30:T451,papers = chiConference.contents.map((d) => { d.id = +d.id; d.sessions = d.sessionIds.map((s) => { const session = maps.get("sessions").get(s); session.room = maps.get("rooms").get(session.roomId); session.timeSlot = maps.get("timeSlots").get(session.timeSlotId); return session; }); d.firstSessionName = d.sessions[0].name; d.firstRoomName = d.sessions[0].room?.name; d.firstTimestamp = new Date(d.sessions[0].timeSlot?.startDate); d.firstSessionNameWord = d.sessions[0].name.split(" ")[0]; d.track = maps.get("tracks").get(d.trackId).name; d.authorsExpanded = d.authors.map((a) => maps.get("people").get(a.personId)); d.authorsList = d.authorsExpanded.map((a) => `${a.firstName?.trim()} ${a.lastName?.trim()}`.toLocaleLowerCase() ); d.authorNames = d.authorsList.join(", "); d.affiliationsList = [ ...new Set( d.authors .map((a) => a.affiliations.map((f) => f.institution.trim().toLocaleLowerCase()) ) .flat() ) ]; d.url = `https://programs.sigchi.org/chi/2024/program/content/${d.id}`; return d; 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2e:{"title":"CHI 2024 Papers","cells":[{"id":0,"value":"# CHI 2024 Papers\n\nPlease try it on https://johnguerra.co/viz/chi2024Papers/ for a better layout.\n\n[CHI 2024 papers](https://programs.sigchi.org/chi/2024) similarity using sentence embeddings via [HuggingFace Transformer.js](https://observablehq.com/@huggingface/sentence-embeddings-and-dimension-reduction-in-the-browse) and dimensionality reduction methods using [Druid.js](https://observablehq.com/@fil/druidjs-worker).\n\nYou can filter for specific paper tracks and, interact with the scatterplot to explore the different papers.\n\nCredits: [Hugging Face and @Radames](https://observablehq.com/@huggingface/sentence-embeddings-and-dimension-reduction-in-the-browse), [Fil](https://observablehq.com/@fil/), [Rene Cutura](https://observablehq.com/@saehrimnir/hello-druidjs), [Vega-Lite team](https://vega.github.io/vega-lite-api/), [@mootari](https://observablehq.com/@mootari/displaying-progress)","pinned":false,"hidden":false,"mode":"md"},{"id":24,"value":"viewof facetedSelectedPhase1 = conditionalShow(\n FacetedSearch(papers, {\n attribs: [\n \"firstSessionName\",\n \"title\",\n \"abstract\",\n \"authorNames\",\n \"track\",\n \"id\",\n \"typeId\"\n ]\n }),\n {\n label: \"Faceted Search\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3035,"value":"### Tracks","pinned":false,"hidden":false,"mode":"md"},{"id":1782,"value":"viewof tracksSelected = \n// PersistInput(\n// \"tracks\",\n scentedCheckbox(facetedSelectedPhase1, (d) => d.track, {\n // value: [\n // \"CHI 2024 Papers\",\n // // \"CHI 2024 Late-Breaking Work\",\n // // \"CHI 2024 Case Studies\",\n // // \"CHI 2024 Workshops & Symposia\",\n // // \"CHI 2024 Interactivity\",\n // // \"CHI 2024 Journals\",\n // // \"CHI 2024 alt.chi\"\n // ],\n cutoff: 5,\n showTotal: false\n })\n// )","pinned":false,"hidden":false,"mode":"ojs"},{"id":3319,"value":"viewof authorsSelected = \n// PersistInput(\n// \"authors\",\n Inputs.bind(multiAutoSelect(authors, { label: \"Authors\", value: initialAuthors }), viewof initialAuthors)\n// )","pinned":false,"hidden":false,"mode":"ojs"},{"id":3479,"value":"viewof affiliationsSelected = // PersistInput(\"affiliations\",\nInputs.bind(\n multiAutoSelect(affiliations, {\n label: \"Affiliations\",\n value: initialAffiliations\n }),\n viewof initialAffiliations\n)\n// )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1429,"value":"## Semantic Search","pinned":false,"hidden":false,"mode":"md"},{"id":1723,"value":"viewof useCachedDR = Inputs.toggle({ label: \"Use cached UMAP\", value: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2647,"value":"viewof restartDR = !useCachedDR\n ? Inputs.button(\"Restart Dimensionality Reduction\")\n : Object.assign(htl.html`

Disable Cached UMAP to use

`, { value: 0 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":" !useCachedDR \n ? viewof druid_method\n : Object.assign(\n htl.html`

Disable Cached UMAP to use

`,\n { value: \"UMAP\" }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":110,"value":"!useCachedDR\n ? viewof druid_params\n : Object.assign(htl.html`

Disable Cached UMAP to use

`, {\n value: {}\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":519,"value":"viewof colorBy = Inputs.select(\n // attrs,\n [\n \"id\",\n \"typeId\",\n // \"title\",\n // \"addons\",\n // \"recognitionIds\",\n // \"isBreak\",\n // \"importedId\",\n // \"source\",\n // \"trackId\",\n // \"tags\",\n // \"keywords\",\n // \"sessionIds\",\n // \"eventIds\",\n // \"abstract\",\n // \"authors\",\n // \"sessions\",\n \"firstSessionName\",\n \"firstSessionNameWord\",\n \"track\",\n // \"authorNames\",\n // \"url\",\n \"score\",\n \"cluster\",\n ],\n { label: \"Color by\", value: \"score\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1924,"value":"viewof colorScheme = Inputs.select(\n [\n \"brownbluegreen\",\n \"blueorange\",\n \"blues\", \n \"purples\",\n \"redyellowblue\", \n \"spectral\",\n \"turbo\",\n \"cividis\",\n \"rainbow\",\n \"category20\",\n \"category10\",\n \"tableau20\",\n \n ],\n { label: \"Color scheme\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1137,"value":"viewof interactive = Inputs.toggle({\n // label: htl.html`Make chart interactive.
Disable to speed up animation`,\n label: \"Interactive chart\",\n value: true,\n width: 600\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2750,"value":"viewof liveSearch = Inputs.toggle({label: \"Live Search\", value: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4268,"value":"viewof computeClusters = Inputs.toggle({ label: \"Compute Clusters\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1586,"value":"viewof maxClusters = Inputs.range([1, 20], {step: 1, label: \"Number of clusters\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1298,"value":"viewof query = PersistInput(\n \"query\",\n Inputs.text({\n label: htl.html`Similarity Search`,\n value: \"information visualization\",\n width: 600,\n submit: !liveSearch\n })\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4042,"value":"viewof drSelection = {\n clusters;\n maxClusters;\n return BrushableScatterPlot(dataToPlot, {\n color: colorBy,\n size: \"score\",\n interactive,\n colorScheme,\n tooltip: [\n \"title\",\n \"award\",\n \"score\",\n \"track\",\n \"firstSessionName\",\n \"authorNames\",\n \"abstract\"\n ]\n });\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4347,"value":"dataToPlot","pinned":true,"hidden":false,"mode":"ojs"},{"id":752,"value":"selectedPapers = htl.html` \n

CHI 2024 artifacts selected

\n ${\n paperClicked.length\n ? htl.html`Clicked:${paperClicked.map(renderItem)}`\n : \"\"\n }\n

\n \n ${papersHighlighted\n .sort((a, b) => b.score - a.score)\n .slice(0, maxPapers)\n .map(renderItem)}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3625,"value":"viewof minScore = Inputs.range([0, 100], {label: \"Min score\", value: 30})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3619,"value":"toAttend = {\n semanticSearchResults;\n const renderSubgroup = (\n v\n ) => htl.html`

${v[0]} (${\n v[1].length\n })

\n${v[1].map((p) => htl.html`

${p.title} ${(p.score*100).toFixed(2)}%

`)}

`;\n const renderGroup = (d) => htl.html`

${d[0]} (${\n d[1].length\n })

\n${d[1].map(renderSubgroup)}\n

`;\n\n return htl.html`\n

Where to go?

\nFor your query ${query} query, with a min score of ${Inputs.bind(\n htl.html``,\n viewof minScore\n )} you should go to: \n

Rooms to go

\n\n

${roomsToGo.map(renderGroup)}\n

\n\n

Sessions you should attend

\n\n

${sessionsToAttend.map(\n renderGroup\n )}\n

\n`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3115,"value":"paperClicked = drSelection.clicked","pinned":true,"hidden":false,"mode":"ojs"},{"id":746,"value":"papersHighlighted = drSelection.brushed","pinned":true,"hidden":false,"mode":"ojs"},{"id":3602,"value":"sessionsToAttend = {\n query;\n semanticSearchResults;\n return d3\n .groups(\n navioSelected.filter((d) => d.score > minScore/100),\n // (v) => v.length,\n (d) => d.firstTimestamp.toLocaleDateString(...dateOptions),\n (d) =>\n `${d.firstTimestamp.toLocaleTimeString(...dateOptions)} ${d.firstRoomName} - ${d.firstSessionName}`\n )\n .sort((a, b) => d3.ascending(a[0], b[0]))\n .map(([g, v]) => [g, v.sort((a, b) => d3.ascending(a[0], b[0]))]);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3785,"value":"dateOptions = [\"en-US\", { timeZone: \"utc\" }]","pinned":true,"hidden":false,"mode":"ojs"},{"id":3621,"value":"roomsToGo = {\n query;\n semanticSearchResults;\n return d3\n .groups(\n navioSelected.filter((d) => d.score > minScore / 100),\n // (v) => v.length,\n (d) => d.firstTimestamp.toLocaleDateString(...dateOptions),\n (d) => `${d.firstRoomName}`\n // (d) => d.title\n )\n .sort((a, b) => d3.ascending(a[0], b[0]))\n .map(([g, v]) => [g, v.sort((a, b) => b[1].length - a[1].length)]);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3117,"value":"viewof maxPapers = Inputs.range([0, navioSelected.length], {\n label: \"Papers to show\",\n step: 1,\n value: 20\n})\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":3154,"value":"viewof maxPapersSpinner = Inputs.bind(htl.html``, viewof maxPapers)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1327,"value":"Inputs.table(semanticSearchResults, {\n columns: [\"title\", \"score\", \"track\"],\n width: { title: \"70%\" },\n sort: \"score\",\n reverse: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":588,"value":"navioSelected = facetedSelected;\n// viewof navioSelected = conditionalShow(await navio(facetedSelected), {\n// label: \"Filter with Navio\"\n// })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1276,"value":"## Embeddings\n\nHere is a visualization of all the embeddings using navio","pinned":false,"hidden":false,"mode":"md"},{"id":1279,"value":"// navio(embeddings.map(d => d.data), {attribWidth: 2, height: 600})","pinned":true,"hidden":false,"mode":"ojs"},{"id":531,"value":"## Compute Embeddings dynamically\n\nYou can compute the embeddings in the browser if you want","pinned":false,"hidden":false,"mode":"md"},{"id":431,"value":"viewof computeEmbeddings = Inputs.toggle({label: \"Compute Embeddings\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":544,"value":"viewof computeEmbeddingsUsing = Inputs.checkbox(attrs, {\n value: [\"title\", \"abstract\"],\n label: \"Compute Embeddings using\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"computeEmbeddings && DOM.download(new Blob(\n [JSON.stringify(embeddings_computed)], \n {type: \"application/json\"}\n ), \"embeddings.json\", \"Download Embeddings\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":261,"value":"viewof restart = computeEmbeddings\n ? html``\n : pleaseEnableComputeEmbeddings()","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"viewof embeddings_computed = {\n if (computeEmbeddings) {\n const before = performance.now();\n restart;\n const values = [];\n const bar = progress({ interval: 50, invalidation });\n yield bar;\n \n let i =0;\n for (let row of sample) {\n \n values.push({id: row.id,\n embedding: await extractor(embeddingAccessor(row), extractorOptions)\n });\n \n await bar.progress(i++, sample.length);\n // console.log(\"i\", i);\n }\n console.log(\"progress completed in\", performance.now() - before);\n bar.resolve(values);\n } else {\n // Not computing embeddgins, return nothing\n yield pleaseEnableComputeEmbeddings();\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":415,"value":"embeddings = {\n if (computeEmbeddings) {\n return embeddings_computed;\n } else {\n return embeddings_cached;\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":407,"value":"embeddings_cached = (\n embeddingsFileSelected === \"Title and Abstract May 13\" ?\n (await FileAttachment(\"chi2024_embeddingsTitleAbstract_May13.json.zip\").zip()) .file(\"chi2024_embeddingsTitleAbstract_May13.json\")\n :\n (await FileAttachment(\"chi2024papers-all-componentized_embeddings.json.zip\").zip()).file(\"chi2024papers-all-componentized_embeddings.json\")\n)\n .json()\n .then((res) => {\n for (let row of res) {\n row.embedding.data = new Float32Array(\n Array.from(Object.values(row.embedding.data))\n );\n // newRes.push({\n // data: new Float32Array(Array.from(Object.values(row.data)))\n // });\n }\n return res;\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":3966,"value":"viewof embeddingsFileSelected = Inputs.select(\n [\n \"Title and Abstract May 13\",\n \"Joel's componentized_embeddings\",\n \n ],\n { label: \"Procomputed embeddings file\"}\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3982,"value":"chi2024papersAllComponentized_embeddingsJson = FileAttachment(\"chi2024papers-all-componentized_embeddings.json.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs"},{"id":169,"value":"embeddingAccessor = (d) => \n// [d.paper_type].join(\"\\n\\n\")\n computeEmbeddingsUsing.map(a => d[a]).join(\"\\n\\n\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1434,"value":"embeddingsHash = {\n console.log(\"computing embHash\");\n return new Map(embeddings.map((d, i) => [d.id, d.embedding]))\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":659,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":1696,"value":"function renderItem(p) {\n return htl.html`\n

\n ${p.title} \n

Similarity score: ${(p.score * 100).toFixed(2)}% ${\n p.awards ? p.awards : \"\"\n }

${p.authorsExpanded\n .map((a) => `${a.firstName} ${a.lastName}`)\n .join(\", \")}

${p.track} - ${p.firstSessionName}

${\n p.abstract\n }

\n`;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1857,"value":"facetedSelected = facetedSelectedPhase1.filter(\n (d) =>\n tracksSelected.includes(d.track) &&\n (!authorsSelected.length ||\n authorsSelected.some((a) => d.authorsList.includes(a))) &&\n (!affiliationsSelected.length ||\n affiliationsSelected.some((a) => d.affiliationsList.includes(a.toLocaleLowerCase())))\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":650,"value":"function pleaseEnableComputeEmbeddings() {\n return Object.assign(htl.html`Please enable compute embeddings`, {\n value: []\n });\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":418,"value":"sample = navioSelected","pinned":true,"hidden":false,"mode":"ojs"},{"id":631,"value":"attrs = Object.keys(papers[0]).concat([\"score\"])","pinned":true,"hidden":false,"mode":"ojs"},{"id":2657,"value":"## Dimmensionality Reduction","pinned":false,"hidden":false,"mode":"md"},{"id":106,"value":"reducerResultComputed = {\n embeddings;\n console.log(\n \"Launching DR Worker\",\n [...embeddingsHash.keys()].length,\n sample.length,\n sample.at(-1),\n tracksSelected\n );\n if (useCachedDR) return [];\n restartDR;\n\n return DruidGenerator(\n sample.map((d) => embeddingsHash.get(d.id).data),\n druid_method,\n druid_params\n );\n\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2058,"value":"reducerResult = {\n console.log(\"got reducerResult\", sample.length)\n return useCachedDR\n ? reducerResultCached\n : Object.fromEntries(\n sample.map((d, id) => [d.id, reducerResultComputed[id]])\n )\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2517,"value":"// DOWNLOAD UMAP \nsample.map((d, i) => {\n const [x, y] = reducerResultComputed[i];\n return ({id: d.id, x, y });\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1719,"value":"reducerResultCached = FileAttachment(\"chi2024_umapTitleAbstract_may13.csv\")\n .csv({ typed: true })\n .then((res) => Object.fromEntries(res.map((d) => [d.id, [d.x, d.y]])))","pinned":true,"hidden":false,"mode":"ojs"},{"id":107,"value":"dataToPlot = {\n // Recompute data to plot when clustering;\n computeClusters;\n maxClusters;\n\n return (semanticSearchResults ? semanticSearchResults : navioSelected).map(\n (row, id) => ({\n ...row,\n x: reducerResult[row.id] && reducerResult[row.id][0],\n y: reducerResult[row.id] && reducerResult[row.id][1]\n })\n );\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2668,"value":"### Semantic Search","pinned":false,"hidden":false,"mode":"md"},{"id":1302,"value":"semanticSearchResults = cosineSimilarity(query, sample)\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":1293,"value":"// https://github.com/xenova/transformers.js/blob/main/examples/semantic-image-search-client/src/app/worker.js#L46\nasync function cosineSimilarity(query, data) {\n if (!query) {\n for (let row of data) {\n row.score = 0;\n }\n return;\n }\n\n const query_embeds = (await extractor(query, extractorOptions)).data;\n\n // const database_embeds = sample.map((d) => embeddingsHash.get(d.id).data);\n\n for (let row of data) {\n const dbVector = embeddingsHash.get(row.id)?.data;\n if (dbVector) {\n row.score = pairCosineSimilarity(query_embeds, dbVector);\n } else {\n console.log(\"no embedding found for\", row.id);\n }\n }\n\n return data;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2069,"value":"// Computes the cosine similarity between two embeddings\n\nfunction pairCosineSimilarity(embeddingA, embeddingB) {\n const EMBED_DIM = embeddingA.length;\n let dotProduct = 0;\n let normEmbeds = 0;\n let normDB = 0;\n\n for (let j = 0; j < EMBED_DIM; ++j) {\n const embedValue = embeddingA[j];\n const dbValue = embeddingB[j];\n\n dotProduct += embedValue * dbValue;\n normEmbeds += embedValue * embedValue;\n normDB += dbValue * dbValue;\n }\n\n const score = dotProduct / (Math.sqrt(normEmbeds) * Math.sqrt(normDB));\n return score;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4148,"value":"import {\n DruidGenerator,\n viewof druid_method,\n viewof druid_params\n} with { default_method } from \"@john-guerra/druidjs-generator\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":4163,"value":"default_method = \"UMAP\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":575,"value":"import {conditionalShow} from \"@john-guerra/conditional-show\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":23,"value":"import {FacetedSearch} from \"@john-guerra/faceted-search\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":256,"value":"import {progress} from \"@mootari/displaying-progress\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":14,"value":"import {navio} from \"@john-guerra/navio\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1801,"value":"import {scentedCheckbox} from \"@john-guerra/scented-checkbox\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":4040,"value":"import { BrushableScatterPlot } from \"@john-guerra/brushable-scatterplot\";","pinned":true,"hidden":false,"mode":"ojs"},{"id":58,"value":"transformers = import(\"https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.1\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":52,"value":"### Transformers.js","pinned":false,"hidden":false,"mode":"md"},{"id":59,"value":"extractor = await transformers.pipeline(\n \"feature-extraction\",\n \"Xenova/all-MiniLM-L6-v2\",\n // \"allenai/specter2\",\n // { quantize: false }\n extractorOptions\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2786,"value":"extractorOptions = ({\n pooling: \"mean\",\n quantize: false, \n // normalize: true,\n // quantize: true,\n // precision: \"binary\"\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":2090,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":2103,"value":"maps = {\n const attrs = [\n // \"conference\",\n // \"publicationInfo\",\n \"sponsors\",\n \"sponsorLevels\",\n \"floors\",\n \"rooms\",\n \"tracks\",\n \"contentTypes\",\n \"timeSlots\",\n \"sessions\",\n \"events\",\n \"contents\",\n \"people\",\n \"recognitions\"\n ];\n\n const maps = new Map(\n attrs.map((a) => [a, new Map(chiConference[a].map((d) => [d.id, d]))])\n );\n return maps;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1641,"value":"chiConference = (await FileAttachment(\"CHI_2024_program_may13.json.zip\").zip()).file(\"CHI_2024_program_may13.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1598,"value":"## Clusters WIP","pinned":false,"hidden":false,"mode":"md"},{"id":4230,"value":"kmeans = import('https://cdn.skypack.dev/ml-kmeans@6.0.0?min')","pinned":true,"hidden":false,"mode":"ojs"},{"id":4234,"value":"clusters = {\n if (!computeClusters) return null;\n console.log(\"Computing clusters\");\n const before = performance.now();\n const clusters = kmeans.kmeans(\n embeddings.map((d) => d.embedding.data),\n maxClusters\n );\n clusters.clusters.map((c, i) => {\n navioSelected[i].cluster = c;\n });\n console.log(\"🤡 clusters computed\", (performance.now() - before) / 1000);\n return clusters;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4242,"value":"clusterPapers = {\n if (!computeClusters) return [];\n const indexes = clusters.clusters.reduce((p, d, i) => {\n if (d === cluster) p.push(i);\n return p;\n }, []);\n \n return indexes.map((i) => papers[i]);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1570,"value":"viewof cluster = Inputs.range([0, maxClusters], {step: 1})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1583,"value":"Inputs.table(clusterPapers)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3302,"value":" authors = [...new Set(papers.map(d => d.authorsList).flat())].sort()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3418,"value":"affiliations = [...new Set(papers.map(d => d.authors.map( a=> a.affiliations[0]?.institution.trim().toLocaleLowerCase())).flat())].sort()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3814,"value":"viewof initialAuthors = {\n const widget = await PersistInput(\n \"authors\",\n Inputs.checkbox(authors, { maxHeight: 100 })\n);\n widget.style.height = \"100px\";\n widget.style.overflow = \"scroll\";\n return widget;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3923,"value":"viewof initialAffiliations = {\n const widget = PersistInput(\n \"affiliations\",\n Inputs.checkbox(affiliations, { maxHeight: \"100px\" })\n );\n widget.style.height = \"100px\";\n widget.style.overflow = \"scroll\";\n return widget;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3303,"value":"import {multiAutoSelect} from \"@john-guerra/multi-auto-select@262\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":3445,"value":"import {PersistInput} from '@john-guerra/persist-input'\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":4018,"value":"## Clustering with hdbscan","pinned":false,"hidden":false,"mode":"md"},{"id":3996,"value":"Clustering = import('https://cdn.skypack.dev/hdbscanjs@1.0.12?min').then(res => res.default)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3999,"value":"clusteredTree = {\n if (!computeClusters) return {};\n const dataset = Object.entries(reducerResult).map(([k, v]) => ({\n data: v,\n id: k\n }));\n\n // two distance measure functions are supported:\n // 1) euclidean\n // 2) geoDist (take inputs as lonlat points)\n const distFunc = Clustering.distFunc.euclidean;\n\n const before = performance.now();\n const cluster = new Clustering(dataset, distFunc);\n const treeNode = cluster.getTree();\n console.log(\"HDBScan run in \", performance.now() - before);\n\n return treeNode;\n\n // const filtered = treeNode.filter((val) => val.data.length === 2, {\n // minX: 0,\n // maxX: width,\n // minY: 0,\n // maxY: innerHeight\n // });\n // filtered.forEach((x) => console.log(x.toString()));\n\n // const filterFunc = val => ...;\n // const bbox = {minX:.., maxX:.., minY:.., maxY:..};\n // const filteredNodes = treeNode.filter(filterFunc, bbox);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4321,"value":"Object.entries(reducerResult).map(([k, v]) => ({\n data: v,\n id: k\n }));","pinned":true,"hidden":false,"mode":"ojs"},{"id":4291,"value":"function convertTree(t) {\n let children = [];\n if (t?.left) children = [convertTree(t.left)];\n if (t?.right) children = [...children, convertTree(t.right)];\n return {\n ...t,\n children\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4302,"value":"convertTree(clusteredTree)","pinned":true,"hidden":false,"mode":"ojs"},{"id":4032,"value":"Tree(convertTree(clusteredTree), { height: 500, width, fit: true, r: 0.5 })","pinned":true,"hidden":false,"mode":"ojs"},{"id":4028,"value":"import {Tree} from \"@john-guerra/tree-fit\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}