DataNet.Fuzzy 0.1.0

DataNet

A data-science toolkit for C#/.NET, built on an honest premise:

Don't rewrite Python. Use the .NET ecosystem where it's strong, and write native code only where .NET has a real gap: text (similarity, vectorization, semantic search). All of it with no Python at runtime.

Why

Python dominates data analysis through its ecosystem and its exploratory notebook workflow — not through the language itself; its performance comes from C/Fortran kernels. C# brings static typing, real parallelism without a global interpreter lock, safe refactoring, and simple deployment. The only objective reason to stay on Python for this domain was the lack of an equivalent .NET library. DataNet removes that reason.

Two deliverables

1. Native code where there's a gap → the packages below (string distances, vectorization, embeddings, fuzzy matching) — allocation-lean, Span-based, SIMD, zero external dependencies in the core.
2. Migration guides for people coming from Python → docs/migration/, which, for each need (NumPy, pandas, scikit-learn, statsmodels, PyTorch, matplotlib, seaborn), points to the right .NET building block and the pitfalls.

See the three-column migration inventory: it's the project map (use / build / decide).

Target: .NET 10 (net10.0). See docs/decisions/0001.

Status — every lot of the project brief is delivered

Every building block is oracle-validated against rapidfuzz / jellyfish / textdistance / difflib / scikit-learn / nltk / HuggingFace tokenizers / sentencepiece / numpy / ONNX Runtime (see docs/equivalence.md).

Getting started

dotnet add package DataNet.Text

using DataNet.Text.Distances;

Levenshtein.Distance("kitten", "sitting");             // 3
Levenshtein.NormalizedSimilarity("kitten", "sitting"); // 0.5714…

Full guide: docs/guides/quickstart.md. See also the vectorization, embeddings and fuzzy-matching guides.

Developing

dotnet build                                   # build the solution
dotnet test                                    # replay oracles + property tests
dotnet run -c Release --project bench/DataNet.Text.Benchmarks -- --filter '*Levenshtein*'

Oracle validation

Conformance to Python behavior is proven, not assumed (§4 of the brief): tools/generate_oracles.py freezes a few thousand reference cases from rapidfuzz/jellyfish/etc. into tests/oracles/*.json (versioned); the C# suite replays them with a 1e-9 tolerance. Python is a development-only dependency. See tools/README.md.

Structure

DataNet.sln
├── src/DataNet.Text/            distances, metrics, tokenizers, vectorizers, stemmers (no dependencies)
├── src/DataNet.Embeddings/      sub-word tokenizers, pooling, SIMD kNN, ONNX inference (ONNX Runtime isolated here)
├── src/DataNet.Fuzzy/           fuzz.*, process.extract, deduplication
├── tests/                       xUnit: oracles + properties (one project per module)
├── tests/oracles/               frozen JSON corpora (generated from Python) + a synthetic ONNX model
├── bench/DataNet.Text.Benchmarks/  BenchmarkDotNet
├── tools/generate_oracles.py    reference generation
├── Directory.Build.props        (root); src|tests/Directory.Packages.props (central package management)
└── docs/                        guides, equivalence table, decision log

Publishing

Three NuGet packages are produced: DataNet.Text, DataNet.Embeddings, DataNet.Fuzzy. Package metadata (version, license, README, repository) is shared in Directory.Build.props.

GitHub Packages (no nuget.org account needed — uses GitHub's automatic token). Tag a version and push; the release workflow packs and publishes:

git tag v0.1.0
git push origin v0.1.0

To consume them, add a source pointing at the owner's feed (with a GitHub token that has read:packages):

dotnet nuget add source "https://nuget.pkg.github.com/CyrilB1531/index.json" \
  --name github --username CyrilB1531 --password <GITHUB_TOKEN>
dotnet add package DataNet.Text

nuget.org (optional, needs a free account + API key). Once you have a key:

dotnet pack src/DataNet.Text -c Release -o artifacts
dotnet nuget push "artifacts/*.nupkg" --source https://api.nuget.org/v3/index.json --api-key <KEY>

License

Apache-2.0. See NOTICE and THIRD-PARTY-NOTICES.md for attributions. The license choice and the code-provenance rule are documented in docs/decisions/0003-provenance-and-licensing.md.

This repository is not legal advice.