glum 3.1.3

High performance Python GLMs with all the features!

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!

The goal of glum is to be 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 when N >> K (there are more observations than predictors), 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.

Why did we choose the name glum? We wanted a name that had the letters GLM and wasn't easily confused with any existing implementation. And we thought glum sounded like a funny name (and not glum at all!). If you need a more professional sounding name, feel free to pronounce it as G-L-um. Or maybe it stands for "Generalized linear... ummm... modeling?"

A classic example predicting housing prices

>>> import pandas as pd
>>> from sklearn.datasets import fetch_openml
>>> from glum import GeneralizedLinearRegressor
>>>
>>> # This dataset contains house sale prices for King County, which includes
>>> # Seattle. It includes homes sold between May 2014 and May 2015.
>>> # The full version of this dataset can be found at:
>>> # https://www.openml.org/search?type=data&status=active&id=42092
>>> house_data = pd.read_parquet("data/housing.parquet")
>>>
>>> # Use only select features
>>> X = house_data[
...     [
...         "bedrooms",
...         "bathrooms",
...         "sqft_living",
...         "floors",
...         "waterfront",
...         "view",
...         "condition",
...         "grade",
...         "yr_built",
...         "yr_renovated",
...     ]
... ].copy()
>>>
>>>
>>> # Model whether a house had an above or below median price via a Binomial
>>> # distribution. We'll be doing L1-regularized logistic regression.
>>> price = house_data["price"]
>>> y = (price < price.median()).values.astype(int)
>>> model = GeneralizedLinearRegressor(
...     family='binomial',
...     l1_ratio=1.0,
...     alpha=0.001
... )
>>>
>>> _ = model.fit(X=X, y=y)
>>>
>>> # .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            0.693091
1            0.489500
2            0.449585
3            0.443681
4            0.443498
5            0.443497
>>>
>>> # Models can also be built with formulas from formulaic.
>>> model_formula = GeneralizedLinearRegressor(
...     family='binomial',
...     l1_ratio=1.0,
...     alpha=0.001,
...     formula="bedrooms + np.log(bathrooms + 1) + bs(sqft_living, 3) + C(waterfront)"
... )
>>> _ = model_formula.fit(X=house_data, y=y)

Installation

Please install the package through conda-forge:

conda install glum -c conda-forge

Performance

For optimal performance on an x86_64 architecture, we recommend using the MKL library (conda install mkl). By default, conda usually installs the openblas version, which is slower, but supported on all major architecture and OS.