Jshaper NPM | npm.io

JShaper

JShaper is a Typescript library that maps source data to a target object using textual feature extraction. To match keys between objects, the cosine similarity of each source embedding to target embeddings is computed.

Source data in JSON and XML file formats are only accepted.

Installation

Npm: install the jshaper package

npm i jshaper

Github: clone the repository and install the necessary dependencies

git clone https://github.com/danmxli/JShaper.git
cd JShaper
npm install

Usage

import { MapObject } from "jshaper";
import exampleJson from "./example.json"
import * as fs from "fs";

async function main() {
    const jsonMapTarget = {
        username: '',
        email: '',
        contact: {
            phone: '',
            address: {
                street: '',
                zipCode: 0
            }
        },
        preferences: {
            newsletter: false,
            notifications: false
        }
    };

    const xmlMapTarget = {
        booktitle: '',
        bookauthor: '',
        bookyear: '',
    }
  
    const objectMapper = new MapObject()
    const mappedJsonData = await objectMapper.mapObject(exampleJson, jsonMapTarget)
    console.log(mappedJsonData)

    fs.readFile(__dirname + '/example.xml', async function read(err, data) {
        if (err) {
            throw err;
        }
      
        const mappedXmlData = await objectMapper.mapObject(data, xmlMapTarget)
        console.log(mappedXmlData)
    });
}

main();

// {
//   email: 'alice@example.com',
//   contact: {
//     phone: '123-456-7890',
//     address: { street: 'Oak St', zipCode: 98765 }
//   },
//   preferences: { newsletter: true, notifications: true }
// }
// {
//   booktitle: 'The Great Gatsby',
//   bookauthor: 'F. Scott Fitzgerald',
//   bookyear: '1925'
// }

API

`MapObject` Class

Constructor

/**
 * 
 * @param {string} model - the Hugging Face model to perform text feature extraction. Default is "Xenova/all-MiniLM-L6-v2"
 * @param {number} similarityThreshold - the cosine similarity threshold. Default is 0.5
 */
new MapObject(model?: string, similarityThreshold?: number): MapObject

Public methods

/**
 * 
 * @param source - the source object. Supports JSON and XML file formats.
 * @param target - the target object that the source maps to.
 * @returns {TJsonObject} the target object with populated values from source.
 */
async mapObject(source: TJsonObject, target: TJsonObject): Promise<TJsonObject>

Supported models

Hugging Face feature extraction models

How it works

MapObject preserves source and target data by flattening nested objects to a depth of 1.

Keys are represented using dot-notation, which is used to unflatten the target object into its original depth.

The text embeddings of the flattened source and target keys are computed using the feature-extraction pipeline from the Transformers.js library.

By computing the cosine similarity of embeddings, it determines which key in the flattened target object is most semantically similar to each key in the flattened source object.

The value from the source object is mapped to the matching key in the target object if the similarity score is higher than the threshold.