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|
%global _empty_manifest_terminate_build 0
Name: python-glum
Version: 2.5.0
Release: 1
Summary: High performance Python GLMs with all the features!
License: BSD
URL: https://github.com/Quantco/glum
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/61/82/e05201187cc571d496d08c628fb159abbdfa52ab2b7241855e27cf6c387b/glum-2.5.0.tar.gz
Requires: python3-joblib
Requires: python3-numexpr
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-scikit-learn
Requires: python3-scipy
Requires: python3-tabmat
%description
# glum
[](https://github.com/Quantco/glum/actions)
[](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
>>> 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
```
# Installation
Please install the package through conda-forge:
```bash
conda install glum -c conda-forge
```
%package -n python3-glum
Summary: High performance Python GLMs with all the features!
Provides: python-glum
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-glum
# glum
[](https://github.com/Quantco/glum/actions)
[](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
>>> 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
```
# Installation
Please install the package through conda-forge:
```bash
conda install glum -c conda-forge
```
%package help
Summary: Development documents and examples for glum
Provides: python3-glum-doc
%description help
# glum
[](https://github.com/Quantco/glum/actions)
[](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
>>> 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
```
# Installation
Please install the package through conda-forge:
```bash
conda install glum -c conda-forge
```
%prep
%autosetup -n glum-2.5.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-glum -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.0-1
- Package Spec generated
|
