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https://github.com/samsonjs/spirit-tracker.git
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link sku
This commit is contained in:
Brennan Wilkes (Text Groove)
parent
2356eb8f8f
commit
9552daeef8
2 changed files with 326 additions and 229 deletions
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@ -257,245 +257,318 @@ export function recommendSimilar(
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export function computeInitialPairsFast(allAgg, mappedSkus, limitPairs, isIgnoredPairFn, sameStoreFn) {
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const itemsAll = allAgg.filter((it) => !!it);
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const seed = (Date.now() ^ ((Math.random() * 1e9) | 0)) >>> 0;
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const rnd = mulberry32(seed);
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const itemsShuf = itemsAll.slice();
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shuffleInPlace(itemsShuf, rnd);
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const WORK_CAP = 5000;
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const workAll = itemsShuf.length > WORK_CAP ? itemsShuf.slice(0, WORK_CAP) : itemsShuf;
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const work = workAll.filter((it) => !(mappedSkus && mappedSkus.has(String(it.sku))));
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function itemRank(it) {
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const stores = it.stores ? it.stores.size : 0;
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const hasPrice = it.cheapestPriceNum != null ? 1 : 0;
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const hasName = it.name ? 1 : 0;
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const unknown = String(it.sku || "").startsWith("u:") ? 1 : 0;
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return stores * 3 + hasPrice * 2 + hasName * 0.5 + unknown * 0.25;
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}
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function smwsPairsFirst(workArr, limit) {
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const buckets = new Map(); // code -> items[]
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for (const it of workArr) {
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if (!it) continue;
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const sku = String(it.sku || "");
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if (!sku) continue;
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const code = smwsKeyFromName(it.name || "");
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if (!code) continue;
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let arr = buckets.get(code);
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if (!arr) buckets.set(code, (arr = []));
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arr.push(it);
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export function computeInitialPairsFast(
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allAgg,
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mappedSkus,
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limitPairs,
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isIgnoredPairFn,
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sameStoreFn,
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sizePenaltyFn // ✅ NEW: pass sizePenaltyForPair in
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) {
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const itemsAll = allAgg.filter((it) => !!it);
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const seed = (Date.now() ^ ((Math.random() * 1e9) | 0)) >>> 0;
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const rnd = mulberry32(seed);
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const itemsShuf = itemsAll.slice();
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shuffleInPlace(itemsShuf, rnd);
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// Bigger cap is fine; still bounded
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const WORK_CAP = Math.min(9000, itemsShuf.length);
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const workAll = itemsShuf.length > WORK_CAP ? itemsShuf.slice(0, WORK_CAP) : itemsShuf;
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// Unmapped-only view for normal similarity stage
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const work = workAll.filter((it) => {
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if (!it) return false;
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return !(mappedSkus && mappedSkus.has(String(it.sku)));
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});
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function itemRank(it) {
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const stores = it.stores ? it.stores.size : 0;
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const hasPrice = it.cheapestPriceNum != null ? 1 : 0;
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const hasName = it.name ? 1 : 0;
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const unknown = String(it.sku || "").startsWith("u:") ? 1 : 0;
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return stores * 3 + hasPrice * 2 + hasName * 0.5 + unknown * 0.25;
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}
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const candPairs = [];
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for (const arr0 of buckets.values()) {
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if (!arr0 || arr0.length < 2) continue;
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const arr = arr0
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.slice()
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.sort((a, b) => itemRank(b) - itemRank(a))
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.slice(0, 80);
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const mapped = [];
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const unmapped = [];
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for (const it of arr) {
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// --- SMWS exact-code pairs first (kept as-is, but apply sameStore/isIgnored) ---
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function smwsPairsFirst(workArr, limit) {
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const buckets = new Map(); // code -> items[]
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for (const it of workArr) {
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if (!it) continue;
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const sku = String(it.sku || "");
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if (mappedSkus && mappedSkus.has(sku)) mapped.push(it);
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else unmapped.push(it);
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if (!sku) continue;
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const code = smwsKeyFromName(it.name || "");
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if (!code) continue;
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let arr = buckets.get(code);
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if (!arr) buckets.set(code, (arr = []));
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arr.push(it);
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}
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const anchor = (mapped.length ? mapped : unmapped)
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.slice()
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.sort((a, b) => itemRank(b) - itemRank(a))[0];
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if (!anchor) continue;
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if (unmapped.length) {
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for (const u of unmapped) {
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const candPairs = [];
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for (const arr0 of buckets.values()) {
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if (!arr0 || arr0.length < 2) continue;
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const arr = arr0
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.slice()
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.sort((a, b) => itemRank(b) - itemRank(a))
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.slice(0, 80);
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// Prefer an unmapped anchor if possible; otherwise best overall
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const anchor = arr.slice().sort((a, b) => itemRank(b) - itemRank(a))[0];
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if (!anchor) continue;
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for (const u of arr) {
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if (u === anchor) continue;
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const a = anchor;
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const b = u;
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const aSku = String(a.sku || "");
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const bSku = String(b.sku || "");
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if (!aSku || !bSku || aSku === bSku) continue;
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// Only link *unmapped* targets in this stage
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if (mappedSkus && mappedSkus.has(bSku)) continue;
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if (typeof sameStoreFn === "function" && sameStoreFn(aSku, bSku)) continue;
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if (typeof isIgnoredPairFn === "function" && isIgnoredPairFn(aSku, bSku)) continue;
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const s = 1e9 + itemRank(a) + itemRank(b);
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candPairs.push({ a, b, score: s, aIsMapped: mappedSkus && mappedSkus.has(aSku) });
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candPairs.push({ a, b, score: s });
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}
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}
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candPairs.sort((x, y) => y.score - x.score);
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const usedUnmapped = new Set();
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const out0 = [];
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for (const p of candPairs) {
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const bSku = String(p.b.sku || "");
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if (!bSku) continue;
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if (usedUnmapped.has(bSku)) continue;
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usedUnmapped.add(bSku);
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out0.push(p);
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if (out0.length >= limit) break;
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}
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return { pairs: out0, usedUnmapped };
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}
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candPairs.sort((x, y) => y.score - x.score);
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const usedUnmapped = new Set();
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const anchorUse = new Map();
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const ANCHOR_REUSE_CAP = 6;
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const out0 = [];
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for (const p of candPairs) {
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const smwsFirst = smwsPairsFirst(workAll, limitPairs);
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const used = new Set(smwsFirst.usedUnmapped);
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const out = smwsFirst.pairs.slice();
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if (out.length >= limitPairs) return out.slice(0, limitPairs);
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// --- Improved general pairing logic (uses same “good” scoring knobs) ---
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const seeds = topSuggestions(work, Math.min(220, work.length), "", mappedSkus).filter(
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(it) => !used.has(String(it?.sku || ""))
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);
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// Build token buckets over *normalized* names (better hits)
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const TOKEN_BUCKET_CAP = 700;
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const tokMap = new Map(); // token -> items[]
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const itemRawToks = new Map(); // sku -> raw tokens
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const itemNorm = new Map(); // sku -> norm name
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const itemFilt = new Map(); // sku -> filtered tokens (for first-token logic)
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for (const it of work) {
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const sku = String(it.sku || "");
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if (!sku) continue;
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const n = normSearchText(it.name || "");
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const raw = tokenizeQuery(n);
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const filt = filterSimTokens(raw);
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itemNorm.set(sku, n);
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itemRawToks.set(sku, raw);
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itemFilt.set(sku, filt);
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// bucket using a handful of filtered tokens (higher signal)
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for (const t of filt.slice(0, 12)) {
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let arr = tokMap.get(t);
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if (!arr) tokMap.set(t, (arr = []));
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if (arr.length < TOKEN_BUCKET_CAP) arr.push(it);
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}
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}
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const bestByPair = new Map();
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const MAX_CAND_TOTAL = 450;
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const MAX_CHEAP = 40;
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const MAX_FINE = 18;
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for (const a of seeds) {
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const aSku = String(a.sku || "");
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if (!aSku || used.has(aSku)) continue;
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const aNorm = itemNorm.get(aSku) || normSearchText(a.name || "");
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const aRaw = itemRawToks.get(aSku) || tokenizeQuery(aNorm);
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const aFilt = itemFilt.get(aSku) || filterSimTokens(aRaw);
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if (!aFilt.length) continue;
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const aBrand = aFilt[0] || "";
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const aAge = extractAgeFromText(aNorm);
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// Gather candidates from token buckets
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const cand = new Map();
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for (const t of aFilt.slice(0, 10)) {
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const arr = tokMap.get(t);
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if (!arr) continue;
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for (let i = 0; i < arr.length && cand.size < MAX_CAND_TOTAL; i++) {
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const b = arr[i];
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if (!b) continue;
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const bSku = String(b.sku || "");
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if (!bSku || bSku === aSku) continue;
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if (used.has(bSku)) continue;
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if (mappedSkus && mappedSkus.has(bSku)) continue;
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if (typeof isIgnoredPairFn === "function" && isIgnoredPairFn(aSku, bSku)) continue;
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if (typeof sameStoreFn === "function" && sameStoreFn(aSku, bSku)) continue;
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cand.set(bSku, b);
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}
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if (cand.size >= MAX_CAND_TOTAL) break;
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}
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if (!cand.size) continue;
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// Cheap score stage (fastSimilarity + containment + size + age + first-token mismatch penalty)
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const cheap = [];
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for (const b of cand.values()) {
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const bSku = String(b.sku || "");
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const bNorm = itemNorm.get(bSku) || normSearchText(b.name || "");
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const bRaw = itemRawToks.get(bSku) || tokenizeQuery(bNorm);
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const bFilt = itemFilt.get(bSku) || filterSimTokens(bRaw);
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if (!bFilt.length) continue;
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const contain = tokenContainmentScore(aRaw, bRaw);
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const bBrand = bFilt[0] || "";
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const firstMatch = aBrand && bBrand && aBrand === bBrand;
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let s = fastSimilarityScore(aRaw, bRaw, aNorm, bNorm);
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if (s <= 0) s = 0.01 + 0.25 * contain;
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if (!firstMatch) {
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const smallN = Math.min(aFilt.length || 0, bFilt.length || 0);
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let mult = 0.10 + 0.95 * contain;
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if (smallN <= 3 && contain < 0.78) mult *= 0.22;
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s *= Math.min(1.0, mult);
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}
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if (typeof sizePenaltyFn === "function") s *= sizePenaltyFn(aSku, bSku);
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const bAge = extractAgeFromText(bNorm);
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if (aAge && bAge) {
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if (aAge === bAge) s *= 1.6;
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else s *= 0.22;
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}
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if (String(aSku).startsWith("u:") || String(bSku).startsWith("u:")) s *= 1.06;
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if (s > 0) cheap.push({ b, s, bNorm, bRaw, bFilt, contain, firstMatch, bAge });
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}
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if (!cheap.length) continue;
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cheap.sort((x, y) => y.s - x.s);
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// Fine stage (expensive similarityScore + same penalties again)
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let bestB = null;
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let bestS = 0;
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for (const x of cheap.slice(0, MAX_FINE)) {
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const b = x.b;
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const bSku = String(b.sku || "");
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let s = similarityScore(a.name || "", b.name || "");
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if (s <= 0) continue;
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// first-token mismatch soft penalty
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if (!x.firstMatch) {
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const smallN = Math.min(aFilt.length || 0, (x.bFilt || []).length || 0);
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let mult = 0.10 + 0.95 * x.contain;
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if (smallN <= 3 && x.contain < 0.78) mult *= 0.22;
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s *= Math.min(1.0, mult);
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if (s <= 0) continue;
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}
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if (typeof sizePenaltyFn === "function") {
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s *= sizePenaltyFn(aSku, bSku);
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if (s <= 0) continue;
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}
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if (aAge && x.bAge) {
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if (aAge === x.bAge) s *= 2.0;
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else s *= 0.15;
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}
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if (String(aSku).startsWith("u:") || String(bSku).startsWith("u:")) s *= 1.10;
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if (s > bestS) {
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bestS = s;
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bestB = b;
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}
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}
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// Threshold (slightly lower than before, because we now punish mismatches more intelligently)
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if (!bestB || bestS < 0.50) continue;
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const bSku = String(bestB.sku || "");
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if (!bSku || used.has(bSku)) continue;
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const key = aSku < bSku ? `${aSku}|${bSku}` : `${bSku}|${aSku}`;
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const prev = bestByPair.get(key);
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if (!prev || bestS > prev.score) bestByPair.set(key, { a, b: bestB, score: bestS });
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}
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const pairs = Array.from(bestByPair.values());
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pairs.sort((x, y) => y.score - x.score);
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// ---- light randomness inside a top band (same behavior as before) ----
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const need = Math.max(0, limitPairs - out.length);
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if (!need) return out.slice(0, limitPairs);
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const TOP_BAND = Math.min(700, pairs.length);
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const JITTER = 0.08;
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const band = pairs.slice(0, TOP_BAND).map((p) => {
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const jitter = (rnd() - 0.5) * JITTER;
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return { ...p, _rank: p.score * (1 + jitter) };
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});
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band.sort((a, b) => b._rank - a._rank);
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function tryTake(p) {
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const aSku = String(p.a.sku || "");
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const bSku = String(p.b.sku || "");
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if (!aSku || !bSku) continue;
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if (usedUnmapped.has(bSku)) continue;
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const k = aSku;
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const n = anchorUse.get(k) || 0;
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if (n >= ANCHOR_REUSE_CAP) continue;
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usedUnmapped.add(bSku);
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anchorUse.set(k, n + 1);
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out0.push(p);
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if (out0.length >= limit) break;
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if (!aSku || !bSku || aSku === bSku) return false;
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if (used.has(aSku) || used.has(bSku)) return false;
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if (typeof sameStoreFn === "function" && sameStoreFn(aSku, bSku)) return false;
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if (typeof isIgnoredPairFn === "function" && isIgnoredPairFn(aSku, bSku)) return false;
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used.add(aSku);
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used.add(bSku);
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out.push({ a: p.a, b: p.b, score: p.score });
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return true;
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}
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return { pairs: out0, usedUnmapped };
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}
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const smwsFirst = smwsPairsFirst(workAll, limitPairs);
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const used = new Set(smwsFirst.usedUnmapped);
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const out = smwsFirst.pairs.slice();
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if (out.length >= limitPairs) return out.slice(0, limitPairs);
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const seeds = topSuggestions(work, Math.min(150, work.length), "", mappedSkus).filter(
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(it) => !used.has(String(it?.sku || ""))
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);
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const TOKEN_BUCKET_CAP = 500;
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const tokMap = new Map();
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const itemTokens = new Map();
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const itemNormName = new Map();
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for (const it of work) {
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const toks = Array.from(new Set(tokenizeQuery(it.name || ""))).filter(Boolean).slice(0, 10);
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itemTokens.set(it.sku, toks);
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itemNormName.set(it.sku, normSearchText(it.name || ""));
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for (const t of toks) {
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let arr = tokMap.get(t);
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if (!arr) tokMap.set(t, (arr = []));
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if (arr.length < TOKEN_BUCKET_CAP) arr.push(it);
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}
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}
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const bestByPair = new Map();
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const MAX_CAND_TOTAL = 250;
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const MAX_FINE = 10;
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for (const a of seeds) {
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const aSku = String(a.sku || "");
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if (!aSku || used.has(aSku)) continue;
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const aToks = itemTokens.get(aSku) || [];
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if (!aToks.length) continue;
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const cand = new Map();
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for (const t of aToks) {
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const arr = tokMap.get(t);
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if (!arr) continue;
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for (let i = 0; i < arr.length && cand.size < MAX_CAND_TOTAL; i++) {
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const b = arr[i];
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if (!b) continue;
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const bSku = String(b.sku || "");
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if (!bSku || bSku === aSku) continue;
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if (used.has(bSku)) continue;
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if (mappedSkus && mappedSkus.has(bSku)) continue;
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if (typeof isIgnoredPairFn === "function" && isIgnoredPairFn(aSku, bSku)) continue;
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if (typeof sameStoreFn === "function" && sameStoreFn(aSku, bSku)) continue;
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cand.set(bSku, b);
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}
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||||
if (cand.size >= MAX_CAND_TOTAL) break;
|
||||
}
|
||||
if (!cand.size) continue;
|
||||
|
||||
const aNameN = itemNormName.get(aSku) || "";
|
||||
const cheap = [];
|
||||
for (const b of cand.values()) {
|
||||
const bSku = String(b.sku || "");
|
||||
const bToks = itemTokens.get(bSku) || [];
|
||||
const bNameN = itemNormName.get(bSku) || "";
|
||||
const s = fastSimilarityScore(aToks, bToks, aNameN, bNameN);
|
||||
if (s > 0) cheap.push({ b, s });
|
||||
}
|
||||
if (!cheap.length) continue;
|
||||
cheap.sort((x, y) => y.s - x.s);
|
||||
|
||||
let bestB = null;
|
||||
let bestS = 0;
|
||||
for (const x of cheap.slice(0, MAX_FINE)) {
|
||||
const s = similarityScore(a.name || "", x.b.name || "");
|
||||
if (s > bestS) {
|
||||
bestS = s;
|
||||
bestB = x.b;
|
||||
}
|
||||
}
|
||||
|
||||
if (!bestB || bestS < 0.6) continue;
|
||||
|
||||
const bSku = String(bestB.sku || "");
|
||||
if (!bSku || used.has(bSku)) continue;
|
||||
|
||||
const key = aSku < bSku ? `${aSku}|${bSku}` : `${bSku}|${aSku}`;
|
||||
const prev = bestByPair.get(key);
|
||||
if (!prev || bestS > prev.score) bestByPair.set(key, { a, b: bestB, score: bestS });
|
||||
}
|
||||
|
||||
const pairs = Array.from(bestByPair.values());
|
||||
pairs.sort((x, y) => y.score - x.score);
|
||||
|
||||
const need = Math.max(0, limitPairs - out.length);
|
||||
if (!need) return out.slice(0, limitPairs);
|
||||
|
||||
const TOP_BAND = Math.min(600, pairs.length);
|
||||
const JITTER = 0.08;
|
||||
|
||||
const band = pairs.slice(0, TOP_BAND).map((p) => {
|
||||
const jitter = (rnd() - 0.5) * JITTER;
|
||||
return { ...p, _rank: p.score * (1 + jitter) };
|
||||
});
|
||||
band.sort((a, b) => b._rank - a._rank);
|
||||
|
||||
function tryTake(p) {
|
||||
const aSku = String(p.a.sku || "");
|
||||
const bSku = String(p.b.sku || "");
|
||||
if (!aSku || !bSku || aSku === bSku) return false;
|
||||
if (used.has(aSku) || used.has(bSku)) return false;
|
||||
if (typeof sameStoreFn === "function" && sameStoreFn(aSku, bSku)) return false;
|
||||
|
||||
used.add(aSku);
|
||||
used.add(bSku);
|
||||
out.push({ a: p.a, b: p.b, score: p.score });
|
||||
return true;
|
||||
}
|
||||
|
||||
for (const p of band) {
|
||||
if (out.length >= limitPairs) break;
|
||||
tryTake(p);
|
||||
}
|
||||
|
||||
if (out.length < limitPairs) {
|
||||
for (let i = TOP_BAND; i < pairs.length; i++) {
|
||||
|
||||
for (const p of band) {
|
||||
if (out.length >= limitPairs) break;
|
||||
tryTake(pairs[i]);
|
||||
tryTake(p);
|
||||
}
|
||||
|
||||
if (out.length < limitPairs) {
|
||||
for (let i = TOP_BAND; i < pairs.length; i++) {
|
||||
if (out.length >= limitPairs) break;
|
||||
tryTake(pairs[i]);
|
||||
}
|
||||
}
|
||||
|
||||
return out.slice(0, limitPairs);
|
||||
}
|
||||
|
||||
|
||||
return out.slice(0, limitPairs);
|
||||
}
|
||||
function fnv1a32u(str) {
|
||||
|
||||
|
||||
|
||||
function fnv1a32u(str) {
|
||||
let h = 0x811c9dc5;
|
||||
str = String(str || "");
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
|
|
|
|||
|
|
@ -139,7 +139,30 @@ export async function renderSkuLinker($app) {
|
|||
return String(rules.canonicalSku(aSku)) === String(rules.canonicalSku(bSku));
|
||||
}
|
||||
|
||||
const initialPairs = computeInitialPairsFast(allAgg, mappedSkus, 28, isIgnoredPair, sameStoreCanon);
|
||||
let initialPairs = null;
|
||||
|
||||
function getInitialPairsIfNeeded() {
|
||||
// never compute if either side is pinned
|
||||
if (pinnedL || pinnedR) return null;
|
||||
|
||||
// never compute if URL query param was used (preselect flow)
|
||||
if (shouldReloadAfterLink) return null;
|
||||
|
||||
if (initialPairs) return initialPairs;
|
||||
|
||||
initialPairs = computeInitialPairsFast(
|
||||
allAgg,
|
||||
mappedSkus,
|
||||
28,
|
||||
isIgnoredPair,
|
||||
sameStoreCanon,
|
||||
sizePenaltyForPair // ✅ NEW
|
||||
);
|
||||
|
||||
return initialPairs;
|
||||
}
|
||||
|
||||
|
||||
|
||||
let pinnedL = null;
|
||||
let pinnedR = null;
|
||||
|
|
@ -221,16 +244,17 @@ export async function renderSkuLinker($app) {
|
|||
sameGroup
|
||||
);
|
||||
|
||||
if (initialPairs && initialPairs.length) {
|
||||
const list = side === "L" ? initialPairs.map((p) => p.a) : initialPairs.map((p) => p.b);
|
||||
return list.filter(
|
||||
(it) =>
|
||||
it &&
|
||||
it.sku !== otherSku &&
|
||||
(!mappedSkus.has(String(it.sku)) || smwsKeyFromName(it.name || ""))
|
||||
);
|
||||
}
|
||||
|
||||
const pairs = getInitialPairsIfNeeded();
|
||||
if (pairs && pairs.length) {
|
||||
const list = side === "L" ? pairs.map((p) => p.a) : pairs.map((p) => p.b);
|
||||
return list.filter(
|
||||
(it) =>
|
||||
it &&
|
||||
it.sku !== otherSku &&
|
||||
(!mappedSkus.has(String(it.sku)) || smwsKeyFromName(it.name || ""))
|
||||
);
|
||||
}
|
||||
|
||||
return topSuggestions(allAgg, 60, otherSku, mappedSkus);
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue