Gravicode.Science.GraviFrame 1.0.0

?# Gravicode.Science

A data science and AI ecosystem for .NET 10 — six libraries that mirror the Python stack, each with a runnable sample, an interactive notebook, benchmarks and tests.

Published from DotNetVibeCoderz/Vibe_ML.

Install

dotnet add package Gravicode.Science.GraviNum      # arrays, linear algebra, autodiff
dotnet add package Gravicode.Science.GraviFrame    # dataframes
dotnet add package Gravicode.Science.GraviLearn    # machine learning
dotnet add package Gravicode.Science.GraviText     # NLP
dotnet add package Gravicode.Science.GraviGraph    # graphs and GNNs
dotnet add package Gravicode.Science.GraviProb     # Bayesian inference

Take only what you need — every package pulls in GraviNum, and nothing else is mandatory. See docs/publishing.md for how a release is cut.

Quick start

git clone https://github.com/DotNetVibeCoderz/Vibe_ML.git
cd Vibe_ML/GravicodeScience
dotnet build Gravicode.Science.sln -c Release
dotnet test

dotnet run --project samples/GraviLearn.Console

using Gravicode.Science.GraviLearn;
using Gravicode.Science.GraviLearn.Decomposition;
using Gravicode.Science.GraviLearn.ModelSelection;
using Gravicode.Science.GraviLearn.Preprocessing;
using Gravicode.Science.GraviLearn.Trees;

var iris = Datasets.LoadIris();
var split = Selection.Split(iris.Features, iris.Target, testSize: 0.3, stratify: true);

var pipeline = new Pipeline()
    .Add(new StandardScaler())
    .Add(new PCA(components: 3))
    .Add(new RandomForestClassifier(nTrees: 100));

pipeline.Fit(split.TrainX, split.TrainY);
Console.WriteLine(Metrics.ClassificationReport(split.TestY, pipeline.Predict(split.TestX), iris.LabelNames));

What is in the box

src/          six class libraries
samples/      six console apps, each printing real results
notebooks/    six .NET Interactive notebooks with charts
benchmarks/   six BenchmarkDotNet suites, plus the Python comparison harness
datasets/     Iris, Titanic, MNIST digits, Cora, plus generated data
tests/        1,049 tests
tools/        ScienceAppGen — an IDE that builds apps from a prompt
docs/         English, with Bahasa Indonesia in docs/id/

ScienceAppGen

A desktop IDE with an assistant that writes the files and runs the build itself rather than printing code for you to copy.

dotnet run --project tools/ScienceAppGen

Editor with syntax highlighting and a file explorer, ten data-science project templates, and a chat panel backed by Semantic Kernel with 16 kernel functions — project and file operations, build and run, web search, scraping, exact arithmetic, the clock, and a curated Gravicode API reference.

Works with OpenAI, Azure OpenAI, Anthropic, Google and Ollama. Claude is served by a hand-written chat completion service, because Semantic Kernel has no official Anthropic connector.

Verified end to end against a live endpoint: from one prompt it created a project, wrote the code and built it — and the test harness independently rebuilt and ran the result to confirm the output. The template path was driven through the UI separately; here is ml-pipeline generated, built and run without a line typed in between.

Details in ScienceAppGen.md.

Design

GraviNum is the foundation. Every other library builds on its array and linear-algebra layer, and nothing above it is ever referenced back down.

GraviNum ──┬── GraviFrame ── GraviLearn ── GraviText ── GraviGraph
           └── GraviProb

Views, not copies. Reshaping, transposing and slicing an NdArray produce views over one shared buffer. Only Copy() moves data.

Three-tier dispatch. Element-wise work automatically picks a Vector<double> SIMD loop, a threaded version of it, or a strided broadcast walk, based on shape and size. Callers never choose.

Honest performance. LinAlg.Dot sustains ~24 GFLOP/s at 512×512 on a mid-range laptop CPU, about 20× the textbook triple loop. GPU support exists through ILGPU but is opt-in: everything here is float64, and on integrated hardware the GPU measured 5–8× slower. See benchmarks.md.

How it compares to NumPy, pandas and scikit-learn

Measured with an identical harness on both sides — same shapes, same fixtures, same warmup and repeat protocol. Full tables in benchmarks.md.

Tally: .NET faster on 12 measurements, Python faster on 22, parity on 3.

The first row used to read 5–140×. Two v0.2 changes moved it:

• Decompositions. SVD and symmetric eigen used Jacobi methods, which sweep the entire matrix until it stops changing. Householder reduction plus a shifted QR/QL iteration made symmetric eigen 23.6× faster and SVD 5.5× faster, taking the worst gaps from 139× and 66× down to 5.6× and 11×.
• Element-wise arithmetic. The parallel path was copying both operands and the result — three extra passes over memory to satisfy a lambda capture. Removing them made it 3× faster and put it ahead of NumPy.

The split is not random. Wherever an operation bottoms out in decades-tuned Fortran, Python wins, and wherever the work is a tight scalar loop that cannot be vectorised into one library call, a JIT-compiled language wins outright — that is most of graph analytics, sampling and text processing.

Those figures are the managed path. If the machine has OpenBLAS or MKL, NativeBlas and NativeLapack find it and the first row changes completely: matmul reaches parity with NumPy and QR goes from 15× behind to 1.3×. Nothing native is bundled, and nothing breaks without it. Both configurations are reported in benchmarks.md.

The comparison paid for itself immediately: it exposed an O(n²) decision-tree split that made the random-forest benchmark run for 83 minutes without finishing. Fixed, it fits in 1.9 s — and now beats scikit-learn. It also caught a benchmark that the JIT had optimised into nothing.

Verified against reference implementations

The test suite pins results that are independently known, so a regression shows up as a test failure rather than as a plausible-looking number:

Documentation

Per-library guides sit alongside them in docs/ and docs/id/.

Roadmap: PLAN.md · Status: Progress.md

Samples

Each prints a hardware report, then does real work on real data:

dotnet run --project samples/GraviNum.Console     # matrix ops, decompositions, GFLOP/s
dotnet run --project samples/GraviFrame.Console   # Titanic: group-by, pivot, joins, time series
dotnet run --project samples/GraviLearn.Console   # Iris: forests, cross-validation, grid search
dotnet run --project samples/GraviText.Console    # bilingual sentiment, embeddings, transformer
dotnet run --project samples/GraviGraph.Console   # Cora: PageRank, centrality, GCN
dotnet run --project samples/GraviProb.Console    # Bayesian coin toss, HMM, Bayesian network

They also render the charts in docs/screenshots/.

Requirements

.NET SDK 10.0 or later. Windows, Linux or macOS. A GPU is optional and never required.

Licence

MIT.

Dibuat oleh Gravicode Studios, dipimpin oleh Kang Fadhil