EasyGLM

EasyGLM is a python package that makes fitting regularised generalised linear models (GLMs) for use in general insurance pricing a cinch. It produces factor relativity tables to inspect, amend and use in a way that naturally fit into standard pricing workflows. It can be used both programmatically within a python session or in the local browser using the GUI workbench.

Install

Requires Python 3.10–3.14. The installation includes the local workbench and CatBoost.

python -m pip install --upgrade easy-glm

Install as easy-glm; import as easy_glm.

Use the workbench

easy-glm-workbench

The workbench opens in your browser and runs locally. Keep the terminal open while you work.

In Project & data, load your data, choose a single model or a sequence of models, and assign the target and any exposure, weight, ID or train/test columns. Choose the model family and predictors. For GLM numeric factors, use automatic bands, set your own cuts or choose a curve, then fit the model.

Inspect the relativities and A/E, search for missing factors or interactions, and compare candidate models. You can amend rates, preview the effect of a change before applying it, and export the tables to Excel.

The workbench walkthrough shows this process on screen.

Use it from Python

PricingSession keeps the data, target, exposure, split and band settings together while you build models. Each fit returns a named model you can inspect, compare or build on.

This example fits driver age and vehicle age to the public French motor claim-frequency data. EasyGLM downloads the data and caches it for later use.

from easy_glm import PricingSession, load_external_dataframe

data = load_external_dataframe().sample(n=10_000, seed=42)

study = PricingSession(
    data,
    family="poisson",
    claims="ClaimNb",
    exposure="Exposure",
    id="IDpol",
)
model = study.fit_glm("Age model", factors=["DrivAge", "VehAge"])

model.summary()
model.relativities("DrivAge").show()
model.ae("DrivAge").show()

The session creates a 70/30 train/holdout split, keeping rows with the same ID together. Fitting and searches use training data; the A/E above is also on training data. Earlier named fits remain available when you change bands or fit another model.

Export the tables and save the fitted model:

model.to_excel("age_model.xlsx")
model.save("age_model.easyglm")

To open the same data in the workbench:

import easy_glm

easy_glm.launch_workbench(data=data)

The pricing walkthrough continues with custom bands, missing-factor searches, interactions, rate amendments and holdout validation.

Add interactions or further models

Add and review interaction tables after fitting the main effects. Each new interaction uses the GLM and earlier tables as its offset, keeping their rates fixed.

You can also fit CatBoost on its own or combine GLMs and CatBoost in a sequence. Each model has its own predictors and uses the complete prediction from all earlier models as its offset. Sequential models support Poisson, Tweedie and Gaussian with a log link.

See the model workflow examples for different orders and tuning settings. The model family examples cover severity, pure premium and claim occurrence.

Save and score

A saved fitted model reuses the rates without refitting. In the workbench you can also export the project settings and a Python script that repeats the fits.

In Score on new data, select the new data and the saved model or sequence to apply. Check the required columns, score the data, and download the results as CSV or Parquet. The scoring example shows this in Python and the workbench.

All examples and workflows · Changelog

Why I built this

This started with my wish to port R's aglm to Python and give it a GUI. It's built primarily for my own pricing work and will be buggy, because… vibecode yo. Check the results before using them.

MIT licence.