easy_glm

EasyGLM fits GLMs. It is designed for insurance pricing and turns fitted rating factors into insurance rate tables.

Yes, its been built with AI (insert Boris Johnson sounds)

Install

pip install easy_glm

Open the workbench

Start the graphical workbench:

easy-glm-workbench

It opens EasyGLM in your browser, normally at http://localhost:8501. Keep this terminal open while you use the workbench.

You can also open it from a Python session:

import easy_glm

easy_glm.launch_workbench()

To open a Polars or pandas dataframe that is already in memory:

easy_glm.launch_workbench(data=df)

The workbench opens with df loaded. Choose the target, weight and predictors on the Variables page, then define and fit the model on the Model page.

For a first run without supplying a file, open Project & data and click Use the French motor sample. EasyGLM downloads that sample once and keeps a local copy for later runs.

To reopen a saved project later, pass its project file after the command:

easy-glm-workbench path/to/project.easyglm-project.json

Fit a Poisson claim-count model

We will fit a Poisson claim count model using the good ol' French Motort Third Party claims frequency dataset. The dataset contains ClaimNb for claim count, Exposure for - uh - yeah no guesses there and insurance-y variables like DrivAge, Region, BonusMalus and Density.

As ever, we love a good train/test set. The code creates a traintest column: 70% of rows teach the model; the other 30% are kept for the check at the end.

import easy_glm

# Downloads the public data once and reuses the local copy later.
df = easy_glm.load_external_dataframe().sample(n=50_000, seed=42)
df = easy_glm.add_train_test_split(df, train_fraction=0.7, seed=42)

predictors = ["DrivAge", "Region", "BonusMalus", "Density"]
model = easy_glm.EasyGLM.fit(
    data=df,
    target="ClaimNb",
    model_type="Poisson",
    predictors=predictors,
    weight_col="Exposure",
    train_test_col="traintest",
    divide_target_by_weight=True,
    cv=5,
)

See the fitted relativity tables

The base claim frequency is the starting level. A relativity of 1.20 means 20% more expected claims than a relativity of 1.00, after taking account of the other fitted factors. exposure shows how much insured time informed each row of the table.

print(f"Base claim frequency: {model.base_rate:.5f} claims per policy-year")
for name, table in model.relativities.items():
    print(f"\n{name}")
    print(table.select("label", "relativity", "exposure"))

The output includes numeric bands and text levels. These are representative rows from the fitted French motor model:

Base claim frequency: 0.04167 claims per policy-year

BonusMalus
band            relativity   exposure
< 53.0            1.000        12108.31
[53.0, 57.0)      1.355          790.70
[57.0, 60.0)      1.830          694.10

Region
level                         relativity   exposure
Centre                          1.000       5218.59
Rhone-Alpes                     1.356       2312.63
Provence-Alpes-Cotes-D'Azur     1.177       1835.53

Plot the fitted shapes

Run the following to open the fitted shapes, then the training and test actual-versus-expected rate charts. The validation charts use the exact fitted bands or category order, draw Actual in red and Expected in blue, and show Exposure behind the rate lines.

easy_glm.plot_all_ratetables(model.relativities)
model.plot_actual_vs_expected(df)

The images below were generated by that example. Expected rates use the complete fitted model, not just the factor named on the figure.

The same walkthrough is available as a standalone basic usage example. It uses the same public French motor data loader and local cache.

MIT licensed. See LICENSE.