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-rw-r--r--src/client/ClientRecommender.tsx95
1 files changed, 27 insertions, 68 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>);
}
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2025-10-11 08:50:24 +0000