superglm 0.18.0

Penalised GLMs and GAM-style pricing models for insurance. SuperGLM combines explicit feature specs, exact REML, large-n discrete REML, solver-backed monotone splines, actuarial validation tooling, and deployable fitted estimators for Poisson, Gamma, NB2, Tweedie, Binomial, and Gaussian models.

Installation

Install SuperGLM from PyPI:

pip install superglm

Plotly-based interactive charts are optional:

pip install "superglm[plotting]"

The local model editor is included in the normal installation.

Recommended Workflow

For spline-based pricing models, the default path is:

1. define explicit feature specs
2. fit with fit_reml() and selection_penalty=0
3. compare candidates with cross_validate(..., fit_mode="fit_reml")
4. refit on all training data
5. evaluate holdout Lorenz and double-lift charts
6. serialize the fitted estimator for scoring

from superglm import Categorical, Numeric, Spline, SuperGLM

features = {
    "DrivAge": Spline(kind="ps", k=14, knot_strategy="quantile_rows"),
    "VehAge": Spline(kind="cr", k=10, knot_strategy="quantile_rows"),
    "BonusMalus": Spline(kind="cr", k=12, knot_strategy="quantile_tempered"),
    "Area": Categorical(base="most_exposed"),
    "LogDensity": Numeric(),
}

model = SuperGLM(
    family="poisson",
    selection_penalty=0.0,
    features=features,
)
model.fit_reml(train_df, y_train, sample_weight=exposure_train, max_reml_iter=30)

mu_holdout = model.predict(holdout_df)
print(model.summary())

Choosing A Fit Path

Selection strength is explicit:

SuperGLM()                                  # no sparse selection
SuperGLM(selection_penalty="auto")         # calibrate from the fit data
SuperGLM(selection_penalty=0.05)           # fixed selection strength

None and 0.0 disable sparse selection. Automatic calibration occurs only when requested with "auto". REML accepts only None or 0.0; use spline select=True when smooth terms should be eligible to shrink inside REML.

fit_reml() with selection_penalty=0

This is the recommended path for spline-heavy GAM-style pricing models. Use it when you want automatic smoothness selection, interpretable smooth terms, and mgcv-style modeling rather than sparse screening.

model = SuperGLM(
    family="poisson",
    selection_penalty=0.0,
    features=features,
)
model.fit_reml(df, y, sample_weight=exposure)

fit_reml(discrete=True)

Use this when the model is still a REML pricing model, but the data is large enough that exact REML becomes expensive. This is the production-scale path for large frequency models.

model = SuperGLM(
    family="poisson",
    selection_penalty=0.0,
    discrete=True,
    n_bins=256,
    features=features,
)
model.fit_reml(df, y, sample_weight=exposure)

fit() with selection_penalty > 0

Use this when you want sparse screening, compression, or fixed-penalty regularisation. This is a different modeling story from REML smoothness selection.

model = SuperGLM(
    family="poisson",
    penalty="group_elastic_net",
    selection_penalty=0.01,
    spline_penalty=0.1,
    features=features,
)
model.fit(df, y, sample_weight=exposure)

select=True

select=True on spline terms adds mgcv-style double-penalty shrinkage. This is the REML-native way to let smooth terms shrink toward linear or zero while staying in the fit_reml() workflow.

features = {
    "DrivAge": Spline(kind="ps", k=14, select=True),
    "VehAge": Spline(kind="cr", k=10, select=True),
    "Area": Categorical(base="most_exposed"),
}
model = SuperGLM(family="poisson", selection_penalty=0.0, features=features)
model.fit_reml(df, y, sample_weight=exposure)

Validation And Model Comparison

cross_validate() should be part of the standard pricing workflow, not an afterthought. It gives fold-level metrics, timing, convergence information, and out-of-fold predictions for challenger comparisons.

from sklearn.model_selection import KFold
from superglm import cross_validate
from superglm.validation import double_lift_chart, lorenz_curve

cv = cross_validate(
    model,
    train_df,
    y_train,
    cv=KFold(n_splits=5, shuffle=True, random_state=42),
    sample_weight=exposure_train,
    fit_mode="fit_reml",
    scoring=("deviance", "nll", "gini"),
    return_oof=True,
)

lorenz = lorenz_curve(y_holdout, mu_holdout, exposure=exposure_holdout)
print(f"Gini ratio: {lorenz.gini_ratio:.4f}")
lift = double_lift_chart(
    y_obs=y_holdout,
    y_pred_model=mu_holdout,
    y_pred_current=mu_baseline,
    exposure=exposure_holdout,
)

Key outputs:

• cv.fold_scores: per-fold metrics, fit time, convergence, and EDF
• cv.mean_scores / cv.std_scores: summary comparisons
• cv.oof_predictions: out-of-fold predictions for the training rows
• lorenz_curve(...): ranking power via Gini
• double_lift_chart(...): business-facing champion/challenger evidence

Monotone Splines

SuperGLM supports solver-backed monotone spline fitting. This is the preferred way to enforce business shape constraints inside the model itself.

• BSplineSmooth(..., constraint=Constraint.fit.increasing): constrained QP path
• CubicRegressionSpline(..., constraint=Constraint.fit.decreasing): constrained QP path
• PSpline(..., constraint=Constraint.fit.increasing): SCOP path

from superglm import BSplineSmooth, Constraint, PSpline, SuperGLM

qp_model = SuperGLM(
    family="gaussian",
    selection_penalty=0.0,
    features={
        "x": BSplineSmooth(
            n_knots=8,
            constraint=Constraint.fit.increasing,
        ),
    },
)

scop_model = SuperGLM(
    family="gaussian",
    selection_penalty=0.0,
    features={
        "x": PSpline(
            n_knots=10,
            constraint=Constraint.fit.increasing,
        ),
    },
)

Post-fit isotonic repair still exists, but it should be treated as a manual fallback rather than the main monotone workflow.

Feature Highlights

• Spline(kind="ps"), Spline(kind="cr"), and Spline(kind="ns") cover the main spline basis choices.
• OrderedCategorical(...) smooths ordered factor levels without forcing a plain one-hot representation and reports one whole-smooth test rather than separate p-values at arbitrary level positions.
• collapse_levels(...) lets you merge sparse categorical levels while still expanding back to original levels for inference and plotting.
• interactions=[(...)] supports spline-categorical, numeric-categorical, tensor, and other interaction types.
• m=(...) supports multi-order spline penalties with separate REML lambdas.

from superglm import Categorical, OrderedCategorical, Spline, collapse_levels

area_grouping = collapse_levels(train_df["Area"], groups={"Rural": ["E", "F"]})

features = {
    "VehAge": Spline(kind="cr", k=10),
    "Area": Categorical(base="most_exposed", grouping=area_grouping),
    "BonusClass": OrderedCategorical(
        order=["A", "B", "C", "D"],
        basis=Spline(kind="ps", k=6),
    ),
}

Weights And Offsets

Weight semantics are family-specific. For Poisson, negative binomial, binomial, Gaussian, and Gamma fits, sample_weight= is a case/frequency weight: once feature geometry is fixed, integer weights have the same likelihood and dispersion semantics as repeating rows. It is not an inverse-variance weight. In the Poisson rate example below, exposure is a frequency weight.

Tweedie is the exception: its weights are strictly positive EDM prior weights, with Var(Y_i | x_i) = phi * mu_i**p / w_i, and are not replication counts. See the families guide before using weighted Gaussian or Gamma averages, where choosing the wrong interpretation changes dispersion and inference.

The replication equivalence remains conditional on the constructed design. Main-effect non-Tweedie spline boundaries and adaptive knots honor frequency mass and omit zero-weight rows. Some adaptive interaction and categorical feature geometry can still depend on the physical row layout, however, so use fixed or preconstructed feature geometry when exact end-to-end replication parity matters.

import numpy as np

# Raw count target: offset absorbs exposure, model estimates a rate
model.fit(df, claim_counts, offset=np.log(exposure))

# Rate target: sample_weight carries exposure
model.fit(df, claim_rate, sample_weight=exposure)

Validation helpers such as lorenz_curve(...) and double_lift_chart(...) still use exposure=..., which is correct for that API.

Deployment

A fitted SuperGLM is the deployment artifact. It already contains:

• registered feature specs
• learned knot geometry and constraints
• fitted coefficients and intercept
• REML smoothing parameters

import pickle

with open("pricing_model.pkl", "wb") as f:
    pickle.dump(model, f)

with open("pricing_model.pkl", "rb") as f:
    loaded = pickle.load(f)

mu = loaded.predict(score_df)

The loaded model can still score, print summaries, rebuild curves, and produce relativity views without refitting.

Advanced Penalty Objects

At the top-level model API, prefer selection_penalty= and spline_penalty=. Low-level penalty objects still expose lambda1, for example:

from superglm import GroupElasticNet

penalty = GroupElasticNet(lambda1=0.01, alpha=0.5)
model = SuperGLM(family="poisson", penalty=penalty, features=features)

That is advanced usage. It should not be your default starting point.

Learn More

• Recommended workflows
• Choosing a fitting path
• Monotone splines
• Validation and model comparison
• Deployment
• Optimization and solver internals