Metadata-Version: 2.4
Name: scikit-verify
Version: 0.1.0
Summary: Lift NumPy/SciPy code to the mathematics it implements, and verify it.
Author: Aadya Chinubhai
License: BSD-3-Clause
Project-URL: Homepage, https://github.com/aadya940/scikit-verify
Project-URL: Issues, https://github.com/aadya940/scikit-verify/issues
Keywords: verification,symbolic,sympy,numpy,scipy,numerical-analysis,scientific-computing
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Quality Assurance
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.26
Requires-Dist: sympy>=1.12
Provides-Extra: mcp
Requires-Dist: mcp<2,>=1.2; extra == "mcp"
Provides-Extra: hypothesis
Requires-Dist: hypothesis>=6; extra == "hypothesis"
Provides-Extra: dev
Requires-Dist: pytest>=8; extra == "dev"
Requires-Dist: hypothesis>=6; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: scipy>=1.12; extra == "dev"
Dynamic: license-file

<p align="center">
  <img src="doc/logos/scikit-verify-lockup.svg" alt="scikit-verify" width="380">
</p>

<p align="center">Translate Python and NumPy programs to symbolic mathematics</p>

![CI](https://github.com/aadya940/scikit-verify/actions/workflows/ci.yml/badge.svg)

* [Source code](https://github.com/aadya940/scikit-verify)
* [Coverage](doc/coverage.md)
* [License](https://github.com/aadya940/scikit-verify/blob/master/LICENSE)
* [skverify-mcp](skverify-mcp/) - MCP for mathematical feedback for coding agents
* [skverify-hypothesis](skverify-hypothesis/) - find every branch, boundary and edge case of your function with Hypothesis

scikit-verify is a tracer for numerical Python. It runs your NumPy
function once and returns the formula it computed, as an ordinary SymPy
expression you can read, simplify, compare against a paper, or evaluate
at any other input. Your code is not modified or annotated. For example:

```python
import numpy as np
from skverify import to_sympy

def weighted_rms(x, w):
    return np.sqrt(np.sum(w * x**2) / np.sum(w))

out = to_sympy(weighted_rms, np.array([1.0, 2.0, 3.0]), np.array([0.5, 0.3, 0.2]))

out.formula
# sqrt(Sum(w[j]*x[j]**2, (j, 0, 2))/Sum(w[j], (j, 0, 2)))
```

Every formula comes as a certificate: the expression, plus the
assumptions it was derived under. When code branches on your data, the
branch taken becomes a stated hypothesis instead of a hidden one:

```python
out = to_sympy(np.median, np.array([3.0, 1.0, 4.0, 1.5]))
print(out.pretty())

# formula    = a[0]/2 + a[3]/2
# assumes[0] = a[0] <= a[2]
# assumes[1] = a[1] <= a[3]
# assumes[2] = a[3] <= a[0]
```

The contract is exact-or-refuse. If an operation has no faithful
symbolic form, scikit-verify raises instead of guessing:

```python
to_sympy(lambda a: a.astype(int).mean(), np.array([1.4, 2.6]))
# NotImplementedError: astype to non-float would change the math
```

This works on real library code, not just kernels: scikit-learn metrics
come back as their defining formulas (precision as its ratio of counting
sums), fitted estimators as their closed forms, iterative solvers as
held recurrences, and compiled routines (LAPACK, FFT, Cython) as named
terms that are checked against their defining equations on every call:
svd against U diag(S) Vh = A, fft against the DFT sum itself.

Randomness stays honest too. A draw like ``rng.normal(0, s)`` enters
the formula as a random variable with that distribution, so
``sympy.stats.E`` and ``variance`` of the result compute in closed
form, while the concrete run keeps the exact numbers drawn.

Tested against numpy, scipy, scikit-learn, statsmodels, cvxpy and
random research code from GitHub; the boards in [coverage](coverage/)
regenerate every number.

## Installation

```bash
pip install scikit-verify
```

Requires Python >= 3.11, `numpy`, and `sympy`. The import name is
`skverify`. The companion layers install as extras:

```bash
pip install "scikit-verify[mcp]"          # MCP server for coding agents
pip install "scikit-verify[hypothesis]"   # testing helpers
```

Pre-alpha; the API may change. Iterative solvers at real sizes can be
slow to trace (minutes, not wrong); the boards in coverage/ carry
timings.

## Lineage

The ideas here are old and good. Pairing a concrete execution with a
symbolic one is King's symbolic execution (CACM 1976), run in the
concolic style of Cadar and Sen. Checking a compiled routine's answer
against its defining equation, instead of trusting its name, is
Blum and Kannan's result checking (1989). Folding a long trace back
into its loop structure follows Larus's whole-program paths (PLDI
1999), with templates recovered by Plotkin's anti-unification (1970).
The stance that code verification means checking code against the
mathematics it claims to implement is Oberkampf and Roy's (2010).
Verified lifting of stencils to summaries was developed by Kamil et
al. (PLDI 2016) for performance; scikit-verify lifts for correctness.
Converting NumPy to SymPy was wished for in
[sympy#2810](https://github.com/sympy/sympy/issues/2810) (2014).

## License

BSD-3-Clause. scikit-verify is an independent project and is not affiliated
with the SciPy developers.
