| Name | glum JSON |
| Version |
3.1.1
JSON |
| download |
| home_page | https://github.com/Quantco/glum |
| Summary | High performance Python GLMs with all the features! |
| upload_time | 2025-01-13 16:13:20 |
| maintainer | None |
| docs_url | None |
| author | QuantCo, Inc. |
| requires_python | >=3.9 |
| license | BSD |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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No coveralls.
|
# glum
[](https://github.com/Quantco/glum/actions)
[](https://github.com/Quantco/glum/actions/workflows/daily.yml)
[](https://glum.readthedocs.io/)
[](https://anaconda.org/conda-forge/glum)
[](https://pypi.org/project/glum)
[](https://pypi.org/project/glum)
[Documentation](https://glum.readthedocs.io/en/latest/)
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](https://github.com/scikit-learn/scikit-learn/pull/9405), 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](src/glum_benchmarks/README.md). 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](https://glum.readthedocs.io/en/latest/).
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
```python
>>> 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:
```bash
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.
Raw data
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