcomesFor(s.flat(), index), { prior }).theta, + anchorIndex, + ); + const bt = + agents.length >= 2 && records.length > 0 + ? stratifiedBootstrapVector(strata, eloOf, { reps, seed }) + : null; + + const standings: AgentStanding[] = agents.map((id, k) => { + const mine = records.filter((g) => g.red.agent === id || g.blue.agent === id); + let wins = 0; + let losses = 0; + let draws = 0; + let forfeits = 0; + let illegalMoves = 0; + let moves = 0; + let nodes = 0; + let searches = 0; + let deep = 0; + const perGameMs: number[] = []; + for (const g of mine) { + // Self-play is never scheduled, so each game has exactly one side for `id`. + const side = g.red.agent === id ? "red" : "blue"; + const s = g[side]; + const w = winningAgent(g); + if (w === null) draws++; + else if (g.winner === side) wins++; + else { + losses++; + if (g.result === "forfeit") forfeits++; + } + illegalMoves += s.illegal; + moves += s.moves; + nodes += s.nodes; + searches += s.searches ?? 0; + deep += s.deepSearches ?? 0; + if (s.moves > 0) perGameMs.push(s.totalMs / s.moves); + } + return { + id, + games: mine.length, + wins, + losses, + draws, + forfeits, + illegalMoves, + moves, + winRate: wilson(wins, mine.length), + elo: bt + ? { estimate: bt.estimate[k], lower: bt.lower[k], upper: bt.upper[k] } + : { estimate: NaN, lower: NaN, upper: NaN }, + moveMs: bootstrapMean(perGameMs, { reps: Math.min(reps, 1000), seed: seed + 1 + k }), + nodesPerMove: moves > 0 ? nodes / moves : 0, + deepSearchShare: searches > 0 ? deep / searches : 0, + }; + }); + standings.sort((x, y) => y.elo.estimate - x.elo.estimate); + + const pairs: PairStanding[] = []; + for (let i = 0; i < agents.length; i++) { + for (let j = i + 1; j < agents.length; j++) { + const a = agents[i]; + const b = agents[j]; + const games = records.filter( + (g) => + (g.red.agent === a && g.blue.agent === b) || (g.red.agent === b && g.blue.agent === a), + ); + if (games.length === 0) continue; + const aWins = games.filter((g) => winningAgent(g) === a).length; + const bWins = games.filter((g) => winningAgent(g) === b).length; + const asRed = games.filter((g) => g.red.agent === a); + const asBlue = games.filter((g) => g.blue.agent === a); + pairs.push({ + a, + b, + games: games.length, + aWins, + bWins, + draws: games.length - aWins - bWins, + aWinRate: wilson(aWins, games.length), + aAsRed: { games: asRed.length, wins: asRed.filter((g) => g.winner === "red").length }, + aAsBlue: { games: asBlue.length, wins: asBlue.filter((g) => g.winner === "blue").length }, + }); + } + } + + const redWins = records.filter((g) => g.winner === "red").length; + const blueWins = records.filter((g) => g.winner === "blue").length; + return { + games: records.length, + sizes: [...new Set(records.map((g) => g.n))].sort((a, b) => a - b), + agents, + standings, + pairs, + colour: { + games: records.length, + redWins, + blueWins, + draws: records.length - redWins - blueWins, + redWinRate: wilson(redWins, records.length), + }, + meanTurns: records.length ? records.reduce((s, g) => s + g.turns, 0) / records.length : 0, + anchor, + bootstrap: { reps, seed, prior }, + }; +} + +/** One row per game, for CSV export. */ +export function gamesCsv(records: readonly GameRecord[]): string { + const rows = records.map((g) => ({ + game_id: g.id, + board_n: g.n, + seed: g.seed, + round: g.round, + red_agent: g.red.agent, + blue_agent: g.blue.agent, + result: g.result, + winner_colour: g.winner ?? "", + winner_agent: winningAgent(g) ?? "", + draw_reason: g.drawReason ?? "", + turns: g.turns, + red_moves: g.red.moves, + red_total_ms: Number(g.red.totalMs.toFixed(3)), + red_nodes: g.red.nodes, + red_illegal: g.red.illegal, + blue_moves: g.blue.moves, + blue_total_ms: Number(g.blue.totalMs.toFixed(3)), + blue_nodes: g.blue.nodes, + blue_illegal: g.blue.illegal, + })); + return toCsv(rows); +} + +/** Leaderboard and pairwise rows, for CSV export. */ +export function summaryCsv(summary: TournamentSummary): string { + const r = (x: number, d = 4) => (Number.isFinite(x) ? Number(x.toFixed(d)) : null); + const rows: Record[] = []; + for (const s of summary.standings) { + rows.push({ + table: "standing", + agent: s.id, + opponent: "", + games: s.games, + wins: s.wins, + losses: s.losses, + draws: s.draws, + win_rate: r(s.winRate.p), + win_rate_lo95: r(s.winRate.lower), + win_rate_hi95: r(s.winRate.upper), + elo: r(s.elo.estimate, 1), + elo_lo95: r(s.elo.lower, 1), + elo_hi95: r(s.elo.upper, 1), + illegal_moves: s.illegalMoves, + mean_move_ms: r(s.moveMs.estimate, 3), + }); + } + for (const p of summary.pairs) { + rows.push({ + table: "pairing", + agent: p.a, + opponent: p.b, + games: p.games, + wins: p.aWins, + losses: p.bWins, + draws: p.draws, + win_rate: r(p.aWinRate.p), + win_rate_lo95: r(p.aWinRate.lower), + win_rate_hi95: r(p.aWinRate.upper), + elo: null, + elo_lo95: null, + elo_hi95: null, + illegal_moves: null, + mean_move_ms: null, + }); + } + return toCsv(rows); +} + +export const agentLabel = (id: AgentId) => AGENTS[id].label; diff --git a/web/src/lib/tournament/crosscheck.ts b/web/src/lib/tournament/crosscheck.ts new file mode 100644 index 0000000..95fe9f7 --- /dev/null +++ b/web/src/lib/tournament/crosscheck.ts @@ -0,0 +1,67 @@ +/** + * Compare the TypeScript reference tournament with the original-Python + * cross-check (scripts/crosscheck_tournament.py) on the pairings and board + * sizes both ran. RNG streams differ between the two, so games differ; the + * question is whether the win rates agree within sampling error. + */ +import { + type DifferenceCI, + type ProportionCI, + newcombeDifference, + wilson, +} from "@/lib/stats/proportion"; +import type { AgentId } from "./agents"; +import { type GameRecord, winningAgent } from "./play"; +import { canonicalAgents } from "./schedule"; + +export interface PythonGame { + n: number; + red: AgentId; + blue: AgentId; + seed: number; + winner: "red" | "blue" | null; + turns: number; +} + +export interface CrosscheckRow { + a: AgentId; + b: AgentId; + sizes: number[]; + python: ProportionCI; + typescript: ProportionCI; + /** Python − TypeScript win rate of `a`, Newcombe 95% interval. */ + difference: DifferenceCI; +} + +const winnerOf = (g: PythonGame): AgentId | null => (g.winner === null ? null : g[g.winner]); + +export function compareImplementations( + ts: readonly GameRecord[], + py: readonly PythonGame[], +): CrosscheckRow[] { + const sizes = [...new Set(py.map((g) => g.n))].sort((x, y) => x - y); + const agents = canonicalAgents([...new Set(py.flatMap((g) => [g.red, g.blue]))]); + const rows: CrosscheckRow[] = []; + for (let i = 0; i < agents.length; i++) { + for (let j = i + 1; j < agents.length; j++) { + const a = agents[i]; + const b = agents[j]; + const isPair = (red: AgentId, blue: AgentId) => + (red === a && blue === b) || (red === b && blue === a); + const pyGames = py.filter((g) => sizes.includes(g.n) && isPair(g.red, g.blue)); + const tsGames = ts.filter((g) => sizes.includes(g.n) && isPair(g.red.agent, g.blue.agent)); + if (!pyGames.length || !tsGames.length) continue; + const pyWins = pyGames.filter((g) => winnerOf(g) === a).length; + const tsWins = tsGames.filter((g) => winningAgent(g) === a).length; + rows.push({ + a, + b, + sizes, + python: wilson(pyWins, pyGames.length), + typescript: wilson(tsWins, tsGames.length), + difference: newcombeDifference(pyWins, pyGames.length, tsWins, tsGames.length), + }); + } + } + return rows; +} diff --git a/web/src/lib/tournament/play.ts b/web/src/lib/tournament/play.ts new file mode 100644 index 0000000..4b6acb6 --- /dev/null +++ b/web/src/lib/tournament/play.ts @@ -0,0 +1,146 @@ +/** + * Plays one tournament game under the ported referee rules. The referee + * validates every action; an exception or an illegal action counts as an + * illegal move and forfeits the game to the opponent. + */ +import { Game } from "@/lib/cachex/game"; +import { type Action, type Colour, opponent } from "@/lib/cachex/types"; +import { createRng, deriveSeed } from "@/lib/rng"; +import { type AgentId, tournamentMove } from "./agents"; +import type { GameSpec } from "./schedule"; + +export interface SideStats { + agent: AgentId; + moves: number; + /** Total wall-clock time spent choosing moves, in ms. */ + totalMs: number; + maxMs: number; + /** Search nodes visited across the game (0 for random / book moves). */ + nodes: number; + /** Moves chosen by a minimax search (not the book, an instant win or random). */ + searches: number; + /** Of those, searches deeper than one ply. */ + deepSearches: number; + illegal: number; +} + +export interface GameRecord { + id: string; + n: number; + seed: number; + round: number; + result: "win" | "draw" | "forfeit"; + winner: Colour | null; + drawReason: "repetition" | "max-turns" | null; + turns: number; + red: SideStats; + blue: SideStats; +} + +export type MoveFn = ( + id: AgentId, + n: number, + history: readonly Action[], + colour: Colour, + seed: number, +) => Action | { action: Action; nodes?: number; depth?: number | null }; + +const defaultMove: MoveFn = (id, n, history, colour, seed) => { + const decision = tournamentMove(id, n, history, colour, createRng(seed)); + const e = decision.explanation; + return { + action: decision.action, + nodes: e.kind === "search" ? e.nodes : 0, + depth: e.kind === "search" ? e.depth : null, + }; +}; + +export function playTournamentGame( + spec: GameSpec, + { move = defaultMove, now = () => performance.now() }: { move?: MoveFn; now?: () => number } = {}, +): GameRecord { + const game = new Game(spec.n); + const history: Action[] = []; + const side = (agent: AgentId): SideStats => ({ + agent, + moves: 0, + totalMs: 0, + maxMs: 0, + nodes: 0, + searches: 0, + deepSearches: 0, + illegal: 0, + }); + const stats: Record = { red: side(spec.red), blue: side(spec.blue) }; + + while (!game.over()) { + const colour = game.turnPlayer(); + const s = stats[colour]; + const started = now(); + let action: Action | null = null; + try { + const out = move(s.agent, spec.n, history, colour, deriveSeed(spec.seed, history.length)); + if (Array.isArray(out)) action = out as Action; + else { + const result = out as { action: Action; nodes?: number; depth?: number | null }; + action = result.action; + s.nodes += result.nodes ?? 0; + if (typeof result.depth === "number") { + s.searches += 1; + if (result.depth > 1) s.deepSearches += 1; + } + } + } catch { + action = null; + } + const elapsed = now() - started; + s.moves += 1; + s.totalMs += elapsed; + s.maxMs = Math.max(s.maxMs, elapsed); + + if (action === null || !game.isLegal(action)) { + s.illegal += 1; + return finish(spec, game, stats, { result: "forfeit", winner: opponent(colour) }); + } + game.update(colour, action); + history.push(action); + } + + const r = game.result!; + return finish( + spec, + game, + stats, + r.kind === "win" + ? { result: "win", winner: r.winner } + : { result: "draw", winner: null, drawReason: r.reason }, + ); +} + +function finish( + spec: GameSpec, + game: Game, + stats: Record, + outcome: { + result: GameRecord["result"]; + winner: Colour | null; + drawReason?: GameRecord["drawReason"]; + }, +): GameRecord { + return { + id: spec.id, + n: spec.n, + seed: spec.seed, + round: spec.round, + result: outcome.result, + winner: outcome.winner, + drawReason: outcome.drawReason ?? null, + turns: game.nturns, + red: stats.red, + blue: stats.blue, + }; +} + +/** The agent that won, or null for a draw. */ +export const winningAgent = (g: GameRecord): AgentId | null => + g.winner === null ? null : g[g.winner].agent; diff --git a/web/src/lib/tournament/schedule.ts b/web/src/lib/tournament/schedule.ts new file mode 100644 index 0000000..4355dc5 --- /dev/null +++ b/web/src/lib/tournament/schedule.ts @@ -0,0 +1,64 @@ +/** + * Round-robin schedule. Every pairing plays `rounds` colour-swapped pairs of + * games on every board size: in each round the same seed is used twice, once + * with each agent as Red, so first-move advantage cancels within a pairing. + */ +import { deriveSeed } from "@/lib/rng"; +import { AGENT_IDS, type AgentId } from "./agents"; + +export interface TournamentConfig { + agents: AgentId[]; + sizes: number[]; + /** Colour-swapped game pairs per pairing per board size (games = 2 × rounds). */ + rounds: number; + seed: number; +} + +export interface GameSpec { + id: string; + n: number; + red: AgentId; + blue: AgentId; + /** Shared by both games of a colour-swapped pair. */ + seed: number; + round: number; +} + +/** Agents in canonical order, without duplicates. */ +export const canonicalAgents = (agents: readonly AgentId[]) => + AGENT_IDS.filter((id) => agents.includes(id)); + +export function pairKey(a: AgentId, b: AgentId): string { + const [x, y] = canonicalAgents([a, b]); + return `${x}|${y}`; +} + +export function buildSchedule(config: TournamentConfig): GameSpec[] { + const agents = canonicalAgents(config.agents); + if (agents.length < 2) throw new RangeError("a round robin needs at least two agents"); + if (!Number.isInteger(config.rounds) || config.rounds < 1) { + throw new RangeError("rounds must be a positive integer"); + } + const sizes = [...new Set(config.sizes)].sort((a, b) => a - b); + const games: GameSpec[] = []; + for (const n of sizes) { + for (let i = 0; i < agents.length; i++) { + for (let j = i + 1; j < agents.length; j++) { + const a = agents[i]; + const b = agents[j]; + for (let round = 0; round < config.rounds; round++) { + const seed = deriveSeed( + config.seed, + n, + AGENT_IDS.indexOf(a), + AGENT_IDS.indexOf(b), + round, + ); + games.push({ id: `${n}:${a}:${b}:${round}:a`, n, red: a, blue: b, seed, round }); + games.push({ id: `${n}:${a}:${b}:${round}:b`, n, red: b, blue: a, seed, round }); + } + } + } + } + return games; +} diff --git a/web/src/lib/tournament/tournament.test.ts b/web/src/lib/tournament/tournament.test.ts new file mode 100644 index 0000000..7ee98d6 --- /dev/null +++ b/web/src/lib/tournament/tournament.test.ts @@ -0,0 +1,289 @@ +import { describe, expect, it } from "vitest"; + +import { AgentBoard } from "@/lib/agent/agent-board"; +import { chooseAgentAction } from "@/lib/agent/player"; +import { runAgent } from "@/lib/agent/run-agent"; +import { place } from "@/lib/cachex/types"; +import { csvCell, toCsv } from "@/lib/csv"; +import { AGENT_IDS, AGENTS, tournamentMove } from "./agents"; +import { gamesCsv, summariseTournament, summaryCsv } from "./analyse"; +import { type GameRecord, playTournamentGame } from "./play"; +import { buildSchedule, pairKey } from "./schedule"; +import { createRng } from "@/lib/rng"; + +describe("round-robin schedule", () => { + const config = { + agents: ["random", "greedy", "minimax-dynamic"] as const, + sizes: [5, 4, 4], + rounds: 3, + seed: 11, + }; + const games = buildSchedule({ ...config, agents: [...config.agents] }); + + it("plays every pairing on every size, colour-swapped", () => { + // 3 pairings × 2 sizes × 3 rounds × 2 colours + expect(games).toHaveLength(36); + const byPair = new Map(); + for (const g of games) { + const key = `${g.n}|${pairKey(g.red, g.blue)}`; + byPair.set(key, [...(byPair.get(key) ?? []), g]); + } + expect(byPair.size).toBe(6); + for (const list of byPair.values()) { + expect(list).toHaveLength(6); + const [a, b] = pairKey(list[0].red, list[0].blue).split("|"); + expect(list.filter((g) => g.red === a)).toHaveLength(3); + expect(list.filter((g) => g.red === b)).toHaveLength(3); + } + }); + + it("shares one seed between the two games of a colour-swapped pair", () => { + for (let i = 0; i < games.length; i += 2) { + expect(games[i].seed).toBe(games[i + 1].seed); + expect(games[i].red).toBe(games[i + 1].blue); + } + expect(new Set(games.map((g) => g.seed)).size).toBe(games.length / 2); + expect(new Set(games.map((g) => g.id)).size).toBe(games.length); + }); + + it("is deterministic and rejects degenerate configs", () => { + expect(buildSchedule({ ...config, agents: [...config.agents] })).toEqual(games); + expect(() => buildSchedule({ ...config, agents: ["random"] })).toThrow(RangeError); + expect(() => buildSchedule({ ...config, agents: [...config.agents], rounds: 0 })).toThrow( + RangeError, + ); + }); +}); + +describe("agents", () => { + it("are the original with one knob changed", () => { + const n = 6; + const history = [place(1, 1), place(4, 2), place(2, 3)] as const; + // The original, unpatched, is exactly what /play and /spectate run. + const original = tournamentMove("minimax-dynamic", n, history, "blue", createRng(3)); + const viaPlayPage = runAgent({ + id: 1, + kind: "minimax", + n, + history: [...history], + colour: "blue", + seed: 3, + }); + if (!viaPlayPage.ok) throw new Error(viaPlayPage.error); + expect(original).toEqual(viaPlayPage.decision); + if (original.explanation.kind !== "search") throw new Error("expected a search"); + expect(original.explanation.depth).toBe(1); + + const d3 = tournamentMove("minimax-d3", n, history, "blue", createRng(3)); + if (d3.explanation.kind !== "search") throw new Error("expected a search"); + expect(d3.explanation.depth).toBe(3); + + // Greedy skips the opening book. + const greedyOpening = tournamentMove("greedy", n, [], "red", createRng(1)); + expect(greedyOpening.explanation.kind).toBe("search"); + const bookOpening = tournamentMove("minimax-d2", n, [], "red", createRng(1)); + expect(bookOpening.explanation.kind).toBe("opening"); + expect(tournamentMove("random", n, [], "red", createRng(1)).explanation.kind).toBe("random"); + expect(AGENT_IDS.every((id) => AGENTS[id].id === id)).toBe(true); + }); + + it("fixed depth 1 never looks past one ply", () => { + const board = AgentBoard.fromActions(5, [place(1, 1), place(3, 3), place(2, 2)]); + const d = chooseAgentAction(5, [place(1, 1), place(3, 3), place(2, 2)], "blue", { + fixedDepth: 1, + }); + if (d.explanation.kind !== "search") throw new Error("expected a search"); + expect(d.explanation.depth).toBe(1); + // depth 1 visits the root plus one node per legal move + expect(d.explanation.nodes).toBe(1 + board.emptyCount()); + }); +}); + +describe("playing a game", () => { + const spec = { + id: "t", + n: 4, + red: "minimax-dynamic", + blue: "random", + seed: 99, + round: 0, + } as const; + + it("is reproducible from its seed", () => { + let t = 0; + const now = () => (t += 1); + const a = playTournamentGame({ ...spec }, { now }); + t = 0; + const b = playTournamentGame({ ...spec }, { now }); + expect(a).toEqual(b); + expect(a.result === "win" || a.result === "draw").toBe(true); + expect(a.red.moves + a.blue.moves).toBe(a.turns); + expect(a.red.illegal + a.blue.illegal).toBe(0); + }); + + it("forfeits the game on an illegal move or an exception", () => { + const illegal = playTournamentGame( + { ...spec }, + { + move: (id, n, history, colour) => + colour === "blue" + ? place(9, 9) + : tournamentMove(id, n, history, colour, createRng(1)).action, + }, + ); + expect(illegal).toMatchObject({ result: "forfeit", winner: "red", turns: 1 }); + expect(illegal.blue.illegal).toBe(1); + + const crash = playTournamentGame( + { ...spec }, + { + move: () => { + throw new Error("boom"); + }, + }, + ); + expect(crash).toMatchObject({ result: "forfeit", winner: "blue", turns: 0 }); + expect(crash.red.illegal).toBe(1); + }); +}); + +function record( + red: GameRecord["red"]["agent"], + blue: GameRecord["blue"]["agent"], + winner: "red" | "blue" | null, + n = 4, + i = 0, +): GameRecord { + const side = (agent: GameRecord["red"]["agent"]) => ({ + agent, + moves: 5, + totalMs: 10, + maxMs: 3, + nodes: 20, + searches: 4, + deepSearches: 1, + illegal: 0, + }); + return { + id: `${red}-${blue}-${i}`, + n, + seed: i, + round: i, + result: winner === null ? "draw" : "win", + winner, + drawReason: winner === null ? "repetition" : null, + turns: 10, + red: side(red), + blue: side(blue), + }; +} + +describe("tournament summary", () => { + // Original beats random 9-1, greedy beats random 7-3, original vs greedy 6-4. + const games: GameRecord[] = []; + const add = ( + a: GameRecord["red"]["agent"], + b: GameRecord["red"]["agent"], + aWins: number, + bWins: number, + ) => { + for (let k = 0; k < aWins; k++) + games.push(record(k % 2 ? a : b, k % 2 ? b : a, k % 2 ? "red" : "blue", 4, games.length)); + for (let k = 0; k < bWins; k++) + games.push(record(k % 2 ? a : b, k % 2 ? b : a, k % 2 ? "blue" : "red", 4, games.length)); + }; + add("minimax-dynamic", "random", 9, 1); + add("greedy", "random", 7, 3); + add("minimax-dynamic", "greedy", 6, 4); + games.push(record("greedy", "random", null, 5, games.length)); + const summary = summariseTournament(games, { reps: 200, seed: 1 }); + + it("counts wins, losses and draws per agent with Wilson intervals", () => { + const original = summary.standings.find((s) => s.id === "minimax-dynamic")!; + expect(original).toMatchObject({ games: 20, wins: 15, losses: 5, draws: 0 }); + expect(original.winRate.p).toBe(0.75); + const greedy = summary.standings.find((s) => s.id === "greedy")!; + expect(greedy).toMatchObject({ games: 21, wins: 11, losses: 9, draws: 1 }); + expect(summary.games).toBe(31); + expect(summary.sizes).toEqual([4, 5]); + }); + + it("anchors Elo at the random agent and orders the leaderboard by strength", () => { + expect(summary.anchor).toBe("random"); + expect(summary.standings.map((s) => s.id)).toEqual(["minimax-dynamic", "greedy", "random"]); + const random = summary.standings.find((s) => s.id === "random")!; + expect(random.elo.estimate).toBe(0); + for (const s of summary.standings) { + expect(s.elo.lower).toBeLessThanOrEqual(s.elo.estimate + 1e-9); + expect(s.elo.upper).toBeGreaterThanOrEqual(s.elo.estimate - 1e-9); + } + }); + + it("reports pairings and the colour effect", () => { + const p = summary.pairs.find((x) => x.a === "minimax-dynamic" && x.b === "random")!; + expect(p).toMatchObject({ games: 10, aWins: 9, bWins: 1, draws: 0 }); + expect(p.aAsRed.games + p.aAsBlue.games).toBe(10); + expect(summary.colour.redWins + summary.colour.blueWins + summary.colour.draws).toBe(31); + }); + + it("filters by board size and is reproducible", () => { + const only5 = summariseTournament(games, { reps: 50, seed: 1, sizes: [5] }); + expect(only5.games).toBe(1); + expect(summariseTournament(games, { reps: 200, seed: 1 })).toEqual(summary); + }); + + it("exports CSV", () => { + const csv = gamesCsv(games); + expect(csv.split("\r\n")[0]).toContain("game_id,board_n,seed"); + expect(csv.trim().split("\r\n")).toHaveLength(games.length + 1); + const s = summaryCsv(summary); + expect(s).toContain("standing,minimax-dynamic"); + expect(s).toContain("pairing,minimax-dynamic,random,10,9,1,0,0.9"); + }); +}); + +describe("CSV writer", () => { + it("quotes, escapes and neutralises formulas", () => { + expect(csvCell('say "hi", ok')).toBe('"say ""hi"", ok"'); + expect(csvCell("=HYPERLINK(1)")).toBe("'=HYPERLINK(1)"); + expect(csvCell(-3)).toBe("-3"); + expect(csvCell(null)).toBe(""); + expect(csvCell(NaN)).toBe(""); + expect(toCsv([{ a: 1, b: "x\ny" }])).toBe('a,b\r\n1,"x\ny"\r\n'); + expect(toCsv([])).toBe("\r\n"); + }); +}); + +describe("Python cross-check comparison", () => { + it("compares win rates per pairing on the shared sizes", async () => { + const { compareImplementations } = await import("./crosscheck"); + const ts: GameRecord[] = [ + record("minimax-dynamic", "random", "red", 4, 1), + record("random", "minimax-dynamic", "blue", 4, 2), + record("minimax-dynamic", "random", "blue", 6, 3), // size not in the Python run + ]; + const py = [ + { + n: 4, + red: "minimax-dynamic" as const, + blue: "random" as const, + seed: 1, + winner: "red" as const, + turns: 9, + }, + { + n: 4, + red: "random" as const, + blue: "minimax-dynamic" as const, + seed: 1, + winner: "red" as const, + turns: 9, + }, + ]; + const [row] = compareImplementations(ts, py); + expect(row).toMatchObject({ a: "minimax-dynamic", b: "random", sizes: [4] }); + expect(row.python.p).toBe(0.5); + expect(row.typescript.p).toBe(1); + expect(row.difference.estimate).toBe(-0.5); + }); +}); diff --git a/web/src/workers/astar-benchmark.worker.ts b/web/src/workers/astar-benchmark.worker.ts index 5f5dea9..09b38a5 100644 --- a/web/src/workers/astar-benchmark.worker.ts +++ b/web/src/workers/astar-benchmark.worker.ts @@ -1,16 +1,17 @@ /** - * Runs the Manhattan vs Euclidean heuristic study off the main thread. + * Runs the paired Manhattan vs Euclidean heuristic study off the main thread. */ -import { type BenchmarkRow, runHeuristicBenchmark } from "@/lib/astar/random-board"; +import { type PairedStudyRow, runPairedStudy } from "@/lib/analysis/astar-paired"; export interface BenchmarkRequest { id: number; dimensions: number[]; + boardsPerDimension: number; seed: number; } export type BenchmarkResponse = - | { id: number; ok: true; rows: BenchmarkRow[]; elapsedMs: number } + | { id: number; ok: true; rows: PairedStudyRow[]; elapsedMs: number } | { id: number; ok: false; error: string }; const ctx = self as unknown as { @@ -24,7 +25,7 @@ const ctx = self as unknown as { ctx.addEventListener("message", ({ data }) => { const started = performance.now(); try { - const rows = runHeuristicBenchmark(data.dimensions, data.seed); + const rows = runPairedStudy(data); ctx.postMessage({ id: data.id, ok: true, rows, elapsedMs: performance.now() - started }); } catch (err) { ctx.postMessage({ diff --git a/web/src/workers/tournament.worker.ts b/web/src/workers/tournament.worker.ts new file mode 100644 index 0000000..9069e7a --- /dev/null +++ b/web/src/workers/tournament.worker.ts @@ -0,0 +1,34 @@ +/** + * Plays tournament games off the main thread. The pool in + * use-tournament-pool.ts runs several of these in parallel. + */ +import { type GameRecord, playTournamentGame } from "@/lib/tournament/play"; +import type { GameSpec } from "@/lib/tournament/schedule"; + +export interface TournamentJob { + id: number; + spec: GameSpec; +} + +export type TournamentJobResult = + { id: number; ok: true; record: GameRecord } | { id: number; ok: false; error: string }; + +const ctx = self as unknown as { + postMessage: (message: TournamentJobResult) => void; + addEventListener: ( + type: "message", + listener: (event: MessageEvent) => void, + ) => void; +}; + +ctx.addEventListener("message", ({ data }) => { + try { + ctx.postMessage({ id: data.id, ok: true, record: playTournamentGame(data.spec) }); + } catch (err) { + ctx.postMessage({ + id: data.id, + ok: false, + error: err instanceof Error ? err.message : String(err), + }); + } +}); From dac6fa13e08fffda3371467048558a5d07c4cd9c Mon Sep 17 00:00:00 2001 From: rNLKJA <62606765+rNLKJA@users.noreply.github.com> Date: Tue, 6 Oct 2026 00:59:11 +1030 Subject: [PATCH 03/18] feat(ai): add bring-your-own-key commentator, LLM arena and audit log - AI settings dialog: Anthropic (default, Claude Haiku 4.5 or Sonnet 5.5) or OpenAI (editable model id). The visitor's key is kept in sessionStorage by default, localStorage only on opt-in, with a forget button. Calls go straight from the browser to the provider; the key never reaches this site, the audit log or the repository. - Provider adapters in lib/ai (official Anthropic SDK with browser access, OpenAI Chat Completions with strict JSON schema), zod validation and friendly errors (invalid key, rate limit, overload, network/CORS, refusal, truncation). - Move commentator grounded in the agent's own evaluation breakdown, with an automatic grounding check and accept / edit / reject review. - LLM Arena: the model plays the minimax agent with JSON moves checked against the legal moves (retry, then forfeit), compared on identical seeds with random and greedy baselines; Wilson CIs, illegal-answer rate, latency and tokens, CSV export. - Every call is audit-logged in IndexedDB and viewable at /ai-log with JSON and CSV export. All output is labelled AI-generated. - Tests mock fetch for both providers and assert the key never appears in log entries or exports. Co-Authored-By: Claude Opus 5.5 --- web/src/app/ai-log/page.tsx | 35 ++ web/src/app/layout.tsx | 13 +- web/src/app/llm-arena/page.tsx | 41 ++ web/src/components/ai/ai-badge.tsx | 19 + web/src/components/ai/ai-log-client.tsx | 228 ++++++++ web/src/components/ai/ai-provider.tsx | 147 +++++ web/src/components/ai/ai-settings-button.tsx | 29 + web/src/components/ai/ai-settings-dialog.tsx | 221 +++++++ web/src/components/ai/commentator-panel.tsx | 252 ++++++++ web/src/components/ai/llm-arena-client.tsx | 493 ++++++++++++++++ web/src/components/layout/site-header.tsx | 23 +- web/src/components/play/explain-panel.tsx | 17 +- web/src/components/play/play-client.tsx | 1 + web/src/components/play/spectate-client.tsx | 1 + web/src/components/ui/dialog.tsx | 95 +++ web/src/lib/ai/ai.test.ts | 575 +++++++++++++++++++ web/src/lib/ai/anthropic.ts | 84 +++ web/src/lib/ai/audit-log.ts | 211 +++++++ web/src/lib/ai/client.ts | 90 +++ web/src/lib/ai/commentator.ts | 258 +++++++++ web/src/lib/ai/llm-player.ts | 340 +++++++++++ web/src/lib/ai/models.ts | 23 + web/src/lib/ai/openai.ts | 128 +++++ web/src/lib/ai/settings.ts | 106 ++++ web/src/lib/ai/types.ts | 100 ++++ 25 files changed, 3522 insertions(+), 8 deletions(-) create mode 100644 web/src/app/ai-log/page.tsx create mode 100644 web/src/app/llm-arena/page.tsx create mode 100644 web/src/components/ai/ai-badge.tsx create mode 100644 web/src/components/ai/ai-log-client.tsx create mode 100644 web/src/components/ai/ai-provider.tsx create mode 100644 web/src/components/ai/ai-settings-button.tsx create mode 100644 web/src/components/ai/ai-settings-dialog.tsx create mode 100644 web/src/components/ai/commentator-panel.tsx create mode 100644 web/src/components/ai/llm-arena-client.tsx create mode 100644 web/src/components/ui/dialog.tsx create mode 100644 web/src/lib/ai/ai.test.ts create mode 100644 web/src/lib/ai/anthropic.ts create mode 100644 web/src/lib/ai/audit-log.ts create mode 100644 web/src/lib/ai/client.ts create mode 100644 web/src/lib/ai/commentator.ts create mode 100644 web/src/lib/ai/llm-player.ts create mode 100644 web/src/lib/ai/models.ts create mode 100644 web/src/lib/ai/openai.ts create mode 100644 web/src/lib/ai/settings.ts create mode 100644 web/src/lib/ai/types.ts diff --git a/web/src/app/ai-log/page.tsx b/web/src/app/ai-log/page.tsx new file mode 100644 index 0000000..ddb608b --- /dev/null +++ b/web/src/app/ai-log/page.tsx @@ -0,0 +1,35 @@ +import type { Metadata } from "next"; +import Link from "next/link"; + +import { AiLogClient } from "@/components/ai/ai-log-client"; +import { PageHeader } from "@/components/layout/page-header"; + +export const metadata: Metadata = { + title: "AI audit log", + description: + "Every AI call made from this browser: feature, provider, model, exact input, output, latency, tokens and your review decision. Exportable as JSON or CSV. API keys are never recorded.", +}; + +export default function AiLogPage() { + return ( +
    + +

    + Every call the optional AI features make is recorded here: what was sent, what came back, + which provider and model, how long it took, the tokens the provider reported, and your + decision on the output (accepted, edited or rejected; automated evaluation calls are + marked n/a). +

    +

    + The log lives only in this browser (IndexedDB). This static site has no server, so nothing + here is sent to us. Your API key is never written to the log.{" "} + + AI use statement + + . +

    +     + +
    + ); +} diff --git a/web/src/app/layout.tsx b/web/src/app/layout.tsx index 0648690..803c404 100644 --- a/web/src/app/layout.tsx +++ b/web/src/app/layout.tsx @@ -1,6 +1,7 @@ import type { Metadata, Viewport } from "next"; import { Bricolage_Grotesque, Geist, Geist_Mono } from "next/font/google"; +import { AiProvider } from "@/components/ai/ai-provider"; import { SiteFooter } from "@/components/layout/site-footer"; import { SiteHeader } from "@/components/layout/site-header"; import { ThemeProvider } from "@/components/layout/theme-provider"; @@ -62,11 +63,13 @@ export default function RootLayout({ children }: { children: React.ReactNode }) enableSystem disableTransitionOnChange > - -
    - {children} -
    - + + +
    + {children} +
    + +     diff --git a/web/src/app/llm-arena/page.tsx b/web/src/app/llm-arena/page.tsx new file mode 100644 index 0000000..f4ecd88 --- /dev/null +++ b/web/src/app/llm-arena/page.tsx @@ -0,0 +1,41 @@ +import type { Metadata } from "next"; +import Link from "next/link"; + +import { LlmArenaClient } from "@/components/ai/llm-arena-client"; +import { PageHeader } from "@/components/layout/page-header"; + +export const metadata: Metadata = { + title: "LLM Arena", + description: + "An evaluation harness: a language model (your own API key) plays short games of Cachex against the original minimax agent, with legal-move validation, Wilson intervals and a full audit log.", +}; + +export default function LlmArenaPage() { + return ( +
    +
    + +

    + The model plays short games against the original minimax agent. Each turn it gets the + rules, the board, the move history and the full list of legal moves, and must answer + with a JSON move. The referee checks every answer: an illegal or malformed one is + counted and the model is asked again (up to three times) before it forfeits. +

    +

    + The same seeds and colours are replayed with the random and greedy agents in the + model's seat, so the comparison is like for like. This page is an evaluation + harness, not a claim: a few games give wide intervals.{" "} + + What is sent to the provider + + . +

    +     + +
    +
    + ); +} diff --git a/web/src/components/ai/ai-badge.tsx b/web/src/components/ai/ai-badge.tsx new file mode 100644 index 0000000..be07f93 --- /dev/null +++ b/web/src/components/ai/ai-badge.tsx @@ -0,0 +1,19 @@ +import { Sparkles } from "lucide-react"; + +import { cn } from "@/lib/utils"; + +/** Visible label on every piece of model output. */ +export function AiBadge({ model, className }: { model?: string; className?: string }) { + return ( + + + AI-generated + {model && · {model}} + + ); +} diff --git a/web/src/components/ai/ai-log-client.tsx b/web/src/components/ai/ai-log-client.tsx new file mode 100644 index 0000000..8529aa3 --- /dev/null +++ b/web/src/components/ai/ai-log-client.tsx @@ -0,0 +1,228 @@ +"use client"; + +import { Download, RefreshCw, Trash2 } from "lucide-react"; +import { useCallback, useEffect, useMemo, useState } from "react"; + +import { Segmented } from "@/components/play/primitives"; +import { Button } from "@/components/ui/button"; +import { + type AuditEntry, + DECISION_LABEL, + auditToCsv, + auditToJson, + onAuditChange, +} from "@/lib/ai/audit-log"; +import { FEATURE_LABEL, PROVIDER_LABEL } from "@/lib/ai/types"; +import { downloadText } from "@/lib/download"; +import { cn } from "@/lib/utils"; +import { AiBadge } from "./ai-badge"; +import { useAi } from "./ai-provider"; + +type Filter = "all" | "commentator" | "llm-player"; + +export function AiLogClient() { + const { audit } = useAi(); + const [entries, setEntries] = useState(null); + const [error, setError] = useState(null); + const [filter, setFilter] = useState("all"); + const [confirmClear, setConfirmClear] = useState(false); + + const refresh = useCallback(() => { + audit + .list() + .then((list) => { + setEntries(list); + setError(null); + }) + .catch((err: unknown) => setError(err instanceof Error ? err.message : String(err))); + }, [audit]); + + useEffect(() => { + // Subscribe first, then load: both callbacks set state asynchronously. + const unsubscribe = onAuditChange(refresh); + const timer = setTimeout(refresh, 0); + return () => { + unsubscribe(); + clearTimeout(timer); + }; + }, [refresh]); + + const shown = useMemo( + () => (entries ?? []).filter((e) => filter === "all" || e.feature === filter), + [entries, filter], + ); + + const stats = useMemo(() => { + const list = entries ?? []; + const reviewed = list.filter((e) => + ["accepted", "edited", "rejected"].includes(e.humanDecision), + ); + return { + calls: list.length, + errors: list.filter((e) => e.error).length, + reviewed: reviewed.length, + accepted: reviewed.filter((e) => e.humanDecision === "accepted").length, + edited: reviewed.filter((e) => e.humanDecision === "edited").length, + rejected: reviewed.filter((e) => e.humanDecision === "rejected").length, + }; + }, [entries]); + + const stamp = new Date().toISOString().slice(0, 10); + + return ( +
    +
    +
    + +
    +
    + + + + {confirmClear ? ( + <> + + + + ) : ( + + )} +
    +
    + +
    + + + + +
    + + {error && ( +

    + Could not read the log: {error} +

    + )} + {entries === null && !error &&

    Loading…

    } + {entries !== null && shown.length === 0 && ( +
    + No AI calls recorded in this browser yet. Use the commentator on /play or /spectate, or + run the LLM Arena, with your own key. +
    + )} + +
      + {shown.map((e) => ( +
    1. +
      + {FEATURE_LABEL[e.feature]} + + {new Date(e.timestamp).toLocaleString("en-AU")} + + + {PROVIDER_LABEL[e.provider]} · {e.model} + + + {Math.round(e.latencyMs).toLocaleString("en-AU")} ms + {e.usage ? ` · ${e.usage.inputTokens} in / ${e.usage.outputTokens} out tokens` : ""} + + + {DECISION_LABEL[e.humanDecision]} + +
      + {e.error ? ( +

      + Error ({e.error.kind}): {e.error.message} +

      + ) : ( +
      + + 
      +                  {JSON.stringify(e.output, null, 2)}
+
      +
      + )} + {e.editedOutput !== undefined && ( +
      +

      Human-edited version

      +

      {e.editedOutput}

      +
      + )} +
      + + Exact input sent (system prompt, user prompt, schema) + + 
      +                {`SYSTEM\n${e.input.system}\n\nUSER\n${e.input.user}\n\nSCHEMA ${e.input.schema}`}
+                {e.context ? `\n\nCONTEXT ${JSON.stringify(e.context)}` : ""}
+
      +
      +
      2. + ))} +
    +
    + ); +} + +function Stat({ label, value }: { label: string; value: string | number }) { + return ( +
    +
    {label}
    +
    {value}
    +
    + ); +} diff --git a/web/src/components/ai/ai-provider.tsx b/web/src/components/ai/ai-provider.tsx new file mode 100644 index 0000000..f6d492e --- /dev/null +++ b/web/src/components/ai/ai-provider.tsx @@ -0,0 +1,147 @@ +"use client"; + +import { + type ReactNode, + createContext, + useCallback, + useContext, + useMemo, + useState, + useSyncExternalStore, +} from "react"; + +import { type AuditStore, getAuditStore } from "@/lib/ai/audit-log"; +import { + type AiPrefs, + DEFAULT_PREFS, + type StoredKey, + forgetKey, + loadKey, + loadPrefs, + saveKey, + savePrefs, +} from "@/lib/ai/settings"; +import type { Credentials } from "@/lib/ai/types"; +import { AiSettingsDialog } from "./ai-settings-dialog"; + +/* + * Browser storage as an external store, so components re-render when the + * settings change (in this tab or another) without effects or hydration + * mismatches: the server snapshot is "not loaded yet". + */ +const listeners = new Set<() => void>(); +let version = 0; +const emit = () => { + version++; + for (const l of listeners) l(); +}; +function subscribe(listener: () => void) { + listeners.add(listener); + const onStorage = () => emit(); + window.addEventListener("storage", onStorage); + return () => { + listeners.delete(listener); + window.removeEventListener("storage", onStorage); + }; +} +const getSnapshot = () => version; +const getServerSnapshot = () => -1; + +const storages = () => ({ + session: typeof sessionStorage === "undefined" ? null : sessionStorage, + local: typeof localStorage === "undefined" ? null : localStorage, +}); + +interface AiContextValue { + /** False until browser storage has been read (always false on the server). */ + ready: boolean; + prefs: AiPrefs; + setPrefs: (prefs: AiPrefs) => void; + /** The stored key for the selected provider, if any. */ + storedKey: StoredKey | null; + saveApiKey: (key: string, remember: boolean) => void; + forgetApiKey: () => void; + /** Ready-to-use credentials, or null when no key is set. */ + credentials: Credentials | null; + audit: AuditStore; + settingsOpen: boolean; + openSettings: () => void; + setSettingsOpen: (open: boolean) => void; +} + +const AiContext = createContext(null); + +export function AiProvider({ children }: { children: ReactNode }) { + const snapshot = useSyncExternalStore(subscribe, getSnapshot, getServerSnapshot); + const ready = snapshot >= 0; + const [settingsOpen, setSettingsOpen] = useState(false); + + const prefs = useMemo( + () => (snapshot >= 0 ? loadPrefs(storages().local) : DEFAULT_PREFS), + [snapshot], + ); + const storedKey = useMemo(() => { + if (snapshot < 0) return null; + const { session, local } = storages(); + return loadKey(prefs.provider, session, local); + }, [snapshot, prefs.provider]); + + const setPrefs = useCallback((next: AiPrefs) => { + savePrefs(storages().local, next); + emit(); + }, []); + const saveApiKey = useCallback( + (key: string, remember: boolean) => { + const { session, local } = storages(); + saveKey(prefs.provider, key, remember, session, local); + emit(); + }, + [prefs.provider], + ); + const forgetApiKey = useCallback(() => { + const { session, local } = storages(); + forgetKey(prefs.provider, session, local); + emit(); + }, [prefs.provider]); + + const credentials = useMemo( + () => + storedKey + ? { + provider: prefs.provider, + model: prefs.provider === "anthropic" ? prefs.anthropicModel : prefs.openaiModel, + apiKey: storedKey.key, + } + : null, + [prefs, storedKey], + ); + + const audit = useMemo(() => getAuditStore(), []); + + const value: AiContextValue = { + ready, + prefs, + setPrefs, + storedKey, + saveApiKey, + forgetApiKey, + credentials, + audit, + settingsOpen, + openSettings: () => setSettingsOpen(true), + setSettingsOpen, + }; + + return ( + + {children} + + + ); +} + +export function useAi(): AiContextValue { + const ctx = useContext(AiContext); + if (!ctx) throw new Error("useAi must be used inside "); + return ctx; +} diff --git a/web/src/components/ai/ai-settings-button.tsx b/web/src/components/ai/ai-settings-button.tsx new file mode 100644 index 0000000..8fbe9f3 --- /dev/null +++ b/web/src/components/ai/ai-settings-button.tsx @@ -0,0 +1,29 @@ +"use client"; + +import { KeyRound } from "lucide-react"; + +import { Button } from "@/components/ui/button"; +import { useAi } from "./ai-provider"; + +export function AiSettingsButton() { + const { openSettings, storedKey, ready } = useAi(); + const active = ready && storedKey !== null; + return ( + + ); +} diff --git a/web/src/components/ai/ai-settings-dialog.tsx b/web/src/components/ai/ai-settings-dialog.tsx new file mode 100644 index 0000000..ba0ef6f --- /dev/null +++ b/web/src/components/ai/ai-settings-dialog.tsx @@ -0,0 +1,221 @@ +"use client"; + +import { Eye, EyeOff, KeyRound, ShieldCheck, Trash2 } from "lucide-react"; +import Link from "next/link"; +import { useState } from "react"; + +import { Segmented } from "@/components/play/primitives"; +import { Button } from "@/components/ui/button"; +import { Checkbox } from "@/components/ui/checkbox"; +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle, +} from "@/components/ui/dialog"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from "@/components/ui/select"; +import { ANTHROPIC_MODELS, DEFAULT_OPENAI_MODEL } from "@/lib/ai/models"; +import { maskKey } from "@/lib/ai/settings"; +import { PROVIDER_LABEL, type Provider } from "@/lib/ai/types"; +import { useAi } from "./ai-provider"; + +export function AiSettingsDialog() { + const { settingsOpen, setSettingsOpen } = useAi(); + return ( + + + {/* Remount the form each time the dialog opens so it starts from saved state. */} + {settingsOpen && setSettingsOpen(false)} />} + + + ); +} + +function SettingsForm({ onDone }: { onDone: () => void }) { + const { prefs, setPrefs, storedKey, saveApiKey, forgetApiKey } = useAi(); + const [draftKey, setDraftKey] = useState(""); + const [remember, setRemember] = useState(storedKey?.remembered ?? false); + const [showKey, setShowKey] = useState(false); + const [openaiModel, setOpenaiModel] = useState(prefs.openaiModel); + + const setProvider = (provider: Provider) => { + setPrefs({ ...prefs, provider }); + setDraftKey(""); + }; + + const save = () => { + setPrefs({ ...prefs, openaiModel: openaiModel.trim() || DEFAULT_OPENAI_MODEL }); + if (draftKey.trim()) saveApiKey(draftKey, remember); + else if (storedKey && storedKey.remembered !== remember) saveApiKey(storedKey.key, remember); + onDone(); + }; + + return ( + <> + + + AI settings + + + Optional. Everything on this site works without a key. With your own key you can turn on + the move commentator and the LLM-as-a-player evaluation. + + + +
    + +

    + Your key stays in this browser. Requests go{" "} + directly from your browser to {PROVIDER_LABEL[prefs.provider]}; this site + has no server that could see the key, and it is never logged or written to the AI audit + log. Calls are billed to your account by the provider. +

    +
    + +
    +
    + + Provider + + +
    + + {prefs.provider === "anthropic" ? ( +
    + + +
    + ) : ( +
    + + setOpenaiModel(e.target.value)} + placeholder={DEFAULT_OPENAI_MODEL} + /> +

    + Any Chat Completions model that supports JSON-schema output. +

    +
    + )} + +
    + +
    + setDraftKey(e.target.value)} + placeholder={storedKey ? `Saved: ${maskKey(storedKey.key)}` : "Paste your key"} + autoComplete="off" + spellCheck={false} + aria-describedby="ai-key-status" + /> + +
    +

    + {storedKey + ? storedKey.remembered + ? `A key (${maskKey(storedKey.key)}) is remembered on this device.` + : `A key (${maskKey(storedKey.key)}) is saved for this tab only.` + : "No key saved."} +

    +
    + +
    + setRemember(v === true)} + className="mt-0.5" + /> + +
    +
    + + + {storedKey && ( + + )} + + + +

    + What the AI features do and never do:{" "} + + AI use statement + + . +

    + + ); +} diff --git a/web/src/components/ai/commentator-panel.tsx b/web/src/components/ai/commentator-panel.tsx new file mode 100644 index 0000000..153d8c7 --- /dev/null +++ b/web/src/components/ai/commentator-panel.tsx @@ -0,0 +1,252 @@ +"use client"; + +import { + Check, + CircleAlert, + CircleCheck, + Loader2, + MessageSquareText, + Pencil, + X, +} from "lucide-react"; +import Link from "next/link"; +import { useState } from "react"; + +import { Button } from "@/components/ui/button"; +import { Textarea } from "@/components/ui/textarea"; +import type { MoveExplanation } from "@/lib/agent/player"; +import { DECISION_LABEL, type HumanDecision } from "@/lib/ai/audit-log"; +import { callStructured } from "@/lib/ai/client"; +import { + type Commentary, + CommentarySchema, + type GroundingResult, + buildCommentaryFacts, + commentaryPrompt, + commentaryToText, + groundingCheck, +} from "@/lib/ai/commentator"; +import { isAiError } from "@/lib/ai/types"; +import type { Action, Colour } from "@/lib/cachex/types"; +import { cn } from "@/lib/utils"; +import { AiBadge } from "./ai-badge"; +import { useAi } from "./ai-provider"; + +type State = + | { status: "idle" } + | { status: "loading" } + | { status: "error"; message: string } + | { + status: "done"; + commentary: Commentary; + grounding: GroundingResult; + entryId: string; + model: string; + decision: HumanDecision; + edited?: string; + }; + +const FEATURE_LABEL: Record = { + empty: "Empty hexes", + triangle: "Triangle formations", + tokens: "Token count", + location: "Positional value", + diamond: "Capturable diamonds", + weakness: "Weak formations", +}; + +/** + * "Explain in plain English": a bring-your-own-key commentary grounded in the + * agent's own evaluation breakdown. Remount (key by move) to reset. + */ +export function CommentatorPanel({ + n, + turn, + colour, + action, + explanation, +}: { + n: number; + turn: number; + colour: Colour; + action: Action; + explanation: Extract; +}) { + const { credentials, ready, openSettings, audit } = useAi(); + const [state, setState] = useState({ status: "idle" }); + const [editing, setEditing] = useState(null); + + const run = async () => { + if (!credentials) return openSettings(); + setState({ status: "loading" }); + const facts = buildCommentaryFacts({ n, turn, colour, action, explanation }); + const prompt = commentaryPrompt(facts); + try { + const res = await callStructured( + credentials, + { + feature: "commentator", + ...prompt, + schema: CommentarySchema, + schemaName: "move_commentary", + maxTokens: 1500, + }, + { audit, context: { boardSize: n, turn, mover: colour } }, + ); + setState({ + status: "done", + commentary: res.data, + grounding: groundingCheck(res.data, facts), + entryId: res.entry.id, + model: res.model, + decision: "pending", + }); + } catch (err) { + setState({ status: "error", message: isAiError(err) ? err.message : String(err) }); + } + }; + + const decide = async (decision: HumanDecision, edited?: string) => { + if (state.status !== "done") return; + await audit.update(state.entryId, { + humanDecision: decision, + decidedAt: new Date().toISOString(), + ...(edited !== undefined ? { editedOutput: edited } : {}), + }); + setState({ ...state, decision, edited }); + setEditing(null); + }; + + return ( +
    +
    +

    + Plain-English commentary +

    + {state.status !== "done" && ( + + )} +
    + + {state.status === "idle" && ( +

    + Optional, with your own API key. The model sees only the numbers above (search depth, + candidate scores and feature breakdown) and is told not to add anything else. +

    + )} + {state.status === "error" && ( +

    + {state.message} +

    + )} + + {state.status === "done" && ( +
    + + {state.decision === "rejected" ? ( +

    You rejected this commentary.

    + ) : editing !== null ? ( +
    +