stacker.news/worker/trust.js

210 lines
7.8 KiB
JavaScript

import * as math from 'mathjs'
import { ANON_USER_ID } from '../lib/constants.js'
export function trust ({ boss, models }) {
return async function () {
try {
console.time('trust')
console.timeLog('trust', 'getting graph')
const graph = await getGraph(models)
console.timeLog('trust', 'computing trust')
const [vGlobal, mPersonal] = await trustGivenGraph(graph)
console.timeLog('trust', 'storing trust')
await storeTrust(models, graph, vGlobal, mPersonal)
} catch (e) {
console.error(e)
throw e
} finally {
console.timeEnd('trust')
}
}
}
const MAX_DEPTH = 10
const MAX_TRUST = 1
const MIN_SUCCESS = 1
// increasing disgree_mult increases distrust when there's disagreement
// ... this cancels DISAGREE_MULT number of "successes" for every disagreement
const DISAGREE_MULT = 10
// https://en.wikipedia.org/wiki/Normal_distribution#Quantile_function
const Z_CONFIDENCE = 6.109410204869 // 99.9999999% confidence
const SEEDS = [616, 6030, 946, 4502]
const GLOBAL_ROOT = 616
const SEED_WEIGHT = 0.25
const AGAINST_MSAT_MIN = 1000
const MSAT_MIN = 1000
const SIG_DIFF = 0.1 // need to differ by at least 10 percent
/*
Given a graph and start this function returns an object where
the keys are the node id and their value is the trust of that node
*/
function trustGivenGraph (graph) {
// empty matrix of proper size nstackers x nstackers
let mat = math.zeros(graph.length, graph.length, 'sparse')
// create a map of user id to position in matrix
const posByUserId = {}
for (const [idx, val] of graph.entries()) {
posByUserId[val.id] = idx
}
// iterate over graph, inserting edges into matrix
for (const [idx, val] of graph.entries()) {
for (const { node, trust } of val.hops) {
try {
mat.set([idx, posByUserId[node]], Number(trust))
} catch (e) {
console.log('error:', idx, node, posByUserId[node], trust)
throw e
}
}
}
// perform random walk over trust matrix
// the resulting matrix columns represent the trust a user (col) has for each other user (rows)
// XXX this scales N^3 and mathjs is slow
let matT = math.transpose(mat)
const original = matT.clone()
for (let i = 0; i < MAX_DEPTH; i++) {
console.timeLog('trust', `matrix multiply ${i}`)
matT = math.multiply(original, matT)
matT = math.add(math.multiply(1 - SEED_WEIGHT, matT), math.multiply(SEED_WEIGHT, original))
}
console.timeLog('trust', 'transforming result')
const seedIdxs = SEEDS.map(id => posByUserId[id])
const isOutlier = (fromIdx, idx) => [...seedIdxs, fromIdx].includes(idx)
const sqapply = (mat, fn) => {
let idx = 0
return math.squeeze(math.apply(mat, 1, d => {
const filtered = math.filter(d, (val, fidx) => {
return val !== 0 && !isOutlier(idx, fidx[0])
})
idx++
if (filtered.length === 0) return 0
return fn(filtered)
}))
}
console.timeLog('trust', 'normalizing')
console.timeLog('trust', 'stats')
mat = math.transpose(matT)
const std = sqapply(mat, math.std) // math.squeeze(math.std(mat, 1))
const mean = sqapply(mat, math.mean) // math.squeeze(math.mean(mat, 1))
const zscore = math.map(mat, (val, idx) => {
const zstd = math.subset(std, math.index(idx[0]))
const zmean = math.subset(mean, math.index(idx[0]))
return zstd ? (val - zmean) / zstd : 0
})
console.timeLog('trust', 'minmax')
const min = sqapply(zscore, math.min) // math.squeeze(math.min(zscore, 1))
const max = sqapply(zscore, math.max) // math.squeeze(math.max(zscore, 1))
const mPersonal = math.map(zscore, (val, idx) => {
const zmin = math.subset(min, math.index(idx[0]))
const zmax = math.subset(max, math.index(idx[0]))
const zrange = zmax - zmin
if (val > zmax) return MAX_TRUST
return zrange ? (val - zmin) / zrange : 0
})
const vGlobal = math.squeeze(math.row(mPersonal, posByUserId[GLOBAL_ROOT]))
return [vGlobal, mPersonal]
}
/*
graph is returned as json in adjacency list where edges are the trust value 0-1
graph = [
{ id: node1, hops: [{node : node2, trust: trust12}, {node: node3, trust: trust13}] },
...
]
*/
async function getGraph (models) {
return await models.$queryRaw`
SELECT id, json_agg(json_build_object(
'node', oid,
'trust', CASE WHEN total_trust > 0 THEN trust / total_trust::float ELSE 0 END)) AS hops
FROM (
WITH user_votes AS (
SELECT "ItemAct"."userId" AS user_id, users.name AS name, "ItemAct"."itemId" AS item_id, min("ItemAct".created_at) AS act_at,
users.created_at AS user_at, "ItemAct".act = 'DONT_LIKE_THIS' AS against,
count(*) OVER (partition by "ItemAct"."userId") AS user_vote_count
FROM "ItemAct"
JOIN "Item" ON "Item".id = "ItemAct"."itemId" AND "ItemAct".act IN ('FEE', 'TIP', 'DONT_LIKE_THIS')
AND "Item"."parentId" IS NULL AND NOT "Item".bio AND "Item"."userId" <> "ItemAct"."userId"
JOIN users ON "ItemAct"."userId" = users.id AND users.id <> ${ANON_USER_ID}
GROUP BY user_id, name, item_id, user_at, against
HAVING CASE WHEN
"ItemAct".act = 'DONT_LIKE_THIS' THEN sum("ItemAct".msats) > ${AGAINST_MSAT_MIN}
ELSE sum("ItemAct".msats) > ${MSAT_MIN} END
),
user_pair AS (
SELECT a.user_id AS a_id, b.user_id AS b_id,
count(*) FILTER(WHERE a.act_at > b.act_at AND a.against = b.against) AS before,
count(*) FILTER(WHERE b.act_at > a.act_at AND a.against = b.against) AS after,
count(*) FILTER(WHERE a.against <> b.against) * ${DISAGREE_MULT} AS disagree,
b.user_vote_count AS b_total, a.user_vote_count AS a_total
FROM user_votes a
JOIN user_votes b ON a.item_id = b.item_id
WHERE a.user_id <> b.user_id
GROUP BY a.user_id, a.user_vote_count, b.user_id, b.user_vote_count
),
trust_pairs AS (
SELECT a_id AS id, b_id AS oid,
CASE WHEN before - disagree >= ${MIN_SUCCESS} AND b_total - after > 0 THEN
confidence(before - disagree, b_total - after, ${Z_CONFIDENCE})
ELSE 0 END AS trust
FROM user_pair
WHERE b_id <> ANY (${SEEDS})
UNION ALL
SELECT a_id AS id, seed_id AS oid, ${MAX_TRUST}::numeric as trust
FROM user_pair, unnest(${SEEDS}::int[]) seed_id
GROUP BY a_id, a_total, seed_id
UNION ALL
SELECT a_id AS id, a_id AS oid, ${MAX_TRUST}::float as trust
FROM user_pair
)
SELECT id, oid, trust, sum(trust) OVER (PARTITION BY id) AS total_trust
FROM trust_pairs
) a
GROUP BY a.id
ORDER BY id ASC`
}
async function storeTrust (models, graph, vGlobal, mPersonal) {
// convert nodeTrust into table literal string
let globalValues = ''
let personalValues = ''
vGlobal.forEach((val, [idx]) => {
if (isNaN(val)) return
if (globalValues) globalValues += ','
globalValues += `(${graph[idx].id}, ${val}::FLOAT)`
if (personalValues) personalValues += ','
personalValues += `(${GLOBAL_ROOT}, ${graph[idx].id}, ${val}::FLOAT)`
})
math.forEach(mPersonal, (val, [fromIdx, toIdx]) => {
const globalVal = vGlobal.get([toIdx])
if (isNaN(val) || val - globalVal <= SIG_DIFF) return
if (personalValues) personalValues += ','
personalValues += `(${graph[fromIdx].id}, ${graph[toIdx].id}, ${val}::FLOAT)`
})
// update the trust of each user in graph
await models.$transaction([
models.$executeRaw`UPDATE users SET trust = 0`,
models.$executeRawUnsafe(
`UPDATE users
SET trust = g.trust
FROM (values ${globalValues}) g(id, trust)
WHERE users.id = g.id`),
models.$executeRawUnsafe(
`INSERT INTO "Arc" ("fromId", "toId", "zapTrust")
SELECT id, oid, trust
FROM (values ${personalValues}) g(id, oid, trust)
ON CONFLICT ("fromId", "toId") DO UPDATE SET "zapTrust" = EXCLUDED."zapTrust"`
)
])
}