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Random forests ported to Javascript with WebAssembly and WebWorkers

Makefile 3.42% JavaScript 82.63% HTML 0.60% C++ 10.40% C 2.96%

random-forest ensembles webassembly webworker multithreading

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gober2021 syedzayyan machine-learning-and-deep

random-forest's Issues

Loaded Model - TypeError: Cannot read properties of undefined (reading 'transform')

TypeError: Cannot read properties of undefined (reading 'transform')
    at RandomForestClassifier.predict (/Users/alasdairbarrie/tradingBots/fantomSimulator/node_modules/random-forest/src/base.js:66:25)
    at module.exports (/Users/alasdairbarrie/tradingBots/fantomSimulator/fun/randomForestClassifier.js:218:32)
    at module.exports (/Users/alasdairbarrie/tradingBots/fantomSimulator/fun/strategyBuilder.js:312:19)
    at module.exports (/Users/alasdairbarrie/tradingBots/fantomSimulator/fun/simulator.js:28:28)
    at signals (/Users/alasdairbarrie/tradingBots/fantomSimulator/index.js:39:11)
    at processTicksAndRejections (node:internal/process/task_queues:96:5)
TypeError: Cannot read properties of undefined (reading 'length')
TypeError: Cannot read properties of undefined (reading 'length')

My Data:
I am using BTCUSDT price data for this. I have added technical indicator features and removed the OHLCV.

I am using Danfo.js (js version of Pandas) to convert my data into a dataframe so its easier to manipulate it. It is then extracted back into data arrays before being used in the model.

The same dataset that was used to train the saved model is now being using on the loaded model...just a different section of the time series.

My Code:

    /* ------------------------------ Define the train and test samples ----------------------------- */
    let XTrain_Cols=[], XTest_Cols=[], yTrain_Cols=[], yTest_Cols=[];
    XTrain_Cols = traindf.loc({ columns: ["ema0","ema1","rsi2","stochk0","stochd0","williamsR0","xBarHigh","xBarLow","historicalReturn1","historicalReturn5","distanceFromEma0","distanceFromEma1"]})['$data']; //
    XTest_Cols = testdf.loc({ columns: ["ema0","ema1","rsi2","stochk0","stochd0","williamsR0","xBarHigh","xBarLow","historicalReturn1","historicalReturn5","distanceFromEma0","distanceFromEma1"]})['$data']; //
    yTrain_Cols = traindf['Prediction']['$data'];
    yTest_Cols = testdf['Prediction']['$data'];
    

    /* -------------------------- Define new random forest classifier model ------------------------- */
    const rfClassifier = new RandomForestClassifier({nEstimators: 400, maxDepth: 20, maxFeatures: 'auto', minSamplesLeaf: 10, minInfoGain: 0});

    /* -------------------------------- Load a previously saved model ------------------------------- */
    var modelName = `./models/rf-BTCUSDT-tf60m-acc0.8603871361073007-epoch1653977716111-ema90-ema140-rsi2-stochk9-stochd3-willr10-pb5-histret1-histret5-forret3-target0.02.model`
    const modelLoaded = new Uint8Array(fs.readFileSync(modelName));
    rfClassifier.load(modelLoaded);

    /* --------------------------------------- Run predictions -------------------------------------- */
    var yPred = rfClassifier.predict(XTest_Cols);

Behaviour:
Everything works completely fine when I save and load the model in the same run. It's when I dont train the model first and just try to load in the saved model I get this error.

Poor Performance Compared to SciKit Learn

Hello!

I am using this library to train a model on bit vectors of small molecules. However, I have had poor performance with similar hyperparameters. Any ideas as to how I could improve?

Regards,
Z

RuntimeError: memory access out of bounds

ERROR [ExceptionsHandler] memory access out of bounds
RuntimeError: memory access out of bounds
    at null.<anonymous> (wasm://wasm/00020232:0:27521)
    at null.<anonymous> (wasm://wasm/00020232:0:31175)
    at null.<anonymous> (wasm://wasm/00020232:0:27022)
    at m (wasm://wasm/00020232:0:25267)
    .
    .
    .

My code

const data = [
      { value: 0, score: 0 },
      { value: 100, score: 10 },
      { value: 500, score: 20 },
      { value: 1000, score: 30 },
      { value: 5000, score: 40 },
      { value: 10000, score: 50 },
      { value: 20000, score: 60 },
      { value: 50000, score: 70 },
      { value: 70000, score: 80 },
      { value: 100000, score: 90 },
      { value: 150000, score: 100 },
      { value: 200000, score: 100 },
      { value: 300000, score: 100 },
      { value: 400000, score: 100 },
      { value: 500000, score: 100 },
    ];

    // * train model
    const rf = new RandomForestClassifier({
      nEstimators: 100,
      maxDepth: 10,
      maxFeatures: 'auto',
      minSamplesLeaf: 5,
      minInfoGain: 0,
    });

    rf.train(
      data.map((obj) => [obj.value]),
      data.map((obj) => [obj.score]),
    );
    const ypred = rf.predict([value]);

    return ypred;

Everything works fine if I change the score values to something like

const data = [
      { value: 0, score: 0 },
      { value: 100, score: 1 },
      { value: 500, score: 2 },
      { value: 1000, score: 3 },
      { value: 5000, score: 4 },
      { value: 10000, score: 5 },
      { value: 20000, score: 6 },
      { value: 50000, score: 7 },
      { value: 70000, score: 8 },
      { value: 100000, score: 9 },
      { value: 150000, score: 10 },
      { value: 200000, score: 10 },
      { value: 300000, score: 10 },
      { value: 400000, score: 10 },
      { value: 500000, score: 10 },
    ];

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