easy-glm 0.1.0

Automate insurance pricing with LASSO-regularised GLMs – blueprint generation, preprocessing, model fitting, rate table extraction & plotting. Built on glum.

easy_glm

Python package to automate building insurance ratetables using (fused) LASSO regularised GLMs. Internally it leverages glum for fitting, providing a higher-level interface tailored to insurance pricing workflows (blueprints, preprocessing, model fitting, rate table extraction & plotting). Inspired by the R package aglm. Packaged with a modern src/ layout.

Installation & Setup

This project uses uv for fast dependency management and venv for virtual environments to ensure reproducibility.

Prerequisites

1. Python 3.10–3.13 - CI tests these versions.
2. uv - Fast Python package installer and resolver

Install uv:

# On Unix/Linux/macOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Installing from Git (Single Command)

You can install the package directly from Git using a single command:

uv pip install git+https://github.com/serband/easy_glm.git

This is the fastest way to get started with easy_glm without cloning the repository.

Quick Setup

Choose one of the following methods to set up your development environment:

Option 1: Cross-platform Python script (Recommended)

python setup_dev.py

Option 2: Direct installation from Git

uv pip install git+https://github.com/serband/easy_glm.git

Option 3: Platform-specific scripts

On Windows (PowerShell):

.\setup_dev.ps1

On Unix/Linux/macOS:

chmod +x setup_dev.sh
./setup_dev.sh

Manual Setup

If you prefer to set up manually:

1. Create virtual environment:

   python -m venv venv
   

2. Activate virtual environment:

   # On Windows
   venv\Scripts\activate
   
   # On Unix/Linux/macOS
   source venv/bin/activate
   

3. Install dependencies:

   uv pip install -r requirements-dev.txt
   uv pip install -e .
   

Usage Example

Here's a complete example of how to use easy_glm to build and visualize insurance rate tables.

For a minimal runnable script, see examples/basic_usage.py.

1. Import Libraries and Load Data

First, import the necessary libraries and load the sample dataset. The package includes a function to load a sample French motor insurance dataset.

import easy_glm 
import polars as pl
import numpy as np

# Load the sample dataset
df = easy_glm.load_external_dataframe()

# Create a train-test split for validation
df = df.with_columns(
    pl.when(pl.lit(np.random.rand(df.height) < 0.7))
    .then(1)
    .otherwise(0)
    .alias("traintest")
)

2. Generate a Preprocessing Blueprint

The generate_blueprint function analyzes the dataframe and creates a "blueprint" that defines how each variable should be preprocessed for modeling.

• Numeric columns: It computes quantile breakpoints.
• Categorical columns: It identifies the levels to keep, lumping rare ones into an 'Other' category.

# Generate the blueprint for the dataset 
blueprint = easy_glm.generate_blueprint(df)

3. Prepare Data for Modeling

Using the blueprint, the prepare_data function transforms the raw data into a feature matrix suitable for the GLM. It applies the transformations defined in the blueprint (binning for numerics, lumping for categoricals).

# Define predictor variables
predictor_variables = ['VehAge', 'Region', 'VehGas', 'DrivAge', 'BonusMalus', 'Density']

# Prepare the dataset for modelling
prepped_data = easy_glm.prepare_data(
    df=df, 
    modelling_variables=predictor_variables, 
    additional_columns=['Exposure', 'ClaimNb'], 
    formats=blueprint, 
    traintest_column='traintest', 
    table_name='cars'
)

4. Fit the LASSO GLM

Fit a LASSO-regularized Generalized Linear Model (GLM) using the prepared data. The fit_lasso_glm function uses cross-validation to find the optimal regularization strength.

# Fit the model
model = easy_glm.fit_lasso_glm(
    dataframe=prepped_data, 
    target="ClaimNb", 
    model_type="Poisson", 
    weight_col="Exposure", 
    train_test_col="traintest",
    divide_target_by_weight=True
)

5. Predict on New Data (Optional)

If you have already prepared data (i.e. ran prepare_data with the same blueprint & predictors) you can obtain predictions using the helper:

# Assume `prepped_data` as above and `model` fitted
new_rows_prepped = prepped_data.head(10).select(pl.all().exclude(["ClaimNb", "Exposure", "traintest"]))
preds = easy_glm.predict_with_model(model, new_rows_prepped)

If you start from raw rows, run prepare_data first with the same formats (blueprint) and predictor list.

6. Generate All Rate Tables

With a fitted model, you can now generate the rate tables for all predictor variables. The generate_all_ratetables function loops through each variable and calculates its relativity.

# Generate rate tables for all predictor variables
all_tables = easy_glm.generate_all_ratetables(
    model=model,
    dataset=df,
    predictor_variables=predictor_variables,
    blueprint=blueprint
)

# You can access the rate table for a specific variable like this:
print(all_tables['VehAge'])

7. Plot the Rate Tables

Finally, visualize the relativities using the plot_all_ratetables function. This will generate a plot for each variable, making it easy to interpret the model's results.

• Numeric variables are shown as line plots.
• Categorical variables are shown as bar plots.

# Plot all rate tables
easy_glm.plot_all_ratetables(all_tables, blueprint)

This will produce a series of plots, one for each variable.

Development

Activating the Environment

After initial setup, activate your environment:

# On Windows
venv\Scripts\activate

# On Unix/Linux/macOS
source venv/bin/activate

Code Quality

The project includes code quality tools:

# Format code
black .

# Lint code
ruff check .

# Run tests
pytest

Project Structure

easy_glm/
├── src/easy_glm/        # Library code (packaged)
│   └── core/            # Core implementation modules
├── tests/               # Pytest test suite
├── examples/            # Usage examples
├── test.py              # Lightweight smoke script
├── pyproject.toml       # Packaging configuration
├── requirements*.txt    # Dependency constraint files
├── setup_dev.*          # Dev environment helpers
└── README.md

Dependencies

Core Dependencies

• duckdb: Fast analytical database for data processing (v1.3+)
• polars: Fast dataframes library (v1.17+)
• numpy: Numerical computing
• pyarrow: Columnar data format
• glum: GLM implementation (v3.0+)
• pandas: Data manipulation and analysis
• matplotlib: Plotting library
• seaborn: Statistical data visualization
• scikit-learn: Machine learning utilities

Development Dependencies

• pytest: Testing framework
• black: Code formatter
• ruff: Fast Python linter
• jupyter: Notebook environment

Additional Usage Ideas

Roadmap ideas:

• Export all ratetables to CSV / Parquet bundle
• Inverse transform scoring for new raw data (auto-prepare + predict)
• Automated monotonic binning / isotonic smoothing option
• CLI entry point (python -m easy_glm build ...)
• Optional caching of downloaded demo dataset
• Configurable blueprint strategies (equal-frequency vs fixed breaks)

Test Performance Tuning

CI sets EASY_GLM_MAX_ROWS=500 to limit dataset size for quicker tests. You can mimic locally:

export EASY_GLM_MAX_ROWS=500
pytest -q

Contributing

See CONTRIBUTING.md for the full guide. Quick checklist:

ruff check .
black .
pytest

License

MIT – see LICENSE.