nspammer

A Naive Bayes spam classifier implementation in Go, enabling text classification system using the Naive Bayes algorithm with Laplace smoothing to classify messages as spam or not spam.

Features

• Naive Bayes Classification: Uses probabilistic classification based on Bayes' theorem with naive independence assumptions
• Laplace Smoothing: Implements additive smoothing to handle zero probabilities for unseen words
• Training & Classification: Simple API for training on labeled datasets and classifying new messages
• Real Dataset Testing: Includes tests with actual spam/ham email datasets

Installation

go get github.com/igomez10/nspammer

Usage

Basic Example

package main

import (
    "fmt"
    "github.com/igomez10/nspammer"
)

func main() {
    // Create training dataset (map[string]bool where true = spam, false = not spam)
    trainingData := map[string]bool{
        "buy viagra now":           true,
        "get rich quick":           true,
        "meeting at 3pm":           false,
        "project update report":    false,
    }

    // Create and train classifier
    classifier := nspammer.NewSpamClassifier(trainingData)

    // Classify new messages
    isSpam := classifier.Classify("buy now")
    fmt.Printf("Is spam: %v\n", isSpam)
}

API

NewSpamClassifier(dataset map[string]bool) *SpamClassifier

Creates a new spam classifier and trains it on the provided dataset. The dataset is a map where keys are text messages and values indicate whether the message is spam (true) or not spam (false).

(*SpamClassifier).Classify(input string) bool

Classifies the input text as spam (true) or not spam (false) based on the trained model.

How It Works

The classifier uses the Naive Bayes algorithm:

1. Training Phase:

   • Calculates prior probabilities: P(spam) and P(not spam)
   • Builds a vocabulary from all training messages
   • Counts word occurrences in spam and non-spam messages
   • Stores word frequencies for likelihood calculations

2. Classification Phase:

   • Calculates log probabilities to avoid numerical underflow
   • Computes: log(P(spam)) + Σ log(P(word|spam))
   • Computes: log(P(not spam)) + Σ log(P(word|not spam))
   • Returns true (spam) if the spam score is higher

3. Laplace Smoothing:

   • Adds a smoothing constant to avoid zero probabilities for unseen words
   • Formula: P(word|class) = (count + α) / (total + α × vocabulary_size)
   • Default α = 1.0

Dataset

The project includes support for the Kaggle Spam Mails Dataset. To download it:

./init.sh

This script requires the Kaggle CLI to be installed and configured.

Testing

Run the test suite:

go test -v

The tests include:

• Simple classification examples
• Real-world email dataset evaluation
• Accuracy measurements on train/test splits