regressionmadesimple 3.0.0

Minimalist machine learning toolkit that wraps `scikit-learn` for quick prototyping. Just `import rms` and go.

RegressionMadeSimple v3.0.0 🚀

A minimalist machine learning toolkit that wraps scikit-learn for quick prototyping. Just import regressionmadesimple as rms and go!

What's New in v3.0.0 🎉

RegressionMadeSimple v3.0.0 brings major improvements to make your regression workflow even simpler and more powerful:

🏗️ Model Registry Pattern

• New API: Access models via rms.models.Linear, rms.models.Quadratic, rms.models.Cubic
• Better Organization: All models now live in a dedicated models submodule
• Type Safety: Use class-based model specification for better IDE support

🔧 Enhanced Base Class

• Common Functionality: Eliminated code duplication across model classes
• Model Serialization: Save and load trained models with save_model() and load_model()
• Additional Metrics: New scoring methods including r2_score(), mae(), rmse()
• Shared Logic: Unified train/test splitting and plotting functionality

🔄 Backward Compatibility

• Legacy Support: Old API still works (with deprecation warnings)
• Smooth Migration: Gradual transition path to new API
• No Breaking Changes: Existing code continues to work

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Installation

pip install regressionmadesimple

Or install from source:

git clone https://github.com/Unknownuserfrommars/regressionmadesimple.git
cd regressionmadesimple
pip install -e .

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Quick Start

Basic Usage (v3.0.0 API - Recommended)

import regressionmadesimple as rms
import pandas as pd

# Load your data
data = pd.DataFrame({
    'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'y': [2.1, 4.2, 6.1, 8.3, 10.2, 12.1, 14.3, 16.2, 18.1, 20.3]
})

# Fit a linear regression model (new API)
model = rms.models.Linear(data, 'x', 'y')

# Make predictions
predictions = model.predict([[11], [12]])
print(predictions)

# Get model summary with metrics
summary = model.summary()
print(f"R² Score: {summary['r2_score']:.4f}")
print(f"RMSE: {summary['rmse']:.4f}")
print(f"MAE: {summary['mae']:.4f}")

# Save the model
model.save_model('my_linear_model.pkl')

# Load it later
loaded_model = rms.models.BaseModel.load_model('my_linear_model.pkl')

Using the Wrapper (Both APIs Supported)

# New v3.0.0 API (recommended)
model = rms.LinearRegressionModel.fit(
    data, 'x', 'y', 
    model=rms.models.Quadratic,
    testsize=0.2
)

# Legacy API (deprecated, shows warning)
model = rms.LinearRegressionModel.fit(
    data, 'x', 'y', 
    model='quadratic',  # String-based (deprecated)
    testsize=0.2
)

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Migration Guide: v2.x → v3.0.0

What Changed?

1. Model Access: Models now accessible via rms.models.*
2. Enhanced Features: New serialization and scoring methods
3. Deprecation Warnings: String-based model specification shows warnings

Migration Examples

Before (v2.x)

import regressionmadesimple as rms

# Direct instantiation
model = rms.Linear(data, 'x', 'y')

# Wrapper with string
model = rms.LinearRegressionModel.fit(data, 'x', 'y', model='linear')

After (v3.0.0 - Recommended)

import regressionmadesimple as rms

# Direct instantiation (still works!)
model = rms.Linear(data, 'x', 'y')  # Backward compatible

# Or use new model registry (recommended)
model = rms.models.Linear(data, 'x', 'y')

# Wrapper with class
model = rms.LinearRegressionModel.fit(data, 'x', 'y', model=rms.models.Linear)

Migration Checklist

• Update imports to use rms.models.* for new projects
• Replace string-based model specification with class-based
• Take advantage of new features: save_model(), r2_score(), mae(), rmse()
• Test existing code (it should still work with deprecation warnings)
• Plan migration before v4.0.0 (when legacy API will be removed)

---

Features

Available Models

Model	Class	Description
Linear	rms.models.Linear	Simple linear regression (y = mx + b)
Quadratic	rms.models.Quadratic	Polynomial regression (degree=2)
Cubic	rms.models.Cubic	Polynomial regression (degree=3)
Custom Curve	rms.models.CustomCurve	Custom basis functions (experimental)

Core Functionality

1. Model Training

# Automatic train/test split (default)
model = rms.models.Linear(data, 'x', 'y', testsize=0.2, randomstate=42)

# Use entire dataset (no split)
model = rms.models.Linear(data, 'x', 'y', train_test_split=False)

# Provide pre-split data
model = rms.models.Linear(
    data, 'x', 'y',
    train_test_split=False,
    X_train=X_train, y_train=y_train,
    X_test=X_test, y_test=y_test
)

2. Predictions

# Single prediction
result = model.predict([[5.5]])

# Multiple predictions
results = model.predict([[5.5], [6.0], [6.5]])

3. Model Evaluation (New in v3.0.0!)

# Get comprehensive summary
summary = model.summary()
print(summary)
# Output: {
#   'model': 'Linear Regression',
#   'mse': 0.0234,
#   'rmse': 0.1530,
#   'mae': 0.1234,
#   'r2_score': 0.9876,
#   'coef': [[2.0123]],
#   'intercept': [0.0987]
# }

# Individual metrics
print(f"R² Score: {model.r2_score()}")
print(f"Mean Absolute Error: {model.mae()}")
print(f"Root Mean Squared Error: {model.rmse()}")
print(f"Mean Squared Error: {model.mse()}")

4. Model Serialization (New in v3.0.0!)

# Save trained model
model.save_model('models/my_regression_model.pkl')

# Load model later
from regressionmadesimple.models import BaseModel
loaded_model = BaseModel.load_model('models/my_regression_model.pkl')

# Use loaded model
predictions = loaded_model.predict(new_data)

5. Visualization

# Plot training/test data with predictions
fig = model.plot_y_train()
fig.show()  # For Plotly backend

# Plot predictions on new data
fig = model.plot_predict(X_new, y_new)
fig.show()

Configuration Options

import regressionmadesimple as rms

# View current options
print(rms.options)

# Change plotting backend
rms.options.plot.backend = 'matplotlib'  # or 'plotly' (default)

# Change training defaults
rms.options.training.test_size = 0.25
rms.options.training.random_state = 42

# Save custom options
rms.save_options('my_config.json')

# Load options later
rms.load_options('my_config.json')

# Reset to defaults
rms.reset_options()

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Advanced Examples

Example 1: Quadratic Regression with Custom Split

import regressionmadesimple as rms
import pandas as pd
import numpy as np

# Generate sample data with quadratic relationship
x = np.linspace(0, 10, 100)
y = 2*x**2 - 3*x + 5 + np.random.normal(0, 5, 100)
data = pd.DataFrame({'x': x, 'y': y})

# Fit quadratic model
model = rms.models.Quadratic(data, 'x', 'y', testsize=0.3, randomstate=123)

# Evaluate
summary = model.summary()
print(f"Model: {summary['model']}")
print(f"R² Score: {summary['r2_score']:.4f}")
print(f"RMSE: {summary['rmse']:.4f}")

# Visualize
fig = model.plot_y_train()
fig.show()

# Save for later use
model.save_model('quadratic_model.pkl')

Example 2: Custom Curve Fitting (Experimental)

import regressionmadesimple as rms
import pandas as pd
import numpy as np

# Generate data with sinusoidal pattern
x = np.linspace(0, 2*np.pi, 100)
y = 3*np.sin(x) + 2*np.cos(x) + np.random.normal(0, 0.5, 100)
data = pd.DataFrame({'x': x, 'y': y})

# Fit custom curve with trigonometric basis functions
model = rms.models.CustomCurve(
    data, 'x', 'y',
    basis_funcs=['x', 'sin(x)', 'cos(x)', 'x^2']
)

# Evaluate
print(f"Basis Functions: {model.basis_funcs}")
print(f"R² Score: {model.r2_score():.4f}")

# Make predictions
x_new = np.linspace(0, 2*np.pi, 50)
predictions = model.predict(x_new.reshape(-1, 1))

Example 3: Comparing Multiple Models

import regressionmadesimple as rms
import pandas as pd

# Load data
data = pd.read_csv('your_data.csv')

# Try different models
models = {
    'Linear': rms.models.Linear(data, 'x', 'y'),
    'Quadratic': rms.models.Quadratic(data, 'x', 'y'),
    'Cubic': rms.models.Cubic(data, 'x', 'y'),
}

# Compare performance
print("Model Comparison:")
print("-" * 60)
for name, model in models.items():
    summary = model.summary()
    print(f"{name:12} | R²: {summary['r2_score']:.4f} | "
          f"RMSE: {summary['rmse']:.4f} | MAE: {summary['mae']:.4f}")

# Save the best model
best_model = models['Quadratic']  # Based on your analysis
best_model.save_model('best_model.pkl')

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API Reference

Model Classes

rms.models.Linear

Linear regression model (y = mx + b)

Methods:

• __init__(dataset, colX, colY, **kwargs) - Initialize and fit model
• predict(X_new) - Make predictions
• summary() - Get model summary with metrics
• save_model(filepath) - Save model to disk
• plot_y_train() - Plot training/test data
• plot_predict(X_new, y_new) - Plot predictions
• r2_score() - Calculate R² score
• mae() - Calculate Mean Absolute Error
• rmse() - Calculate Root Mean Squared Error
• mse() - Get Mean Squared Error

rms.models.Quadratic

Quadratic regression model (polynomial degree=2)

Same methods as Linear.

rms.models.Cubic

Cubic regression model (polynomial degree=3)

Same methods as Linear.

rms.models.CustomCurve

Custom curve fitting with user-defined basis functions Still testing! Some features may not be working!

Additional Parameters:

• basis_funcs - List of basis functions: ["x", "x^2", "x^3", "cos(x)", "sin(x)", "log(x)", "exp(x)", "sqrt(x)"]

Wrapper Class

rms.LinearRegressionModel

Methods:

• fit(dataset, colX, colY, model, **kwargs) - Fit a regression model
  • model can be a class (e.g., rms.models.Linear) or string (deprecated)

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Configuration

Options Object

rms.options.plot.backend          # 'plotly' or 'matplotlib'
rms.options.training.test_size    # Default: 0.15
rms.options.training.random_state # Default: 1
rms.options.linear.auto_split     # Default: True
rms.options.quadratic.auto_split  # Default: True

Functions

• rms.save_options(filepath) - Save current options to JSON
• rms.load_options(filepath) - Load options from JSON
• rms.reset_options() - Reset to default options

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Roadmap

Planned for v3.x

• Additional models
• KNN regression support
• Cross-validation utilities
• Hyperparameter tuning helpers
• More visualization options
• Support for the creation of a validation dataset (apart from training & testing dataset)

Future (v4.0.0 and later)

• Remove deprecated string-based API
• Classification models
• Pipeline support
• Feature engineering utilities

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

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License

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

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Acknowledgments

• Built on top of scikit-learn
• Visualization powered by Plotly and Matplotlib (Matplotlib support will be added in future versions!)
• Data handling with pandas and NumPy

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Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Changelog

v3.0.0 (2025-01-01)

• ✨ New: Model registry pattern (rms.models.*)
• ✨ New: Enhanced BaseModel with common functionality
• ✨ New: Model serialization (save_model(), load_model())
• ✨ New: Additional scoring metrics (r2_score(), mae(), rmse())
• 🔧 Improved: Refactored codebase to eliminate duplication
• 🔧 Improved: Better type hints throughout
• ⚠️ Deprecated: String-based model specification (still works with warnings)
• 📚 Docs: Comprehensive README with migration guide

v2.0.0 (Previous Release)

• Initial public release
• Linear, Quadratic, Cubic regression models
• Basic plotting and evaluation
• Options configuration system

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Made with ❤️ by Unknownuserfrommars