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README.md
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README.md

TensorLogic Python Tutorials

This directory contains comprehensive Jupyter notebooks demonstrating the TensorLogic Python API.

Getting Started

Prerequisites

# Install TensorLogic Python bindings
cd crates/pytensorlogic
maturin develop

# Install tutorial dependencies
pip install jupyter matplotlib numpy

Launching Jupyter

cd crates/pytensorlogic/tutorials
jupyter notebook

Tutorials

01_getting_started.ipynb

Level: Beginner Duration: 45-60 minutes Topics:

  • Basic logical expressions (predicates, AND, OR, NOT)
  • Compilation and execution workflow
  • Compilation strategies (soft, hard, fuzzy, probabilistic)
  • Quantifiers (EXISTS, FORALL)
  • Arithmetic operations (add, sub, mul, div)
  • Comparison operations (eq, lt, gt, lte, gte)
  • Conditional expressions (IF-THEN-ELSE)
  • Complex nested expressions (De Morgan's laws, implication)
  • Adapter types (DomainInfo, PredicateInfo, SymbolTable, CompilerContext)
  • Practical example: Social network reasoning

Learning Outcomes:

  • Understand the TensorLogic compilation model
  • Create and execute logical expressions
  • Choose appropriate compilation strategies
  • Visualize results with matplotlib

02_advanced_topics.ipynb

Level: Advanced Duration: 60-90 minutes Topics:

  • Multi-arity predicates (binary, ternary, n-ary)
  • Relational reasoning (transitive closure, path queries)
  • Nested quantifiers (double, triple quantification)
  • Performance optimization and benchmarking
  • Graph inspection and analysis
  • Strategy selection guide with use cases
  • Integration patterns (iterative reasoning, multi-rule systems)
  • Error handling and debugging techniques
  • Best practices and performance tips
  • Type safety patterns

Learning Outcomes:

  • Master complex relational queries
  • Optimize performance for production use
  • Build robust integration pipelines
  • Debug compilation and execution issues
  • Apply best practices for maintainable code

Tutorial Structure

Each notebook follows this structure:

  1. Setup - Import dependencies and verify installation
  2. Conceptual Introduction - Explain the topic with examples
  3. Code Examples - Executable code demonstrating features
  4. Visualizations - Plots and diagrams for understanding
  5. Practice - Exercises and challenges (where applicable)
  6. Summary - Key takeaways and next steps

Running the Tutorials

Option 1: Interactive (Recommended)

jupyter notebook

Open the desired notebook and execute cells interactively.

Option 2: Non-Interactive

jupyter nbconvert --to notebook --execute 01_getting_started.ipynb

This executes the notebook and saves the output.

Option 3: Python Script

jupyter nbconvert --to python 01_getting_started.ipynb
python 01_getting_started.py

Converts the notebook to a Python script for non-interactive execution.

Tips for Learning

  1. Execute cells in order - Notebooks have state, cells depend on previous execution
  2. Experiment - Modify examples to test understanding
  3. Visualize - Use matplotlib to understand tensor operations
  4. Debug - Use the debug helpers provided in advanced tutorials
  5. Read tests - The tests/ directory has additional examples
  6. Ask questions - File issues on GitHub for clarification

Common Issues

Issue: ModuleNotFoundError: No module named 'pytensorlogic'

Solution:

cd crates/pytensorlogic
maturin develop

Issue: Compilation fails with Rust errors

Solution: Ensure you have Rust toolchain installed:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Issue: Jupyter kernel crashes

Solution: Install kernel dependencies:

pip install ipykernel
python -m ipykernel install --user

Issue: Matplotlib plots not showing

Solution: For notebook:

%matplotlib inline

For JupyterLab:

pip install jupyterlab

Additional Resources

  • API Reference: See pytensorlogic.pyi for complete type signatures
  • Test Suite: tests/ directory for comprehensive usage examples
  • Examples: examples/ directory for standalone demonstrations
  • Documentation: README.md and CLAUDE.md in repository root
  • Paper: TensorLogic research paper (link in main README)

Contributing

Found an error or have a suggestion? Please:

  1. Check existing issues on GitHub
  2. File a new issue with details
  3. Submit a pull request with fixes/improvements

We welcome contributions to improve these tutorials!

License

These tutorials are part of the TensorLogic project and are licensed under Apache-2.0.

Last Updated: 2025-11-04 Maintainer: COOLJAPAN ecosystem Feedback: https://github.com/cool-japan/tensorlogic/issues