@jsonstack/jsonm

JSONM

Description

JSONM is a module that creates Tensorflow models with JSON.

JSONM is designed so software engineers and machine learning engineers can quickly build, train, save, and load Tensorflow based AI & ML Models. The JSONM Library includes a JSONM UMD module with batteries included so you can use JSONM in the browser without transpilers or any additional setup/configuration. The JSONM UMD is ideal for JAMstack Applications.

Usage

The idea behind JSONM is to enable rapid model development. JSONM attempts to automate the data pre-processing and feature engineering needed for most modeling tasks.

Data Scientists who are more comfortable finely tuning hyperparameters and controlling pre-processing, scaling and normalization of datasets can also configure JSONM to meet specific model requirements.

What's included

JSONM currently supports the following models

General purpose multivariate based predictions
Multivariate linear regression based predictions
General purpose multivariate classification based descriptions
General purpose multivariate timeseries based forecasts
General purpose content based recommendations (coming soon)
General purpose cohort analysis (coming soon)

Installation

$ npm i @jsonstack/jsonm

JSONM Manual

Getting Started
Working With Data
- Data Fetching
- Feature Engineering
Working With Models
Advanced Topics

Basic Usage

import * as tf from '@tensorflow/tfjs-node';
import { getModel, setBackend, } from '@jsonstack/jsonm';

//set tensorflow
scikit.setBackend(tf);
setBackend(tf);

//Iris Dataset e.g from https://raw.githubusercontent.com/repetere/modelx-model/master/src/test/mock/data/iris_data.csv
const type = 'ai-classification';
const dataset = [
  {
    "sepal_length_cm": 5.1,
    "sepal_width_cm": 3.5,
    "petal_length_cm": 1.4,
    "petal_width_cm": 0.2,
    "plant": "Iris-setosa",
  },
//  ...
  {
    "sepal_length_cm": 7.0,
    "sepal_width_cm": 3.2,
    "petal_length_cm": 4.7,
    "petal_width_cm": 1.4,
    "plant": "Iris-versicolor",
  },
  // ...
  {
    "sepal_length_cm": 5.9,
    "sepal_width_cm": 3.0,
    "petal_length_cm": 5.1,
    "petal_width_cm": 1.8,
    "plant": "virginica",
  }
]
const inputs = ['sepal_length_cm','sepal_width_cm','petal_length_cm','petal_width_cm', ];
const outputs = [ 'plant',];
const on_progress = ({ completion_percentage, loss, epoch, status, logs, defaultLog, }) => { 
  console.log({ completion_percentage, loss, epoch, status, logs, defaultLog, });
}
const IrisModel = await getModel({
  type,
  dataset,
  inputs,
  outputs,
  on_progress,
}); 
await IrisModel.trainModel()
const predictions = await IrisModel.predictModel({ 
  prediction_inputs:[
    { sepal_length_cm: 5.1, sepal_width_cm: 3.5, petal_length_cm: 1.4, petal_width_cm: 0.2, },
    { sepal_length_cm: 5.9, sepal_width_cm: 3.0, petal_length_cm: 5.1, petal_width_cm: 1.8, },
  ],
}); // => [ { plant:'Iris-setosa' }, { plant:'Iris-virginica' }, ]