🎯 LASSO Regression in Go

Efficient parallel implementation of LASSO (Least Absolute Shrinkage and Selection Operator) regression using coordinate descent optimization. Designed for performance and scalability with Go's concurrency model.

Features ✨

• ⚡ Parallel coordinate descent - Leverages goroutines for concurrent feature updates
• 📉 L1 regularization - Automatic feature selection and model simplification
• 🎯 Early stopping - Terminates training when convergence is detected
• 📊 Metrics tracking - Records MSE, R², and weight deltas during training
• 🔧 Feature standardization - Automatic data preprocessing
• 📈 Comprehensive evaluation - Supports R², MSE, and MAE metrics
• 📝 Training history - Access detailed logs of each iteration
• ⚙️ Configurable parameters - Tune lambda, tolerance, and parallel jobs

Installation 📦

go get github.com/CausalGo/lasso

Quick Start 🚀

package main

import (
	"fmt"
	
	"github.com/CausalGo/lasso"
	"gonum.org/v1/gonum/mat"
)

func main() {
	// Training data
	X := mat.NewDense(4, 2, []float64{
		1, 2,
		3, 4,
		5, 6,
		7, 8,
	})
	y := []float64{3, 7, 11, 15}

	// Configure training
	cfg := lasso.NewDefaultConfig()
	cfg.Lambda = 0.1      // Regularization strength
	cfg.Verbose = true    // Enable training logs
	cfg.NJobs = 2         // Use 2 parallel workers

	// Train model
	model := lasso.Fit(X, y, cfg)

	// Make predictions
	newX := mat.NewDense(2, 2, []float64{
		2, 3,
		4, 5,
	})
	predictions := model.Predict(newX)
	fmt.Println("Predictions:", predictions) // [5.0001, 9.0000]
	
	// Evaluate model
	score := model.Score(X, y)
	fmt.Printf("R² score: %.4f\n", score) // 1.0000
}

Advanced Usage 🧠

Custom Configuration

cfg := &lasso.Config{
	Lambda:     0.05,    // Regularization parameter
	MaxIter:    2000,    // Maximum iterations
	Tol:        1e-5,    // Convergence tolerance
	NJobs:      4,       // Parallel workers
	Standardize: true,   // Standardize features
	Verbose:    true,    // Show training logs
	LogStep:    50,      // Log every 50 iterations
	EarlyStop:  true,    // Enable early stopping
	StopAfter:  15,      // Stop after 15 iterations without improvement
}

Accessing Training History

model := lasso.Fit(X, y, cfg)

// Analyze training progress
for _, log := range model.History {
	if log.Iteration%100 == 0 {
		fmt.Printf("Iter %d: MSE=%.4f R²=%.4f\n", 
			log.Iteration, log.MSE, log.R2)
	}
}

Saving and Loading Models

// Save model to JSON
err := model.Save("model.json")
if err != nil {
	panic(err)
}

// Load model from JSON
loadedModel, err := lasso.Load("model.json")
if err != nil {
	panic(err)
}

Performance Benchmarks ⏱️

Benchmark results on synthetic dataset (10,000 samples, 100 features):

Lambda	Iterations	Time (ms)	Active Features
0.01	142	245	73
0.1	78	132	45
1.0	35	58	12

BenchmarkFit-8   	      50	  24823612 ns/op

Documentation 📚

Full documentation is available on pkg.go.dev

Contributing 🤝

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a pull request

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

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CausalGo - Machine learning tools for causal analysis in Go