Can't manage to have a string get analysed by brain.js

[mohammedmulazada]

What is wrong?

The training network doesn't train, it returns NaN for the data that it should have analysed.

Where does it happen?

In the app.js run through node, after receiving data from the Twitter API

How do we replicate the issue?

1. Use the dummy data below

const trainingData =[  
   {  
      input:'RT @ObamaFoundation: This week—50 years since Dr. Martin Luther King, Jr. was killed—@BarackObama and @RepJohnLewis sat down with a group o…',
      output:{  
         Barack:1
      }
   },
   {  
      input:'Incredible to have a Chicago team in the Final Four. I’ll take that over an intact bracket any day! Congratulations to everybody @LoyolaChicago - let’s keep it going!',
      output:{  
         Barack:1
      }
   },
   {  
      input:'In Singapore with young people who are advocating for education, empowering young women, and getting involved all over Southeast Asia with a profoundly optimistic commitment to building the world they want to see. ',
      output:{  
         Barack:1
      }
   },
   {  
      input:'Very thankful for President Xi of China’s kind words on tarrifs and automobile barriers...also, his enlightenment on intellectual property and technology transfers. We will make great progress together!',
      output:{  
         Donald:1
      }
   },
   {  
      input:'Last night, it was my great honor to host America’s senior defense and military leaders for dinner at the White House. America’s military is the GREATEST fighting force in the history of the world. They all have my pledge of unwavering commitment to our men and women in uniform! ',
      output:{  
         Donald:1
      }
   },
   {  
      input:'A TOTAL WITCH HUNT!!!',
      output:{  
         Donald:1
      }
   }
]

2. Convert the input using encode

function encode(arg) {
return arg.split('').map(x => (x.charCodeAt(0) / 400));
}

3. Run it through net.trainAsync()

How important is this (1-5)?

A 4, since I think this should be able to work, but it could be my bad too!

Expected behavior (i.e. solution)

The data should have been analysed, but somewhere inbetween something went wrong!

Other Comments

I'm think the encoding method might not be correct here.

[robertleeplummerjr]

Do you have a code sample, perhaps even a jsfiddle?

[mohammedmulazada]

I do, it's a Node project so not sure what would be the best way to go about this, but if you know a better way, please let me know and I'll gladly do it!

The only difference between this code and mine is that mine uses the twitter API to get the data I provided above.

Basically my ideal goal would be for it to be able to separate the tweets by author and then be able to feed it some text for it to make a prediction on who it belongs to.

const brain = require('brain.js')
const http = require('http')
const express = require('express')
const app = express()

const path = require('path')
app.set('view engine', 'ejs')
const publicPath = path.join(__dirname, './public')
app.use(express.static(publicPath))
let trainedNet
let net = new brain.NeuralNetwork()

const trainingdata = require('./js/training-data.js')

function train(data) {
    console.log('training')
    net.trainAsync(processTrainingData(data, {
        iterations: 1,
        log:true,
        learningRate: 0.1,
        timeout: 500
    }))
    trainedNet = net.toFunction();
}

function encode(arg) {
    return arg.split('').map(x => (x.charCodeAt(0) / 400));
}

function processTrainingData(data) {
    return data.map(d => {
        return {
            input: encode(d.input),
            output: d.output
        }
    })
}

function execute(input) {
    console.log(input, 'input')
    let results = trainedNet(encode(input));
    let output;
    let certainty;
    console.log(results)
    console.log(results.Donald)
    if (results.Donald > results.Barack) {
        output = 'Donald Trump'
        certainty = Math.floor(results.Donald * 100)
    } else { 
        output = 'Barack Obama'
        certainty = Math.floor(results.Barack * 100)
    }

    return "I'm " + certainty + "% sure that tweet was written by " + output;
}

app.get('/', (req, res) => {
    train(trainingdata)
    
    console.log(execute("After years of rebuilding OTHER nations, we are finally rebuilding OUR nation - and we are restoring our confidence and our pride!"));
    res.render('index')
})

server.listen(3000, () => {
    console.log('Example app listening on port 3000!')
})

[robertleeplummerjr]

The main problem here is the inputs are going to have varying sizes, which won't train correctly.

[robertleeplummerjr]

Here is a working prototype: https://jsfiddle.net/8Lvynxz5/36/

[robertleeplummerjr]

Here is a better working prototype (the other one I forgot to click save on) https://jsfiddle.net/8Lvynxz5/38/

[robertleeplummerjr]

Let me know if that isn't enough of an example to get this right.
Things to be aware of:

• Likely this is limited enough data that the net will probably over-train on
• The recurrent neural network will likely give better results

[mohammedmulazada]

Thank you very much for your help and pointers. I will have a good look at this tomorrow morning, really looking forward to it! :)

[mubaidr]

If I am not mistaken, this step (to encode and fix length of input data) is not required with LSTM network and is more suitable for these type of problems.
Example here: https://github.com/BrainJS/brain.js/blob/develop/examples/childrens-book.js

[mohammedmulazada]

@mubaidr the unfortunate thing is that it seems like I can't use this, since I am running this on Node and as far as I understand that isn't available yet.

Currently the app is working while getting data from the twitter API, though for some reason it leans heavily to one side at the moment!

It seems like I am very close, but still a few tweaks needed here and there.

const dotenv = require('dotenv').config()
const brain = require('brain.js')
const Twitter = require('twitter')
const http = require('http')
const express = require('express')
const app = express()
const socketIO = require('socket.io')
const server = http.createServer(app)
const io = socketIO(server);
const path = require('path')
let dataTweet = []
app.set('view engine', 'ejs')
const publicPath = path.join(__dirname, './public')
app.use(express.static(publicPath))

let net = new brain.NeuralNetwork();
let trainedNet;
let longest;

const params = { screen_name: 'realdonaldtrump', count: 10, result_type: 'recent', tweet_mode: 'extended' };
const paramsObama = { screen_name: 'barackobama', count: 10, result_type: 'recent', tweet_mode: 'extended' };

const tokens = {
	consumer_key: process.env.CONSUMERKEY,
	consumer_secret: process.env.consumer_secret,
	access_token: process.env.access_token,
	access_token_key: process.env.access_token_key,
	access_token_secret: process.env.access_token_secret
}

const client = new Twitter(tokens)

function getTweets(user) {
	const promise = new Promise((resolve, reject) => {
		const params = { screen_name: `${user}`, count: 500, result_type: 'recent', tweet_mode: 'extended' };
		console.log(params)
		client.get('statuses/user_timeline', params)
		.then((data) => {
			if (data) {
				resolve(data)
			}
		})
	})
	
	return promise
}

getTweets('realdonaldtrump')
	.then((data) => {
		data.forEach((tweet) => {
			dataTweet.push({
				input: tweet.full_text.split('https:')[0],
				output: {[tweet.user.name.split(' ')[0]]: 1}
			})
		})
	}).then(() => {
		// train(getTrainingData(dataTweet))
	}).then(() => {
		

		getTweets('barackobama').then((data) => {
			data.forEach((tweet) => {
				dataTweet.push({
					input: tweet.full_text.split('https:')[0],
					output: {[tweet.user.name.split(' ')[0]]: 1}
				})
			})
		}).then(() => {
			train(getTrainingData(dataTweet))
	console.log(trainedNet(encode(adjustSize('A TOTAL WITCH HUNT!!!'))));

	console.log(trainedNet(encode(adjustSize('Incredible to have a Chicago team in the Final Four. I’ll take that over an intact bracket any day! Congratulations to everybody @loyolachicago - let’s keep it going!'))));
		})
	}).catch((e) => {
		console.log(e)
	})



// console.log(trainedNet(encode(adjustSize('Last night, it was my great honor to host America’s senior defense and military leaders for dinner at the White House. America’s military is the GREATEST fighting force in the history of the world. They all have my pledge of unwavering commitment to our men and women in uniform! '))));

// console.log(trainedNet(encode(adjustSize('Incredible to have a Chicago team in the Final Four. I’ll take that over an intact bracket any day! Congratulations to everybody @loyolachicago - let’s keep it going!'))));

function train(data) {
    net.train(processTrainingData(data), {
        iterations: 2000,
        log:true,
        learningRate: 0.1,
        timeout: 5000
    });
    trainedNet = net.toFunction();
}

function encode(arg) {
    return arg.split('').map(x => (x.charCodeAt(0) / 400));
}

function processTrainingData(data) {
    const processedValues = data.map(d => {
        return {
            input: encode(d.input),
            output: d.output
        }
    });
    // console.log(processedValues);
    return processedValues;
}

function getTrainingData(data) {
	const trainingData = data
  longest = trainingData.reduce((a, b) =>
    a.input.length > b.input.length ? a : b).input.length;
  for (let i = 0; i < trainingData.length; i++) {
    trainingData[i].input = adjustSize(trainingData[i].input);
  }
  return trainingData;
}

function adjustSize(string) {
  while (string.length < longest) {
    string += ' ';
  }
  return string;  
}

[mubaidr]

Api is same whether you use in browser or node.js. I am currently on mobile, will look into code later.

[robertleeplummerjr]

The api is the same in whatever javascript you are on. I started experimenting with a lstm version, which I believe is the answer here, I'll see what I can come up with as well.

[mohammedmulazada]

In case you would like to see where I am at right now, here is a link to the repo: https://github.com/moniac/real-time-web

Thanks for the help already, it's really exciting to see this develop!

[robertleeplummerjr]

It has been a while since I looked at the api (even though I built it, lol). We really should document stuff like this better but here you go: https://jsfiddle.net/j638LfLd/

Outputs:

iterations: 0 training error: 102.38539514646675
iterations: 10 training error: 22.821843183158208
iterations: 20 training error: 11.492725393552938
iterations: 30 training error: 8.732626567498885
iterations: 40 training error: 6.30779809294381
iterations: 50 training error: 5.643141357916936
iterations: 60 training error: 4.7329927373696865
iterations: 70 training error: 3.923181896715045
iterations: 80 training error: 3.5271946017753346
iterations: 90 training error: 3.713248549303406
iterations: 100 training error: 3.9557870752674535
iterations: 110 training error: 3.034900554836554
iterations: 120 training error: 2.9839399371764377
iterations: 130 training error: 2.6454871466040264
iterations: 140 training error: 3.4238002357793262
iterations: 150 training error: 2.4393232345710634
iterations: 160 training error: 2.554969571658671
iterations: 170 training error: 2.3398548776565775
iterations: 180 training error: 2.1756967055287038
iterations: 190 training error: 2.267156470151439
Donald
Barack

Definition:

let net = new brain.recurrent.LSTM();
net.train([
  {
    input: 'I say yes!',
    output: 'positive'
  },
  {
    input: 'I say no!'.
    output: 'negative'
  }
]);

const standaloneFunction = net.toFunction();

// for the curious at heart and want the non-readable version:
console.log(standaloneFunction.toString());