gamfit 0.1.194

Formula-first generalized additive models with a high-performance Rust core

gamfit

Formula-based generalized additive models for Python, backed by a Rust engine.

gamfit fits Gaussian, binomial (including Bernoulli marginal-slope), Poisson, and Gamma GLMs with smooth terms, random effects, bounded/constrained coefficients, location-scale extensions, survival likelihoods, and flexible/learnable links. Smoothing parameters are selected by REML or LAML. Posterior sampling uses NUTS where supported, and a Gaussian Laplace approximation otherwise.

Manifold smooths handle predictor spaces that wrap or close: circles, cylinders, tori, and the sphere (intrinsic Wahba and spherical-harmonic kernels), plus periodic tensor products and boundary-conditioned B-splines. The Möbius example in the gallery is a 4π-periodic double-cover parameterization, not a twisted Möbius-strip basis.

Docs: https://gamfit.readthedocs.io/.

Install

uv add gamfit

Wheels are published for Linux (x86_64, aarch64), macOS (x86_64, Apple silicon), and Windows. No Rust toolchain is required.

Example

import gamfit

# Smooth fits need enough rows for the basis to be identified; ~20 rows
# is the minimum the default `s(x)` basis (cubic B-spline) is well-posed
# on. Use more rows when the signal is noisier.
train = [
    {"y": 1.05, "x": 0.0}, {"y": 1.32, "x": 0.5}, {"y": 1.78, "x": 1.0},
    {"y": 2.41, "x": 1.5}, {"y": 3.10, "x": 2.0}, {"y": 3.95, "x": 2.5},
    {"y": 4.80, "x": 3.0}, {"y": 5.62, "x": 3.5}, {"y": 6.25, "x": 4.0},
    {"y": 6.71, "x": 4.5}, {"y": 6.94, "x": 5.0}, {"y": 6.88, "x": 5.5},
    {"y": 6.55, "x": 6.0}, {"y": 5.99, "x": 6.5}, {"y": 5.20, "x": 7.0},
    {"y": 4.30, "x": 7.5}, {"y": 3.42, "x": 8.0}, {"y": 2.65, "x": 8.5},
    {"y": 2.10, "x": 9.0}, {"y": 1.82, "x": 9.5},
]

model = gamfit.fit(train, "y ~ s(x)")
print(model.predict([{"x": 1.5}, {"x": 5.0}], interval=0.95))
print(model.summary())
model.save("model.gam")

pandas, polars, pyarrow, numpy, dict-of-columns, and list-of-records inputs are all accepted without conversion.

Features

• Polyharmonic / Duchon smooths combine magnitude, gradient, and curvature penalty operators on the same basis. P-spline and thin-plate smooths use their standard derivative penalties. Each penalized block has its own smoothing parameter.
• Flexible link functions: flexible(base) adds a spline offset on a base link; blended(...) learns a mixture weight; sas and beta-logistic learn shape parameters.
• Surface smooths in arbitrary dimension: thin-plate, Duchon (scale-free by default, hybrid with length_scale=...), and Matérn, with automatic knot placement.
• Manifold smooths: periodic 1-D, cylinder / torus tensor products, intrinsic sphere (Wahba kernel or spherical harmonics), and boundary-conditioned B-splines.
• Per-axis anisotropy inside a single joint smooth.
• Marginal-slope models that separate baseline risk from a calibrated score's effect, for Bernoulli and survival outcomes.
• Posterior sampling via NUTS where supported, Gaussian Laplace otherwise, behind one API.

API examples

import gamfit
from gamfit.sklearn import GAMRegressor, GAMClassifier

# Validate before you fit
gamfit.validate_formula(train, "y ~ s(x) + group(site)")

# Posterior sampling and mean bands
posterior = model.sample(train, seed=42)
bands = posterior.predict(test, level=0.95)

# Survival
gamfit.fit(df,
    "Surv(entry, exit, event) ~ s(age) + bmi + timewiggle(internal_knots=6)",
    survival_likelihood="transformation",
    baseline_target="weibull",
)

# scikit-learn
est = GAMRegressor(formula="y ~ s(x)")
est.fit(X, y)

# Diagnose, plot, report
model.diagnose(train).metrics
model.plot(train, x="x", kind="prediction")
model.report("report.html")

Public API

Symbol	Purpose
gamfit.fit(data, formula, **kwargs)	Fit a model.
gamfit.load(path) / gamfit.loads(bytes)	Reload a saved model.
gamfit.load_posterior(path)	Reload a PosteriorSamples archive.
gamfit.validate_formula(data, formula, ...)	Type-check a formula without fitting.
gamfit.build_info()	Native extension build metadata.
gamfit.cuda_diagnostics() / gamfit.format_cuda_diagnostics()	CUDA probe results.
gamfit.explain_error(exc)	Human-readable hint for a gamfit exception.
gamfit.Model	Fitted model: predict, summary, check, diagnose, plot, report, sample, save.
gamfit.SurvivalPrediction	Per-row hazard / survival surface.
gamfit.CompetingRisksPrediction, competing_risks_cif	Competing-risks CIF evaluation.
gamfit.SamplingConfig, PosteriorSamples, PosteriorPredictive, PairedPosteriorSamples	Posterior interface.
gamfit.ResponseGeometryModel, sphere_frechet_mean, simplex_frechet_mean, alr, clr, closure	Response-geometry utilities.
gamfit.sklearn.GAMRegressor / GAMClassifier	scikit-learn estimators.

Full reference: https://gamfit.readthedocs.io/en/latest/api-reference/.

Optional extras

uv add "gamfit[pandas]"     # pandas + pyarrow input/output
uv add "gamfit[plot]"       # matplotlib-based plotting
uv add "gamfit[sklearn]"    # scikit-learn integration
uv add "gamfit[torch]"      # PyTorch bridge
uv add "gamfit[all]"        # everything

GPU acceleration

CUDA support (cuBLAS / cuSOLVER / cuSPARSE) is built into the same wheel; there is no separate gamfit-gpu package. Per-op dispatch thresholds are derived at probe time from measured GPU FP64 throughput, CPU FP64 throughput, and PCIe bandwidth, so small kernels stay on the CPU. Inspect the calibrated thresholds with gamfit.build_info()["cuda_diagnostics"] or gamfit.format_cuda_diagnostics().

If both a system CUDA toolkit and pip nvidia-*-cu12 wheels are present in the same environment, gamfit warns once per conflict-set and continues; glibc resolves dlopen(SONAME) to a single file, so this is usually benign. If you use gamfit with torch, install a torch build whose CUDA suffix matches your driver.

License

AGPL-3.0-or-later. See LICENSE.