python-glmnet

Python wrapper for glmnet

Project description

Fork of python-glmnet with support for more recent Python versions.

This is a Python wrapper for the fortran library used in the R package glmnet. While the library includes linear, logistic, Cox, Poisson, and multiple-response Gaussian, only linear and logistic are implemented in this package.

The API follows the conventions of Scikit-Learn, so it is expected to work with tools from that ecosystem.

Installation

requirements

python-glmnet requires Python version >= 3.6, scikit-learn, numpy, and scipy. Installation from source or via pip requires a Fortran compiler.

conda

conda install -c conda-forge glmnet

pip

pip install glmnet

source

glmnet depends on numpy, scikit-learn and scipy. A working Fortran compiler is also required to build the package. For Mac users, brew install gcc will take care of this requirement.

git clone git@github.com:replicahq/python-glmnet.git
cd python-glmnet
python setup.py install

Usage

General

By default, LogitNet and ElasticNet fit a series of models using the lasso penalty (α = 1) and up to 100 values for λ (determined by the algorithm). In addition, after computing the path of λ values, performance metrics for each value of λ are computed using 3-fold cross validation. The value of λ corresponding to the best performing model is saved as the lambda_max_ attribute and the largest value of λ such that the model performance is within cut_point * standard_error of the best scoring model is saved as the lambda_best_ attribute.

The predict and predict_proba methods accept an optional parameter lamb which is used to select which model(s) will be used to make predictions. If lamb is omitted, lambda_best_ is used.

Both models will accept dense or sparse arrays.

Regularized Logistic Regression

from glmnet import LogitNet

m = LogitNet()
m = m.fit(x, y)

Prediction is similar to Scikit-Learn:

# predict labels
p = m.predict(x)
# or probability estimates
p = m.predict_proba(x)

Regularized Linear Regression

from glmnet import ElasticNet

m = ElasticNet()
m = m.fit(x, y)

Predict:

p = m.predict(x)

Project details

Release history Release notifications | RSS feed

2.2.2.post2

May 10, 2024

This version

2.2.2.post1

May 10, 2024

2.2.2 yanked

May 10, 2024

Reason this release was yanked:

build error

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

python_glmnet-2.2.2.post1.tar.gz (89.7 kB view details)

Uploaded May 10, 2024 Source

Built Distributions

python_glmnet-2.2.2.post1-cp311-cp311-musllinux_1_1_x86_64.whl (1.4 MB view details)

Uploaded May 10, 2024 CPython 3.11 musllinux: musl 1.1+ x86-64

python_glmnet-2.2.2.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded May 10, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

python_glmnet-2.2.2.post1-cp311-cp311-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded May 10, 2024 CPython 3.11 macOS 11.0+ ARM64

python_glmnet-2.2.2.post1-cp311-cp311-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded May 10, 2024 CPython 3.11 macOS 10.9+ x86-64

python_glmnet-2.2.2.post1-cp310-cp310-musllinux_1_1_x86_64.whl (1.4 MB view details)

Uploaded May 10, 2024 CPython 3.10 musllinux: musl 1.1+ x86-64

python_glmnet-2.2.2.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded May 10, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

python_glmnet-2.2.2.post1-cp310-cp310-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded May 10, 2024 CPython 3.10 macOS 11.0+ ARM64

python_glmnet-2.2.2.post1-cp310-cp310-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded May 10, 2024 CPython 3.10 macOS 10.9+ x86-64

python_glmnet-2.2.2.post1-cp39-cp39-musllinux_1_1_x86_64.whl (1.4 MB view details)

Uploaded May 10, 2024 CPython 3.9 musllinux: musl 1.1+ x86-64

python_glmnet-2.2.2.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB view details)

Uploaded May 10, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

python_glmnet-2.2.2.post1-cp39-cp39-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded May 10, 2024 CPython 3.9 macOS 11.0+ ARM64

python_glmnet-2.2.2.post1-cp39-cp39-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded May 10, 2024 CPython 3.9 macOS 10.9+ x86-64

File details

Details for the file python_glmnet-2.2.2.post1.tar.gz.

File metadata

Download URL: python_glmnet-2.2.2.post1.tar.gz
Upload date: May 10, 2024
Size: 89.7 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1.tar.gz
Algorithm	Hash digest
SHA256	`54e68a9499f70b9df4f0ed4b11c2e2bba9095b290d2faf4a2f6cc2cc50d2bcc1`
MD5	`d08010f0e4831cc04c5d7c9151b20bbc`
BLAKE2b-256	`580e1ca3d04182fbcfd24f380e35820793899a92ba000043eab2ffa80dde944e`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp311-cp311-musllinux_1_1_x86_64.whl
Upload date: May 10, 2024
Size: 1.4 MB
Tags: CPython 3.11, musllinux: musl 1.1+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`b6f51193b3f02f25fb0411bad1c37c4fcd62ddcbe62bbcbdca0a2b8f39c61bc0`
MD5	`5ab551091ce571964a437169597735bf`
BLAKE2b-256	`827345b86b4b6e844016a28db78f5fa92330f13c98087b25bc760e3ec60eaa88`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: May 10, 2024
Size: 1.7 MB
Tags: CPython 3.11, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`666061b8b1b5d1d9359346bcf3ad73a0274217cfd795c9bbaf23b77340492ba3`
MD5	`b23a11c69fb75885dee8222cd3f7c3b9`
BLAKE2b-256	`fa43e3eb73ac1813f15fe7096960e4daf080ec6e57ce79ca72bce11ed65b9393`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp311-cp311-macosx_11_0_arm64.whl
Upload date: May 10, 2024
Size: 1.1 MB
Tags: CPython 3.11, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`dbf623a606bf932928d4f18716dc545551158e6202f4e2db080063891448aed5`
MD5	`f552876486679592226e48f02e41904a`
BLAKE2b-256	`4ad4a3223a7b328d4a98c859ebc8ed625a5bc75db4fa0b3d02d68f867f0bed52`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp311-cp311-macosx_10_9_x86_64.whl
Upload date: May 10, 2024
Size: 1.8 MB
Tags: CPython 3.11, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`7c382f4dac22e051edff6911e0f0164acbb1037eb3bebc54e96dbaaaa3d6febd`
MD5	`9cf9cf1e8562718136c7eb76945b786e`
BLAKE2b-256	`75431cfcdeb307d932512dc1bb5934e12ac23a6942dfd240583d470afd3c95f2`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp310-cp310-musllinux_1_1_x86_64.whl
Upload date: May 10, 2024
Size: 1.4 MB
Tags: CPython 3.10, musllinux: musl 1.1+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`253d1dc6a66494433292613fc4a430c96eb6fe586f232a7f69b3059b4e06ff29`
MD5	`bea282465c2306548f164e40521bb200`
BLAKE2b-256	`9b6ec0c6bec20f61fad71f4c35cdaa30ae7df244d05e2ea2988e73b15bc06d9f`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: May 10, 2024
Size: 1.7 MB
Tags: CPython 3.10, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`b074e918676e01aa5530763bdd49143070d68e22d46f4cfd86adbef511abe949`
MD5	`ca7a875b4f65bb67de3809c300c0423a`
BLAKE2b-256	`90f5d68e05da27049a6457dafdd05e7f099d5822d064de6afb327d7b0fe79fdf`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp310-cp310-macosx_11_0_arm64.whl
Upload date: May 10, 2024
Size: 1.1 MB
Tags: CPython 3.10, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`bf680b8497983c192b73ab91dd75fff499621433ba540c10dfdf98ac07641fa6`
MD5	`a257b423fb56b345d58dce82fc009bf5`
BLAKE2b-256	`0d1ba194241d01517e1976e50fa642d2aa39102cff04d2b217e22d4b5921fde6`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp310-cp310-macosx_10_9_x86_64.whl
Upload date: May 10, 2024
Size: 1.8 MB
Tags: CPython 3.10, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`a20b7844810a598edb997f327c9681b3e6039f10090184139fb4d676ccc0aa43`
MD5	`7ff4bdb92d3dd68363a3444812de7933`
BLAKE2b-256	`a45e1fca8accb50794902d27caa9d4396cb4cee6c766c7fef0bb4685291ffcd9`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp39-cp39-musllinux_1_1_x86_64.whl
Upload date: May 10, 2024
Size: 1.4 MB
Tags: CPython 3.9, musllinux: musl 1.1+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`55a537b0bb90d578bc139b2729684b0b626d4001204002d418b9bfbf159e1cd7`
MD5	`dbb65af7e418dead392002000ab828a2`
BLAKE2b-256	`3187dc89e53e79b6cdd06eb4cd8fc288072b0f0cc2e6c97a3a417b7df1da2d70`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: May 10, 2024
Size: 1.7 MB
Tags: CPython 3.9, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`207c9d7909e6439682c960a34e8f8cbc5f7097122debb2485c44df1a325131c3`
MD5	`80df74f56c7fe795f0c92a8eae008648`
BLAKE2b-256	`03c7f935e8ffb3a2d7883ef11af98f927b7c7b925d4305bf73cdfd575905a36a`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp39-cp39-macosx_11_0_arm64.whl
Upload date: May 10, 2024
Size: 1.1 MB
Tags: CPython 3.9, macOS 11.0+ ARM64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`40d0b6e3c71054272f2c33aac7b20476f95bd2fd76eb7ab4871f35ebfae2378d`
MD5	`cbcbf6f91e6ba64fd599cfc76e41acc4`
BLAKE2b-256	`b96edb698e633ea570e1d89e3bc7a6667d5539df4efc82d180f224a0855fc9c2`

See more details on using hashes here.

File details

Details for the file python_glmnet-2.2.2.post1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

Download URL: python_glmnet-2.2.2.post1-cp39-cp39-macosx_10_9_x86_64.whl
Upload date: May 10, 2024
Size: 1.8 MB
Tags: CPython 3.9, macOS 10.9+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for python_glmnet-2.2.2.post1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`6aaa8c9ebf4846762b63c6ee2df3b700f5ff25ffdfa7f55152c74e5a5d43abdb`
MD5	`27d00475a3ebfcc3fe518588d9aceb74`
BLAKE2b-256	`a8d8e62d1ea2013163648737d1d6b1e22c50ff57463e30112772d764ec7e186b`

See more details on using hashes here.

python-glmnet 2.2.2.post1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Project description

Installation

requirements

conda

pip

source

Usage

General

Regularized Logistic Regression

Regularized Linear Regression

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes