infopy-estimators 0.1.2

Information theory related estimators

infopy-estimators

A comprehensive Python library for estimating mutual information (MI) and entropy in discrete, continuous, and mixed random variables. Built with a focus on performance and ease of use, infopy-estimators provides implementations of various information-theoretic estimators.

Features

• Multiple MI Estimators: Support for discrete-discrete, continuous-discrete, continuous-continuous, and mixed variable types
• Multidimensional Support: All estimators handle multidimensional random vectors (X, Y ∈ ℝⁿ)
• Pointwise MI: Calculate mutual information per sample, not just averages
• Conditional MI: Compute conditional mutual information I(X;Y|Z)
• Automatic Estimator Selection: Let the library choose the best estimator based on your data types
• Entropy Estimation: Dedicated entropy estimators for continuous and discrete variables
• Type-Safe: Full type hints and mypy compliance

Installation

Using pip

pip install infopy-estimators

Using uv (recommended for development)

uv add infopy-estimators

From source

git clone https://github.com/jurrutiag/infopy.git
cd infopy
uv sync  # or pip install -e .

Quick Start

Basic Usage

import numpy as np
from infopy import get_mi_estimator

# Generate sample data
X = np.random.randn(1000, 2)  # Continuous 2D variable
Y = np.random.randn(1000, 1)  # Continuous 1D variable

# Automatically select appropriate estimator
estimator = get_mi_estimator(x_type="continuous", y_type="continuous")

# Estimate mutual information
mi = estimator.estimate(X, Y)
print(f"Mutual Information: {mi:.4f}")

Pointwise Mutual Information

# Get MI for each sample instead of average
estimator = get_mi_estimator(x_type="continuous", y_type="continuous", pointwise_suited=True)
pointwise_mi = estimator.estimate(X, Y, pointwise=True)
print(f"Pointwise MI shape: {pointwise_mi.shape}")  # (1000,)

Mixed Variable Types

# Continuous and discrete variables
X_continuous = np.random.randn(1000, 3)
Y_discrete = np.random.randint(0, 5, size=(1000, 1))

estimator = get_mi_estimator(x_type="continuous", y_type="discrete")
mi = estimator.estimate(X_continuous, Y_discrete)
print(f"Continuous-Discrete MI: {mi:.4f}")

Conditional Mutual Information

# Compute I(X;Y|Z)
X = np.random.randn(1000, 2)
Y = np.random.randn(1000, 2)
Z = np.random.randn(1000, 1)

estimator = get_mi_estimator(x_type="continuous", y_type="continuous")
cmi = estimator.estimate_conditional(X, Y, Z)
print(f"Conditional MI: {cmi:.4f}")

Entropy Estimation

from infopy import ContinuousEntropyEstimator, DiscreteEntropyEstimator

# Continuous entropy
X_continuous = np.random.randn(1000, 3)
cont_estimator = ContinuousEntropyEstimator()
entropy = cont_estimator.estimate(X_continuous)
print(f"Continuous Entropy: {entropy:.4f}")

# Discrete entropy
X_discrete = np.random.randint(0, 10, size=(1000, 2))
disc_estimator = DiscreteEntropyEstimator()
entropy = disc_estimator.estimate(X_discrete)
print(f"Discrete Entropy: {entropy:.4f}")

Available Estimators

Mutual Information Estimators

Estimator	Variable Types	Method	Reference
DDMIEstimator	Discrete-Discrete	Maximum likelihood PMF estimation	-
CDMIRossEstimator	Continuous-Discrete	Ross method	Ross (2014) [1]
CDMIEntropyBasedEstimator	Continuous-Discrete	Kozachenko-Leonenko entropy	-
CCMIEstimator	Continuous-Continuous	Kraskov estimator	Kraskov et al. (2004) [2]
MixedMIEstimator	Mixed types	Gao estimator (experimental)	Gao et al. (2018) [3]

Entropy Estimators

Estimator	Variable Type	Method
ContinuousEntropyEstimator	Continuous	Kozachenko-Leonenko k-NN
DiscreteEntropyEstimator	Discrete	Maximum likelihood

Automatic Estimator Selection

The get_mi_estimator() function automatically selects the appropriate estimator:

from infopy import get_mi_estimator

# For continuous-continuous variables
estimator = get_mi_estimator(x_type="continuous", y_type="continuous")

# For discrete-discrete variables
estimator = get_mi_estimator(x_type="discrete", y_type="discrete")

# For mixed types
estimator = get_mi_estimator(x_type="continuous", y_type="discrete")

# For pointwise MI calculation
estimator = get_mi_estimator(x_type="continuous", y_type="continuous", pointwise_suited=True)

API Reference

BaseMIEstimator

All MI estimators inherit from BaseMIEstimator and implement:

class BaseMIEstimator:
    def estimate(self, X: np.ndarray, Y: np.ndarray, pointwise: bool = False) -> Union[float, np.ndarray]:
        """Estimate mutual information between X and Y."""

    def estimate_conditional(self, X: np.ndarray, Y: np.ndarray, Z: np.ndarray) -> float:
        """Estimate conditional mutual information I(X;Y|Z)."""

Parameters

• X, Y: Input arrays of shape (n_samples, n_features)
• pointwise: If True, return MI for each sample; if False, return average MI
• Z: Conditioning variable for conditional MI

Returns

• estimate(): Float (average MI) or array of shape (n_samples,) (pointwise MI)
• estimate_conditional(): Float (conditional MI)

Advanced Usage

Custom k-NN Parameters

from infopy import CCMIEstimator

# Use custom k for k-NN estimation
estimator = CCMIEstimator(k=5)  # Default is k=3
mi = estimator.estimate(X, Y)

Handling High-Dimensional Data

# For high-dimensional continuous data
X = np.random.randn(1000, 50)  # 50-dimensional
Y = np.random.randn(1000, 30)  # 30-dimensional

# CCMIEstimator handles high dimensions well
estimator = CCMIEstimator(k=10)  # Increase k for stability
mi = estimator.estimate(X, Y)

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/jurrutiag/infopy.git
cd infopy

# Install with development dependencies
uv sync --extra testing

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=infopy

# Run specific test
uv run pytest tests/test_estimators.py::TestCCMIEstimator

Code Quality

# Format code
uv run ruff format

# Lint code
uv run ruff check

# Type checking
uv run mypy src/

Contributing

We welcome contributions! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Ensure all tests pass and code is formatted
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Development Standards

• Formatter: ruff format (100 char line length)
• Linter: ruff with comprehensive rules
• Type Checker: mypy with strict settings
• Test Framework: pytest with coverage
• Package Manager: uv

Citation

If you use this library in your research, please cite:

@software{infopy_estimators,
  author = {Urrutia, Juan},
  title = {infopy-estimators: Information Theory Estimators for Python},
  url = {https://github.com/jurrutiag/infopy},
  year = {2024}
}

References

1. B. C. Ross "Mutual Information between Discrete and Continuous Data Sets". PLoS ONE 9(2), 2014.
2. A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual information". Phys. Rev. E 69, 2004.
3. Gao, Weihao, et al. "Estimating Mutual Information for Discrete-Continuous Mixtures". NeurIPS, 2018.

License

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

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]