linreg-from-scratch 0.2.0

A clean implementation of linear regression algorithms from scratch

Linear Regression from Scratch

A production-quality, educational implementation of linear regression algorithms built from scratch using NumPy. This library provides clean, well-documented implementations for learning the mathematical foundations of linear regression while maintaining professional-grade code quality.

🎯 Project Overview

This project implements linear regression algorithms from first principles without using high-level ML libraries like scikit-learn. It's designed as both an educational tool and a functional library that demonstrates professional Python package development practices.

🌟 What Makes This Special

• 📚 Educational Focus: Understand the mathematics behind linear regression
• 🏗️ Production Quality: Professional package structure ready for PyPI
• 🔬 From Scratch: Only NumPy used for mathematical operations
• 🧪 Fully Tested: Comprehensive test suite with edge case handling
• 📦 Complete Package: Installable via pip with proper dependency management

📁 Project Architecture

See the full project architecture in DEVELOPMENT.md.

📐 Mathematical Background

For a detailed explanation of the mathematical foundations behind linear regression, see mathematical_background.md.

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip package manager

Installation

Option 1: Install from PyPI (recommended)

pip install linreg-from-scratch

Option 2: Clone & Setup for development

git clone https://github.com/illoonego/linear-regression-from-scratch.git
cd linear-regression-from-scratch

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install all dependencies using pyproject.toml (PEP 621)
pip install -e .[dev]
# For optional dependencies (notebooks, docs):
pip install -e ".[notebooks,docs]"

Note: All dependencies are managed via pyproject.toml. The legacy requirements.txt file has been removed for clarity and modern Python packaging best practices.

Running Examples

# Run all examples
python examples/basic_linear_regression.py

# Run specific examples
python examples/basic_linear_regression.py 1d    # Simple regression
python examples/basic_linear_regression.py 2d    # Multiple regression

Basic Usage

Simple Linear Regression

import numpy as np
from linear_regression import LinearRegression, StandardScaler, r2_score

# Create sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2.1, 3.9, 6.1, 8.0, 9.9])  # y ≈ 2x with noise

# Option 1: Direct usage
model = LinearRegression(learning_rate=0.01, n_iterations=1000)
model.fit(X, y, method='gradient_descent')
predictions = model.predict(X)
print(f"Weights: {model.weights_}")
print(f"R² Score: {r2_score(y, predictions):.4f}")

# Option 2: With preprocessing
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X)
model.fit(X_scaled, y)
predictions_scaled = model.predict(X_scaled)
print(f"Weights (scaled): {model.weights_}")
print(f"R² Score (scaled): {r2_score(y, predictions_scaled):.4f}")

Multiple Linear Regression

import numpy as np
from linear_regression import LinearRegression, r2_score

# House price prediction example
np.random.seed(42)
size_sqft = np.random.uniform(800, 2500, 100)
bedrooms = np.random.randint(1, 5, 100)
X = np.column_stack((size_sqft, bedrooms))

# True relationship: price = 150*size + 10000*bedrooms + 20000 + noise
price = 150 * size_sqft + 10000 * bedrooms + 20000 + np.random.randn(100) * 10000

model = LinearRegression(learning_rate=1e-7, n_iterations=5000)
model.fit(X, price)
predictions = model.predict(X)

print(f"Learned coefficients: {model.weights_[1:]}")  # [size_coef, bedroom_coef]
print(f"Intercept: {model.weights_[0]}")
print(f"R² Score: {r2_score(price, predictions):.4f}")

Polynomial Regression Example

import numpy as np
from linear_regression import PolynomialRegression, r2_score

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 4, 9, 16, 25])  # y = x^2
model = PolynomialRegression(degree=2)
model.fit(X, y)
predictions = model.predict(X)
print(f"Predictions: {predictions}")
print(f"R² Score: {r2_score(y, predictions):.4f}")

📊 Current Features

✅ Implemented & Tested

• LinearRegression: Complete implementation with both gradient descent and normal equation (closed-form solution)
• PolynomialRegression: Full implementation with feature expansion, robust input validation, and edge case handling
• StandardScaler: Feature standardization with robust validation
• Metrics: r2_score, mean_squared_error, mean_absolute_error (100% coverage)
• Examples: Working 1D, 2D, and polynomial regression demonstrations
• Error Handling: Comprehensive input validation and edge case management
• Verbose Training Output: Control progress printing with the verbose flag
• Professional Structure: PyPI-ready package with proper metadata

🚧 Planned Features

See the DEVELOPMENT.md for the full roadmap and planned features.

🧪 Testing & Development

Run Tests & Coverage

# Run all tests and coverage
pytest --cov=src/linear_regression tests/ -v
# Coverage: LinearRegression 99%, PolynomialRegression & metrics 100%

Continuous Integration & Delivery (CI/CD)

This project uses GitHub Actions for:

• CI: Automatic tests, linting (ruff), formatting checks (black), and coverage reporting on every push and pull request. See .github/workflows/python-ci.yml.
• CD: Automated publishing to PyPI on new version tags. See .github/workflows/python-cd.yml.

How releases work:

• When a new version tag (e.g., v1.0.0) is pushed, the CD workflow builds and publishes the package to PyPI using secure repository secrets.
• See DEVELOPMENT.md for more on the release workflow.

Code Quality

# Format code
black src/ tests/ examples/

# Sort imports  
isort src/ tests/ examples/

# Lint code
flake8 src/ tests/ examples/

Development Installation

# Install with development dependencies
pip install -e ".[dev,notebooks,docs]"

🎯 Example Output

$ python examples/basic_linear_regression.py 2d

2D Multiple Linear Regression Example
----------------------------------------

Generating synthetic data...
Data points: 100
True weights: size coefficient=150, bedroom coefficient=10000, intercept=20000

Training model with Gradient Descent...
Iteration 0: Cost = 1250000000.0000
Iteration 500: Cost = 125678923.4567  
Iteration 1000: Cost = 89234567.1234

Training completed!

Results:
Learned weights: size coefficient=149.87, bedroom coefficient=9989.23, intercept=20145.67
R² Score:        0.9234
MSE:             89234567.12

Comparison with True Values:
model_gd = LinearRegression(learning_rate=0.01, n_iterations=1000, verbose=True)
model_gd.fit(X, y, method='gradient_descent')
predictions_gd = model_gd.predict(X)
print(f"GD Weights: {model_gd.weights_}")
print(f"GD R² Score: {r2_score(y, predictions_gd):.4f}")

# Option 2: Normal Equation (closed-form)
model_ne = LinearRegression(verbose=False)
model_ne.fit(X, y, method='normal_equation')
predictions_ne = model_ne.predict(X)
print(f"NE Weights: {model_ne.weights_}")
print(f"NE R² Score: {r2_score(y, predictions_ne):.4f}")
True:    size=150.00, bedroom=10000.00, intercept=20000.00  
Learned: size=149.87, bedroom=9989.23, intercept=20145.67
Error:   size=0.13, bedroom=10.77, intercept=145.67

🎓 Educational Value

This project demonstrates:

• Mathematical Understanding: Implement algorithms from equations, including polynomial feature expansion
• Software Engineering: Professional Python package development
• Machine Learning: Core concepts without library abstractions
• Numerical Computing: Efficient NumPy vectorized operations
• Testing: Comprehensive test coverage with edge cases and fixtures
• Documentation: Clear code documentation and user guides

📏 Metrics Usage Example

from linear_regression import r2_score, mean_squared_error, mean_absolute_error
print(r2_score(y, predictions))
print(mean_squared_error(y, predictions))
print(mean_absolute_error(y, predictions))

🤝 Contributing

We welcome contributions! Please see:

• CONTRIBUTING.md for guidelines and onboarding
• DEVELOPMENT.md for development workflow
• Issues for bug reports and feature requests

🙏 Acknowledgments

• Built for educational purposes to understand ML fundamentals
• Mathematical foundations from "The Elements of Statistical Learning"
• Inspired by the need for transparent, understandable ML implementations

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Note: This is primarily an educational project. For production ML workflows, consider using established libraries like scikit-learn, though this implementation is production-quality and could be used in real applications.