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
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?"
>>> 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.
>>> house_data = fetch_openml(name="house_sales", version=3, as_frame=True)
>>>
>>> # Use only select features
>>> X = house_data.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.target
>>> 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.data, y=y)
Please install the package through conda-forge:
conda install glum -c conda-forge
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.