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ydf-inference

v0.0.4

Published

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

Downloads

28

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("ydf-inference")();

    // 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 YDFInference from 'ydf-inference';

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

// 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/ydf-inference/dist/inference.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.0/jszip.min.js"></script>
<script>
YDFInference()
    .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