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.
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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