diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/client/ClientRecommender.tsx | 95 | ||||
| -rw-r--r-- | src/client/cognitive_services/CognitiveServices.ts | 6 | ||||
| -rw-r--r-- | src/client/views/Recommendations.tsx | 22 | ||||
| -rw-r--r-- | src/server/Recommender.ts | 2 |
4 files changed, 37 insertions, 88 deletions
diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx index 9ce7df366..bc1cd139c 100644 --- a/src/client/ClientRecommender.tsx +++ b/src/client/ClientRecommender.tsx @@ -35,7 +35,6 @@ export class ClientRecommender extends React.Component<RecommenderProps> { static Instance: ClientRecommender; private mainDoc?: RecommenderDocument; private docVectors: Set<RecommenderDocument> = new Set(); - private highKP: string[] = []; @observable private corr_matrix = [[0, 0], [0, 0]]; @@ -94,7 +93,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { public computeSimilarities() { ClientRecommender.Instance.docVectors.forEach((doc: RecommenderDocument) => { if (ClientRecommender.Instance.mainDoc) { - const distance = ClientRecommender.Instance.distance(ClientRecommender.Instance.mainDoc.vectorDoc, doc.vectorDoc, "euclidian"); + const distance = ClientRecommender.Instance.distance(ClientRecommender.Instance.mainDoc.vectorDoc, doc.vectorDoc, "cosine"); doc.score = distance; } } @@ -151,17 +150,21 @@ export class ClientRecommender extends React.Component<RecommenderProps> { let converter = async (results: any, data: string) => { let keyterms = new List<string>(); // raw keywords let keyterms_counted = new List<string>(); // keywords, where each keyword is repeated as - let highKP: string[] = [""]; // most frequent + let kp_string: string = ""; + let highKP: string[] = [""]; // most frequent keyphrase let high = 0; results.documents.forEach((doc: any) => { let keyPhrases = doc.keyPhrases; keyPhrases.map((kp: string) => { const frequency = this.countFrequencies(kp, data); keyterms.push(kp); + kp_string += kp + ", "; + // replaces highKP with new one if (frequency > high) { high = frequency; highKP = [kp]; } + // appends to current highKP phrase else if (frequency === high) { highKP.push(kp); } @@ -175,13 +178,11 @@ export class ClientRecommender extends React.Component<RecommenderProps> { }); }); }); - this.highKP = highKP; - //console.log(highKP); const kts_counted = new List<string>(); keyterms_counted.forEach(kt => kts_counted.push(kt.toLowerCase())); let values = ""; if (!internal) values = await this.sendRequest(highKP); - return { keyterms: keyterms, keyterms_counted: kts_counted, values }; + return { keyterms: keyterms, keyterms_counted: kts_counted, values, kp_string: [kp_string] }; }; if (data != "") { return CognitiveServices.Text.Appliers.analyzer(dataDoc, extDoc, ["key words"], data, converter, mainDoc, internal); @@ -189,6 +190,10 @@ export class ClientRecommender extends React.Component<RecommenderProps> { return; } + /** + * + * Counts frequencies of keyphrase in paragraph. + */ private countFrequencies(keyphrase: string, paragraph: string) { let data = paragraph.split(" "); @@ -196,8 +201,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { let num_keywords = kp_array.length; let par_length = data.length; let frequency = 0; - // console.log("Paragraph: ", data); - // console.log("Keyphrases:", kp_array); + // slides keyphrase windows across paragraph and checks if it matches with corresponding paragraph slice for (let i = 0; i <= par_length - num_keywords; i++) { const window = data.slice(i, i + num_keywords); if (JSON.stringify(window).toLowerCase() === JSON.stringify(kp_array).toLowerCase() || kp_array.every(val => window.includes(val))) { @@ -207,11 +211,21 @@ export class ClientRecommender extends React.Component<RecommenderProps> { return frequency; } + /** + * + * Removes stopwords from list of strings representing a sentence + */ + private removeStopWords(word_array: string[]) { //console.log(sw.removeStopwords(word_array)); return sw.removeStopwords(word_array); } + /** + * + * API for sending arXiv request. + */ + private async sendRequest(keywords: string[]) { let query = ""; keywords.forEach((kp: string) => query += " " + kp); @@ -221,13 +235,14 @@ export class ClientRecommender extends React.Component<RecommenderProps> { } /** - * Request to the arXiv server for ML articles. + * Actual request to the arXiv server for ML articles. */ arxivrequest = async (query: string) => { let xhttp = new XMLHttpRequest(); let serveraddress = "http://export.arxiv.org/api"; - let endpoint = serveraddress + "/query?search_query=all:" + query + "&start=0&max_results=5"; + const maxresults = 5; + let endpoint = serveraddress + "/query?search_query=all:" + query + "&start=0&max_results=" + maxresults.toString(); let promisified = (resolve: any, reject: any) => { xhttp.onreadystatechange = function () { if (this.readyState === 4) { @@ -243,7 +258,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { let titles = xml.getElementsByTagName("title"); let counter = 1; if (titles && titles.length > 1) { - while (counter <= 5) { + while (counter <= maxresults) { const title = titles[counter].childNodes[0].nodeValue!; console.log(title) title_vals.push(title); @@ -253,7 +268,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> { let ids = xml.getElementsByTagName("id"); counter = 1; if (ids && ids.length > 1) { - while (counter <= 5) { + while (counter <= maxresults) { const url = ids[counter].childNodes[0].nodeValue!; console.log(url); url_vals.push(url); @@ -280,64 +295,8 @@ export class ClientRecommender extends React.Component<RecommenderProps> { console.log(text); } - /*** - * Creates distance matrix for all Documents analyzed - */ - - @action - public createDistanceMatrix(documents: Set<RecommenderDocument> = ClientRecommender.Instance.docVectors) { - const documents_list = Array.from(documents); - const n = documents_list.length; - var matrix = new Array<number>(n).fill(0).map(() => new Array<number>(n).fill(0)); - for (let i = 0; i < n; i++) { - var doc1 = documents_list[i]; - for (let j = 0; j < n; j++) { - var doc2 = documents_list[j]; - matrix[i][j] = ClientRecommender.Instance.distance(doc1.vectorDoc, doc2.vectorDoc, "euclidian"); - } - } - ClientRecommender.Instance.corr_matrix = matrix; - return matrix; - } - - @computed - private get generateRows() { - const n = ClientRecommender.Instance.corr_matrix.length; - let rows: JSX.Element[] = []; - for (let i = 0; i < n; i++) { - let children: JSX.Element[] = []; - for (let j = 0; j < n; j++) { - //let cell = React.createElement("td", ClientRecommender.Instance.corr_matrix[i][j]); - let cell = <td>{ClientRecommender.Instance.corr_matrix[i][j].toFixed(4)}</td>; - children.push(cell); - } - //let row = React.createElement("tr", { children: children, key: i }); - let row = <tr>{children}</tr>; - rows.push(row); - } - return rows; - } - render() { return (<div className="wrapper"> - <h3 >{ClientRecommender.Instance.props.title ? ClientRecommender.Instance.props.title : "hello"}</h3> - {/* <table className="space" > - <tbody> - <tr key="1"> - <td key="1">{ClientRecommender.Instance.corr_matrix[0][0].toFixed(4)}</td> - <td key="2">{ClientRecommender.Instance.corr_matrix[0][1].toFixed(4)}</td> - </tr> - <tr key="2"> - <td key="1">{ClientRecommender.Instance.corr_matrix[1][0].toFixed(4)}</td> - <td key="2">{ClientRecommender.Instance.corr_matrix[1][1].toFixed(4)}</td> - </tr> - </tbody> - </table> */} - <table className="space"> - <tbody> - {ClientRecommender.Instance.generateRows} - </tbody> - </table> </div>); } diff --git a/src/client/cognitive_services/CognitiveServices.ts b/src/client/cognitive_services/CognitiveServices.ts index c138c68b7..eb088763d 100644 --- a/src/client/cognitive_services/CognitiveServices.ts +++ b/src/client/cognitive_services/CognitiveServices.ts @@ -15,7 +15,7 @@ type RequestExecutor = (apiKey: string, body: string, service: Service) => Promi type AnalysisApplier<D> = (target: Doc, relevantKeys: string[], data: D, ...args: any) => any; type BodyConverter<D> = (data: D) => string; type Converter = (results: any) => Field; -type TextConverter = (results: any, data: string) => Promise<{ keyterms: Field, keyterms_counted: Field, values: any }>; +type TextConverter = (results: any, data: string) => Promise<{ keyterms: Field, keyterms_counted: Field, values: any, kp_string: string[] }>; export type Tag = { name: string, confidence: number }; export type Rectangle = { top: number, left: number, width: number, height: number }; @@ -290,12 +290,12 @@ export namespace CognitiveServices { export const analyzer = async (dataDoc: Doc, target: Doc, keys: string[], data: string, converter: TextConverter, mainDoc: boolean = false, internal: boolean = true) => { let results = await ExecuteQuery(Service.Text, Manager, data); console.log(results); - let { keyterms, values, keyterms_counted } = await converter(results, data); + let { keyterms, values, keyterms_counted, kp_string } = await converter(results, data); //target[keys[0]] = Docs.Get.DocumentHierarchyFromJson(results, "Key Word Analysis"); target[keys[0]] = keyterms; console.log("analyzed!"); if (internal) { - await vectorize(keyterms, dataDoc, mainDoc, data); + await vectorize(kp_string, dataDoc, mainDoc, data); } else { return values; } diff --git a/src/client/views/Recommendations.tsx b/src/client/views/Recommendations.tsx index 5dc62105d..f965d655b 100644 --- a/src/client/views/Recommendations.tsx +++ b/src/client/views/Recommendations.tsx @@ -70,15 +70,9 @@ export class RecommendationsBox extends React.Component<FieldViewProps> { newRenderDoc.height = NumCast(this.props.Document.documentIconHeight); newRenderDoc.autoHeight = false; const docview = <div> - {/* onPointerDown={action(() => { - this._useIcons = !this._useIcons; - this._displayDim = this._useIcons ? 50 : Number(SEARCH_THUMBNAIL_SIZE); - })} - onPointerEnter={action(() => this._displayDim = this._useIcons ? 50 : Number(SEARCH_THUMBNAIL_SIZE))} - onPointerLeave={action(() => this._displayDim = 50)} > */} <DocumentView - fitToBox={StrCast(doc).indexOf(DocumentType.COL) !== -1} - Document={doc} + fitToBox={StrCast(doc.type).indexOf(DocumentType.COL) !== -1} + Document={newRenderDoc} addDocument={returnFalse} removeDocument={returnFalse} ruleProvider={undefined} @@ -100,10 +94,6 @@ export class RecommendationsBox extends React.Component<FieldViewProps> { ContentScaling={scale} /> </div>; - // const data = renderDoc.data; - // if (data instanceof ObjectField) newRenderDoc.data = ObjectField.MakeCopy(data); - // newRenderDoc.preview = true; - // this.previewDocs.push(newRenderDoc); return docview; } @@ -171,16 +161,16 @@ export class RecommendationsBox extends React.Component<FieldViewProps> { {DocListCast(this.props.Document.data).map(doc => { return ( <div className="content"> - {/* <span style={{ height: NumCast(this.props.Document.documentIconHeight) }} className="image-background"> + <span style={{ height: NumCast(this.props.Document.documentIconHeight) }} className="image-background"> {this.DocumentIcon(doc)} - </span> */} + </span> <span className="score">{NumCast(doc.score).toFixed(4)}</span> <div style={{ marginRight: 50 }} onClick={() => DocumentManager.Instance.jumpToDocument(doc, false)}> <FontAwesomeIcon className="documentdecorations-icon" icon={"bullseye"} size="sm" /> </div> - {/* <div style={{ marginRight: 50 }} onClick={() => DocUtils.MakeLink({ doc: this.props.Document.sourceDoc as Doc }, { doc: doc }, "User Selected Link", "Generated from Recommender", undefined)}> + <div style={{ marginRight: 50 }} onClick={() => DocUtils.MakeLink({ doc: this.props.Document.sourceDoc as Doc }, { doc: doc }, "User Selected Link", "Generated from Recommender", undefined)}> <FontAwesomeIcon className="documentdecorations-icon" icon={"link"} size="sm" /> - </div> */} + </div> </div> ); })} diff --git a/src/server/Recommender.ts b/src/server/Recommender.ts index d014ba344..d974d7ef6 100644 --- a/src/server/Recommender.ts +++ b/src/server/Recommender.ts @@ -17,7 +17,7 @@ export class Recommender { private _model: any; static Instance: Recommender; private dimension: number = 0; - private choice: string = ""; + private choice: string = ""; // Tensorflow or Word2Vec constructor() { console.log("creating recommender..."); |