A Python package for automated mathematical conjecturing.
Project description
GraffitiAI
GraffitiAI is a Python package for automated mathematical conjecturing, inspired by the legacy of GRAFFITI. It provides tools for exploring relationships between mathematical invariants and properties, with a focus on graph theory and polytopes. This package supports generating conjectures, applying heuristics, and visualizing results.
Features
- Load and preprocess datasets with ease.
- Identify possible invariants and hypotheses for conjecturing.
- Generate upper and lower bounds for a target invariant.
- Apply customizable heuristics to refine conjectures.
- Export results to PDF for presentation and sharing.
- Includes a sample dataset of 3-regular polytopes for experimentation.
Installation
To install GraffitiAI, use pip:
# Install GraffitiAI with pip
pip install graffitiai
Quick Start
Here's a simple example to get you started:
from graffitiai import TxGraffiti
# Initialize the Optimist instance
ai = TxGraffiti()
# Load a custom dataset
ai.read_csv("<path_to_your_data>.csv")
# Describe available invariants and hypotheses
ai.describe_invariants_and_hypotheses()
# Generate conjectures
ai.conjecture(
target_invariants=[
"zero_forcing_number",
"total_domination_number",
],
other_invariants=[
"independence_number",
"diameter",
"radius",
"domination_number"
],
hypothesis=[
"a_connected_cubic_and_diamond_free_graph",
"a_connected_and_cubic_graph_which_is_not_k_4",
],
complexity_range=(1, 3),
lower_b_max=None,
upper_b_max=2,
)
# Write the conjectures to the wall!
ai.write_on_the_wall()
# Save conjectures to a PDF
ai.save_conjectures_to_pdf("custom_conjectures.pdf")
Contributing
Contributions are welcome! If you have suggestions, find bugs, or want to add features, feel free to create an issue or submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments
GraffitiAI is inspired by the pioneering work of GRAFFITI and built using the ideas of TxGraffiti and the Optimist.
Author
Randy R. Davila, PhD
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