Starlight Classifier

Starlight Classifier is a lightweight text classification library for Starlight, built on top of starlight-vec and starlight-ml. It uses TF-IDF vectorization and a simple Naive Bayes approach for classifying documents quickly and efficiently.

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Features

* Vectorizes text using TF-IDF (via starlight-vec)
* Simple Naive Bayes classifier for text
* Predict labels for single or multiple documents
* Works seamlessly with Starlight ML ecosystem
* Customizable stopwords support

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Installation

``bash
npm install starlight-classifier starlight-vec starlight-ml
`

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Usage

$3

`javascript
import { NaiveBayesClassifier, classify } from "starlight-classifier";
import * as ml from "starlight-ml";
`

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$3

`javascript
// Sample documents
const docs = [
"I love machine learning",
"Starlight ML is amazing",
"Python is great for AI",
"I enjoy coding in JavaScript"
];

// Corresponding labels
const labels = ["tech", "tech", "tech", "programming"];

// Stopwords (optional)
const stopwords = ["is", "in"];

// Train classifier
const clf = classify(docs, labels, stopwords);

// Predict a new document
const newDoc = "I love AI and machine learning";
const prediction = clf.predict(newDoc);

console.log("Predicted class:", prediction); // e.g., "tech"

// Predict multiple documents
const batch = ["I code every day", "Starlight ML is cool"];
const batchPredictions = clf.predictBatch(batch);

console.log("Batch predictions:", batchPredictions);
`

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$3

`javascript
import { NaiveBayesClassifier } from "starlight-classifier";

// Create classifier with stopwords
const clf = new NaiveBayesClassifier(["is", "and"]);

// Fit on documents
clf.fit(docs, labels);

// Transform and analyze similarity
const vec1 = clf.vectorizer.transform(docs[0]);
const vec2 = clf.vectorizer.transform(docs[1]);
const similarity = NaiveBayesClassifier.cosine(vec1, vec2);

console.log("Cosine similarity between doc1 and doc2:", similarity);
`

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API

$3

* Creates a classifier instance.
* stopwords — optional array of words to ignore during vectorization.

$3

* Train classifier on an array of documents with corresponding labels.

$3

* Predict the class of a single document.

$3

* Predict the classes for multiple documents.

$3

* Convenience function: creates a classifier, fits it, and returns it.

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Dependencies

* starlight-vec — TF-IDF vectorization for Starlight
* starlight-ml` — Text preprocessing utilities

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License

MIT © Dominex Macedon