SimpliPy:
Efficient Simplification of Mathematical Expressions

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Usage

pip install simplipy

The compiled Rust extension (simplipy._core) is required: the inline phase (simplify, conversions, validation) runs on it exclusively, and there is no pure-Python fallback. Prebuilt wheels are published for Linux (x86_64/aarch64), macOS (x86_64/arm64) and Windows (x64) on CPython ≥ 3.12, so pip install simplipy does not compile anything for most users. Installing from the source distribution (an unsupported platform, or --no-binary) requires a Rust toolchain (rustup, MSRV 1.83). If the extension is missing at runtime, constructing an engine raises ImportError.

import simplipy as sp

engine = sp.SimpliPyEngine.load("acj-5-4-llm", install=True)   # a published ruleset artifact

# Simplify infix expressions
engine.simplify('x3 * sin(<constant> + 1) / (x3 * x3)')
# > '<constant>/x3'

# Simplify prefix expressions
engine.simplify(('/', '<constant>', '*', '/', '*', 'x3', '<constant>', 'x3', 'log', 'x3'))
# > ('/', '<constant>', 'log', 'x3')

simplify only simplifies: it answers in the form it was given -- a str in, a str out; explicit binary prefix in, explicit binary prefix out; the engine's native tagged form in (n-ary +/* bags are delimited: <add> ... </add>, <mul> ... </mul>), tagged out. To change the NOTATION, convert -- to_infix / to_prefix / to_tagged are pure syntactic conversions that never simplify -- and compose the two:

expr = ('/', '<constant>', '*', '/', '*', 'x3', '<constant>', 'x3', 'log', 'x3')

engine.to_infix(engine.simplify(expr))       # simplify, then render
# > '<constant> / log(x3)'

engine.simplify(engine.to_tagged(expr))      # convert, then simplify: the tagged answer
# > ['<mul>', '<constant>', '<div>', 'log', 'x3', '</mul>']

Normalization

The root-exported to_skeleton, to_expression, and normalize_variable_token helpers (also available as simplipy.normalization) canonicalize an expression so that two expressions that are "the same" up to variable renaming / constant values compare equal. Each takes all three forms (infix str, explicit prefix, tagged) and returns the one it was given; the canonicalization runs through the engine's internal state, so the answer does not depend on the dialect you passed.

import simplipy as sp

# Skeleton form: variables -> x{n}, EVERY numeric literal -> <constant>
sp.to_skeleton(['+', 'v1', '2.5'], engine)
# > ['+', 'x1', '<constant>']

# Expression form: variables canonicalized, numeric values kept
sp.to_expression(['+', 'V1', '3'], engine)
# > ['+', 'x1', '3']

# Classify / canonicalize a single token -> (normalized_token, is_variable)
sp.normalize_variable_token('X3')
# > ('x3', True)
sp.normalize_variable_token('sin')
# > ('sin', False)

More examples can be found in the documentation.

Performance

On a 65,536-expression symbolic-regression benchmark, paired per-row against SymPy's simplify (serial single-core, 1 s cap):

SimpliPy SymPy
Mean size ratio (lower is better) 0.97 1.09
Expressions strictly simplified 13.2% 22.3%
Expressions made bigger 0.00% 45.1%
Median per-row speedup ≈260× 1×

Compression on the SR benchmark

Full results, figures, and methodology: simplify guide · paper.

Development

Setup

To set up the development environment, run the following commands:

pip install -e .[dev]
pre-commit install

Tests

Test the package with pytest:

pytest tests --cov src --cov-report html

or to skip integration tests,

pytest tests --cov src --cov-report html -m "not integration"

Citation

@inproceedings{saegert2026breakingsimplificationbottleneckamortized,
  title   = {Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression},
  author  = {Paul Saegert and Ullrich Köthe},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year    = {2026},
  eprint  = {2602.08885},
  archivePrefix =  {arXiv},
  primaryClass  = {cs.LG},
  url     = {https://arxiv.org/abs/2602.08885},
}

% Optionally
@software{simplipy-2026,
    author = {Paul Saegert},
    title = {Efficient Simplification of Mathematical Expressions},
    year = 2026,
    publisher = {GitHub},
    version = {0.14.0},
    url = {https://github.com/psaegert/simplipy}
}

Project links

Data verified by PyPI on Sep 2, 2026
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  • Released:
Newer release available (0.14.6)

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Credits

Author: Paul Saegert

GitHub Statistics

Data verified by PyPI on Sep 2, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.12

Provides Extra

dev torch

Tags

symbolic-regression simplification expression prefix rewriting rust

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