starlight-preprocess

Starlight Preprocess is a lightweight preprocessing library for machine learning in the Starlight ecosystem.
It provides common data transformation tools such as scaling, encoding, and dataset splitting — designed to work seamlessly with other Starlight ML packages.

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Features

* Feature Scaling

* StandardScaler (mean = 0, std = 1)
* MinMaxScaler (range 0–1)

* Label Encoding

* LabelEncoder (class → integer)
* OneHotEncoder (categorical → binary vectors)

* Dataset Splitting

* Train / Test split utility

* Pipeline Friendly

* Works with starlight-regression, starlight-classifier, starlight-cluster
* Designed for starlight-pipeline

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Installation

bash

npm install starlight-preprocess





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 Usage Examples



$3

js

import { StandardScaler } from "starlight-preprocess";



const X = [

  [1, 10],

  [2, 20],

  [3, 30]

];



const scaler = new StandardScaler();

const XScaled = scaler.fitTransform(X);



console.log(XScaled);





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$3

js

import { MinMaxScaler } from "starlight-preprocess";



const scaler = new MinMaxScaler();

const normalized = scaler.fitTransform(X);





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$3

js

import { LabelEncoder } from "starlight-preprocess";



const labels = ["cat", "dog", "cat", "bird"];



const encoder = new LabelEncoder();

const encoded = encoder.fitTransform(labels);



console.log(encoded);

// [0, 1, 0, 2]





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$3

js

import { OneHotEncoder } from "starlight-preprocess";



const encoder = new OneHotEncoder();

const oneHot = encoder.fitTransform(["red", "blue", "red"]);



console.log(oneHot);

// [[1,0],[0,1],[1,0]]





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$3

js

import { trainTestSplit } from "starlight-preprocess";



const X = [[1], [2], [3], [4], [5]];

const y = [2, 4, 6, 8, 10];



const { XTrain, XTest, yTrain, yTest } =

  trainTestSplit(X, y, 0.2);



console.log(XTrain, XTest);





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 API Overview



$3



*

StandardScaler

MinMaxScaler





Methods



*

fit(X)

transform(X)

fitTransform(X)





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$3



*

LabelEncoder

OneHotEncoder





Methods



*

fit(values)

transform(values)

fitTransform(values)

inverseTransform()

(LabelEncoder)*



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$3



*

trainTestSplit(X, y, testSize = 0.2, shuffle = true)





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 Works Great With



*

starlight-ml

starlight-vec

starlight-classifier

starlight-regression

starlight-cluster

starlight-pipeline`

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License

MIT License © Dominex Macedon

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Roadmap

* Missing value handling
* Text normalization helpers
* Image preprocessing
* Auto-preprocessing in pipelines