migration

From Python to .NET — migration inventory

This page is Lodestar's migration hub. It answers a simple question: "I do this in Python, what do I do in C#?"

The project's guiding principle (see the rationale) is honest: we don't rewrite Python's data-science ecosystem. Most of it already exists in .NET, and Python's dense linear algebra relies on Fortran BLAS/LAPACK kernels there's no point reimplementing. We use what exists, and only write native code where .NET has a real gap: text (similarity, vectorization).

The four columns

Python	Role	.NET recommendation	Verdict
PyTorch	tensors, autograd, training, GPU	TorchSharp (= libtorch); ONNX Runtime for inference only	✅ Use
matplotlib	plotting	ScottPlot, Plotly.NET, OxyPlot	✅ Use
NumPy	N-dim arrays, dense algebra	Math.NET Numerics (+ native MKL/OpenBLAS provider); System.Numerics.Tensors	✅ Use
scikit-learn	classical ML, pipelines, metrics	ML.NET; SharpLearning	✅ Use except text vectorization → Lodestar.Text and classification metrics → Lodestar.Metrics
pandas	DataFrame, groupby, IO	Microsoft.Data.Analysis; Deedle	🟡 Use (rougher)
statsmodels	econometric regression, time series, tests	Math.NET (basics); Accord.NET	🟠 Decide — rich econometrics is a gap
seaborn	tidy statistical viz	ScottPlot / Plotly.NET (charts rebuilt)	🟠 Decide — statistical presets missing

Legend. ✅ a solid equivalent exists, use it as is. 🟡 an equivalent exists but is less mature than Python; expect some glue. 🟠 the foundation exists but a whole area is missing: a candidate for native code if your usage justifies it.

What Lodestar writes natively

One area truly justifies native code — text — and one more turned out to be a gap the .NET options do not fill honestly: the evaluation metrics every sklearn user reaches for. That's Lodestar.Text and its siblings, delivered as lots (see the brief):

1. String distances & similarity — Levenshtein, Damerau-Levenshtein, Jaro-Winkler, Jaccard, Ratcliff-Obershelp, phonetics… (done)
2. Tokenization & sparse vectorization — CountVectorizer, TfidfVectorizer (exact sklearn semantics), home-grown CSR matrix. (done)
3. Embeddings & semantic search — ONNX Runtime + sub-word tokenizers. (done)
4. Applied fuzzy matching — rapidfuzz.fuzz / process equivalents. (done)
5. Classification metrics — sklearn-parity precision, recall, F1, confusion matrix, report and ROC-AUC. (done)

Per-library guides

Guide	Status
NumPy → .NET	draft
pandas → .NET	draft
scikit-learn → .NET	draft
statsmodels → .NET	draft
PyTorch → .NET	draft
matplotlib → .NET	draft
seaborn → .NET	draft

The detailed equivalence table (Python call → C# call, behavioral differences, performance notes), filled in as we go, is in equivalence.md.

This document is not legal advice; third-party dependency licenses are recorded in THIRD-PARTY-NOTICES.md.