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nextbrain

v1.4.0

Published

Convenient access to the NextBrain API from javascript

Downloads

8

Readme

NextBrain AI

Convenient access to the NextBrain AI API from Javascript

Installation

npm install nextbrain

All steps in one.

import { NextBrain } from 'nextbrain'

const nb = new NextBrain({
  access_token: '<YOUR-ACCESS-TOKEN-HERE>',
})

// You can create your custom table and predict table by your own from any source
// It is a list of list, where the first row contains the header
// Example:
// [
//   [ Column1, Column2, Column3 ],
//   [       1,       2,       3 ],
//   [       4,       5,       6 ]
// ]
const table = await nb.loadCsv('<PATH-TO-YOUR-TRAINING-CSV>')
const predictTable = await nb.loadCsv('<PATH-TO-YOUR-PREDICTING-CSV>')

const [modelId, response] = await nb.uploadAndPredict(table, predictTable, '<YOUR-TARGET-COLUMN>')
console.log('Response:', response)

// You can optionally delete the model
await nb.deleteModel(modelId)

Step by step

import { NextBrain } from 'nextbrain'

const nb = new NextBrain({
  access_token: '<YOUR-ACCESS-TOKEN-HERE>',
})

// You can create your custom table and predict table by your own from any source
const table = await nb.loadCsv('<PATH-TO-YOUR-TRAINING-CSV>')
// Upload the model to NextBrain service
const modelId = await nb.uploadModel(table)
// Train the model
// You can re-train a previous model
await nb.trainModel(modelId, '<YOUR-TARGET-COLUMN>')

const predictTable = await nb.loadCsv('<PATH-TO-YOUR-PREDICTING-CSV>')
// You can predict multiple using the same model (don't need to create a new model each time)
const response = await nb.predictModel(modelId, predictTable)
console.log('Response:', response)

Extra notes

Everytime you train, you can select an option to create lightning models. isLightning is an optional parameter that by default is set to false but can be overrided in trainModel and uploadAndPredict.

We also recommend that you investigate all the methods that the class provides you with to make the most of the functionalities we offer. For example, you can use the getAccuracy method to obtain all the information about the performance of your model.