HTtoHP 1.0.0

A Python package that generates code templates for ML lab questions - HTtoHP

HTtoHP - ML Lab Code Generator

A Python package that generates clean, well-formatted code templates for Machine Learning lab questions. This package does NOT execute ML code, but instead returns Python/ML code as strings that can be used as templates or starting points for ML assignments.

🎯 Features

• 8 Complete Sets of ML lab questions covering fundamental topics
• Python & ML Code Generation for each set
• Dynamic Templates with customizable parameters
• Clean, Ready-to-Execute Code with proper imports and structure
• Educational Focus - perfect for learning and teaching ML concepts
• Easy Installation via pip

📦 Installation

From PyPI (Recommended)

pip install HTtoHP

From Source

git clone https://github.com/yourusername/HTtoHP.git
cd HTtoHP
pip install .

Development Installation

git clone https://github.com/yourusername/HTtoHP.git
cd HTtoHP
pip install -e .

🚀 Quick Start

import HTtoHP

# Get code for SET 1 Machine Learning question
ml_code = HTtoHP.get_code(1, "ml")
print(ml_code)

# Get code for SET 3 Python question
python_code = HTtoHP.get_code(3, "python")
print(python_code)

# Save code to a file
file_path = HTtoHP.save_code_to_file(2, "ml", "my_regression.py")
print(f"Code saved to: {file_path}")

# List all available sets
sets_info = HTtoHP.list_available_sets()
for set_num, description in sets_info.items():
    print(f"SET {set_num}: {description}")

📚 Available Sets

Set	Python Topic	ML Topic
SET 1	2D Arrays & Indexing	Simple Linear Regression
SET 2	1D Arrays & Reversal	Multiple Linear Regression
SET 3	Statistics & Boxplots	Binary Logistic Regression
SET 4	Matrix Operations	Decision Tree
SET 5	Matrix Creation & Slicing	Gradient Descent for Linear Regression
SET 6	DataFrame & Missing Values	Logistic Regression
SET 7	Line Plots & Labeling	K-Nearest Neighbors (KNN)
SET 8	Scatter Plots & Styling	K-Means Clustering

🔧 API Reference

Core Functions

get_code(set_number, part, **kwargs)

Generate code template for a specific set and part.

Parameters:

• set_number (int): Set number (1-8)
• part (str): Either "python" or "ml"
• **kwargs: Additional parameters for dynamic templates

Returns: str - Generated code template

Example:

# Basic usage
code = HTtoHP.get_code(1, "ml")

# With custom parameters
code = HTtoHP.get_code(1, "ml", 
                      dataset_name="iris.csv", 
                      x_column="sepal_length",
                      y_column="sepal_width")

save_code_to_file(set_number, part, filename=None, **kwargs)

Save generated code to a Python file.

Parameters:

• set_number (int): Set number (1-8)
• part (str): Either "python" or "ml"
• filename (str, optional): Output filename. Auto-generated if None.
• **kwargs: Additional parameters for dynamic templates

Returns: str - Path to the saved file

list_available_sets()

List all available sets and their descriptions.

Returns: dict - Dictionary with set numbers as keys and descriptions as values

🎨 Dynamic Templates

Many code templates support dynamic parameters:

import HTtoHP

# Customize dataset name and columns
ml_code = HTtoHP.get_code(2, "ml", 
                         dataset_name="housing.csv",
                         target_column="price")

# Customize gradient descent parameters
gd_code = HTtoHP.get_code(5, "ml",
                         learning_rate=0.001,
                         iterations=2000)

# Customize matrix value
matrix_code = HTtoHP.get_code(1, "python", value=99)

📋 Example Usage

Generate and Execute ML Code

import HTtoHP

# Generate logistic regression code
log_reg_code = HTtoHP.get_code(6, "ml", 
                              dataset_name="titanic.csv",
                              target_column="survived")

# Save to file
HTtoHP.save_code_to_file(6, "ml", "logistic_regression_analysis.py",
                        dataset_name="titanic.csv",
                        target_column="survived")

# The generated file is ready to run!
# python logistic_regression_analysis.py

Batch Generate All Sets

import HTtoHP

# Generate all Python questions
for set_num in range(1, 9):
    code = HTtoHP.get_code(set_num, "python")
    filename = f"set{set_num}_python.py"
    HTtoHP.save_code_to_file(set_num, "python", filename)
    print(f"Generated {filename}")

# Generate all ML questions
for set_num in range(1, 9):
    code = HTtoHP.get_code(set_num, "ml")
    filename = f"set{set_num}_ml.py"
    HTtoHP.save_code_to_file(set_num, "ml", filename)
    print(f"Generated {filename}")

🛠️ Development

Setting up Development Environment

# Clone the repository
git clone https://github.com/yourusername/HTtoHP.git
cd HTtoHP

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

# Install in development mode
pip install -e .

# Install development dependencies
pip install -e .[dev]

Running Tests

pytest tests/

Code Formatting

black HTtoHP/

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the Project
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

📞 Support

If you encounter any problems or have questions, please:

1. Check the documentation
2. Search existing issues
3. Create a new issue

🙏 Acknowledgments

• Built for educational purposes to help students learn Machine Learning
• Inspired by common ML lab assignments and best practices
• Uses popular ML libraries: scikit-learn, pandas, numpy, matplotlib

📊 Package Statistics

• 8 Sets of questions
• 16 Code templates (Python + ML for each set)
• Clean, documented code ready for execution
• Customizable parameters for dynamic templates
• Educational focus with detailed comments and explanations

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Happy Learning! 🎓