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?"
>>> 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-forgeFor 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.
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