204 lines
7.7 KiB
JavaScript
204 lines
7.7 KiB
JavaScript
import * as math from 'mathjs'
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import { USER_ID, SN_USER_IDS } from '@/lib/constants.js'
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export async function trust ({ boss, models }) {
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try {
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console.time('trust')
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console.timeLog('trust', 'getting graph')
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const graph = await getGraph(models)
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console.timeLog('trust', 'computing trust')
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const [vGlobal, mPersonal] = await trustGivenGraph(graph)
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console.timeLog('trust', 'storing trust')
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await storeTrust(models, graph, vGlobal, mPersonal)
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} finally {
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console.timeEnd('trust')
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}
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}
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const MAX_DEPTH = 10
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const MAX_TRUST = 1
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const MIN_SUCCESS = 1
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// increasing disgree_mult increases distrust when there's disagreement
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// ... this cancels DISAGREE_MULT number of "successes" for every disagreement
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const DISAGREE_MULT = 10
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// https://en.wikipedia.org/wiki/Normal_distribution#Quantile_function
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const Z_CONFIDENCE = 6.109410204869 // 99.9999999% confidence
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const GLOBAL_ROOT = 616
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const SEED_WEIGHT = 0.25
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const AGAINST_MSAT_MIN = 1000
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const MSAT_MIN = 1000
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const SIG_DIFF = 0.1 // need to differ by at least 10 percent
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/*
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Given a graph and start this function returns an object where
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the keys are the node id and their value is the trust of that node
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*/
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function trustGivenGraph (graph) {
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// empty matrix of proper size nstackers x nstackers
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let mat = math.zeros(graph.length, graph.length, 'sparse')
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// create a map of user id to position in matrix
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const posByUserId = {}
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for (const [idx, val] of graph.entries()) {
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posByUserId[val.id] = idx
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}
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// iterate over graph, inserting edges into matrix
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for (const [idx, val] of graph.entries()) {
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for (const { node, trust } of val.hops) {
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try {
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mat.set([idx, posByUserId[node]], Number(trust))
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} catch (e) {
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console.log('error:', idx, node, posByUserId[node], trust)
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throw e
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}
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}
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}
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// perform random walk over trust matrix
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// the resulting matrix columns represent the trust a user (col) has for each other user (rows)
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// XXX this scales N^3 and mathjs is slow
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let matT = math.transpose(mat)
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const original = matT.clone()
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for (let i = 0; i < MAX_DEPTH; i++) {
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console.timeLog('trust', `matrix multiply ${i}`)
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matT = math.multiply(original, matT)
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matT = math.add(math.multiply(1 - SEED_WEIGHT, matT), math.multiply(SEED_WEIGHT, original))
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}
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console.timeLog('trust', 'transforming result')
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const seedIdxs = SN_USER_IDS.map(id => posByUserId[id])
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const isOutlier = (fromIdx, idx) => [...seedIdxs, fromIdx].includes(idx)
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const sqapply = (mat, fn) => {
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let idx = 0
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return math.squeeze(math.apply(mat, 1, d => {
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const filtered = math.filter(d, (val, fidx) => {
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return val !== 0 && !isOutlier(idx, fidx[0])
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})
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idx++
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if (filtered.length === 0) return 0
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return fn(filtered)
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}))
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}
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console.timeLog('trust', 'normalizing')
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console.timeLog('trust', 'stats')
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mat = math.transpose(matT)
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const std = sqapply(mat, math.std) // math.squeeze(math.std(mat, 1))
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const mean = sqapply(mat, math.mean) // math.squeeze(math.mean(mat, 1))
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const zscore = math.map(mat, (val, idx) => {
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const zstd = math.subset(std, math.index(idx[0]))
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const zmean = math.subset(mean, math.index(idx[0]))
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return zstd ? (val - zmean) / zstd : 0
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})
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console.timeLog('trust', 'minmax')
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const min = sqapply(zscore, math.min) // math.squeeze(math.min(zscore, 1))
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const max = sqapply(zscore, math.max) // math.squeeze(math.max(zscore, 1))
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const mPersonal = math.map(zscore, (val, idx) => {
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const zmin = math.subset(min, math.index(idx[0]))
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const zmax = math.subset(max, math.index(idx[0]))
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const zrange = zmax - zmin
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if (val > zmax) return MAX_TRUST
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return zrange ? (val - zmin) / zrange : 0
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})
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const vGlobal = math.squeeze(math.row(mPersonal, posByUserId[GLOBAL_ROOT]))
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return [vGlobal, mPersonal]
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}
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/*
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graph is returned as json in adjacency list where edges are the trust value 0-1
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graph = [
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{ id: node1, hops: [{node : node2, trust: trust12}, {node: node3, trust: trust13}] },
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...
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]
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*/
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async function getGraph (models) {
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return await models.$queryRaw`
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SELECT id, json_agg(json_build_object(
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'node', oid,
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'trust', CASE WHEN total_trust > 0 THEN trust / total_trust::float ELSE 0 END)) AS hops
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FROM (
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WITH user_votes AS (
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SELECT "ItemAct"."userId" AS user_id, users.name AS name, "ItemAct"."itemId" AS item_id, min("ItemAct".created_at) AS act_at,
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users.created_at AS user_at, "ItemAct".act = 'DONT_LIKE_THIS' AS against,
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count(*) OVER (partition by "ItemAct"."userId") AS user_vote_count
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FROM "ItemAct"
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JOIN "Item" ON "Item".id = "ItemAct"."itemId" AND "ItemAct".act IN ('FEE', 'TIP', 'DONT_LIKE_THIS')
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AND "Item"."parentId" IS NULL AND NOT "Item".bio AND "Item"."userId" <> "ItemAct"."userId"
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JOIN users ON "ItemAct"."userId" = users.id AND users.id <> ${USER_ID.anon}
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GROUP BY user_id, name, item_id, user_at, against
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HAVING CASE WHEN
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"ItemAct".act = 'DONT_LIKE_THIS' THEN sum("ItemAct".msats) > ${AGAINST_MSAT_MIN}
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ELSE sum("ItemAct".msats) > ${MSAT_MIN} END
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),
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user_pair AS (
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SELECT a.user_id AS a_id, b.user_id AS b_id,
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count(*) FILTER(WHERE a.act_at > b.act_at AND a.against = b.against) AS before,
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count(*) FILTER(WHERE b.act_at > a.act_at AND a.against = b.against) AS after,
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count(*) FILTER(WHERE a.against <> b.against) * ${DISAGREE_MULT} AS disagree,
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b.user_vote_count AS b_total, a.user_vote_count AS a_total
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FROM user_votes a
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JOIN user_votes b ON a.item_id = b.item_id
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WHERE a.user_id <> b.user_id
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GROUP BY a.user_id, a.user_vote_count, b.user_id, b.user_vote_count
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),
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trust_pairs AS (
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SELECT a_id AS id, b_id AS oid,
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CASE WHEN before - disagree >= ${MIN_SUCCESS} AND b_total - after > 0 THEN
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confidence(before - disagree, b_total - after, ${Z_CONFIDENCE})
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ELSE 0 END AS trust
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FROM user_pair
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WHERE NOT (b_id = ANY (${SN_USER_IDS}))
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UNION ALL
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SELECT a_id AS id, seed_id AS oid, ${MAX_TRUST}::numeric as trust
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FROM user_pair, unnest(${SN_USER_IDS}::int[]) seed_id
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GROUP BY a_id, a_total, seed_id
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UNION ALL
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SELECT a_id AS id, a_id AS oid, ${MAX_TRUST}::float as trust
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FROM user_pair
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)
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SELECT id, oid, trust, sum(trust) OVER (PARTITION BY id) AS total_trust
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FROM trust_pairs
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) a
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GROUP BY a.id
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ORDER BY id ASC`
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}
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async function storeTrust (models, graph, vGlobal, mPersonal) {
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// convert nodeTrust into table literal string
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let globalValues = ''
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let personalValues = ''
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vGlobal.forEach((val, [idx]) => {
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if (isNaN(val)) return
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if (globalValues) globalValues += ','
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globalValues += `(${graph[idx].id}, ${val}::FLOAT)`
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if (personalValues) personalValues += ','
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personalValues += `(${GLOBAL_ROOT}, ${graph[idx].id}, ${val}::FLOAT)`
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})
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math.forEach(mPersonal, (val, [fromIdx, toIdx]) => {
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const globalVal = vGlobal.get([toIdx])
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if (isNaN(val) || val - globalVal <= SIG_DIFF) return
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if (personalValues) personalValues += ','
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personalValues += `(${graph[fromIdx].id}, ${graph[toIdx].id}, ${val}::FLOAT)`
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})
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// update the trust of each user in graph
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await models.$transaction([
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models.$executeRaw`UPDATE users SET trust = 0`,
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models.$executeRawUnsafe(
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`UPDATE users
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SET trust = g.trust
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FROM (values ${globalValues}) g(id, trust)
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WHERE users.id = g.id`),
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models.$executeRawUnsafe(
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`INSERT INTO "Arc" ("fromId", "toId", "zapTrust")
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SELECT id, oid, trust
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FROM (values ${personalValues}) g(id, oid, trust)
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ON CONFLICT ("fromId", "toId") DO UPDATE SET "zapTrust" = EXCLUDED."zapTrust"`
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)
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])
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}
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