stacker.news/worker/trust.js

175 lines
6.6 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 trust = await trustGivenGraph(graph)
console.timeLog('trust', 'storing trust')
await storeTrust(models, trust)
console.timeEnd('trust')
} catch (e) {
console.error(e)
throw e
}
}
}
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 SEED_WEIGHT = 0.25
const AGAINST_MSAT_MIN = 1000
const MSAT_MIN = 1000
/*
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
const 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 ${i}`)
matT = math.multiply(original, matT)
matT = math.add(math.multiply(1 - SEED_WEIGHT, matT), math.multiply(SEED_WEIGHT, original))
}
console.timeLog('trust', 'normalizing result')
// we normalize the result taking the z-score, then min-max to [0,1]
// we remove seeds and 0 trust people from the result because they are known outliers
// but we need to keep them in the result to keep positions correct
function resultForId (id) {
let result = math.squeeze(math.subset(math.transpose(matT), math.index(posByUserId[id], math.range(0, graph.length))))
const outliers = SEEDS.concat([id])
outliers.forEach(id => result.set([posByUserId[id]], 0))
const withoutZero = math.filter(result, val => val > 0)
// NOTE: this might be improved by using median and mad (modified z score)
// given the distribution is skewed
const mean = math.mean(withoutZero)
const std = math.std(withoutZero)
result = result.map(val => val >= 0 ? (val - mean) / std : 0)
const min = math.min(result)
const max = math.max(result)
result = math.map(result, val => (val - min) / (max - min))
outliers.forEach(id => result.set([posByUserId[id]], MAX_TRUST))
return result
}
// turn the result vector into an object
const result = {}
resultForId(616).forEach((val, idx) => {
result[graph[idx].id] = val
})
return result
}
/*
graph is returned as json in adjacency list where edges are the trust value 0-1
graph = [
{ id: node1, hops: [{node : node2, trust: trust12}, {node: node3, trust: trust13}] },
...
]
*/
async function getGraph (models) {
return await models.$queryRaw`
SELECT id, array_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, ${MAX_TRUST}::numeric/ARRAY_LENGTH(${SEEDS}::int[], 1) as trust
FROM user_pair, unnest(${SEEDS}::int[]) seed_id
GROUP BY a_id, a_total, seed_id
)
SELECT id, oid, trust, sum(trust) OVER (PARTITION BY id) AS total_trust
FROM trust_pairs
) a
GROUP BY a.id
ORDER BY id ASC`
}
async function storeTrust (models, nodeTrust) {
// convert nodeTrust into table literal string
let values = ''
for (const [id, trust] of Object.entries(nodeTrust)) {
if (values) values += ','
values += `(${id}, ${trust})`
}
// update the trust of each user in graph
await models.$transaction([
models.$executeRaw`UPDATE users SET trust = 0`,
models.$executeRawUnsafe(
`UPDATE users
SET trust = g.trust
FROM (values ${values}) g(id, trust)
WHERE users.id = g.id`)])
}