| Name | coxdev JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Library for computing Cox deviance |
| upload_time | 2025-07-11 20:38:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD-3-Clause |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# coxdev
A high-performance Python library for computing Cox proportional hazards model deviance, gradients, and Hessian information matrices. Built with C++ and Eigen for optimal performance, this library provides efficient survival analysis computations with support for different tie-breaking methods.
## Features
- **High Performance**: C++ implementation with Eigen linear algebra library
- **Comprehensive Support**: Handles both Efron and Breslow tie-breaking methods
- **Left-Truncated Data**: Support for left-truncated survival data
- **Efficient Computations**: Optimized algorithms for deviance, gradient, and Hessian calculations
- **Memory Efficient**: Uses linear operators for large-scale computations
- **Cross-Platform**: Works on Linux, macOS, and Windows
## Installation
### Prerequisites
This package requires the Eigen C++ library headers. The Eigen library is included as a git submodule.
1. **Initialize Eigen submodule**: The Eigen library is included as a git submodule. Make sure it's initialized:
```bash
git submodule update --init --recursive
```
2. **Check Eigen availability**: Run the check script to verify Eigen headers are available:
```bash
python check_eigen.py
```
### Standard Installation
```bash
pip install .
```
### With Custom Eigen Path
If you have Eigen installed elsewhere, you can specify its location:
```bash
env EIGEN_LIBRARY_PATH=/path/to/eigen pip install .
```
### Development Installation
```bash
pip install -e .
```
## Quick Start
```python
import numpy as np
from coxdev import CoxDeviance
# Generate sample survival data
n_samples = 1000
event_times = np.random.exponential(1.0, n_samples)
status = np.random.binomial(1, 0.7, n_samples) # 70% events, 30% censored
linear_predictor = np.random.normal(0, 1, n_samples)
# Create CoxDeviance object
coxdev = CoxDeviance(event=event_times, status=status, tie_breaking='efron')
# Compute deviance and related quantities
result = coxdev(linear_predictor)
print(f"Deviance: {result.deviance:.4f}")
print(f"Saturated log-likelihood: {result.loglik_sat:.4f}")
print(f"Gradient norm: {np.linalg.norm(result.gradient):.4f}")
```
## Advanced Usage
### Left-Truncated Data
```python
# With start times (left-truncated data)
start_times = np.random.exponential(0.5, n_samples)
coxdev = CoxDeviance(
event=event_times,
status=status,
start=start_times,
tie_breaking='efron'
)
```
### Computing Information Matrix
```python
# Get information matrix as a linear operator
info_matrix = coxdev.information(linear_predictor)
# Matrix-vector multiplication
v = np.random.normal(0, 1, n_samples)
result_vector = info_matrix @ v
# For small problems, you can compute the full matrix
X = np.random.normal(0, 1, (n_samples, 10))
beta = np.random.normal(0, 1, 10)
eta = X @ beta
# Information matrix for coefficients: X^T @ I @ X
I = info_matrix @ X
information_matrix = X.T @ I
```
### Different Tie-Breaking Methods
```python
# Efron's method (default)
coxdev_efron = CoxDeviance(event=event_times, status=status, tie_breaking='efron')
# Breslow's method
coxdev_breslow = CoxDeviance(event=event_times, status=status, tie_breaking='breslow')
```
## API Reference
### CoxDeviance
The main class for computing Cox model quantities.
#### Parameters
- **event**: Event times (failure times) for each observation
- **status**: Event indicators (1 for event occurred, 0 for censored)
- **start**: Start times for left-truncated data (optional)
- **tie_breaking**: Method for handling tied event times ('efron' or 'breslow')
#### Methods
- **`__call__(linear_predictor, sample_weight=None)`**: Compute deviance and related quantities
- **`information(linear_predictor, sample_weight=None)`**: Get information matrix as linear operator
### CoxDevianceResult
Result object containing computation results.
#### Attributes
- **linear_predictor**: The linear predictor values used
- **sample_weight**: Sample weights used
- **loglik_sat**: Saturated log-likelihood value
- **deviance**: Computed deviance value
- **gradient**: Gradient of deviance with respect to linear predictor
- **diag_hessian**: Diagonal of Hessian matrix
## Performance
The library is optimized for performance:
- **C++ Implementation**: Core computations in C++ with Eigen
- **Memory Efficient**: Reuses buffers and uses linear operators
- **Vectorized Operations**: Leverages Eigen's optimized linear algebra
- **Minimal Python Overhead**: Heavy computations done in C++
## Building from Source
### Prerequisites
- Python 3.9+
- C++ compiler with C++17 support
- Eigen library headers
- pybind11
### Build Steps
1. Clone the repository with submodules:
```bash
git clone --recursive https://github.com/jonathan-taylor/coxdev.git
cd coxdev
```
2. Install build dependencies:
```bash
pip install build wheel setuptools pybind11 numpy
```
3. Build the package:
```bash
python -m build
```
### Building Wheels
For wheel building:
```bash
# Standard wheel build
python -m build
# With custom Eigen path
env EIGEN_LIBRARY_PATH=/path/to/eigen python -m build
```
## Testing
Run the test suite:
```bash
python -m pytest tests/
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request
## License
This project is licensed under the BSD-3-Clause License - see the [LICENSE](LICENSE) file for details.
## Citation
If you use this library in your research, please cite:
```bibtex
@software{coxdev2024,
title={coxdev: High-performance Cox proportional hazards deviance computation},
author={Taylor, Jonathan and Hastie, Trevor and Narasimhan, Balasubramanian},
year={2024},
url={https://github.com/jonathan-taylor/coxdev}
}
```
## Acknowledgments
- Built with [Eigen](http://eigen.tuxfamily.org/) for efficient linear algebra
- Uses [pybind11](https://pybind11.readthedocs.io/) for Python bindings
- Inspired by the R `glmnet` package for survival analysis
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"description": "# coxdev\n\nA high-performance Python library for computing Cox proportional hazards model deviance, gradients, and Hessian information matrices. Built with C++ and Eigen for optimal performance, this library provides efficient survival analysis computations with support for different tie-breaking methods.\n\n## Features\n\n- **High Performance**: C++ implementation with Eigen linear algebra library\n- **Comprehensive Support**: Handles both Efron and Breslow tie-breaking methods\n- **Left-Truncated Data**: Support for left-truncated survival data\n- **Efficient Computations**: Optimized algorithms for deviance, gradient, and Hessian calculations\n- **Memory Efficient**: Uses linear operators for large-scale computations\n- **Cross-Platform**: Works on Linux, macOS, and Windows\n\n## Installation\n\n### Prerequisites\n\nThis package requires the Eigen C++ library headers. The Eigen library is included as a git submodule.\n\n1. **Initialize Eigen submodule**: The Eigen library is included as a git submodule. Make sure it's initialized:\n ```bash\n git submodule update --init --recursive\n ```\n\n2. **Check Eigen availability**: Run the check script to verify Eigen headers are available:\n ```bash\n python check_eigen.py\n ```\n\n### Standard Installation\n\n```bash\npip install .\n```\n\n### With Custom Eigen Path\n\nIf you have Eigen installed elsewhere, you can specify its location:\n```bash\nenv EIGEN_LIBRARY_PATH=/path/to/eigen pip install .\n```\n\n### Development Installation\n\n```bash\npip install -e .\n```\n\n## Quick Start\n\n```python\nimport numpy as np\nfrom coxdev import CoxDeviance\n\n# Generate sample survival data\nn_samples = 1000\nevent_times = np.random.exponential(1.0, n_samples)\nstatus = np.random.binomial(1, 0.7, n_samples) # 70% events, 30% censored\nlinear_predictor = np.random.normal(0, 1, n_samples)\n\n# Create CoxDeviance object\ncoxdev = CoxDeviance(event=event_times, status=status, tie_breaking='efron')\n\n# Compute deviance and related quantities\nresult = coxdev(linear_predictor)\n\nprint(f\"Deviance: {result.deviance:.4f}\")\nprint(f\"Saturated log-likelihood: {result.loglik_sat:.4f}\")\nprint(f\"Gradient norm: {np.linalg.norm(result.gradient):.4f}\")\n```\n\n## Advanced Usage\n\n### Left-Truncated Data\n\n```python\n# With start times (left-truncated data)\nstart_times = np.random.exponential(0.5, n_samples)\ncoxdev = CoxDeviance(\n event=event_times, \n status=status, \n start=start_times,\n tie_breaking='efron'\n)\n```\n\n### Computing Information Matrix\n\n```python\n# Get information matrix as a linear operator\ninfo_matrix = coxdev.information(linear_predictor)\n\n# Matrix-vector multiplication\nv = np.random.normal(0, 1, n_samples)\nresult_vector = info_matrix @ v\n\n# For small problems, you can compute the full matrix\nX = np.random.normal(0, 1, (n_samples, 10))\nbeta = np.random.normal(0, 1, 10)\neta = X @ beta\n\n# Information matrix for coefficients: X^T @ I @ X\nI = info_matrix @ X\ninformation_matrix = X.T @ I\n```\n\n### Different Tie-Breaking Methods\n\n```python\n# Efron's method (default)\ncoxdev_efron = CoxDeviance(event=event_times, status=status, tie_breaking='efron')\n\n# Breslow's method\ncoxdev_breslow = CoxDeviance(event=event_times, status=status, tie_breaking='breslow')\n```\n\n## API Reference\n\n### CoxDeviance\n\nThe main class for computing Cox model quantities.\n\n#### Parameters\n\n- **event**: Event times (failure times) for each observation\n- **status**: Event indicators (1 for event occurred, 0 for censored)\n- **start**: Start times for left-truncated data (optional)\n- **tie_breaking**: Method for handling tied event times ('efron' or 'breslow')\n\n#### Methods\n\n- **`__call__(linear_predictor, sample_weight=None)`**: Compute deviance and related quantities\n- **`information(linear_predictor, sample_weight=None)`**: Get information matrix as linear operator\n\n### CoxDevianceResult\n\nResult object containing computation results.\n\n#### Attributes\n\n- **linear_predictor**: The linear predictor values used\n- **sample_weight**: Sample weights used\n- **loglik_sat**: Saturated log-likelihood value\n- **deviance**: Computed deviance value\n- **gradient**: Gradient of deviance with respect to linear predictor\n- **diag_hessian**: Diagonal of Hessian matrix\n\n## Performance\n\nThe library is optimized for performance:\n\n- **C++ Implementation**: Core computations in C++ with Eigen\n- **Memory Efficient**: Reuses buffers and uses linear operators\n- **Vectorized Operations**: Leverages Eigen's optimized linear algebra\n- **Minimal Python Overhead**: Heavy computations done in C++\n\n## Building from Source\n\n### Prerequisites\n\n- Python 3.9+\n- C++ compiler with C++17 support\n- Eigen library headers\n- pybind11\n\n### Build Steps\n\n1. Clone the repository with submodules:\n ```bash\n git clone --recursive https://github.com/jonathan-taylor/coxdev.git\n cd coxdev\n ```\n\n2. Install build dependencies:\n ```bash\n pip install build wheel setuptools pybind11 numpy\n ```\n\n3. Build the package:\n ```bash\n python -m build\n ```\n\n### Building Wheels\n\nFor wheel building:\n```bash\n# Standard wheel build\npython -m build\n\n# With custom Eigen path\nenv EIGEN_LIBRARY_PATH=/path/to/eigen python -m build\n```\n\n## Testing\n\nRun the test suite:\n```bash\npython -m pytest tests/\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests for new functionality\n5. Ensure all tests pass\n6. 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