torchvine

GPU-ready, differentiable vine copula modelling in pure PyTorch.
Drop-in replacement for pyvinecopulib — same API, but with autograd and CUDA support.

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✨ Why torchvine?

	torchvine	pyvinecopulib
Backend	Pure PyTorch (GPU / CPU)	C++ with Python bindings
Differentiable	✅ Autograd-compatible	❌
GPU acceleration	✅ CUDA tensors	❌ CPU only
API	Drop-in replacement	Reference
Copula families	13 (full parity)	13

Zero C/C++ dependencies — everything is implemented in pure PyTorch, making it easy to install, debug, and extend.

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🚀 Installation

pip install torchvine

From source:

git clone https://github.com/Bluerrror/torchvine.git
cd torchvine
pip install -e .

Requirements: Python ≥ 3.9  |  PyTorch ≥ 2.0  |  matplotlib ≥ 3.5

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📖 Quick Start

Bivariate Copula

import torch
import torchvine as tv

# Create a Gaussian copula with correlation 0.7
cop = tv.Bicop(tv.BicopFamily.gaussian, parameters=torch.tensor([0.7]))
print(cop.str())               # <torchvine.Bicop> family: gaussian, parameters: [0.7000]
print(cop.parameters_to_tau()) # Kendall's tau ≈ 0.494

# Evaluate density and simulate
u = torch.rand(1000, 2, dtype=torch.float64)
pdf_vals = cop.pdf(u)
samples  = cop.simulate(1000)

# Fit from data (automatic family selection)
fitted = tv.Bicop()
fitted.select(samples)
print(fitted.str())

Vine Copula

# Fit a 5-dimensional vine copula
data = torch.rand(500, 5, dtype=torch.float64)
vine = tv.Vinecop.from_dimension(5)
vine.select(data, controls=tv.FitControlsVinecop(family_set=tv.parametric))

print(vine.str())
print(f"Log-likelihood: {vine.loglik(data):.2f}")
print(f"AIC: {vine.aic(data):.2f}")

# Simulate and transform
sim = vine.simulate(1000)
pit = vine.rosenblatt(data)      # probability integral transform

Dependence Measures

x = torch.randn(1000, dtype=torch.float64)
y = 0.6 * x + 0.8 * torch.randn(1000, dtype=torch.float64)

print(tv.kendall_tau(x, y))    # Kendall's tau
print(tv.spearman_rho(x, y))   # Spearman's rho
print(tv.pearson_cor(x, y))    # Pearson correlation
print(tv.blomqvist_beta(x, y)) # Blomqvist's beta
print(tv.hoeffding_d(x, y))    # Hoeffding's D

GPU Acceleration

device = "cuda" if torch.cuda.is_available() else "cpu"
u_gpu = torch.rand(10000, 2, dtype=torch.float64, device=device)

cop = tv.Bicop(tv.BicopFamily.clayton, parameters=torch.tensor([3.0], device=device))
pdf_gpu = cop.pdf(u_gpu)  # runs entirely on GPU

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📋 Supported Copula Families

Family	Parameters	Type
Independence	0	—
Gaussian	1 (ρ)	Elliptical
Student-t	2 (ρ, ν)	Elliptical
Clayton	1 (θ)	Archimedean
Gumbel	1 (θ)	Archimedean / Extreme-value
Frank	1 (θ)	Archimedean
Joe	1 (θ)	Archimedean
BB1	2 (θ, δ)	Archimedean
BB6	2 (θ, δ)	Archimedean
BB7	2 (θ, δ)	Archimedean
BB8	2 (θ, δ)	Archimedean
Tawn	3 (ψ₁, ψ₂, θ)	Extreme-value
TLL	nonparametric	Kernel-based

All asymmetric families support rotations (0°, 90°, 180°, 270°).

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📚 API Reference

Core Classes

Class	Description
tv.Bicop	Bivariate copula — create, fit, evaluate, simulate
tv.Vinecop	Vine copula model — select, pdf, simulate, rosenblatt
tv.Kde1d	1-D kernel density estimation — fit, pdf, cdf, quantile
tv.RVineStructure	R-vine structure matrix
tv.DVineStructure	D-vine structure (convenience subclass)
tv.CVineStructure	C-vine structure (convenience subclass)
tv.FitControlsBicop	Fitting options for bivariate copulas
tv.FitControlsVinecop	Fitting options for vine copulas
tv.BicopFamily	Enum of all copula families

Dependence Measures

Function	Description
tv.kendall_tau(x, y)	Kendall's rank correlation
tv.spearman_rho(x, y)	Spearman's rank correlation
tv.pearson_cor(x, y)	Pearson linear correlation
tv.blomqvist_beta(x, y)	Blomqvist's beta (medial correlation)
tv.hoeffding_d(x, y)	Hoeffding's D statistic
tv.wdm(x, y, method)	Unified interface for all measures

Utilities

Function	Description
tv.to_pseudo_obs(data)	Rank-transform to pseudo-observations
tv.simulate_uniform(n, d)	Uniform random / quasi-random samples
tv.pairs_copula_data(data)	Pairs plot with copula density contours

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📓 Examples

See the examples/ directory for Jupyter notebooks:

Notebook	Topics
01 — Getting Started	Imports, copula basics, simulation, fitting
02 — Bivariate Copulas	All families, rotations, Student-t, model selection
03 — Vine Copulas	Vine fitting, structure, simulation, Rosenblatt transform
04 — Kde1d & Statistics	KDE, dependence measures, pairs plot visualization

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🤝 Contributing

1. Fork the repo
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m "Add amazing feature"
4. Push: git push origin feature/amazing-feature
5. Open a Pull Request

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📄 License

MIT License — see LICENSE for details.

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🙏 Acknowledgements

• API design follows vinecopulib / pyvinecopulib by Thomas Nagler and Thibault Vatter.