gamfit 0.1.60

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

gamfit

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

gamfit fits Gaussian, binomial, 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, with a Gaussian Laplace fallback elsewhere.

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

Install

uv add gamfit

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

30-second tour

import gamfit

train = [
    {"y": 1.2, "x": 0.0},
    {"y": 1.9, "x": 1.0},
    {"y": 3.1, "x": 2.0},
    {"y": 4.5, "x": 3.0},
]

model = gamfit.fit(train, "y ~ s(x)")
print(model.predict([{"x": 1.5}, {"x": 2.5}], interval=0.95))
print(model.summary())
model.save("model.gam")

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

What's different

• Three-part penalty structure. Each smooth gets separate penalties for magnitude, gradient, and curvature. Most GAM libraries use one or two.
• Flexible link functions. Spline offsets from a base link (link(type=flexible(probit))), blended mixture links, and SAS / beta-logistic learnable shapes.
• Surface smooths in arbitrary dimension. Thin-plate, Duchon (with triple-operator regularization), and Matérn covariance, with automatic knot placement.
• Adaptive anisotropy. Per-axis spatial anisotropy shrinks or stretches each feature axis independently inside a single joint smooth.
• Composable basis/kernel. Combine a spline kernel with a length-scale behaviour (e.g. Duchon kernel with Matérn-style global κ).
• Marginal-slope models. Separate baseline risk from a calibrated score's effect, for both Bernoulli and survival outcomes.
• Posterior sampling. model.sample(...) runs NUTS where supported, with a Gaussian Laplace fallback elsewhere, behind one API.

Highlights from the API

import gamfit
from gamfit.sklearn import GAMRegressor, GAMClassifier

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

# Posterior sampling and predictive 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",
)

# 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.validate_formula(data, formula, ...)	Type-check a formula without fitting.
gamfit.build_info()	Native extension build metadata.
gamfit.explain_error(exc)	Human-readable hint for a gamfit exception.
gamfit.Model	Fitted-model handle: predict, summary, check, diagnose, plot, report, sample, save.
gamfit.SurvivalPrediction	Per-row hazard / survival surface; on-demand evaluation.
gamfit.SamplingConfig, PosteriorSamples, PosteriorPredictive	NUTS / posterior interface.
gamfit.sklearn.GAMRegressor / GAMClassifier	scikit-learn estimators.

Full reference at 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[all]"        # everything

License

AGPL-3.0-or-later. See LICENSE.