txgraffiti 0.4.0

A Python package for automated mathematical conjecturing

TxGraffiti: Automated Conjecture Generation in Python

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TxGraffiti is a Python package for automated mathematical conjecture generation.

It uncovers patterns, equalities, and inequalities in structured datasets by forming symbolic expressions and proposing data-backed conjectures. While originally developed to explore graph-theoretic invariants, TxGraffiti is domain-agnostic and can be applied to any tabular data where mathematical relationships may be discovered.

Built on principles from the Graffiti family of programs, TxGraffiti blends logic, optimization, and heuristics to create meaningful, testable mathematical statements. It is designed for:

• 📐 Mathematicians exploring new bounds and relationships
• 📊 Data scientists modeling symbolic structure in tabular data
• 🤖 AI researchers studying machine-driven discovery
• 📚 Educators demonstrating the intersection of math and computation

The system combines symbolic logic, heuristic filtering, and optimization techniques to produce clear, interpretable conjectures—making it a powerful tool for researchers, educators, and AI-assisted discovery.

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Features

• Work with properties (numeric features), predicates (boolean tests), and inequalities
• Automatically generate conjectures using convex hull, LP, and ratio methods
• Apply heuristics to reduce noise and prioritize meaningful conjectures
• Compose logical hypotheses and filter conjectures by truth and significance
• Use built-in datasets on graphs and integers, or plug in your own
• Export results to Lean4, search for counterexamples, and iterate

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📦 Installation

Install the latest release from PyPI:

pip install txgraffiti

To install the development version from source:

git clone https://github.com/RandyRDavila/TxGraffiti2.git
cd TxGraffiti2

# Optional: create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate   # On Windows, use: .venv\Scripts\activate

# Install the package in editable mode with development dependencies
pip install -e .[dev]

TxGraffiti requires Python 3.8 or later.

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Example: Graph Theory Conjectures

Below is a minimal example of using txgraffiti on a built in dataset of precomputed values on simple, connected, and nontrivial graphs.

from txgraffiti.playground    import ConjecturePlayground  # main interface for discovery
from txgraffiti.generators    import convex_hull, ratios
from txgraffiti.heuristics    import morgan_accept, dalmatian_accept
from txgraffiti.processing    import remove_duplicates, sort_by_touch_count
from txgraffiti.example_data  import graph_data            # bundled toy dataset

# 1. Instantiate your playground
ai = ConjecturePlayground(
    graph_data,
    object_symbol='G'  # used in pretty-printing: ∀ G: ...
)

# 2. (Optional) Define custom predicates
regular = (ai.max_degree == ai.min_degree)
cubic   = regular & (ai.max_degree == 3)

# 3. Run conjecture discovery
ai.discover(
    methods         = [convex_hull, ratios],
    features        = ['order', 'matching_number', 'min_degree'],
    target          = 'independence_number',
    hypothesis      = [ai.connected & ai.bipartite,
                       ai.connected & regular],
    heuristics      = [morgan_accept, dalmatian_accept],
    post_processors = [remove_duplicates, sort_by_touch_count],
)

# 4. Print your top conjectures
for idx, conj in enumerate(ai.conjectures[:10], start=1):
    print(f"Conjecture {idx}. {ai.forall(conj)}\n")

The output of the above code should look something like the following:

Conjecture 1. ∀ G: ((connected) ∧ (bipartite)) → (independence_number == ((-1 * matching_number) + order))

Conjecture 2. ∀ G: ((connected) ∧ (max_degree == min_degree) ∧ (bipartite)) → (independence_number == matching_number)

Example: Integer Dataset

Next, we conjecture on the built in integer dataset.

from txgraffiti.playground    import ConjecturePlayground
from txgraffiti.generators    import convex_hull, ratios
from txgraffiti.heuristics    import morgan_accept, dalmatian_accept
from txgraffiti.processing    import remove_duplicates, sort_by_touch_count
from txgraffiti.example_data  import integer_data   # bundled toy dataset

# 2) Instantiate your playground
#    object_symbol will be used when you pretty-print "∀ G.connected: …"
ai = ConjecturePlayground(
    integer_data,
    object_symbol='n.PositiveInteger'
)

ai.discover(
    methods         = [convex_hull, ratios],
    features        = ['sum_divisors', 'divisor_count', 'totient', 'prime_factor_count'],
    target          = 'collatz_steps',
    hypothesis      = [ai.is_square, ai.is_fibonacci, ai.is_power_of_two],
    heuristics      = [morgan_accept, dalmatian_accept],
    post_processors = [remove_duplicates, sort_by_touch_count],
)

# 5) Print your top conjectures
for idx, conj in enumerate(ai.conjectures[:10], start=1):
    # wrap in ∀-notation for readability
    formula = ai.forall(conj)
    print(f"Conjecture {idx}. {formula}\n")

The output of the above code should look something like the following:

Conjecture 1. ∀ n.PositiveInteger: ((is_power_of_two) ∧ (is_fibonacci)) → (collatz_steps == prime_factor_count)

Conjecture 2. ∀ n.PositiveInteger: (is_square) → (collatz_steps >= (((17/8 * divisor_count) + -17/8) + (-9/8 * prime_factor_count)))

Conjecture 3. ∀ n.PositiveInteger: (is_square) → (collatz_steps <= (((((-17/10 * sum_divisors) + -391/8) + (1887/40 * divisor_count)) + (34/5 * totient)) + (-1847/40 * prime_factor_count)))

Conjecture 4. ∀ n.PositiveInteger: (is_power_of_two) → (collatz_steps <= prime_factor_count)

Conjecture 5. ∀ n.PositiveInteger: (is_square) → (collatz_steps >= prime_factor_count)

Conjecture 6. ∀ n.PositiveInteger: (is_fibonacci) → (collatz_steps >= prime_factor_count)

Graffiti3 (New)

TxGraffiti now supports native non-linear conjecturing and conjecturing of sufficient conditions. This is all done via the new Graffiti3 class. See the example below.

# In the terminal run the command: PYTHONPATH=src python demo/polytope_demo.py
from __future__ import annotations

import pandas as pd

# at the top of graffiti4.py
from txgraffiti.graffiti3.heuristics.morgan import morgan_filter#, dalmatian_filter
from txgraffiti.graffiti3.heuristics.dalmatian import dalmatian_filter
from txgraffiti.graffiti3.graffiti3 import Graffiti3, print_g3_result, Stage
from txgraffiti.example_data import polytope_data as df




df.drop(columns=['temperature(p6)', 'p4_odd', 'p5_odd', 'p3_odd', ], inplace=True)


g3 = Graffiti3(
    df,
    max_boolean_arity=2,
    morgan_filter=morgan_filter,
    dalmatian_filter=dalmatian_filter,
    sophie_cfg=dict(
        eq_tol=1e-4,
        min_target_support=5,
        min_h_support=3,
        max_violations=0,
        min_new_coverage=1,
    ),
)


STAGES = [
    Stage.CONSTANT,
    Stage.RATIO,
    Stage.LP1,
    Stage.LP2,
    Stage.LP3,
    Stage.LP4,
    Stage.POLY_SINGLE,
    Stage.MIXED,
    Stage.SQRT,
    Stage.LOG,
    Stage.SQRT_LOG,
    Stage.GEOM_MEAN,
    Stage.LOG_SUM,
    Stage.SQRT_PAIR,
    Stage.SQRT_SUM,
    Stage.EXP_EXPONENT,

]

# Target invariants to conjecture on: p5 and p6.
TARGETS = [
        "p5",
        "p6",
    ]

# Conjecture on the target invariants using the stages defined above.
result = g3.conjecture(
    targets=TARGETS,
    stages=STAGES,
    include_invariant_products=False,
    include_abs=False,
    include_min_max=False,
    include_log=False,
    enable_sophie=True,
    sophie_stages=STAGES,
    quick=True,
    show=True,
)

Testing

Run the existing pytest suite:

pytest

Contributing

Contributions, ideas, and suggestions are welcome! To get involved:

1. Fork the repository
2. Create a new branch
3. Submit a pull request

See CONTRIBUTING.md for details.

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License

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

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Authors

• Randy Davila, PhD – Lead developer

• Jillian Eddy – Co-developer, logic design