# Contributing

Want to add your favorite estimator in JS? Want to make Javascript a better language for machine learning? Awesome! In this article, we'll add an example Estimator to this project. In doing so, we'll learn

1. How this project is setup
2. How to create / document your new estimator
3. What are the types in this library
4. What are the deploy targets

## DummyRegressorβ

Let's make a DummyRegressor. It's an Estimator that predicts a y value based on simple rules. So for example, if you pass a strategy of "mean", then it will look at the response variable (y), compute the mean, and return that value on any input. Here's some example usage.

import { DummyRegressor } from 'scikitjs'let myReg = new DummyRegressor({ strategy: 'mean' })await myReg.fit(  [    [1, 2],    [3, 4],    [5, 6]  ],  [10, 20, 30]) // The mean is 20, so anything that we call predict on should return 20const expect = myReg.predict([  [2, 10],  [1, 10]]) // returns a 1D Tensor which is basically [20, 20]

Based on the scikit-learn DummyRegressor documentation, we see that the DummyRegressor supports strategy, constant, and quantile as constructor inputs. For simplicity let's only support strategy and constant in this walkthrough.

Moreover, the only strategy values that we will support will be "mean" and "constant".

## First Passβ

Without further ado, let's create a class

class DummyRegressor {  constructor(args) {    // does stuff  }  fit(X, y) {    // fits stuff  }  predict(X) {    // predicts stuff  }}

The first order of business, is how do we pass constructor arguments to our class? We could pass those arguments positionally, like constructor(strategy, constant), or we can pass it as an object like constructor({strategy, constant}). As explained in Coming from python, I chose to do objects because there are some Estimators that take in a large number of options with sane defaults like DecisionTreeClassifier and I didn't want users to have to type in all the defaults.

## What types though?β

The second order of business, is what exactly are the types that we pass into fit and predict? Looking at the Estimators in scikit-learn it's clear that the X is a 2D Array, and the y is a 1D Array. We also want to support the DataFrame, and Series objects from danfo (the pandas equivalent in JS), and Tensor2D and Tensor1D (Tensors are the JS version of numpy arrays. They ship with Tensorflow.).

After some deliberation, here are the types that we use in this library to represent the following things.

// The Types that Scikit usesexport type TypedArray = Float32Array | Int32Array | Uint8Arrayexport type ScikitLike1D = TypedArray | number[] | boolean[] | string[]export type ScikitLike2D = TypedArray[] | number[][] | boolean[][] | string[][]export type Scikit1D = ScikitLike1D | Tensor1D | Seriesexport type Scikit2D = ScikitLike2D | Tensor2D | DataFrameexport type ScikitVecOrMatrix = Scikit1D | Scikit2D

## What we have so farβ

After putting in the proper types for the arguments, we have the following

class DummyRegressor {  constructor({ strategy = 'mean', constant }) {    // does stuff  }  fit(X: Scikit2D, y: Scikit1D) {    // fits stuff  }  predict(X: Scikit2D) {    // predicts stuff  }}

Note also, that fit should return a reference to the class itself, and predict usually returns a numpy array, which in javascript land is a Tensor2D. Let's save our constructor args, and add those typings.

class DummyRegressor {  constructor({ strategy = 'mean', constant }) {    this.strategy = strategy    this.constant = constant  }  fit(X: Scikit2D, y: Scikit1D): DummyRegressor {    // fit stuff  }  predict(X: Scikit2D): Tensor1D {    // predicts stuff  }}

Now let's write the fit function. If the user has set the strategy to "mean", than we will construct a Tensor1D from the y array, and calculate the mean of it. If the strategy is "constant", then we do nothing, because the constant class property already contains the fill value that we will use for prediction.

class DummyRegressor {  constructor({ strategy = 'mean', constant }) {    this.strategy = strategy    this.constant = constant  }  fit(X: Scikit2D, y: Scikit1D): DummyRegressor {    const newY = convertToNumericTensor1D(y)    if (this.strategy === 'mean') {      this.constant = newY.mean().dataSync()[0]      return this    }    // constant case    return this  }  predict(X: Scikit2D): Tensor1D {    // predicts stuff  }}

As you can see, there are a bunch of nice utility functions that we can use to convert user input into formats more amenable to number crunching. In this case, I use convertToNumericTensor1D to turn y into a Tensor1D.

Then I call mean on that Tensor and then use the Tensorflow API dataSync function to get the actual returned value as a single number instead of a Tensor1D.

Let's finish writing predict. In this case, I'm simply going to take the input, convert it to a 2D Tensor, and then get the number of rows. From there, I'm going to simply create a Tensor1D and fill it with whatever is in my constant attribute. That looks like this.

class DummyRegressor {  constructor({ strategy = 'mean', constant }) {    this.strategy = strategy    this.constant = constant  }  fit(X: Scikit2D, y: Scikit1D): DummyRegressor {    const newY = convertToNumericTensor1D(y)    if (this.strategy === 'mean') {      this.constant = newY.mean().dataSync()[0]      return this    }    // constant case    return this  }  predict(X: Scikit2D): Tensor1D {    let newData = convertToNumericTensor2D(X)    let length = newData.shape[0]    return tensor1d(Array(length).fill(this.constant))  }}

And there you go! Certainly there is more to do. We haven't talked about asserting on bad user input, or supporting all the options for DummyRegressor (both constructor args, and class methods), but it's a start. And we can start testing.

I think the next question is "Where do I put this code in the repo?". And in order to answer that question we need to talk about the project setup and deploy targets.

## Project setupβ

This single project needs to serve both frontend and backend needs. It has 3 deploy targets.

1. It needs to be useful in a modern frontend framework which includes a bundler. This means that you need to be able to
yarn add scikitjs

and have it just work.

1. It needs to be compatible with script tags. Someone should be able to just
<script src="https://unpkg.com/scikitjs/dist/web/index.min.js"></script>
1. It needs to be compatible with backend Node.js environments, and use the C++ Tensorflow.js bindings for speed improvements there.
yarn add scikitjs

And then use it easily with

import { LinearRegression } from 'scikitjs/node'

Because there are multiple deploy environments (frontend / backend and eventually gpu), we have multiple output directories. We build an esm (ES Modules), cjs (Commonjs modules), (ES5 build) for older browsers, and a full-fledged script tag bundle which bundles up tensorflow, and other dependencies.

The repo basically looks like this.

scikitjsβ   README.mdβ   package.jsonβββββsrcβ   β   package.jsonβ   ββββsharedβ       β    globals.ts||   ββββshared-esm|       |    globals.ts||   ββββshared-node|       |    globals.tsβββββdocsβ   β   package.jsonβ   ββββsrcβ       β    globals.tsβ

The src directory contains all of the important algorithmic code. The build scripts swap the shared/globals.ts file for other shared-*/globals.ts files that have different versions of our dependencies to build different versions of the library (esm, cjs, etc).

## There ya have itβ

So that's the basic idea. There are things I skipped over (testing, using mixins, asserting on bad input), but this will get the gravy train rolling.