MLxtend

MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack - Published in JOSS (2018)

https://github.com/rasbt/mlxtend

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 6 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
9 of 112 committers (8.0%) from academic institutions
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Institutional organization owner
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

association-rules data-mining data-science machine-learning python supervised-learning unsupervised-learning

Keywords from Contributors

neuroscience

Scientific Fields

Mathematics Computer Science - 84% confidence

Last synced: 4 months ago · JSON representation

Repository

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Basic Info

Host: GitHub
Owner: rasbt
License: other
Language: Python
Default Branch: master
Homepage: https://rasbt.github.io/mlxtend/
Size: 94.3 MB

Statistics

Stars: 5,061
Watchers: 116
Forks: 884
Open Issues: 151
Releases: 32

Topics

association-rules data-mining data-science machine-learning python supervised-learning unsupervised-learning

Created over 11 years ago · Last pushed 6 months ago

Metadata Files

Readme License Code of conduct

README.md

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

Sebastian Raschka 2014-2024

Installing mlxtend

PyPI

To install mlxtend, just execute

bash pip install mlxtend

Alternatively, you could download the package manually from the Python Package Index https://pypi.python.org/pypi/mlxtend, unzip it, navigate into the package, and use the command:

bash python setup.py install

Conda

If you use conda, to install mlxtend just execute

bash conda install -c conda-forge mlxtend

Dev Version

The mlxtend version on PyPI may always be one step behind; you can install the latest development version from the GitHub repository by executing

bash pip install git+git://github.com/rasbt/mlxtend.git#egg=mlxtend

Or, you can fork the GitHub repository from https://github.com/rasbt/mlxtend and install mlxtend from your local drive via

bash python setup.py install

Examples

```python import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import itertools from sklearn.linearmodel import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from mlxtend.classifier import EnsembleVoteClassifier from mlxtend.data import irisdata from mlxtend.plotting import plotdecisionregions

Initializing Classifiers

clf1 = LogisticRegression(randomstate=0) clf2 = RandomForestClassifier(randomstate=0) clf3 = SVC(random_state=0, probability=True) eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft')

Loading some example data

X, y = iris_data() X = X[:,[0, 2]]

Plotting Decision Regions

gs = gridspec.GridSpec(2, 2) fig = plt.figure(figsize=(10, 8))

for clf, lab, grd in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble'], itertools.product([0, 1], repeat=2)): clf.fit(X, y) ax = plt.subplot(gs[grd[0], grd[1]]) fig = plotdecisionregions(X=X, y=y, clf=clf, legend=2) plt.title(lab) plt.show() ```

If you use mlxtend as part of your workflow in a scientific publication, please consider citing the mlxtend repository with the following DOI:

@article{raschkas_2018_mlxtend, author = {Sebastian Raschka}, title = {MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack}, journal = {The Journal of Open Source Software}, volume = {3}, number = {24}, month = apr, year = 2018, publisher = {The Open Journal}, doi = {10.21105/joss.00638}, url = {https://joss.theoj.org/papers/10.21105/joss.00638} }

Raschka, Sebastian (2018) MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack. J Open Source Softw 3(24).

License

This project is released under a permissive new BSD open source license (LICENSE-BSD3.txt) and commercially usable. There is no warranty; not even for merchantability or fitness for a particular purpose.
In addition, you may use, copy, modify and redistribute all artistic creative works (figures and images) included in this distribution under the directory according to the terms and conditions of the Creative Commons Attribution 4.0 International License. See the file LICENSE-CC-BY.txt for details. (Computer-generated graphics such as the plots produced by matplotlib fall under the BSD license mentioned above).

Contact

The best way to ask questions is via the GitHub Discussions channel. In case you encounter usage bugs, please don't hesitate to use the GitHub's issue tracker directly.

Owner

Name: Sebastian Raschka
Login: rasbt
Kind: user
Location: Madison, WI
Company: @Lightning-AI , University of Wisconsin-Madison

Website: https://magazine.sebastianraschka.com
Twitter: rasbt
Repositories: 136
Profile: https://github.com/rasbt

Machine Learning and AI researcher & currently research engineer at a startup

JOSS Publication

MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack

Published

April 22, 2018

DOI

10.21105/joss.00638

Volume 3, Issue 24, Page 638

Authors

Sebastian Raschka

Michigan State University

Editor

Ariel Rokem

GitHub Events

Total

Create event: 5
Release event: 3
Issues event: 11
Watch event: 180
Delete event: 3
Issue comment event: 62
Push event: 20
Pull request event: 28
Pull request review event: 25
Pull request review comment event: 13
Fork event: 35

Last Year

Create event: 5
Release event: 3
Issues event: 11
Watch event: 180
Delete event: 3
Issue comment event: 62
Push event: 20
Pull request event: 28
Pull request review event: 25
Pull request review comment event: 13
Fork event: 35

Committers

Last synced: 5 months ago

All Time

Total Commits: 1,360
Total Committers: 112
Avg Commits per committer: 12.143
Development Distribution Score (DDS): 0.317

Past Year

Commits: 15
Committers: 6
Avg Commits per committer: 2.5
Development Distribution Score (DDS): 0.6

Top Committers

Name	Email	Commits
rasbt	m**l@s**m	929
SolidAhmad	n**r@g**m	110
Dmitry Labazkin	l**y@g**m	25
James Bourbeau	j**u@g**m	18
Sebastian Raschka	r**s@m**u	16
reiinakano	r**o@y**m	13
Maitreyee Mhasakar	m**r@g**m	12
Takashi Oya	t**a@T**l	10
Kota Mori	k**5@g**m	10
JJLWHarrison	j**9@g**m	9
Will McGinnis	w**l@p**m	8
Zach Griffith	z**h@i**u	8
fatihsen20	f**0@g**m	8
jaksmid	j**d@b**i	8
zuari1993	l**n@w**u	7
Ne-oL	3****L	7
Daniel Kleine	5****e	6
ftnext	t**p@g**m	6
Daniel	d**9@g**m	5
F.J.H. Heras	f**s@g**m	5
Guillaume Poirier-Morency	g**y@g**m	5
Qiang Gu	3****u	5
Colin Carroll	c**l@g**m	4
Floris Hoogenboom	f**s@d**l	4
Steve Harenberg	h**d@g**m	4
naman.mistry	6****y	4
Vahid Mirjalili	2****y	4
geo	a**e@g**m	3
Marco Tiraboschi	m**i@h**t	3
Denis Barbier	b**r@i**r	3
and 82 more...

Committer Domains (Top 20 + Academic)

msu.edu: 2 wisc.edu: 2 sebastianraschka.com: 1 predikto.com: 1 icecube.wisc.edu: 1 blindspot.ai: 1 digitaldreamworks.nl: 1 hotmail.it: 1 imacs.polytechnique.fr: 1 ulb.ac.be: 1 zoho.com: 1 peterbloem.nl: 1 ulaval.ca: 1 tribalgroup.com: 1 upserve.com: 1 scortex.io: 1 lsv.uni-saarland.de: 1 mathewsavage.com: 1 rachum.com: 1 montevideo.com.uy: 1 ucdavis.edu: 1 college.harvard.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 91
Total pull requests: 98
Average time to close issues: 5 months
Average time to close pull requests: 2 months
Total issue authors: 67
Total pull request authors: 34
Average comments per issue: 3.36
Average comments per pull request: 3.72
Merged pull requests: 81
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 7
Pull requests: 19
Average time to close issues: 12 days
Average time to close pull requests: 13 days
Issue authors: 7
Pull request authors: 8
Average comments per issue: 1.29
Average comments per pull request: 2.05
Merged pull requests: 16
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

rasbt (17)
NimaSarajpoor (4)
ftnext (3)
carlsmedstad (2)
it176131 (2)
arilwan (2)
andysingal (1)
prateek-bricklane (1)
huanfachen (1)
labdmitriy (1)
zuari1993 (1)
ltbd78 (1)
josejub (1)
rich-ard (1)
nucsit026 (1)

Pull Request Authors

rasbt (43)
d-kleine (12)
fkdosilovic (4)
NimaSarajpoor (4)
it176131 (4)
namanmistry (4)
zuari1993 (4)
HassaneSkikri (4)
zazass8 (4)
ftnext (3)
muhammadanas0716 (2)
soumyadsanyal (2)
danickblouin (2)
MarceloNunesAlves (2)
Bot-wxt1221 (2)

Top Labels

Issue Labels

Bug (27) New Feature (19) Question (12) Documentation (11) Enhancement (9) help wanted (5) easy (4)

Pull Request Labels

Enhancement (2) in progress (2)

Packages

Total packages: 4
Total downloads:
- pypi 746,190 last-month
Total docker downloads: 109,005

Total dependent packages: 58
(may contain duplicates)
Total dependent repositories: 1,311
(may contain duplicates)
Total versions: 101
Total maintainers: 1

pypi.org: mlxtend

Machine Learning Library Extensions

Homepage: https://github.com/rasbt/mlxtend
Documentation: https://rasbt.github.io/mlxtend
License: BSD 3-Clause
Latest release: 0.23.4
published 11 months ago

Versions: 53
Dependent Packages: 56
Dependent Repositories: 1,293
Downloads: 746,190 Last month
Docker Downloads: 109,005

Rankings

Dependent repos count: 0.3%

Dependent packages count: 0.4%

Downloads: 0.4%

Average: 0.8%

Stargazers count: 1.0%

Docker downloads count: 1.1%

Forks count: 1.5%

Maintainers (1)

rasbt

Last synced: 4 months ago

proxy.golang.org: github.com/rasbt/mlxtend

Documentation: https://pkg.go.dev/github.com/rasbt/mlxtend#section-documentation
License: other
Latest release: v0.23.4
published 11 months ago

Versions: 28
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 9.0%

Average: 9.6%

Dependent repos count: 10.2%

Last synced: 4 months ago

conda-forge.org: mlxtend

A library of Python tools and extensions for data science and machine learning. Contact ============= If you have any questions or comments about mlxtend, please feel free to contact me via eMail: mail@sebastianraschka.com or Twitter: https://twitter.com/rasbt This project is hosted at https://github.com/rasbt/mlxtend The documentation can be found at http://rasbt.github.io/mlxtend/

Homepage: https://github.com/rasbt/mlxtend
License: BSD-3-Clause
Latest release: 0.21.0
published over 3 years ago

Versions: 17
Dependent Packages: 2
Dependent Repositories: 9

Rankings

Stargazers count: 5.4%

Forks count: 5.8%

Average: 10.6%

Dependent repos count: 11.6%

Dependent packages count: 19.6%

Last synced: 4 months ago

anaconda.org: mlxtend

A library of Python tools and extensions for data science and machine learning.

Homepage: https://github.com/rasbt/mlxtend
License: BSD-3-Clause
Latest release: 0.23.4
published 11 months ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 9

Rankings

Stargazers count: 12.0%

Forks count: 12.3%

Average: 28.6%

Dependent repos count: 38.9%

Dependent packages count: 51.2%

Last synced: 4 months ago

Dependencies

requirements-test.txt pypi

dlib * test
imageio * test
isort * test
joblib * test
scikit-image * test

requirements.txt pypi

joblib >=0.13.2
matplotlib >=3.0.0
numpy >=1.16.2
pandas >=0.24.2
scikit-learn >=1.0.2
scipy >=1.2.1

.github/workflows/linter.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/python-package-conda.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite
codecov/codecov-action v2 composite

requirements.lock pypi

cycler ==0.11.0
fonttools ==4.34.4
joblib ==1.1.0
kiwisolver ==1.4.4
matplotlib ==3.5.2
numpy ==1.23.1
packaging ==21.3
pandas ==1.4.3
pillow ==9.2.0
pyparsing ==3.0.9
python-dateutil ==2.8.2
pytz ==2022.1
scikit-learn ==1.1.1
scipy ==1.8.1
six ==1.16.0
threadpoolctl ==3.1.0

environment.yml pypi

markdown >=3.3.6

setup.py pypi

MLxtend

Science Score: 95.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Links

Installing mlxtend

PyPI

Conda

Dev Version

Examples

Initializing Classifiers

Loading some example data

Plotting Decision Regions

License

Contact

Owner

JOSS Publication

MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: mlxtend

Rankings

Maintainers (1)

proxy.golang.org: github.com/rasbt/mlxtend

Rankings

conda-forge.org: mlxtend

Rankings

anaconda.org: mlxtend

Rankings

Dependencies