datascience

Data Science and Machine Learning in Go
A zero-dependency Go library for probabilistic models, deep learning, numerical computing, and quantitative analysis

---

Overview

datascience is a comprehensive pure Go library for data science and machine learning. It provides probabilistic graphical models (full pgmpy parity), a TensorFlow-compatible deep learning framework, BLAS-optimized numerical computing, and quantitative finance tools — all with zero third-party dependencies.

Every numerical, graph-theoretic, statistical, and tabular operation is implemented from scratch in Go within the project's internal libraries. This eliminates supply-chain risk and simplifies deployment.

Installation

go get github.com/asymmetric-effort/datascience

Requires Go 1.26 or later.

Quick Start

Build a Bayesian network and run inference:

package main

import (
    "fmt"
    "log"

    "github.com/asymmetric-effort/datascience/example_models"
    "github.com/asymmetric-effort/datascience/lib/pgm/inference"
)

func main() {
    bn := example_models.Student()
    if err := bn.CheckModel(); err != nil {
        log.Fatalf("Model validation failed: %v", err)
    }

    markovFactors, _ := bn.ToMarkovFactors()
    ve := inference.NewVariableElimination(markovFactors)
    evidence := map[string]int{"D": 0, "I": 1}
    result, _ := ve.Query([]string{"G"}, evidence)

    for i, state := range bn.GetStates("G") {
        prob := result.GetValue(map[string]int{"G": i})
        fmt.Printf("P(G=%s | D=Easy, I=High) = %.4f\n", state, prob)
    }
}

Train a neural network:

package main

import (
    "github.com/asymmetric-effort/datascience/lib/numgo"
    "github.com/asymmetric-effort/datascience/lib/tensorflow/keras"
    "github.com/asymmetric-effort/datascience/lib/tensorflow/nn/layer"
    "github.com/asymmetric-effort/datascience/lib/tensorflow/nn/loss"
)

func main() {
    model := keras.NewSequential()
    model.Add(layer.NewDense(128, "relu"))
    model.Add(layer.NewDense(10, "softmax"))
    model.Compile(loss.CategoricalCrossEntropy, 0.001)

    X := numgo.NewNDArray([]int{100, 784}, nil) // training data
    Y := numgo.NewNDArray([]int{100, 10}, nil)  // labels
    model.Fit(X, Y, 10, 32) // epochs=10, batch=32
}

Libraries

Library	Replaces	Description
lib/numgo	NumPy	N-dimensional arrays, broadcasting, linear algebra, BLAS L1/2/3
lib/scigo	SciPy	Statistics, distributions, optimization, FFT, SDE solvers, Black-Scholes, portfolio optimization
lib/tabgo	Pandas	DataFrames, CSV I/O, filtering, aggregation, rolling analytics
lib/graphgo	NetworkX	Graph data structures, algorithms, d-separation, moralization
lib/gpu	PyTorch/Pyro	Compute backend abstraction (CPU fallback included)
lib/pgm	pgmpy	Probabilistic graphical models — 13 model types, 7 inference algorithms, 11 learning algorithms
lib/tensorflow	TensorFlow/Keras	Neural networks — Dense, Conv2D, LSTM, GRU, Attention, BatchNorm, optimizers, loss functions

Probabilistic Graphical Models (lib/pgm)

Models (13 types)

Bayesian Network, Discrete Bayesian Network, Markov Network, Discrete Markov Network, Dynamic Bayesian Network, Factor Graph, Cluster Graph, Junction Tree, Naive Bayes, Markov Chain, Linear Gaussian BN, Functional BN, Structural Equation Model (SEM)

Inference (7 algorithms)

Variable Elimination, Belief Propagation, MPLP, Approximate Inference, Causal Inference (do-calculus, backdoor/frontdoor), Dynamic BN Inference, MAP/MPE queries

Learning (11+ algorithms)

MLE, Bayesian Estimation, EM, Linear Gaussian MLE, SEM Estimation, Hill Climb, Exhaustive Search, PC, GES, MMHC, Tree Search, Expert-in-the-Loop, LLM-assisted discovery, IV estimation, Mirror Descent

File I/O (7 formats)

BIF, XMLBIF, UAI, NET, XBN, XDSL, POMDPX

Deep Learning (lib/tensorflow)

Layers

Dense, Conv2D, LSTM, GRU, Attention, BatchNorm, Dropout, Embedding, Flatten, MaxPool2D

Training

Sequential model, SGD/Adam/RMSProp optimizers, MSE/CrossEntropy/Huber loss, callbacks, learning rate schedules, regularizers, metrics

Utilities

GradientTape (automatic differentiation), Variable (trainable state), model save/load, dataset loading, image processing, weight initializers

Quantitative Finance (lib/scigo, lib/tabgo)

• Black-Scholes pricing (European calls/puts, Greeks, implied volatility)
• Binomial tree and Monte Carlo pricing
• SDE solvers (Euler-Maruyama, Milstein)
• Ito calculus (Brownian motion, GBM, Ornstein-Uhlenbeck)
• Markowitz mean-variance portfolio optimization
• QP solver with active-set method
• Rolling correlation, beta, alpha, R-squared, PCA
• Rolling Sharpe, Sortino, max drawdown, VaR, CVaR

Project Structure

datascience/
  lib/
    numgo/             Numerical arrays and BLAS
    scigo/             Scientific computing
    graphgo/           Graph algorithms
    tabgo/             Tabular data and analytics
    gpu/               Compute backend
    pgm/               Probabilistic graphical models
      models/            13 model types
      inference/         7 inference algorithms
      learning/          11+ learning algorithms
      sampling/          Forward and Gibbs sampling
      readwrite/         7 file format readers/writers
      factors/           CPD/JPD representations
      metrics/           Scoring and evaluation
      ...
    tensorflow/        Deep learning
      keras/             Sequential model, training
      nn/                Layers, loss, optimizers, activations
      variable/          Trainable variables
      gradtape/          Automatic differentiation
      ...
  examples/            Runnable example programs
  example_models/      Pre-built canonical networks
  tests/               Cross-validation fixtures
  website/             Project website

Documentation

• Project Website
• GoDoc
• Examples

Contributing

Contributions are welcome. Please read CONTRIBUTING.md for guidelines on development workflow, testing, commit conventions, and the zero-dependency policy.

Security

To report a security vulnerability, see SECURITY.md. Do not open public issues for security concerns.

License

datascience is released under the MIT License.

Copyright (c) 2026 Asymmetric Effort, LLC.