torch-linear-regression 0.2.0

Closed-form linear regression models (OLS, Ridge, Ridge variants) using PyTorch.

torch_linear_regression

A very simple library containing closed-form linear regression models using PyTorch. Accepts both NumPy arrays and PyTorch tensors, follows the scikit-learn estimator interface (fit/predict/score), and can run on GPU.

Models

• OLS -- Ordinary Least Squares: (X'X)^-1 X'Y
• Ridge -- Ridge regression with a fixed scalar penalty: (X'X + lambda*I)^-1 X'Y
• ReducedRankRidgeRegression -- Ridge regression followed by SVD truncation of the weight matrix
• RidgeMML -- Ridge regression with automatic per-column lambda estimation (Karabatsos 2017).

The closed-form approach results in fast and accurate results under most conditions. However, when n_features is large and/or very underdetermined (n_samples << n_features), the closed-form solution will start to diverge from other solutions. Also, if the input X matrix is singular (has redundant columns), an error will be thrown. If you encounter these issues, consider using SVD / PCA to reduce the redundancy in your input matrix.

OLS, Ridge, and ReducedRankRidgeRegression include a model.prefit() method that precomputes the inverse matrix. This is useful when fitting the same X to multiple targets.

Installation

Install stable version:

pip install torch_linear_regression

Install development version:

pip install git+https://github.com/RichieHakim/torch_linear_regression.git

Usage

OLS / Ridge

See the demo notebook for more examples.

import torch_linear_regression as tlr
import numpy as np
from sklearn.datasets import make_regression

X, Y = make_regression(n_samples=100, n_features=10, noise=5, random_state=42)

model = tlr.OLS()
model.fit(X=X, y=Y)
Y_pred = model.predict(X)
print(f"R^2: {model.score(X=X, y=Y):.4f}")

RidgeMML

Ridge regression where each column of Y gets its own regularization parameter, estimated automatically via marginal maximum likelihood.

X columns are z-scored internally (required by the algorithm for lambda comparability across features). Coefficients are un-z-scored before storage, so predict(X_raw) works on raw data.

import torch_linear_regression as tlr
import numpy as np

## Multi-target regression
X = np.random.randn(500, 20)
beta_true = np.random.randn(20, 5)
Y = X @ beta_true + 0.5 * np.random.randn(500, 5)

model = tlr.RidgeMML()
model.fit(X, Y)

print(f"R^2: {model.score(X, Y):.4f}")
print(f"Lambdas: {model.lambdas_}")          ## per-column regularization
print(f"Converged: {~model.convergence_failures_.any()}")

## Pre-supplied lambdas (skip MML optimization)
model_fixed = tlr.RidgeMML(lambdas=np.ones(5) * 10.0)
model_fixed.fit(X, Y)

## GPU acceleration
model_gpu = tlr.RidgeMML(device="cuda")
model_gpu.fit(X, Y)

## Control memory for large p_y
model_batched = tlr.RidgeMML(batch_size_solve=50)
model_batched.fit(X, Y)

Key differences from Ridge:

	Ridge	RidgeMML
Lambda	Single scalar, user-specified	Per-column of Y, estimated via MML
X standardization	None	Intrinsic z-scoring (ddof=1)
Intercept	Optional	Always (derived from column means)
Dependencies	numpy, torch	numpy, torch