glum 2.0.0

Python package to benchmark GLM implementations.

glum

Documentation

Generalized linear models (GLM) are a core statistical tool that include many common methods like least-squares regression, Poisson regression and logistic regression as special cases. At QuantCo, we have used GLMs in e-commerce pricing, insurance claims prediction and more. We have developed glum, a fast Python-first GLM library. The development was based on a fork of scikit-learn, so it has a scikit-learn-like API. We are thankful for the starting point provided by Christian Lorentzen in that PR!

glum is at least as feature-complete as existing GLM libraries like glmnet or h2o. It supports

• Built-in cross validation for optimal regularization, efficiently exploiting a “regularization path”
• L1 regularization, which produces sparse and easily interpretable solutions
• L2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effects
• Elastic net regularization
• Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions
• Box constraints, linear inequality constraints, sample weights, offsets

This repo also includes tools for benchmarking GLM implementations in the glum_benchmarks module. For details on the benchmarking, see here. Although the performance of glum relative to glmnet and h2o depends on the specific problem, we find that it is consistently much faster for a wide range of problems.

For more information on glum, including tutorials and API reference, please see the documentation.

An example: predicting car insurance claim frequency using Poisson regression.

This example uses a public French car insurance dataset.

>>> import pandas as pd
>>> import numpy as np
>>> from glum_benchmarks.problems import load_data, generate_narrow_insurance_dataset
>>> from glum_benchmarks.util import get_obj_val
>>> from glum import GeneralizedLinearRegressor
>>>
>>> # Load the French Motor Insurance dataset
>>> dat = load_data(generate_narrow_insurance_dataset)
>>> X, y, sample_weight = dat['X'], dat['y'], dat['sample_weight']
>>>
>>> # Model the number of claims per year as Poisson and regularize using a L1-penalty.
>>> model = GeneralizedLinearRegressor(
...     family='poisson',
...     l1_ratio=1.0,
...     alpha=0.001
... )
>>>
>>> _ = model.fit(X=X, y=y, sample_weight=sample_weight)
>>>
>>> # .report_diagnostics shows details about the steps taken by the iterative solver
>>> diags = model.get_formatted_diagnostics(full_report=True)
>>> diags[['objective_fct']]
        objective_fct
n_iter               
0            0.331670
1            0.328841
2            0.319605
3            0.318660
4            0.318641
5            0.318641

Installation

Please install the package through conda-forge:

conda install glum -c conda-forge