npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

yggdrasil-decision-forests

v0.0.3

Published

With this package, you can generate predictions of machine learning models trained with YDF in browser and with NodeJS.

Downloads

3,505

Readme

YDF in JS

With this package, you can generate predictions of machine learning models trained with YDF in the browser and with NodeJS.

Usage example

First, let's train a machine learning model in python. For more details, read YDF's documentation.

In Python in a Colab or in a Jupyter Notebook, run:

# Install YDF
!pip install ydf pandas

import ydf
import pandas as pd

# Download a training dataset
ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/"
train_ds = pd.read_csv(ds_path + "adult_train.csv")

# Train a Gradient Boosted Trees model
learner = ydf.GradientBoostedTreesLearner(label="income", pure_serving_model=True)
model = learner.train(train_ds)

# Save the model
model.save("/tmp/my_model")

# Zip the model
# Important: Use -j to not include the directory structure.
!zip -rj /tmp/my_model.zip /tmp/my_model

Then:

Run the model with NodeJS and CommonJS

(async function (){
    // Load the YDF library
    const ydf = await require("yggdrasil-decision-forests")();

    // Load the model
    const fs = require("node:fs");
    let model = await ydf.loadModelFromZipBlob(fs.readFileSync("./model.zip"));

    // Create a batch of examples.
    let examples = {
        "age": [39, 40, 40, 35],
        "workclass": ["State-gov", "Private", "Private", "Federal-gov"],
        "fnlwgt": [77516, 121772, 193524, 76845],
        "education": ["Bachelors", "Assoc-voc", "Doctorate", "9th"],
        "education_num": ["13", "11", "16", "5"],
        "marital_status": ["Never-married", "Married-civ-spouse", "Married-civ-spouse", "Married-civ-spouse"],
        "occupation": ["Adm-clerical", "Craft-repair", "Prof-specialty", "Farming-fishing"],
        "relationship": ["Not-in-family", "Husband", "Husband", "Husband"],
        "race": ["White", "Asian-Pac-Islander", "White", "Black"],
        "sex": ["Male", "Male", "Male", "Male"],
        "capital_gain": [2174, 0, 0, 0],
        "capital_loss": [0, 0, 0, 0],
        "hours_per_week": [40, 40, 60, 40],
        "native_country": ["United-States", null, "United-States", "United-States"]
    };

    // Make predictions
    let predictions = model.predict(examples);
    console.log("predictions:", predictions);

    // Release model
    model.unload();
}())

Run the model with NodeJS and ES6

import * as fs from "node:fs";
import YggdrasilDecisionForests from 'yggdrasil-decision-forests';

// Load the YDF library
let ydf = await YggdrasilDecisionForests();

// Load the model
let model = await ydf.loadModelFromZipBlob(fs.readFileSync("./model.zip"));

// Create a batch of examples.
let examples = {
    "age": [39, 40, 40, 35],
    "workclass": ["State-gov", "Private", "Private", "Federal-gov"],
    "fnlwgt": [77516, 121772, 193524, 76845],
    "education": ["Bachelors", "Assoc-voc", "Doctorate", "9th"],
    "education_num": ["13", "11", "16", "5"],
    "marital_status": ["Never-married", "Married-civ-spouse", "Married-civ-spouse", "Married-civ-spouse"],
    "occupation": ["Adm-clerical", "Craft-repair", "Prof-specialty", "Farming-fishing"],
    "relationship": ["Not-in-family", "Husband", "Husband", "Husband"],
    "race": ["White", "Asian-Pac-Islander", "White", "Black"],
    "sex": ["Male", "Male", "Male", "Male"],
    "capital_gain": [2174, 0, 0, 0],
    "capital_loss": [0, 0, 0, 0],
    "hours_per_week": [40, 40, 60, 40],
    "native_country": ["United-States", null, "United-States", "United-States"]
};

// Make predictions
let predictions = model.predict(examples);
console.log("predictions:", predictions);

// Release model
model.unload();

Run the model with in Browser

<script src="./node_modules/yggdrasil-decision-forests/dist/inference.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.0/jszip.min.js"></script>
<script>
YggdrasilDecisionForests()
    .then(ydf => ydf.loadModelFromUrl("http://localhost:3000/model.zip"))
    .then(model => {
        let examples = {
            "age": [39, 40, 40, 35],
            "workclass": ["State-gov", "Private", "Private", "Federal-gov"],
            "fnlwgt": [77516, 121772, 193524, 76845],
            "education": ["Bachelors", "Assoc-voc", "Doctorate", "9th"],
            "education_num": ["13", "11", "16", "5"],
            "marital_status": ["Never-married", "Married-civ-spouse", "Married-civ-spouse", "Married-civ-spouse"],
            "occupation": ["Adm-clerical", "Craft-repair", "Prof-specialty", "Farming-fishing"],
            "relationship": ["Not-in-family", "Husband", "Husband", "Husband"],
            "race": ["White", "Asian-Pac-Islander", "White", "Black"],
            "sex": ["Male", "Male", "Male", "Male"],
            "capital_gain": [2174, 0, 0, 0],
            "capital_loss": [0, 0, 0, 0],
            "hours_per_week": [40, 40, 60, 40],
            "native_country": ["United-States", null, "United-States", "United-States"]
        };
        predictions = model.predict(examples);
        model.unload();
    });
</script>

For developers

Run unit tests

npm test

Update the binary bundle

# Assume the shell is located in a clone of:
# https://github.com/google/yggdrasil-decision-forests.git

# Compile the YDF with WebAssembly
yggdrasil_decision_forests/port/javascript/tools/build_zipped_library.sh

# Extract the the content of `dist` in `yggdrasil_decision_forests/port/javascript/npm/dist`.
unzip dist/ydf.zip -d yggdrasil_decision_forests/port/javascript/npm/dist