Merge remote-tracking branch 'origin/speedups2' into temporalmedia-mehek

1 files changed, 0 insertions, 421 deletions

diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx
deleted file mode 100644
index 1d4653471..000000000
--- a/src/client/ClientRecommender.tsx
+++ /dev/null
@@ -1,421 +0,0 @@
-import { Doc, FieldResult } from "../fields/Doc";
-import { StrCast, Cast } from "../fields/Types";
-import { List } from "../fields/List";
-import { CognitiveServices, Confidence, Tag, Service } from "./cognitive_services/CognitiveServices";
-import React = require("react");
-import { observer } from "mobx-react";
-import { observable, action, computed, reaction } from "mobx";
-// var assert = require('assert');
-// var sw = require('stopword');
-// var FeedParser = require('feedparser');
-// var https = require('https');
-import "./ClientRecommender.scss";
-import { JSXElement } from "babel-types";
-import { RichTextField } from "../fields/RichTextField";
-import { ToPlainText } from "../fields/FieldSymbols";
-import { listSpec } from "../fields/Schema";
-import { ComputedField } from "../fields/ScriptField";
-import { ImageField } from "../fields/URLField";
-import { KeyphraseQueryView } from "./views/KeyphraseQueryView";
-import { Networking } from "./Network";
-
-export interface RecommenderProps {
-    title: string;
-}
-
-/**
- * actualDoc: datadoc
- * vectorDoc: mean vector of text
- * score: similarity score to main doc
- */
-
-export interface RecommenderDocument {
-    actualDoc: Doc;
-    vectorDoc: number[];
-    score: number;
-}
-
-const fieldkey = "data";
-
-@observer
-export class ClientRecommender extends React.Component<RecommenderProps> {
-
-    static Instance: ClientRecommender;
-    private mainDoc?: RecommenderDocument;
-    private docVectors: Set<RecommenderDocument> = new Set();
-    public _queries: string[] = [];
-
-    @observable private corr_matrix = [[0, 0], [0, 0]]; // for testing
-
-    constructor(props: RecommenderProps) {
-        super(props);
-        if (!ClientRecommender.Instance) ClientRecommender.Instance = this;
-        ClientRecommender.Instance.docVectors = new Set();
-        //ClientRecommender.Instance.corr_matrix = [[0, 0], [0, 0]];
-    }
-
-    @action
-    public reset_docs() {
-        ClientRecommender.Instance.docVectors = new Set();
-        ClientRecommender.Instance.mainDoc = undefined;
-        ClientRecommender.Instance.corr_matrix = [[0, 0], [0, 0]];
-    }
-
-    /***
-     * Computes the cosine similarity between two vectors in Euclidean space. 
-     */
-
-    private distance(vector1: number[], vector2: number[], metric: string = "cosine") {
-        // assert(vector1.length === vector2.length, "Vectors are not the same length");
-        let similarity: number;
-        switch (metric) {
-            case "cosine":
-                var dotproduct = 0;
-                var mA = 0;
-                var mB = 0;
-                for (let i = 0; i < vector1.length; i++) { // here you missed the i++
-                    dotproduct += (vector1[i] * vector2[i]);
-                    mA += (vector1[i] * vector1[i]);
-                    mB += (vector2[i] * vector2[i]);
-                }
-                mA = Math.sqrt(mA);
-                mB = Math.sqrt(mB);
-                similarity = (dotproduct) / ((mA) * (mB)); // here you needed extra brackets
-                return similarity;
-            case "euclidian":
-                var sum = 0;
-                for (let i = 0; i < vector1.length; i++) {
-                    sum += Math.pow(vector1[i] - vector2[i], 2);
-                }
-                similarity = Math.sqrt(sum);
-                return similarity;
-            default:
-                return 0;
-        }
-    }
-
-    /**
-     * Returns list of {doc, similarity (to main doc)} in increasing score
-     */
-
-    public computeSimilarities(distance_metric: string) {
-        const parameters: any = {};
-        Networking.PostToServer("/IBMAnalysis", parameters).then(response => {
-            console.log("ANALYSIS RESULTS! ", response);
-        });
-        ClientRecommender.Instance.docVectors.forEach((doc: RecommenderDocument) => {
-            if (ClientRecommender.Instance.mainDoc) {
-                const distance = ClientRecommender.Instance.distance(ClientRecommender.Instance.mainDoc.vectorDoc, doc.vectorDoc, distance_metric);
-                doc.score = distance;
-            }
-        }
-        );
-        const doclist = Array.from(ClientRecommender.Instance.docVectors);
-        if (distance_metric === "euclidian") {
-            doclist.sort((a: RecommenderDocument, b: RecommenderDocument) => a.score - b.score);
-        }
-        else {
-            doclist.sort((a: RecommenderDocument, b: RecommenderDocument) => b.score - a.score);
-        }
-        return doclist;
-    }
-
-    /***
-     * Computes the mean of a set of vectors
-     */
-
-    public mean(paragraph: Set<number[]>) {
-        const n = 512;
-        const num_words = paragraph.size;
-        let meanVector = new Array<number>(n).fill(0); // mean vector
-        if (num_words > 0) { // check to see if paragraph actually was vectorized
-            paragraph.forEach((wordvec: number[]) => {
-                for (let i = 0; i < n; i++) {
-                    meanVector[i] += wordvec[i];
-                }
-            });
-            meanVector = meanVector.map(x => x / num_words);
-        }
-        return meanVector;
-    }
-
-    /***
-     * Processes sentence vector as Recommender Document, adds to Doc Set. 
-     */
-
-    public processVector(vector: number[], dataDoc: Doc, isMainDoc: boolean) {
-        if (vector.length > 0) {
-            const internalDoc: RecommenderDocument = { actualDoc: dataDoc, vectorDoc: vector, score: 0 };
-            ClientRecommender.Instance.addToDocSet(internalDoc, isMainDoc);
-        }
-    }
-
-    /***
-     * Adds to Doc set. Updates mainDoc (one clicked) if necessary.
-     */
-
-    private addToDocSet(internalDoc: RecommenderDocument, isMainDoc: boolean) {
-        if (ClientRecommender.Instance.docVectors) {
-            if (isMainDoc) ClientRecommender.Instance.mainDoc = internalDoc;
-            ClientRecommender.Instance.docVectors.add(internalDoc);
-        }
-    }
-
-    /***
-     * Generates tags for an image using Cognitive Services
-     */
-
-    generateMetadata = async (dataDoc: Doc, extDoc: Doc, threshold: Confidence = Confidence.Excellent) => {
-        const converter = (results: any) => {
-            const tagDoc = new Doc;
-            const tagsList = new List();
-            results.tags.map((tag: Tag) => {
-                tagsList.push(tag.name);
-                const sanitized = tag.name.replace(" ", "_");
-                tagDoc[sanitized] = ComputedField.MakeFunction(`(${tag.confidence} >= this.confidence) ? ${tag.confidence} : "${ComputedField.undefined}"`);
-            });
-            extDoc.generatedTags = tagsList;
-            tagDoc.title = "Generated Tags Doc";
-            tagDoc.confidence = threshold;
-            return tagDoc;
-        };
-        const url = this.url(dataDoc);
-        if (url) {
-            return CognitiveServices.Image.Appliers.ProcessImage(extDoc, ["generatedTagsDoc"], url, Service.ComputerVision, converter);
-        }
-    }
-
-    /***
-     * Gets URL of image
-     */
-
-    private url(dataDoc: Doc) {
-        const data = Cast(Doc.GetProto(dataDoc)[fieldkey], ImageField);
-        return data ? data.url.href : undefined;
-    }
-
-    /***
-     * Uses Cognitive Services to extract keywords from a document
-     */
-
-    public async extractText(dataDoc: Doc, extDoc: Doc, internal: boolean = true, api: string = "bing", isMainDoc: boolean = false, image: boolean = false) {
-        // STEP 1. Consolidate data of document. Depends on type of document. 
-        let data: string = "";
-        let taglist: FieldResult<List<string>> = undefined;
-        if (image) {
-            if (!extDoc.generatedTags) await this.generateMetadata(dataDoc, extDoc);  // TODO: Automatically generate tags. Need to ask Sam about this.
-            if (extDoc.generatedTags) {
-                taglist = Cast(extDoc.generatedTags, listSpec("string"));
-                taglist!.forEach(tag => {
-                    data += tag + ", ";
-                });
-            }
-        }
-        else {
-            const fielddata = Cast(dataDoc.data, RichTextField, null);
-            data = fielddata?.Text || "";
-        }
-
-        // STEP 2. Upon receiving response from Text Cognitive Services, do additional processing on keywords.
-        // Currently we are still using Cognitive Services for internal recommendations, but in the future this might not be necessary. 
-
-        const converter = async (results: any, data: string, isImage: boolean = false) => {
-            let keyterms = new List<string>(); // raw keywords
-            let kp_string: string = ""; // keywords*frequency concatenated into a string. input into TF
-            let highKP: string[] = [""]; // most frequent keyphrase
-            let high = 0;
-
-            if (isImage) { // no keyphrase processing necessary
-                kp_string = data;
-                if (taglist) {
-                    keyterms = taglist;
-                    highKP = [taglist[0]];
-                }
-            }
-            else { // text processing
-                results.documents.forEach((doc: any) => {
-                    const keyPhrases = doc.keyPhrases; // returned by Cognitive Services
-                    keyPhrases.map((kp: string) => {
-                        keyterms.push(kp);
-                        const frequency = this.countFrequencies(kp, data); // frequency of keyphrase in paragraph
-                        kp_string += kp + ", "; // ensures that if frequency is 0 for some reason kp is still added
-                        for (let i = 0; i < frequency - 1; i++) {
-                            kp_string += kp + ", "; // weights repeated keywords higher
-                        }
-                        // replaces highKP with new one
-                        if (frequency > high) {
-                            high = frequency;
-                            highKP = [kp];
-                        }
-                        // appends to current highKP phrase
-                        else if (frequency === high) {
-                            highKP.push(kp);
-                        }
-                    });
-                });
-            }
-            if (kp_string.length > 2) kp_string = kp_string.substring(0, kp_string.length - 2); // strips extra comma and space if there are a lot of keywords
-            console.log("kp_string: ", kp_string);
-
-            let ext_recs = "";
-            // Pushing keyword extraction to IBM for external recommendations. Should shift to internal eventually.
-            if (!internal) {
-                const parameters: any = {
-                    'language': 'en',
-                    'text': data,
-                    'features': {
-                        'keywords': {
-                            'sentiment': true,
-                            'emotion': true,
-                            'limit': 3
-                        }
-                    }
-                };
-                await Networking.PostToServer("/IBMAnalysis", parameters).then(response => {
-                    const sorted_keywords = response.result.keywords;
-                    if (sorted_keywords.length > 0) {
-                        console.log("IBM keyphrase", sorted_keywords[0]);
-                        highKP = [];
-                        for (let i = 0; i < 5; i++) {
-                            if (sorted_keywords[i]) {
-                                highKP.push(sorted_keywords[i].text);
-                            }
-                        }
-                        keyterms = new List<string>(highKP);
-                    }
-                });
-                //let kpqv = new KeyphraseQueryView({ keyphrases: ["hello"] });
-                ext_recs = await this.sendRequest([highKP[0]], api);
-            }
-
-            // keyterms: list for extDoc, kp_string: input to TF, ext_recs: {titles, urls} of retrieved results from highKP query
-            return { keyterms: keyterms, external_recommendations: ext_recs, kp_string: [kp_string] };
-        };
-
-        // STEP 3: Start recommendation pipeline. Branches off into internal and external in Cognitive Services 
-        if (data !== "") {
-            return CognitiveServices.Text.Appliers.analyzer(dataDoc, extDoc, ["key words"], data, converter, isMainDoc, internal);
-        }
-        return;
-    }
-
-    /**
-     * 
-     * Counts frequencies of keyphrase in paragraph. 
-     */
-
-    private countFrequencies(keyphrase: string, paragraph: string) {
-        const data = paragraph.split(/ |\n/); // splits by new lines and spaces
-        const kp_array = keyphrase.split(" ");
-        const num_keywords = kp_array.length;
-        const par_length = data.length;
-        let frequency = 0;
-        // 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))) {
-                frequency++;
-            }
-        }
-        return frequency;
-    }
-
-    /**
-     * 
-     * API for sending arXiv request.
-     */
-
-    private async sendRequest(keywords: string[], api: string) {
-        let query = "";
-        keywords.forEach((kp: string) => query += " " + kp);
-        if (api === "arxiv") {
-            return new Promise<any>(resolve => {
-                this.arxivrequest(query).then(resolve);
-            });
-        }
-        else if (api === "bing") {
-            return new Promise<any>(resolve => {
-                this.bingWebSearch(query).then(resolve);
-            });
-        }
-        else {
-            console.log("no api specified :(");
-        }
-
-    }
-
-    /**
-     * Request to Bing API. Most of code is in Cognitive Services. 
-     */
-
-    bingWebSearch = async (query: string) => {
-        const converter = async (results: any) => {
-            const title_vals: string[] = [];
-            const url_vals: string[] = [];
-            results.webPages.value.forEach((doc: any) => {
-                title_vals.push(doc.name);
-                url_vals.push(doc.url);
-            });
-            return { title_vals, url_vals };
-        };
-        return CognitiveServices.BingSearch.Appliers.analyzer(query, converter);
-    }
-
-    /**
-     * Actual request to the arXiv server for ML articles.
-     */
-
-    arxivrequest = async (query: string) => {
-        const xhttp = new XMLHttpRequest();
-        const serveraddress = "http://export.arxiv.org/api";
-        const maxresults = 5;
-        const endpoint = serveraddress + "/query?search_query=all:" + query + "&start=0&max_results=" + maxresults.toString();
-        const promisified = (resolve: any, reject: any) => {
-            xhttp.onreadystatechange = function () {
-                if (this.readyState === 4) {
-                    const result = xhttp.response;
-                    const xml = xhttp.responseXML;
-                    console.log("arXiv Result: ", xml);
-                    switch (this.status) {
-                        case 200:
-                            const title_vals: string[] = [];
-                            const url_vals: string[] = [];
-                            if (xml) {
-                                const titles = xml.getElementsByTagName("title");
-                                let counter = 1;
-                                if (titles && titles.length > 1) {
-                                    while (counter <= maxresults) {
-                                        const title = titles[counter].childNodes[0].nodeValue!;
-                                        title_vals.push(title);
-                                        counter++;
-                                    }
-                                }
-                                const ids = xml.getElementsByTagName("id");
-                                counter = 1;
-                                if (ids && ids.length > 1) {
-                                    while (counter <= maxresults) {
-                                        const url = ids[counter].childNodes[0].nodeValue!;
-                                        url_vals.push(url);
-                                        counter++;
-                                    }
-                                }
-                            }
-                            return resolve({ title_vals, url_vals });
-                        case 400:
-                        default:
-                            return reject(result);
-                    }
-                }
-            };
-            xhttp.open("GET", endpoint, true);
-            xhttp.send();
-        };
-        return new Promise<any>(promisified);
-    }
-
-    render() {
-        return (<div className="wrapper">
-        </div>);
-    }
-
-}
\ No newline at end of file