GridSearchHelper 0.5.0

A library for hyperparameter tuning using grid search for machine learning models.

GridSearchHelper

A Python library to simplify hyperparameter tuning using scikit-learn's GridSearchCV. Automate parameter grid generation for various machine learning models and optimize your model's performance with ease.

🚀 Quick Start

pip install DridSearchHelper

from GridSearchHelper import get_param_grid, perform_grid_search
from sklearn.ensemble import RandomForestRegressor

# Initialize your model
model = RandomForestRegressor()

# Get parameter grid automatically
param_grid = get_param_grid(model)

# Perform grid search
best_params, best_score = perform_grid_search(model, param_grid, X_train, y_train)

🔑 Key Features

• 🤖 Automatic Parameter Grid Generation: No more manual parameter grid definition
• 🔄 Seamless scikit-learn Integration: Works directly with GridSearchCV
• 📊 Multiple Model Support: Compatible with various scikit-learn models
• 🛠 Easy to Use: Simple API with just two main functions

📋 Requirements

• Python 3.6+
• scikit-learn
• numpy
• pandas

💻 Installation

From PyPI

pip install gridsearchhelper

From Source

git clone https://github.com/alimovabdulla/GridSearchHelper.git
cd GridSearchHelper
pip install .

📖 Usage Examples

ElasticNet Example

from GridSearchHelper import get_param_grid, perform_grid_search
from sklearn.linear_model import ElasticNet
from sklearn.preprocessing import StandardScaler

# Prepare your model
model = ElasticNet(alpha=1)

# Scale your features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# Get parameter grid and perform search
param_grid = get_param_grid(model)
best_params, best_score = perform_grid_search(model, param_grid, X_train_scaled, y_train)

RandomForest Example

from GridSearchHelper import get_param_grid, perform_grid_search
from sklearn.ensemble import RandomForestRegressor

# Initialize model
model = RandomForestRegressor()

# Get grid and optimize
param_grid = get_param_grid(model)
best_params, best_score = perform_grid_search(model, param_grid, X_train, y_train)

🛠 API Reference

get_param_grid(model)

Generates parameter grid based on model type.

Parameters:

• model: scikit-learn model instance

Returns:

• Dictionary containing parameter grid

perform_grid_search(model, param_grid, X_train, y_train)

Performs grid search with cross-validation.

Parameters:

• model: scikit-learn model instance
• param_grid: Parameter grid dictionary
• X_train: Training features
• y_train: Target values

Returns:

• Tuple of (best_parameters, best_score)

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. 🍴 Fork the repository
2. 🌿 Create a feature branch (git checkout -b feature/amazing-feature)
3. 💻 Make your changes
4. 📝 Commit your changes (git commit -m 'Add amazing feature')
5. 📤 Push to the branch (git push origin feature/amazing-feature)
6. 🔄 Open a Pull Request

📄 License

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

📞 Contact

Abdullah Alimov - @alimovabdulla

Project Link: https://github.com/alimovabdulla/GridSearchHelper

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