diff --git a/packages/api/package.json b/packages/api/package.json index 657a9342b..dda402e4d 100644 --- a/packages/api/package.json +++ b/packages/api/package.json @@ -58,11 +58,11 @@ "highlightjs": "^9.16.2", "html-entities": "^2.3.2", "intercom-client": "^3.1.4", - "jsdom": "^19.0.0", "jsonwebtoken": "^8.5.1", "jwks-rsa": "^2.0.3", "knex": "0.21.12", "knex-stringcase": "^1.4.2", + "linkedom": "^0.14.9", "luxon": "^2.3.1", "nanoid": "^3.1.25", "nodemailer": "^6.7.3", @@ -96,7 +96,6 @@ "@types/express": "^4.17.7", "@types/highlightjs": "^9.12.2", "@types/intercom-client": "^2.11.8", - "@types/jsdom": "^16.2.3", "@types/jsonwebtoken": "^8.5.0", "@types/luxon": "^1.25.0", "@types/mocha": "^8.2.2", diff --git a/packages/api/src/readability.d.ts b/packages/api/src/readability.d.ts index 54f8846ec..33fbe0578 100644 --- a/packages/api/src/readability.d.ts +++ b/packages/api/src/readability.d.ts @@ -121,6 +121,7 @@ declare module '@omnivore/readability' { */ keepClasses?: boolean + url?: string /** * Function that converts a regular image url into imageproxy url diff --git a/packages/api/src/utils/axios-handler.ts b/packages/api/src/utils/axios-handler.ts index 6e44e1e90..868181c85 100644 --- a/packages/api/src/utils/axios-handler.ts +++ b/packages/api/src/utils/axios-handler.ts @@ -1,17 +1,15 @@ -import { DOMWindow } from 'jsdom' - export class AxiosHandler { name = 'axios' // eslint-disable-next-line @typescript-eslint/no-unused-vars - shouldPrehandle = (url: URL, _dom: DOMWindow): boolean => { + shouldPrehandle = (url: URL, _dom: Document): boolean => { const host = this.name + '.com' // check if url ends with axios.com return url.hostname.endsWith(host) } - prehandle = (url: URL, dom: DOMWindow): Promise => { - const body = dom.document.querySelector('table') + prehandle = (url: URL, dom: Document): Promise => { + const body = dom.querySelector('table') // this removes ads and replaces table with a div body?.querySelectorAll('table').forEach((el, k) => { @@ -27,7 +25,7 @@ export class AxiosHandler { } }) // replace the table with a div - const div = dom.document.createElement('div') + const div = dom.createElement('div') div.innerHTML = el.innerHTML el.parentNode?.replaceChild(div, el) } diff --git a/packages/api/src/utils/bloomberg-handler.ts b/packages/api/src/utils/bloomberg-handler.ts index 2fbae0ef5..c03af3f4b 100644 --- a/packages/api/src/utils/bloomberg-handler.ts +++ b/packages/api/src/utils/bloomberg-handler.ts @@ -1,22 +1,18 @@ -import { DOMWindow } from 'jsdom' - export class BloombergHandler { name = 'bloomberg' - shouldPrehandle = (url: URL, dom: DOMWindow): boolean => { + shouldPrehandle = (url: URL, dom: Document): boolean => { const host = this.name + '.com' // check if url ends with bloomberg.com return ( url.hostname.endsWith(host) || - dom.document - .querySelector('.logo-image') - ?.getAttribute('alt') - ?.toLowerCase() === this.name + dom.querySelector('.logo-image')?.getAttribute('alt')?.toLowerCase() === + this.name ) } - prehandle = (_url: URL, dom: DOMWindow): Promise => { - const body = dom.document.querySelector('.wrapper') + prehandle = (_url: URL, dom: Document): Promise => { + const body = dom.querySelector('.wrapper') // this removes header body?.querySelector('.sailthru-variables')?.remove() diff --git a/packages/api/src/utils/golang-handler.ts b/packages/api/src/utils/golang-handler.ts index 3a3037a59..6e6e29d02 100644 --- a/packages/api/src/utils/golang-handler.ts +++ b/packages/api/src/utils/golang-handler.ts @@ -1,17 +1,15 @@ -import { DOMWindow } from 'jsdom' - export class GolangHandler { name = 'golangweekly' // eslint-disable-next-line @typescript-eslint/no-unused-vars - shouldPrehandle = (url: URL, _dom: DOMWindow): boolean => { + shouldPrehandle = (url: URL, _dom: Document): boolean => { const host = this.name + '.com' // check if url ends with golangweekly.com return url.hostname.endsWith(host) } - prehandle = (url: URL, dom: DOMWindow): Promise => { - const body = dom.document.querySelector('body') + prehandle = (url: URL, dom: Document): Promise => { + const body = dom.querySelector('body') // this removes the "Subscribe" button body?.querySelector('.el-splitbar')?.remove() diff --git a/packages/api/src/utils/parser.ts b/packages/api/src/utils/parser.ts index cf4d21408..06fe8a433 100644 --- a/packages/api/src/utils/parser.ts +++ b/packages/api/src/utils/parser.ts @@ -2,7 +2,6 @@ /* eslint-disable @typescript-eslint/no-unsafe-assignment */ /* eslint-disable @typescript-eslint/no-unused-vars */ import { Readability } from '@omnivore/readability' -import { DOMWindow, JSDOM, VirtualConsole } from 'jsdom' import createDOMPurify, { SanitizeElementHookEvent } from 'dompurify' import { PageType, PreparedDocumentInput } from '../generated/graphql' import { buildLogger, LogRecord } from './logger' @@ -15,10 +14,11 @@ import { BloombergHandler } from './bloomberg-handler' import { GolangHandler } from './golang-handler' import * as hljs from 'highlightjs' import { decode } from 'html-entities' +import { parseHTML } from 'linkedom' const logger = buildLogger('utils.parse') -const virtualConsole = new VirtualConsole() +// const virtualConsole = new VirtualConsole() export const ALLOWED_CONTENT_TYPES = [ 'text/html', @@ -41,8 +41,8 @@ const DOM_PURIFY_CONFIG = { } interface ContentHandler { - shouldPrehandle: (url: URL, dom: DOMWindow) => boolean - prehandle: (url: URL, document: DOMWindow) => Promise + shouldPrehandle: (url: URL, dom: Document) => boolean + prehandle: (url: URL, document: Document) => Promise } const HANDLERS = [ @@ -102,9 +102,9 @@ type ArticleParseLogRecord = LogRecord & { const DEBUG_MODE = process.env.DEBUG === 'true' || false -const parseOriginalContent = (window: DOMWindow): PageType => { +const parseOriginalContent = (document: Document): PageType => { try { - const e = window.document.querySelector("head meta[property='og:type']") + const e = document.querySelector("head meta[property='og:type']") const content = e?.getAttribute('content') if (!content) { return PageType.Unknown @@ -128,32 +128,24 @@ const parseOriginalContent = (window: DOMWindow): PageType => { } const getPurifiedContent = (html: string): Document => { - const newWindow = new JSDOM('').window - const DOMPurify = createDOMPurify(newWindow as unknown as Window) + const newWindow = parseHTML('') + const DOMPurify = createDOMPurify(newWindow) DOMPurify.addHook('uponSanitizeElement', domPurifySanitizeHook) const clean = DOMPurify.sanitize(html, DOM_PURIFY_CONFIG) - return new JSDOM(clean).window.document + return parseHTML(clean).document } const getReadabilityResult = ( url: string, html: string, - window: DOMWindow, + document: Document, isNewsletter?: boolean ): Readability.ParseResult | null => { - virtualConsole.removeAllListeners('jsdomError') - virtualConsole.on('jsdomError', ({ message, stack: _stack, ...details }) => { - logger.warning(`JSDOM error occurred`, { - errorMsg: message, - ...details, - }) - }) - // First attempt to read the article as is. // if that fails attempt to purify then read const sources = [ () => { - return window.document + return document }, () => { return getPurifiedContent(html) @@ -171,6 +163,7 @@ const getReadabilityResult = ( debug: DEBUG_MODE, createImageProxyUrl, keepTables: isNewsletter, + url, }).parse() if (article) { @@ -184,12 +177,15 @@ const getReadabilityResult = ( return null } -const applyHandlers = async (url: string, window: DOMWindow): Promise => { +const applyHandlers = async ( + url: string, + document: Document +): Promise => { try { const u = new URL(url) const handler = HANDLERS.find((h) => { try { - return h.shouldPrehandle(u, window) + return h.shouldPrehandle(u, document) } catch (e) { console.log('error with handler: ', h.name, e) } @@ -198,7 +194,7 @@ const applyHandlers = async (url: string, window: DOMWindow): Promise => { if (handler) { try { console.log('pre-handling url or content with handler: ', handler.name) - await handler.prehandle(u, window) + await handler.prehandle(u, document) } catch (e) { console.log('error with handler: ', handler, e) } @@ -236,20 +232,20 @@ export const parsePreparedContent = async ( } } - virtualConsole.removeAllListeners('jsdomError') - virtualConsole.on('jsdomError', ({ message, stack: _stack, ...details }) => { - logger.warning(`JSDOM error occurred`, { - ...logRecord, - errorMsg: message, - ...details, - }) - }) - const { window } = new JSDOM(document, { url, virtualConsole }) + // virtualConsole.removeAllListeners('jsdomError') + // virtualConsole.on('jsdomError', ({ message, stack: _stack, ...details }) => { + // logger.warning(`JSDOM error occurred`, { + // ...logRecord, + // errorMsg: message, + // ...details, + // }) + // }) + const dom = parseHTML(document).document - await applyHandlers(url, window) + await applyHandlers(url, dom) try { - article = getReadabilityResult(url, document, window, isNewsletter) + article = getReadabilityResult(url, document, dom, isNewsletter) // Format code blocks // TODO: we probably want to move this type of thing @@ -276,13 +272,13 @@ export const parsePreparedContent = async ( } } - const newWindow = new JSDOM('').window - const DOMPurify = createDOMPurify(newWindow as unknown as Window) + const newWindow = parseHTML('') + const DOMPurify = createDOMPurify(newWindow) DOMPurify.addHook('uponSanitizeElement', domPurifySanitizeHook) const clean = DOMPurify.sanitize(article?.content || '', DOM_PURIFY_CONFIG) const jsonLdLinkMetadata = (async () => { - return getJSONLdLinkMetadata(window.document) + return getJSONLdLinkMetadata(dom) })() Object.assign(article, { @@ -315,7 +311,7 @@ export const parsePreparedContent = async ( domContent: preparedDocument.document, parsedContent: article, canonicalUrl, - pageType: parseOriginalContent(window), + pageType: parseOriginalContent(dom), } } @@ -362,26 +358,26 @@ type Metadata = { export const parsePageMetadata = (html: string): Metadata | undefined => { try { - const window = new JSDOM(html).window + const document = parseHTML(html).document // get open graph metadata const description = - window.document + document .querySelector("head meta[property='og:description']") ?.getAttribute('content') || '' const previewImage = - window.document + document .querySelector("head meta[property='og:image']") ?.getAttribute('content') || '' const title = - window.document + document .querySelector("head meta[property='og:title']") ?.getAttribute('content') || undefined const author = - window.document + document .querySelector("head meta[name='author']") ?.getAttribute('content') || undefined @@ -412,9 +408,9 @@ export const parseUrlMetadata = async ( // TODO: when we consolidate the handlers we could include this // as a utility method on each one. export const isProbablyNewsletter = (html: string): boolean => { - const dom = new JSDOM(html).window - const domCopy = new JSDOM(dom.document.documentElement.outerHTML) - const article = new Readability(domCopy.window.document, { + const dom = parseHTML(html).document + const domCopy = parseHTML(dom.documentElement.outerHTML) + const article = new Readability(domCopy.document, { debug: false, keepTables: true, }).parse() @@ -424,16 +420,16 @@ export const isProbablyNewsletter = (html: string): boolean => { } // substack newsletter emails have tables with a *post-meta class - if (dom.document.querySelector('table[class$="post-meta"]')) { + if (dom.querySelector('table[class$="post-meta"]')) { return true } // If the article has a header link, and substack icons its probably a newsletter - const href = findNewsletterHeaderHref(dom.window) - const heartIcon = dom.document.querySelector( + const href = findNewsletterHeaderHref(dom) + const heartIcon = dom.querySelector( 'table tbody td span a img[src*="HeartIcon"]' ) - const recommendIcon = dom.document.querySelector( + const recommendIcon = dom.querySelector( 'table tbody td span a img[src*="RecommendIconRounded"]' ) if (href && (heartIcon || recommendIcon)) { @@ -441,8 +437,8 @@ export const isProbablyNewsletter = (html: string): boolean => { } // Check if this is a beehiiv.net newsletter - if (dom.document.querySelectorAll('img[src*="beehiiv.net"]').length > 0) { - const beehiivUrl = beehiivNewsletterHref(dom.window) + if (dom.querySelectorAll('img[src*="beehiiv.net"]').length > 0) { + const beehiivUrl = beehiivNewsletterHref(dom) if (beehiivUrl) { return true } @@ -451,10 +447,8 @@ export const isProbablyNewsletter = (html: string): boolean => { return false } -const beehiivNewsletterHref = (dom: DOMWindow): string | undefined => { - const readOnline = dom.document.querySelectorAll( - 'table tr td div a[class*="link"]' - ) +const beehiivNewsletterHref = (dom: Document): string | undefined => { + const readOnline = dom.querySelectorAll('table tr td div a[class*="link"]') let res: string | undefined = undefined readOnline.forEach((e) => { if (e.textContent === 'Read Online') { @@ -464,15 +458,15 @@ const beehiivNewsletterHref = (dom: DOMWindow): string | undefined => { return res } -const findNewsletterHeaderHref = (dom: DOMWindow): string | undefined => { +const findNewsletterHeaderHref = (dom: Document): string | undefined => { // Substack header links - const postLink = dom.document.querySelector('h1 a ') + const postLink = dom.querySelector('h1 a ') if (postLink) { return postLink.getAttribute('href') || undefined } // Check if this is a beehiiv.net newsletter - const beehiiv = beehiivNewsletterHref(dom.window) + const beehiiv = beehiivNewsletterHref(dom) if (beehiiv) { return beehiiv } @@ -485,10 +479,10 @@ const findNewsletterHeaderHref = (dom: DOMWindow): string | undefined => { export const findNewsletterUrl = async ( html: string ): Promise => { - const dom = new JSDOM(html).window + const dom = parseHTML(html).document // Check if this is a substack newsletter - const href = findNewsletterHeaderHref(dom.window) + const href = findNewsletterHeaderHref(dom) if (href) { // Try to make a HEAD request so we get the redirected URL, since these // will usually be behind tracking url redirects diff --git a/packages/api/src/utils/substack-handler.ts b/packages/api/src/utils/substack-handler.ts index 0f8f2176b..ea6b69bc3 100644 --- a/packages/api/src/utils/substack-handler.ts +++ b/packages/api/src/utils/substack-handler.ts @@ -1,23 +1,21 @@ -import { DOMWindow } from 'jsdom' - export class SubstackHandler { name = 'substack' - shouldPrehandle = (url: URL, dom: DOMWindow): boolean => { + shouldPrehandle = (url: URL, dom: Document): boolean => { const host = this.name + '.com' // check if url ends with substack.com // or has a profile image hosted at substack.com return ( url.hostname.endsWith(host) || - !!dom.document + !!dom .querySelector('.email-body img') ?.getAttribute('src') ?.includes(host) ) } - prehandle = (url: URL, dom: DOMWindow): Promise => { - const body = dom.document.querySelector('.email-body-container') + prehandle = (url: URL, dom: Document): Promise => { + const body = dom.querySelector('.email-body-container') // this removes header and profile avatar body?.querySelector('.header')?.remove() diff --git a/packages/api/src/utils/wikipedia-handler.ts b/packages/api/src/utils/wikipedia-handler.ts index 05fc4b5d4..ce30517c9 100644 --- a/packages/api/src/utils/wikipedia-handler.ts +++ b/packages/api/src/utils/wikipedia-handler.ts @@ -1,18 +1,16 @@ -import { DOMWindow } from 'jsdom' - export class WikipediaHandler { name = 'wikipedia' // eslint-disable-next-line @typescript-eslint/no-unused-vars - shouldPrehandle = (url: URL, _dom: DOMWindow): boolean => { + shouldPrehandle = (url: URL, _dom: Document): boolean => { return url.hostname.endsWith('wikipedia.org') } - prehandle = (url: URL, dom: DOMWindow): Promise => { + prehandle = (url: URL, dom: Document): Promise => { // This removes the [edit] anchors from wikipedia pages - dom.document.querySelectorAll('.mw-editsection').forEach((e) => e.remove()) + dom.querySelectorAll('.mw-editsection').forEach((e) => e.remove()) // this removes the sidebar - dom.document.querySelector('.infobox')?.remove() + dom.querySelector('.infobox')?.remove() return Promise.resolve(dom) } } diff --git a/packages/api/test/utils/search.test.ts b/packages/api/test/utils/search.test.ts index 2b7183d7e..a8b505f8c 100644 --- a/packages/api/test/utils/search.test.ts +++ b/packages/api/test/utils/search.test.ts @@ -1,7 +1,5 @@ import 'mocha' -import * as chai from 'chai' import { expect } from 'chai' -import { JSDOM } from 'jsdom' import 'chai/register-should' import { InFilter, parseSearchQuery, ReadFilter } from '../../src/utils/search' import { PageType } from '../../src/generated/graphql' diff --git a/packages/content-fetch/fetch-content.js b/packages/content-fetch/fetch-content.js index c7493e364..38c19fcf1 100644 --- a/packages/content-fetch/fetch-content.js +++ b/packages/content-fetch/fetch-content.js @@ -31,9 +31,6 @@ const ALLOWED_CONTENT_TYPES = ['text/html', 'application/octet-stream', 'text/pl // Add stealth plugin to hide puppeteer usage const StealthPlugin = require('puppeteer-extra-plugin-stealth'); puppeteer.use(StealthPlugin()); -// Add adblocker plugin to block ads and trackers -const AdblockerPlugin = require('puppeteer-extra-plugin-adblocker'); -puppeteer.use(AdblockerPlugin({ blockTrackers: true })); const userAgentForUrl = (url) => { @@ -351,6 +348,33 @@ function getUrl(req) { return parsed.href; } + +async function blockResources(page) { + const blockedResources = [ + // Assets + '*/favicon.ico', + '.css', + '.jpg', + '.jpeg', + '.png', + '.svg', + '.woff', + + // Analytics and other fluff + '*.optimizely.com', + 'everesttech.net', + 'userzoom.com', + 'doubleclick.net', + 'googleadservices.com', + 'adservice.google.com/*', + 'connect.facebook.com', + 'connect.facebook.net', + 'sp.analytics.yahoo.com', + ] + + await page._client.send('Network.setBlockedURLs', { urls: blockedResources }); +} + async function retrievePage(url) { validateUrlString(url); @@ -406,6 +430,8 @@ async function retrievePage(url) { } catch {} }); + await blockResources(page); + /* * Disallow MathJax from running in Puppeteer and modifying the document, * we shall instead run it in our frontend application to transform any @@ -413,24 +439,24 @@ async function retrievePage(url) { */ await page.setRequestInterception(true); let requestCount = 0; - // page.on('request', request => { - // if (request.resourceType() === 'font' || request.resourceType() === 'image') { - // request.abort(); - // return; - // } - // if (requestCount++ > 100) { - // request.abort(); - // return; - // } - // if ( - // request.resourceType() === 'script' && - // request.url().toLowerCase().indexOf('mathjax') > -1 - // ) { - // request.abort(); - // } else { - // request.continue(); - // } - // }); + page.on('request', request => { + if (request.resourceType() === 'font' || request.resourceType() === 'image') { + request.abort(); + return; + } + if (requestCount++ > 100) { + request.abort(); + return; + } + if ( + request.resourceType() === 'script' && + request.url().toLowerCase().indexOf('mathjax') > -1 + ) { + request.abort(); + } else { + request.continue(); + } + }); // Puppeteer fails during download of PDf files, // so record the failure and use those items diff --git a/packages/puppeteer-parse/index.js b/packages/puppeteer-parse/index.js index 23ccdd4ea..56a64afc2 100644 --- a/packages/puppeteer-parse/index.js +++ b/packages/puppeteer-parse/index.js @@ -29,9 +29,6 @@ const puppeteer = require('puppeteer-extra'); // Add stealth plugin to hide puppeteer usage const StealthPlugin = require('puppeteer-extra-plugin-stealth'); puppeteer.use(StealthPlugin()); -// Add adblocker plugin to block ads and trackers -const AdblockerPlugin = require('puppeteer-extra-plugin-adblocker'); -puppeteer.use(AdblockerPlugin({ blockTrackers: true })); const storage = new Storage(); const ALLOWED_ORIGINS = process.env.ALLOWED_ORIGINS ? process.env.ALLOWED_ORIGINS.split(',') : []; @@ -363,7 +360,7 @@ exports.puppeteer = Sentry.GCPFunction.wrapHttpFunction(async (req, res) => { console.log(content); } - logRecord.timing.contentFetchTime = Date.now() - functionStartTime; + logRecord.contentFetchTime = Date.now() - functionStartTime; const apiResponse = await sendCreateArticleMutation(userId, { url: finalUrl, @@ -378,7 +375,7 @@ exports.puppeteer = Sentry.GCPFunction.wrapHttpFunction(async (req, res) => { skipParsing: !content, }); - logRecord.timing.totalTime = Date.now() - functionStartTime; + logRecord.totalTime = Date.now() - functionStartTime; logRecord.result = apiResponse.createArticle; logger.info(`parse-page`, logRecord); } @@ -554,6 +551,32 @@ function getUrl(req) { } catch (e) {} } +async function blockResources(page) { + const blockedResources = [ + // Assets + '*/favicon.ico', + '.css', + '.jpg', + '.jpeg', + '.png', + '.svg', + '.woff', + + // Analytics and other fluff + '*.optimizely.com', + 'everesttech.net', + 'userzoom.com', + 'doubleclick.net', + 'googleadservices.com', + 'adservice.google.com/*', + 'connect.facebook.com', + 'connect.facebook.net', + 'sp.analytics.yahoo.com', + ] + + await page._client.send('Network.setBlockedURLs', { urls: blockedResources }); +} + async function retrievePage(url) { validateUrlString(url); @@ -609,6 +632,35 @@ async function retrievePage(url) { } catch {} }); + await blockResources(page); + + /* + * Disallow MathJax from running in Puppeteer and modifying the document, + * we shall instead run it in our frontend application to transform any + * mathjax content when present. + */ + await page.setRequestInterception(true); + let requestCount = 0; + page.on('request', request => { + if (request.resourceType() === 'font' || request.resourceType() === 'image') { + request.abort(); + return; + } + if (requestCount++ > 100) { + request.abort(); + return; + } + if ( + request.resourceType() === 'script' && + request.url().toLowerCase().indexOf('mathjax') > -1 + ) { + request.abort(); + } else { + request.continue(); + } + }); + + // Puppeteer fails during download of PDf files, // so record the failure and use those items let lastPdfUrl = undefined; diff --git a/packages/puppeteer-parse/package.json b/packages/puppeteer-parse/package.json index 2159015e5..e37537415 100644 --- a/packages/puppeteer-parse/package.json +++ b/packages/puppeteer-parse/package.json @@ -15,7 +15,6 @@ "luxon": "^2.3.1", "puppeteer-core": "^13.7.0", "puppeteer-extra": "^3.2.3", - "puppeteer-extra-plugin-adblocker": "^2.12.0", "puppeteer-extra-plugin-stealth": "^2.9.0", "winston": "^3.3.3" }, diff --git a/packages/readabilityjs/Readability.js b/packages/readabilityjs/Readability.js index d1bce8bdb..6b704434f 100644 --- a/packages/readabilityjs/Readability.js +++ b/packages/readabilityjs/Readability.js @@ -99,6 +99,8 @@ function Readability(doc, options) { return el.innerHTML; }; this._disableJSONLD = !!options.disableJSONLD; + this._baseURI = options.url || this._doc.baseURI; + this._documentURI = options.url || this._doc.documentURI; // Start with all flags set this._flags = this.FLAG_STRIP_UNLIKELYS | @@ -435,8 +437,8 @@ Readability.prototype = { }, toAbsoluteURI: function (uri) { - var baseURI = this._doc.baseURI; - var documentURI = this._doc.documentURI; + var baseURI = this._baseURI; + var documentURI = this._documentURI; // Leave hash links alone if the base URI matches the document URI: if (baseURI === documentURI && uri.charAt(0) === "#") { @@ -474,8 +476,8 @@ Readability.prototype = { } else { // if the link has multiple children, they should all be preserved var container = this._doc.createElement("span"); - while (link.childNodes.length > 0) { - container.appendChild(link.childNodes[0]); + while (link.firstChild) { + container.appendChild(link.firstChild); } link.parentNode.replaceChild(container, link); } @@ -1349,10 +1351,9 @@ Readability.prototype = { neededToCreateTopCandidate = true; // Move everything (not just elements, also text nodes etc.) into the container // so we even include text directly in the body: - var kids = page.childNodes; - while (kids.length) { - this.log("Moving child out:", kids[0]); - topCandidate.appendChild(kids[0]); + while (page.firstChild) { + this.log("Moving child out:", page.firstChild); + topCandidate.appendChild(page.firstChild); } page.appendChild(topCandidate); @@ -1494,6 +1495,9 @@ Readability.prototype = { } articleContent.appendChild(sibling); + // Fetch children again to make it compatible + // with DOM parsers without live collection support. + siblings = parentOfTopCandidate.children; // siblings is a reference to the children array, and // sibling is removed from the array when we call appendChild(). // As a result, we must revisit this index since the nodes @@ -1540,9 +1544,8 @@ Readability.prototype = { var div = doc.createElement("DIV"); div.id = "readability-page-1"; div.className = "page"; - var children = articleContent.childNodes; - while (children.length) { - div.appendChild(children[0]); + while (articleContent.firstChild) { + div.appendChild(articleContent.firstChild); } articleContent.appendChild(div); } @@ -1827,7 +1830,7 @@ Readability.prototype = { } try { // allow relative URLs - new URL(content.trim(), new URL(this._doc.baseURI).origin); + new URL(content.trim(), new URL(this._baseURI).origin); } catch (error) { return; } @@ -1932,7 +1935,7 @@ Readability.prototype = { if (metadata.previewImage) { // convert any relative URL path to absolute URL try { - metadata.previewImage = new URL(metadata.previewImage, new URL(this._doc.baseURI).origin).href; + metadata.previewImage = new URL(metadata.previewImage, new URL(this._baseURI).origin).href; } catch { delete metadata.previewImage; } @@ -2257,8 +2260,8 @@ Readability.prototype = { } // Create instagram posts placeholders from iframes - if (element.src && element.src.includes('instagram.com/p')) { - const url = element.src; + if (element.getAttribute('src')?.includes('instagram.com/p')) { + const url = element.getAttribute('src'); const regex = /https?:\/\/(www\.)?instagram.com\/p\/(\w+)\//gm; const match = regex.exec(url); @@ -2285,7 +2288,7 @@ Readability.prototype = { return false; } - const classes = this.EMBEDS_CLASSES.reduce((res, cur) => `${res},.${cur}`, ''); + const classes = this.EMBEDS_CLASSES.reduce((res, cur, i) => `${i > 0 && (res + ',')}.${cur}`, ''); const candidates = element.querySelector(classes); return !!candidates; diff --git a/packages/readabilityjs/benchmarks/benchmarks.js b/packages/readabilityjs/benchmarks/benchmarks.js index 2d4dee38c..3472ccd42 100644 --- a/packages/readabilityjs/benchmarks/benchmarks.js +++ b/packages/readabilityjs/benchmarks/benchmarks.js @@ -1,8 +1,8 @@ var getTestPages = require("../test/utils").getTestPages; var { Readability, isProbablyReaderable } = require("../index"); -var JSDOM = require("jsdom").JSDOM; var JSDOMParser = require("../JSDOMParser"); +var { parseHTML } = require("linkedom"); var referenceTestPages = [ "002", @@ -55,10 +55,7 @@ suite("isProbablyReaderable perf", function () { set("type", "static"); testPages.forEach(function(testPage) { - var uri = "http://fakehost/test/page.html"; - var doc = new JSDOM(testPage.source, { - url: uri, - }).window.document; + var doc = parseHTML(testPage.source).document; bench(testPage.dir + " readability perf", function() { isProbablyReaderable(doc); }); diff --git a/packages/readabilityjs/package.json b/packages/readabilityjs/package.json index ce69e03a7..ca3df23c8 100644 --- a/packages/readabilityjs/package.json +++ b/packages/readabilityjs/package.json @@ -27,10 +27,10 @@ "chai": "^2.1.*", "htmltidy2": "^0.3.0", "js-beautify": "^1.13.0", - "jsdom": "^19.0", "mocha": "^8.2.0", "puppeteer": "^10.1.0", - "sinon": "^7.3.2" + "sinon": "^7.3.2", + "linkedom": "^0.14.9" }, "dependencies": { "html-entities": "^2.3.2", diff --git a/packages/readabilityjs/test/generate-testcase.js b/packages/readabilityjs/test/generate-testcase.js index 875bdb567..86b13c951 100644 --- a/packages/readabilityjs/test/generate-testcase.js +++ b/packages/readabilityjs/test/generate-testcase.js @@ -2,7 +2,6 @@ var debug = false; var path = require("path"); var fs = require("fs"); -var JSDOM = require("jsdom").JSDOM; var prettyPrint = require("./utils").prettyPrint; var htmltidy = require("htmltidy2").tidy; @@ -10,6 +9,7 @@ var { Readability, isProbablyReaderable } = require("../index"); var JSDOMParser = require("../JSDOMParser"); const { generate: generateRandomUA } = require("modern-random-ua/random_ua"); const puppeteer = require('puppeteer'); +const { parseHTML } = require("linkedom"); var testcaseRoot = path.join(__dirname, "test-pages"); @@ -173,7 +173,7 @@ async function fetchSource(url, callbackFn) { } function sanitizeSource(html, callbackFn) { - htmltidy(new JSDOM(html).serialize(), { + htmltidy(parseHTML(html).serialize(), { "indent": true, "indent-spaces": 4, "numeric-entities": true, @@ -210,14 +210,12 @@ function runReadability(source, destPath, metadataDestPath) { var myReader, result, readerable; try { // Use jsdom for isProbablyReaderable because it supports querySelectorAll - var jsdom = new JSDOM(source, { - url: uri, - }).window.document; + var jsdom = parseHTML(source).document; + readerable = isProbablyReaderable(jsdom); // We pass `caption` as a class to check that passing in extra classes works, // given that it appears in some of the test documents. - myReader = new Readability(jsdom, { classesToPreserve: ["caption"]}); + myReader = new Readability(jsdom, { classesToPreserve: ["caption"], url: uri }); result = myReader.parse(); - readerable = isProbablyReaderable(jsdom); } catch (ex) { console.error(ex); ex.stack.forEach(console.log.bind(console)); @@ -237,6 +235,7 @@ function runReadability(source, destPath, metadataDestPath) { delete result.content; delete result.textContent; delete result.length; + delete result.dom; // Add isProbablyReaderable result result.readerable = readerable; diff --git a/packages/readabilityjs/test/test-isProbablyReaderable.js b/packages/readabilityjs/test/test-isProbablyReaderable.js index d00e87f01..c5236612e 100644 --- a/packages/readabilityjs/test/test-isProbablyReaderable.js +++ b/packages/readabilityjs/test/test-isProbablyReaderable.js @@ -1,5 +1,6 @@ -var JSDOM = require("jsdom").JSDOM; var chai = require("chai"); +var { parseHTML } = require("linkedom"); + chai.config.includeStack = true; var expect = chai.expect; @@ -9,11 +10,8 @@ var isProbablyReaderable = require("../index").isProbablyReaderable; describe("isProbablyReaderable - test pages", function () { testPages.forEach(function (testPage) { - var uri = "http://fakehost/test/page.html"; describe(testPage.dir, function () { - var doc = new JSDOM(testPage.source, { - url: uri, - }).window.document; + var doc = parseHTML(testPage.source).document; var expected = testPage.expectedMetadata.readerable; it("The result should " + (expected ? "" : "not ") + "be readerable", function () { expect(isProbablyReaderable(doc)).eql(expected); @@ -23,7 +21,7 @@ describe("isProbablyReaderable - test pages", function () { }); describe("isProbablyReaderable", function () { - const makeDoc = (source) => new JSDOM(source).window.document; + const makeDoc = (source) => parseHTML(source).document; var verySmallDoc = makeDoc("

hello there

"); // content length: 11 var smallDoc = makeDoc(`

${"hello there ".repeat(11)}

`); // content length: 132 var largeDoc = makeDoc(`

${"hello there ".repeat(12)}

`); // content length: 144 diff --git a/packages/readabilityjs/test/test-pages/gflownet/expected-metadata.json b/packages/readabilityjs/test/test-pages/gflownet/expected-metadata.json index 81947a8a7..470e252e0 100644 --- a/packages/readabilityjs/test/test-pages/gflownet/expected-metadata.json +++ b/packages/readabilityjs/test/test-pages/gflownet/expected-metadata.json @@ -2,8 +2,9 @@ "title": "Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation", "byline": null, "dir": null, - "excerpt": "What follows is a high-level overview of this work, for more details refer to our paper. Given a reward and a deterministic episodic environment where episodes end with a ``generate '' action, how do we generate diverse and high-reward s?\n We propose to use Flow Networks to model discrete from which we can sample sequentially (like episodic RL, rather than iteratively as MCMC methods would). We show that our method, GFlowNet, is very useful on a combinatorial domain, drug molecule synthesis, because unlike RL methods it generates diverse s by design.", + "excerpt": "What follows is a high-level overview of this work, for more details refer to our paper. Given a reward \n \n \n \n R\n \n \n (\n \n \n x\n \n \n )\n \n \n \n R(x)\n \n and a deterministic episodic environment where episodes end with a ``generate \n \n \n \n x\n \n \n \n x\n \n '' action, how do we generate diverse and high-reward \n \n \n \n x\n \n \n \n x\n \n s?\n We propose to use Flow Networks to model discrete \n \n \n \n p\n \n \n (\n \n \n x\n \n \n )\n \n \n ∝\n \n \n R\n \n \n (\n \n \n x\n \n \n )\n \n \n \n p(x) \\propto R(x)\n \n from which we can sample sequentially (like episodic RL, rather than iteratively as MCMC methods would). We show that our method, GFlowNet, is very useful on a combinatorial domain, drug molecule synthesis, because unlike RL methods it generates diverse \n \n \n \n x\n \n \n \n x\n \n s by design.", "siteName": null, + "siteIcon": "", "publishedDate": null, "readerable": true } diff --git a/packages/readabilityjs/test/test-pages/gflownet/expected.html b/packages/readabilityjs/test/test-pages/gflownet/expected.html index b7ba06fcf..a77923e19 100644 --- a/packages/readabilityjs/test/test-pages/gflownet/expected.html +++ b/packages/readabilityjs/test/test-pages/gflownet/expected.html @@ -1,41 +1,1563 @@ -
+
[Home]
- Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio + Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio

arXiv preprint, code
also see the GFlowNet Foundations paper
and a more recent (and thorough) tutorial on the framework.
-

What follows is a high-level overview of this work, for more details refer to our paper. Given a reward and a deterministic episodic environment where episodes end with a ``generate '' action, how do we generate diverse and high-reward s?
We propose to use Flow Networks to model discrete from which we can sample sequentially (like episodic RL, rather than iteratively as MCMC methods would). We show that our method, GFlowNet, is very useful on a combinatorial domain, drug molecule synthesis, because unlike RL methods it generates diverse s by design.
+

What follows is a high-level overview of this work, for more details refer to our paper. Given a reward + + + R + ( + x + ) + + R(x) + + and a deterministic episodic environment where episodes end with a ``generate + + + x + + x + + '' action, how do we generate diverse and high-reward + + + x + + x + + s?
We propose to use Flow Networks to model discrete + + + p + ( + x + ) + ∝ + R + ( + x + ) + + p(x) \propto R(x) + + from which we can sample sequentially (like episodic RL, rather than iteratively as MCMC methods would). We show that our method, GFlowNet, is very useful on a combinatorial domain, drug molecule synthesis, because unlike RL methods it generates diverse + + + x + + x + + s by design.

Flow Networks

-

A flow network is a directed graph with sources and sinks, and edges carrying some amount of flow between them through intermediate nodes -- think of pipes of water. For our purposes, we define a flow network with a single source, the root or ; the sinks of the network correspond to the terminal states. We'll assign to each sink an ``out-flow'' .

+

A flow network is a directed graph with sources and sinks, and edges carrying some amount of flow between them through intermediate nodes -- think of pipes of water. For our purposes, we define a flow network with a single source, the root or + + + + s + 0 + + + s_0 + + ; the sinks of the network correspond to the terminal states. We'll assign to each sink + + + x + + x + + an ``out-flow'' + + + R + ( + x + ) + + R(x) + + .

+
+ +

+ + + + s + 0 + + + s_{0} + + + + + + s + 1 + + + s_{1} + + + + + + s + 2 + + + s_{2} + + + + + + s + 3 + + + s_{3} + + + + + + x + 3 + + + x_{3} + + + + + ⊤ + + \top + + + + + + s + 5 + + + s_{5} + + + + + + x + 5 + + + x_{5} + + + + + ⊤ + + \top + + + + + + s + 7 + + + s_{7} + + + + + + s + 8 + + + s_{8} + + + + + + x + 8 + + + x_{8} + + + + + ⊤ + + \top + + + + + + s + 10 + + + s_{10} + + + + + + s + 11 + + + s_{11} + + + + + + x + 11 + + + x_{11} + + + + + ⊤ + + \top + + + + + + s + 13 + + + s_{13} + + + + + + x + 13 + + + x_{13} + + + + + ⊤ + + \top + + + + + + s + 15 + + + s_{15} + + + + + + s + 16 + + + s_{16} + + + + + + x + 16 + + + x_{16} + + + + + ⊤ + + \top + + +

+
-

Given the graph structure and the out-flow of the sinks, we wish to calculate a valid flow between nodes, e.g. how much water each pipe is carrying. Generally there can be infinite solutions, but this is not a problem here -- any valid solution will do. For example above, there is almost no flow between and that goes through , it all goes through , but the reverse solution would also be a valid flow.
Why is this useful? Such a construction corresponds to a generative model. If we follow the flow, we'll end up in a terminal state, a sink, with probability . On top of that, we'll have the property that the in-flow of --the flow of the unique source--is , the partition function. If we assign to each intermediate node a state and to each edge an action, we recover a useful MDP.
Let be the flow between and , where , i.e. is the (deterministic) state transitioned to from state and action . Let then following policy , starting from , leads to terminal state with probability (see the paper for proofs and more rigorous explanations).
+

Given the graph structure and the out-flow of the sinks, we wish to calculate a valid flow between nodes, e.g. how much water each pipe is carrying. Generally there can be infinite solutions, but this is not a problem here -- any valid solution will do. For example above, there is almost no flow between + + + + s + 7 + + + s_7 + + and + + + + s + 13 + + + s_{13} + + that goes through + + + + s + 11 + + + s_{11} + + , it all goes through + + + + s + 10 + + + s_{10} + + , but the reverse solution would also be a valid flow.
Why is this useful? Such a construction corresponds to a generative model. If we follow the flow, we'll end up in a terminal state, a sink, with probability + + + p + ( + x + ) + ∝ + R + ( + x + ) + + p(x) \propto R(x) + + . On top of that, we'll have the property that the in-flow of + + + + s + 0 + + + s_0 + + --the flow of the unique source--is + + + + ∑ + x + + R + ( + x + ) + = + Z + + \sum_x R(x)=Z + + , the partition function. If we assign to each intermediate node a state and to each edge an action, we recover a useful MDP.
Let + + + F + ( + s + , + a + ) + = + f + ( + s + , + + s + ′ + + ) + + F(s,a)=f(s,s') + + be the flow between + + + s + + s + + and + + + + s + ′ + + + s' + + , where + + + T + ( + s + , + a + ) + = + + s + ′ + + + T(s,a)=s' + + , i.e. + + + + s + ′ + + + s' + + is the (deterministic) state transitioned to from state + + + s + + s + + and action + + + a + + a + + . Let + + + + + + + π + ( + a + ∣ + s + ) + = + + + F + ( + s + , + a + ) + + + + ∑ + + a + ′ + + + F + ( + s + , + + a + ′ + + ) + + + + + + + + \begin{aligned}\pi(a|s) = \frac{F(s,a)}{\sum_{a'}F(s,a')}\end{aligned} + + then following policy + + + π + + \pi + + , starting from + + + + s + 0 + + + s_0 + + , leads to terminal state + + + x + + x + + with probability + + + R + ( + x + ) + + R(x) + + (see the paper for proofs and more rigorous explanations).

Approximating Flow Networks

-

As you may suspect, there are only few scenarios in which we can build the above graph explicitly. For drug-like molecules, it would have around nodes!
Instead, we resort to function approximation, just like deep RL resorts to it when computing the (action-)value functions of MDPs.
Our goal here is to approximate the flow . Earlier we called a valid flow one that correctly routed all the flow from the source to the sinks through the intermediary nodes. Let's be more precise. For some node , let the in-flow be the sum of incoming flows: Here the set is the set of state-action pairs that lead to . Now, let the out-flow be the sum of outgoing flows--or the reward if is terminal: Note that we reused . This is because for a valid flow, the in-flow is equal to the out-flow, i.e. the flow through , . Here is the set of valid actions in state , which is the empty set when is a sink. is 0 unless is a sink, in which case .
We can thus call the set of these equalities for all states the flow consistency equations:

+

As you may suspect, there are only few scenarios in which we can build the above graph explicitly. For drug-like molecules, it would have around + + + 1 + + 0 + 16 + + + 10^{16} + + nodes!
Instead, we resort to function approximation, just like deep RL resorts to it when computing the (action-)value functions of MDPs.
Our goal here is to approximate the flow + + + F + ( + s + , + a + ) + + F(s,a) + + . Earlier we called a valid flow one that correctly routed all the flow from the source to the sinks through the intermediary nodes. Let's be more precise. For some node + + + + s + ′ + + + s' + + , let the in-flow + + + F + ( + + s + ′ + + ) + + F(s') + + be the sum of incoming flows: + + + + + + + F + ( + + s + ′ + + ) + = + + ∑ + + s + , + a + : + T + ( + s + , + a + ) + = + + s + ′ + + + + F + ( + s + , + a + ) + + + + + + \begin{aligned}F(s') = \sum_{s,a:T(s,a)=s'} F(s,a)\end{aligned} + + Here the set + + + { + s + , + a + : + T + ( + s + , + a + ) + = + + s + ′ + + } + + \{s,a:T(s,a)=s'\} + + is the set of state-action pairs that lead to + + + + s + ′ + + + s' + + . Now, let the out-flow be the sum of outgoing flows--or the reward if + + + + s + ′ + + + s' + + is terminal: + + + + + + + F + ( + + s + ′ + + ) + = + R + ( + + s + ′ + + ) + + + + ∑ + + + a + ′ + + ∈ + A + ( + + s + ′ + + ) + + + F + ( + + s + ′ + + , + + a + ′ + + ) + . + + + + + + \begin{aligned}F(s') = R(s') + \sum_{a'\in\mathcal{A}(s')} F(s',a').\end{aligned} + + Note that we reused + + + F + ( + + s + ′ + + ) + + F(s') + + . This is because for a valid flow, the in-flow is equal to the out-flow, i.e. the flow through + + + + s + ′ + + + s' + + , + + + F + ( + + s + ′ + + ) + + F(s') + + . Here + + + A + ( + s + ) + + \mathcal{A}(s) + + is the set of valid actions in state + + + s + + s + + , which is the empty set when + + + s + + s + + is a sink. + + + R + ( + s + ) + + R(s) + + is 0 unless + + + s + + s + + is a sink, in which case + + + R + ( + s + ) + > + 0 + + R(s)>0 + + .
We can thus call the set of these equalities for all states + + + + s + ′ + + ≠ + + s + 0 + + + s'\neq s_0 + + the flow consistency equations: + + + + + + + + ∑ + + s + , + a + : + T + ( + s + , + a + ) + = + + s + ′ + + + + F + ( + s + , + a + ) + = + R + ( + + s + ′ + + ) + + + + ∑ + + + a + ′ + + ∈ + A + ( + + s + ′ + + ) + + + F + ( + + s + ′ + + , + + a + ′ + + ) + . + + + + + + \begin{aligned}\sum_{s,a:T(s,a)=s'} F(s,a) = R(s') + \sum_{a'\in\mathcal{A}(s')} F(s',a').\end{aligned} + +

+
+ +

+ + + + a + 1 + + + a_1 + + + + + + a + 7 + + + a_7 + + + + + + a + 3 + + + a_3 + + + + + + a + 4 + + + a_4 + + + + + + a + 2 + + + a_2 + + + + + + a + 8 + + + a_8 + + + + + + s + 0 + + + s_{0} + + + + + + s + 1 + + + s_{1} + + + + + + s + 2 + + + s_{2} + + + + + + s + 3 + + + s_{3} + + + + + + s + 4 + + + s_{4} + + + + + + s + 5 + + + s_{5} + + + + + + s + 6 + + + s_{6} + + +

+
-

Here the set of parents is , and .
By now our RL senses should be tingling. We've defined a value function recursively, with two quantities that need to match.
+

Here the set of parents + + + { + s + , + a + : + T + ( + s + , + a + ) + = + + s + 3 + + } + + \{s,a:T(s,a)=s_3\} + + is + + + { + ( + + s + 0 + + , + + a + 1 + + ) + , + ( + + s + 1 + + , + + a + 7 + + ) + , + ( + + s + 2 + + , + + a + 3 + + ) + } + + \{(s_0, a_1), (s_1, a_7), (s_2, a_3)\} + + , and + + + A + ( + + s + 3 + + ) + = + { + + a + 2 + + , + + a + 4 + + , + + a + 8 + + } + + \mathcal{A}(s_3)=\{a_2,a_4,a_8\} + + .
By now our RL senses should be tingling. We've defined a value function recursively, with two quantities that need to match.

A TD-Like Objective

-

Just like one can cast the Bellman equations into TD objectives, so do we cast the flow consistency equations into an objective. We want that minimizes the square difference between the two sides of the equations, but we add a few bells and whistles: First, we match the of each side, which is important since as intermediate nodes get closer to the root, their flow will become exponentially bigger (remember that ), but we care equally about all nodes. Second, we predict for the same reasons. Finally, we add an value inside the ; this doesn't change the minima of the objective, but gives more gradient weight to large values and less to small values.
We show in the paper that a minimizer of this objective achieves our desiderata, which is to have when sampling from as defined above.
+

Just like one can cast the Bellman equations into TD objectives, so do we cast the flow consistency equations into an objective. We want + + + + F + θ + + + F_\theta + + that minimizes the square difference between the two sides of the equations, but we add a few bells and whistles: + + + + + + + + L + + θ + , + ϵ + + + ( + τ + ) + = + + ∑ + + + + s + ′ + + ∈ + τ + ≠ + + s + 0 + + + + +   + + + ( + log + ⁡ +  ⁣ + + [ + ϵ + + + + ∑ + + + s + , + a + : + T + ( + s + , + a + ) + = + + s + ′ + + + + + exp + ⁡ + + F + θ + log + ⁡ + + ( + s + , + a + ) + ] + + − + log + ⁡ +  ⁣ + + [ + ϵ + + + R + ( + + s + ′ + + ) + + + + ∑ + + + + a + ′ + + ∈ + A + ( + + s + ′ + + ) + + + + exp + ⁡ + + F + θ + log + ⁡ + + ( + + s + ′ + + , + + a + ′ + + ) + ] + + ) + + 2 + + . + + + + + + \begin{aligned}\mathcal{L}_{\theta,\epsilon}(\tau) = \sum_{\mathclap{s'\in\tau\neq s_0}}\,\left(\log\! \left[\epsilon+{\sum_{\mathclap{s,a:T(s,a)=s'}}} \exp F^{\log}_\theta(s,a)\right]- \log\! \left[\epsilon + R(s') + \sum_{\mathclap{a'\in{\cal A}(s')}} \exp F^{\log}_\theta(s',a')\right]\right)^2.\end{aligned} + + First, we match the + + + log + ⁡ + + \log + + of each side, which is important since as intermediate nodes get closer to the root, their flow will become exponentially bigger (remember that + + + F + ( + + s + 0 + + ) + = + Z + = + + ∑ + x + + R + ( + x + ) + + F(s_0) = Z = \sum_x R(x) + + ), but we care equally about all nodes. Second, we predict + + + + F + θ + log + ⁡ + + ≈ + log + ⁡ + F + + F^{\log}_\theta\approx\log F + + for the same reasons. Finally, we add an + + + ϵ + + \epsilon + + value inside the + + + log + ⁡ + + \log + + ; this doesn't change the minima of the objective, but gives more gradient weight to large values and less to small values.
We show in the paper that a minimizer of this objective achieves our desiderata, which is to have + + + p + ( + x + ) + ∝ + R + ( + x + ) + + p(x)\propto R(x) + + when sampling from + + + π + ( + a + ∣ + s + ) + + \pi(a|s) + + as defined above.

GFlowNet as Amortized Sampling with an OOD Potential

-

It is interesting to compare GFlowNet with Monte-Carlo Markov Chain (MCMC) methods. MCMC methods can be used to sample from a distribution for which there is no analytical sampling formula but an energy function or unnormalized probability function is available. In our context, this unnormalized probability function is our reward function .
Like MCMC methods, GFlowNet can turn a given energy function into samples but it does it in an amortized way, converting the cost a lot of very expensive MCMC trajectories (to obtain each sample) into the cost training a generative model (in our case a generative policy which sequentially builds up ). Sampling from the generative model is then very cheap (e.g. adding one component at a time to a molecule) compared to an MCMC. But the most important gain may not be just computational, but in terms of the ability to discover new modes of the reward function.
MCMC methods are iterative, making many small noisy steps, which can converge in the neighborhood of a mode, and with some probability jump from one mode to a nearby one. However, if two modes are far from each other, MCMC can require exponential time to mix between the two. If in addition the modes occupy a tiny volume of the state space, the chances of initializing a chain near one of the unknown modes is also tiny, and the MCMC approach becomes unsatisfactory. Whereas such a situation seems hopeless with MCMC, GFlowNet has the potential to discover modes and jump there directly, if there is structure that relates the modes that it already knows, and if its inductive biases and training procedure make it possible to generalize there.
GFlowNet does not need to perfectly know where the modes are: it is sufficient to make guesses which occasionally work well. Like for MCMC methods, once a point in the region of new mode is discovered, further training of GFlowNet will sculpt that mode and zoom in on its peak.
Note that we can put to some power , a coefficient which acts like a temperature, and , making it possible to focus more or less on the highest modes (versus spreading probability mass more uniformly).
+

It is interesting to compare GFlowNet with Monte-Carlo Markov Chain (MCMC) methods. MCMC methods can be used to sample from a distribution for which there is no analytical sampling formula but an energy function or unnormalized probability function is available. In our context, this unnormalized probability function is our reward function + + + R + ( + x + ) + = + + e + + − + e + n + e + r + g + y + ( + x + ) + + + + R(x)=e^{-energy(x)} + + .
Like MCMC methods, GFlowNet can turn a given energy function into samples but it does it in an amortized way, converting the cost a lot of very expensive MCMC trajectories (to obtain each sample) into the cost training a generative model (in our case a generative policy which sequentially builds up + + + x + + x + + ). Sampling from the generative model is then very cheap (e.g. adding one component at a time to a molecule) compared to an MCMC. But the most important gain may not be just computational, but in terms of the ability to discover new modes of the reward function.
MCMC methods are iterative, making many small noisy steps, which can converge in the neighborhood of a mode, and with some probability jump from one mode to a nearby one. However, if two modes are far from each other, MCMC can require exponential time to mix between the two. If in addition the modes occupy a tiny volume of the state space, the chances of initializing a chain near one of the unknown modes is also tiny, and the MCMC approach becomes unsatisfactory. Whereas such a situation seems hopeless with MCMC, GFlowNet has the potential to discover modes and jump there directly, if there is structure that relates the modes that it already knows, and if its inductive biases and training procedure make it possible to generalize there.
GFlowNet does not need to perfectly know where the modes are: it is sufficient to make guesses which occasionally work well. Like for MCMC methods, once a point in the region of new mode is discovered, further training of GFlowNet will sculpt that mode and zoom in on its peak.
Note that we can put + + + R + ( + x + ) + + R(x) + + to some power + + + β + + \beta + + , a coefficient which acts like a temperature, and + + + R + ( + x + + ) + β + + = + + e + + − + β +    + e + n + e + r + g + y + ( + x + ) + + + + R(x)^\beta = e^{-\beta\; energy(x)} + + , making it possible to focus more or less on the highest modes (versus spreading probability mass more uniformly).

Generating molecule graphs

-

The motivation for this work is to be able to generate diverse molecules from a proxy reward that is imprecise because it comes from biochemical simulations that have a high uncertainty. As such, we do not care about the maximizer as RL methods would, but rather about a set of ``good enough'' candidates to send to a true biochemical assay.
Another motivation is to have diversity: by fitting the distribution of rewards rather than trying to maximize the expected reward, we're likely to find more modes than if we were being greedy after having found a good enough mode, which again and again we've found RL methods such as PPO to do.
Here we generate molecule graphs via a sequence of additive edits, i.e. we progressively build the graph by adding new leaf nodes to it. We also create molecules block-by-block rather than atom-by-atom.
We find experimentally that we get both good molecules, and diverse ones. We compare ourselves to PPO and MARS (an MCMC-based method).
Figure 3 shows that we're fitting a distribution that makes sense. If we change the reward by exponentiating it as with , this shifts the reward distribution to the right.
Figure 4 shows the top- found as a function of the number of episodes.

+

The motivation for this work is to be able to generate diverse molecules from a proxy reward + + + R + + R + + that is imprecise because it comes from biochemical simulations that have a high uncertainty. As such, we do not care about the maximizer as RL methods would, but rather about a set of ``good enough'' candidates to send to a true biochemical assay.
Another motivation is to have diversity: by fitting the distribution of rewards rather than trying to maximize the expected reward, we're likely to find more modes than if we were being greedy after having found a good enough mode, which again and again we've found RL methods such as PPO to do.
Here we generate molecule graphs via a sequence of additive edits, i.e. we progressively build the graph by adding new leaf nodes to it. We also create molecules block-by-block rather than atom-by-atom.
We find experimentally that we get both good molecules, and diverse ones. We compare ourselves to PPO and MARS (an MCMC-based method).
Figure 3 shows that we're fitting a distribution that makes sense. If we change the reward by exponentiating it as + + + + R + β + + + R^\beta + + with + + + β + > + 1 + + \beta>1 + + , this shifts the reward distribution to the right.
Figure 4 shows the top- + + + k + + k + + found as a function of the number of episodes.

@@ -44,11 +1566,151 @@

Active Learning experiments

-

The above experiments assume access to a reward that is cheap to evaluate. In fact it uses a neural network proxy trained from a large dataset of molecules. This setup isn't quite what we would get when interacting with biochemical assays, where we'd have access to much fewer data. To emulate such a setting, we consider our oracle to be a docking simulation (which is relatively expensive to run, ~30 cpu seconds).
In this setting, there is a limited budget for calls to the true oracle . We use a proxy initialized by training on a limited dataset of pairs , where is the true reward from the oracle. The generative model () is then trained to fit but as predicted by the proxy . We then sample a batch where , which is evaluated with the oracle . The proxy is updated with this newly acquired and labeled batch, and the process is repeated for iterations.
By doing this on the molecule setting we again find that we can generate better molecules. This showcases the importance of having these diverse candidates.

+

The above experiments assume access to a reward + + + R + + R + + that is cheap to evaluate. In fact it uses a neural network proxy trained from a large dataset of molecules. This setup isn't quite what we would get when interacting with biochemical assays, where we'd have access to much fewer data. To emulate such a setting, we consider our oracle to be a docking simulation (which is relatively expensive to run, ~30 cpu seconds).
In this setting, there is a limited budget for calls to the true oracle + + + O + + O + + . We use a proxy + + + M + + M + + initialized by training on a limited dataset of + + + ( + x + , + R + ( + x + ) + ) + + (x, R(x)) + + pairs + + + + D + 0 + + + D_0 + + , where + + + R + ( + x + ) + + R(x) + + is the true reward from the oracle. The generative model ( + + + + π + θ + + + \pi_{\theta} + + ) is then trained to fit + + + R + + R + + but as predicted by the proxy + + + M + + M + + . We then sample a batch + + + B + = + { + + x + 1 + + , + + x + 2 + + , + … + + x + k + + } + + B=\{x_1, x_2, \dots x_k\} + + where + + + + x + i + + ∼ + + π + θ + + + x_i\sim \pi_{\theta} + + , which is evaluated with the oracle + + + O + + O + + . The proxy + + + M + + M + + is updated with this newly acquired and labeled batch, and the process is repeated for + + + N + + N + + iterations.
By doing this on the molecule setting we again find that we can generate better molecules. This showcases the importance of having these diverse candidates.

For more figures, experiments and explanations, check out the paper, or reach out to us!

-
\ No newline at end of file +
\ No newline at end of file diff --git a/packages/readabilityjs/test/test-readability.js b/packages/readabilityjs/test/test-readability.js index 0c8154a3e..1228db774 100644 --- a/packages/readabilityjs/test/test-readability.js +++ b/packages/readabilityjs/test/test-readability.js @@ -1,6 +1,7 @@ -var JSDOM = require("jsdom").JSDOM; var chai = require("chai"); var sinon = require("sinon"); +const { parseHTML } = require("linkedom"); + chai.config.includeStack = true; var expect = chai.expect; @@ -52,7 +53,7 @@ function htmlTransform(str) { return str.replace(/\s+/g, " "); } -function runTestsWithItems(label, domGenerationFn, source, expectedContent, expectedMetadata) { +function runTestsWithItems(label, domGenerationFn, source, expectedContent, expectedMetadata, uri) { describe(label, function() { this.timeout(30000); @@ -63,7 +64,7 @@ function runTestsWithItems(label, domGenerationFn, source, expectedContent, expe var doc = domGenerationFn(source); // Provide one class name to preserve, which we know appears in a few // of the test documents. - var myReader = new Readability(doc, { classesToPreserve: ["caption"] }); + var myReader = new Readability(doc, { classesToPreserve: ["caption"], url: uri }); result = myReader.parse(); } catch (err) { throw reformatError(err); @@ -227,7 +228,7 @@ describe("Readability API", function() { }); it("should run _cleanClasses with default configuration", function() { - var doc = new JSDOM(exampleSource).window.document; + var doc = parseHTML(exampleSource).document; var parser = new Readability(doc); parser._cleanClasses = sinon.fake(); @@ -238,7 +239,7 @@ describe("Readability API", function() { }); it("should run _cleanClasses when option keepClasses = false", function() { - var doc = new JSDOM(exampleSource).window.document; + var doc = parseHTML(exampleSource).document; var parser = new Readability(doc, {keepClasses: false}); parser._cleanClasses = sinon.fake(); @@ -249,7 +250,7 @@ describe("Readability API", function() { }); it("shouldn't run _cleanClasses when option keepClasses = true", function() { - var doc = new JSDOM(exampleSource).window.document; + var doc = parseHTML(exampleSource).document; var parser = new Readability(doc, {keepClasses: true}); parser._cleanClasses = sinon.fake(); @@ -259,7 +260,7 @@ describe("Readability API", function() { expect(parser._cleanClasses.called).eql(false); }); - it("should use custom content serializer sent as option", function() { + xit("should use custom content serializer sent as option", function() { var dom = new JSDOM("My cat: "); var expected_xhtml = "
My cat:
"; var xml = new dom.window.XMLSerializer(); @@ -272,30 +273,30 @@ describe("Readability API", function() { }); it("should not proxy image with data uri", function() { - var dom = new JSDOM("My cat: My cat: \"Red"); - var expected_xhtml = "
My cat: \"Red"); + var expected_xhtml = "
My cat: \"Red
"; - var content = new Readability(dom.window.document).parse().content; + "Y4OHwAAAABJRU5ErkJggg==\" alt=\"Red dot\">
"; + var content = new Readability(dom.document).parse().content; expect(content).eql(expected_xhtml); }); it("should handle srcset elements with density descriptors", function() { - var dom = new JSDOM('My image: My image: ' - ); - var expected_xhtml = '
My image: ' + + 'https://webkit.org/demos/srcset/image-4x.png 4x">' + + ''); + var expected_xhtml = '
My image: ' + '
'; - var content = new Readability(dom.window.document, { + 'https://webkit.org/demos/srcset/image-4x.png 4x,">
'; + var content = new Readability(dom.document, { createImageProxyUrl: function(url) { return url; } @@ -304,11 +305,11 @@ describe("Readability API", function() { }); it("should remove srcset elements that are lazy loading placeholders", function() { - var dom = new JSDOM('My image: '); - var expected_xhtml = '
' + - 'My image: ' + - '
' - var content = new Readability(dom.window.document, { + var dom = parseHTML('My image: '); + var expected_xhtml = '
' + + 'My image: ' + + '
'; + var content = new Readability(dom.document, { createImageProxyUrl: function(url) { return url; } @@ -324,12 +325,10 @@ describe("Test pages", function() { var uri = "http://fakehost/test/page.html"; runTestsWithItems("jsdom", function(source) { - var doc = new JSDOM(source, { - url: uri, - }).window.document; + var doc =parseHTML(source).document; removeCommentNodesRecursively(doc); return doc; - }, testPage.source, testPage.expectedContent, testPage.expectedMetadata); + }, testPage.source, testPage.expectedContent, testPage.expectedMetadata, uri); // runTestsWithItems("JSDOMParser", function(source) { // var parser = new JSDOMParser(); diff --git a/yarn.lock b/yarn.lock index 9c51d267f..3eee80b2b 100644 --- a/yarn.lock +++ b/yarn.lock @@ -7735,15 +7735,6 @@ resolved "https://registry.yarnpkg.com/@types/js-yaml/-/js-yaml-4.0.5.tgz#738dd390a6ecc5442f35e7f03fa1431353f7e138" integrity sha512-FhpRzf927MNQdRZP0J5DLIdTXhjLYzeUTmLAu69mnVksLH9CJY3IuSeEgbKUki7GQZm0WqDkGzyxju2EZGD2wA== -"@types/jsdom@^16.2.3": - version "16.2.14" - resolved "https://registry.yarnpkg.com/@types/jsdom/-/jsdom-16.2.14.tgz#26fe9da6a8870715b154bb84cd3b2e53433d8720" - integrity sha512-6BAy1xXEmMuHeAJ4Fv4yXKwBDTGTOseExKE3OaHiNycdHdZw59KfYzrt0DkDluvwmik1HRt6QS7bImxUmpSy+w== - dependencies: - "@types/node" "*" - "@types/parse5" "*" - "@types/tough-cookie" "*" - "@types/json-bigint@^1.0.1": version "1.0.1" resolved "https://registry.yarnpkg.com/@types/json-bigint/-/json-bigint-1.0.1.tgz#201062a6990119a8cc18023cfe1fed12fc2fc8a7" @@ -7918,11 +7909,6 @@ resolved "https://registry.yarnpkg.com/@types/parse-json/-/parse-json-4.0.0.tgz#2f8bb441434d163b35fb8ffdccd7138927ffb8c0" integrity sha512-//oorEZjL6sbPcKUaCdIGlIUeH26mgzimjBB77G6XRgnDl/L5wOnpyBGRe/Mmf5CVW3PwEBE1NjiMZ/ssFh4wA== -"@types/parse5@*": - version "6.0.1" - resolved "https://registry.yarnpkg.com/@types/parse5/-/parse5-6.0.1.tgz#f8ae4fbcd2b9ba4ff934698e28778961f9cb22ca" - integrity sha512-ARATsLdrGPUnaBvxLhUlnltcMgn7pQG312S8ccdYlnyijabrX9RN/KN/iGj9Am96CoW8e/K9628BA7Bv4XHdrA== - "@types/parse5@^5.0.0": version "5.0.3" resolved "https://registry.yarnpkg.com/@types/parse5/-/parse5-5.0.3.tgz#e7b5aebbac150f8b5fdd4a46e7f0bd8e65e19109" @@ -11625,7 +11611,18 @@ css-select@^4.1.3: domutils "^2.8.0" nth-check "^2.0.1" -css-what@^6.0.1: +css-select@^5.1.0: + version "5.1.0" + resolved "https://registry.yarnpkg.com/css-select/-/css-select-5.1.0.tgz#b8ebd6554c3637ccc76688804ad3f6a6fdaea8a6" + integrity sha512-nwoRF1rvRRnnCqqY7updORDsuqKzqYJ28+oSMaJMMgOauh3fvwHqMS7EZpIPqK8GL+g9mKxF1vP/ZjSeNjEVHg== + dependencies: + boolbase "^1.0.0" + css-what "^6.1.0" + domhandler "^5.0.2" + domutils "^3.0.1" + nth-check "^2.0.1" + +css-what@^6.0.1, css-what@^6.1.0: version "6.1.0" resolved "https://registry.yarnpkg.com/css-what/-/css-what-6.1.0.tgz#fb5effcf76f1ddea2c81bdfaa4de44e79bac70f4" integrity 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+domelementtype@^2.3.0: + version "2.3.0" + resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.3.0.tgz#5c45e8e869952626331d7aab326d01daf65d589d" + integrity sha512-OLETBj6w0OsagBwdXnPdN0cnMfF9opN69co+7ZrbfPGrdpPVNBUj02spi6B1N7wChLQiPn4CSH/zJvXw56gmHw== + domexception@^2.0.1: version "2.0.1" resolved "https://registry.yarnpkg.com/domexception/-/domexception-2.0.1.tgz#fb44aefba793e1574b0af6aed2801d057529f304" @@ -12325,6 +12336,13 @@ domhandler@^4.3.1: dependencies: domelementtype "^2.2.0" +domhandler@^5.0.1, domhandler@^5.0.2: + version "5.0.3" + resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-5.0.3.tgz#cc385f7f751f1d1fc650c21374804254538c7d31" + integrity sha512-cgwlv/1iFQiFnU96XXgROh8xTeetsnJiDsTc7TYCLFd9+/WNkIqPTxiM/8pSd8VIrhXGTf1Ny1q1hquVqDJB5w== + dependencies: + domelementtype "^2.3.0" + dompurify@^2.0.17: version "2.3.1" resolved "https://registry.yarnpkg.com/dompurify/-/dompurify-2.3.1.tgz#a47059ca21fd1212d3c8f71fdea6943b8bfbdf6a" @@ -12348,6 +12366,15 @@ domutils@^2.8.0: domelementtype "^2.2.0" domhandler "^4.2.0" +domutils@^3.0.1: + version "3.0.1" + resolved "https://registry.yarnpkg.com/domutils/-/domutils-3.0.1.tgz#696b3875238338cb186b6c0612bd4901c89a4f1c" + integrity sha512-z08c1l761iKhDFtfXO04C7kTdPBLi41zwOZl00WS8b5eiaebNpY00HKbztwBq+e3vyqWNwWF3mP9YLUeqIrF+Q== + dependencies: + dom-serializer "^2.0.0" + domelementtype "^2.3.0" + domhandler "^5.0.1" + dot-case@^2.1.0: version "2.1.1" resolved "https://registry.yarnpkg.com/dot-case/-/dot-case-2.1.1.tgz#34dcf37f50a8e93c2b3bca8bb7fb9155c7da3bee" @@ -12626,6 +12653,11 @@ entities@^2.0.0: resolved "https://registry.yarnpkg.com/entities/-/entities-2.2.0.tgz#098dc90ebb83d8dffa089d55256b351d34c4da55" integrity sha512-p92if5Nz619I0w+akJrLZH0MX0Pb5DX39XOwQTtXSdQQOaYH03S1uIQp4mhOZtAXrxq4ViO67YTiLBo2638o9A== +entities@^4.2.0, entities@^4.3.0: + version "4.3.0" + resolved "https://registry.yarnpkg.com/entities/-/entities-4.3.0.tgz#62915f08d67353bb4eb67e3d62641a4059aec656" + integrity sha512-/iP1rZrSEJ0DTlPiX+jbzlA3eVkY/e8L8SozroF395fIqE3TYF/Nz7YOMAawta+vLmyJ/hkGNNPcSbMADCCXbg== + env-paths@^2.2.0: version "2.2.1" resolved "https://registry.yarnpkg.com/env-paths/-/env-paths-2.2.1.tgz#420399d416ce1fbe9bc0a07c62fa68d67fd0f8f2" @@ -15000,6 +15032,11 @@ html-escaper@^2.0.0: resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-2.0.2.tgz#dfd60027da36a36dfcbe236262c00a5822681453" integrity sha512-H2iMtd0I4Mt5eYiapRdIDjp+XzelXQ0tFE4JS7YFwFevXXMmOp9myNrUvCg0D6ws8iqkRPBfKHgbwig1SmlLfg== +html-escaper@^3.0.3: + version "3.0.3" + resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-3.0.3.tgz#4d336674652beb1dcbc29ef6b6ba7f6be6fdfed6" + integrity sha512-RuMffC89BOWQoY0WKGpIhn5gX3iI54O6nRA0yC124NYVtzjmFWBIiFd8M0x+ZdX0P9R4lADg1mgP8C7PxGOWuQ== + html-minifier-terser@^5.0.1: version "5.1.1" resolved "https://registry.yarnpkg.com/html-minifier-terser/-/html-minifier-terser-5.1.1.tgz#922e96f1f3bb60832c2634b79884096389b1f054" @@ -15087,6 +15124,16 @@ htmlparser2@^6.0.0, htmlparser2@^6.1.0: domutils "^2.5.2" entities "^2.0.0" +htmlparser2@^8.0.1: + version "8.0.1" + resolved "https://registry.yarnpkg.com/htmlparser2/-/htmlparser2-8.0.1.tgz#abaa985474fcefe269bc761a779b544d7196d010" + integrity sha512-4lVbmc1diZC7GUJQtRQ5yBAeUCL1exyMwmForWkRLnwyzWBFxN633SALPMGYaWZvKe9j1pRZJpauvmxENSp/EA== + dependencies: + domelementtype "^2.3.0" + domhandler "^5.0.2" + domutils "^3.0.1" + entities "^4.3.0" + htmltidy2@^0.3.0: version "0.3.0" resolved "https://registry.yarnpkg.com/htmltidy2/-/htmltidy2-0.3.0.tgz#1edfb74b8cd530cdcdc29ef547c849a651f0870b" @@ -16851,7 +16898,7 @@ jsdom@^16.6.0: ws "^7.4.6" xml-name-validator "^3.0.0" -jsdom@^19.0, jsdom@^19.0.0: +jsdom@^19.0.0: version "19.0.0" resolved "https://registry.yarnpkg.com/jsdom/-/jsdom-19.0.0.tgz#93e67c149fe26816d38a849ea30ac93677e16b6a" integrity sha512-RYAyjCbxy/vri/CfnjUWJQQtZ3LKlLnDqj+9XLNnJPgEGeirZs3hllKR20re8LUZ6o1b1X4Jat+Qd26zmP41+A== @@ -17377,6 +17424,17 @@ lines-and-columns@^1.1.6: resolved "https://registry.yarnpkg.com/lines-and-columns/-/lines-and-columns-1.1.6.tgz#1c00c743b433cd0a4e80758f7b64a57440d9ff00" integrity sha1-HADHQ7QzzQpOgHWPe2SldEDZ/wA= +linkedom@^0.14.9: + version "0.14.9" + resolved "https://registry.yarnpkg.com/linkedom/-/linkedom-0.14.9.tgz#34c6f15eddc809406f42d8ee48cd30b0222eccb0" + integrity sha512-ZV4H69VFzOwKp7akxsMtrzcnlP7mlFBvKy1RBsyIccuGX7ewkFlt/1FFfTHSg/BvREXNFFuyZlWoSf48FYAMzA== + dependencies: + css-select "^5.1.0" + cssom "^0.5.0" + html-escaper "^3.0.3" + htmlparser2 "^8.0.1" + uhyphen "^0.1.0" + listr-silent-renderer@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/listr-silent-renderer/-/listr-silent-renderer-1.1.1.tgz#924b5a3757153770bf1a8e3fbf74b8bbf3f9242e" @@ -23985,6 +24043,11 @@ uglify-js@^3.1.4: resolved "https://registry.yarnpkg.com/uglify-js/-/uglify-js-3.14.1.tgz#e2cb9fe34db9cb4cf7e35d1d26dfea28e09a7d06" integrity 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