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Open Source Science

https://github.com/interactive-media-lab-data-science-team/vampyr-mtl

Scalable, Portable Multi-task Learning Library for Python

https://github.com/interactive-media-lab-data-science-team/vampyr-mtl

Science Score: 23.0%

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

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, acm.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.4%) to scientific vocabulary

Keywords

machine-learning machine-learning-algorithms multitask-learning python3
Last synced: 5 months ago · JSON representation

Repository

Scalable, Portable Multi-task Learning Library for Python

Basic Info
  • Host: GitHub
  • Owner: Interactive-Media-Lab-Data-Science-Team
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 3.33 MB
Statistics
  • Stars: 2
  • Watchers: 0
  • Forks: 2
  • Open Issues: 9
  • Releases: 0
Topics
machine-learning machine-learning-algorithms multitask-learning python3
Created over 5 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

MD-MTL: An Ensemble Med-Multi-Task Learning Package

Python Pandas Plotly Numpy Scikit-learn GitHub Last Commit GitHub Issues GitHub Stars GitHub Forks

Github License

Vampire Squid

MD-MTL is a machine learning python package inspired by MALSAR multi-task learning Matlab algorithm, combined with up-to-date multi-task learning researches and algorithm for public research purposes.

Demo

Demo for runing Clustered Multitask Learning algorithm with risk factor analysis, pls copy to your playground and do not ask for change authorizations

Functionality

  • Algorithms:
    • Multitask Binary Logistic Regression
    • Hinge Loss
    • L21 normalization
    • Multitask Linear Regression
    • Mean Square Error
    • L21 normalization
    • Cluster Multitask Least Square Regression
    • L21 Normalization
  • Util Functions:
    • MTLdatasplit
    • Split data set inside each task with predefined proportions, build on sklearn traintestsplit
    • MTLdataextract
    • Extract data from pandas.DataFrame to desired data matrix format, with desired target and task
    • Cross Validation with k Folds:
    • Cross validation with predefined k folds and scoring methods
    • RFA
    • Risk Factor Analysis with Plotly fig returned

more see Documentation

Related Reseaches

Accelerated Gredient Method

Clustered Multi-Task Learning: a Convex Formulation

Regularized Multi-task Learning

Installation (test version)

pip install -i https://test.pypi.org/simple/ MD-MTL==0.0.8

Dependency

Auto generated by pigar - scikit_learn == 0.22.1

  • setuptools == 45.2.0

  • tqdm == 4.46.1

  • plotly == 4.8.1

  • numpy == 1.18.1

  • pandas == 1.0.4

  • pytest == 5.3.5

  • scipy == 1.4.1

Package Update

  • Manual Deployment:

    • test-pypi manual
    • python setup.py sdist bdist_wheel
    • twine check dist/*
    • twine upload --repository-url https://test.pypi.org/legacy/ dist/*

or rewrite .pypirc file with credencials and

  • python3 twine upload -r pypi dist/*

  • python3 setup.py dist bdist_wheel

    • Automation(Linux):

  • deploy: ./build_deploy.sh

  • test: ./build_deploy.sh --test

Development

Windows ```$ git clone https://github.com/Interactive-Media-Lab-Data-Science-Team/Vampyr-MTL.git

$ cd Vampyr_MTL

$ python3 -m venv myenv

$ myenv/Scripts/activate

$ pip3 install -r requirements.txt ```

Doc

https://test.pypi.org/project/MD-MTL/0.0.8/

powered by Sphinx with Google comment style, compile with napoleon: sphinx-apidoc -f -o docs/source Vampyr_MTL

Owner

  • Name: Interactive Media Lab Data Science Team
  • Login: Interactive-Media-Lab-Data-Science-Team
  • Kind: organization
  • Email: haoyanhy.jiang@mail.utoronto.ca
  • Location: Toronto, ON

IML Data Science Team Repo for Code Contribution and Collaboration

GitHub Events

Total
Last Year

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 74
  • Total Committers: 3
  • Avg Commits per committer: 24.667
  • Development Distribution Score (DDS): 0.284
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
DaPraxis h****9@g****m 53
Max H****9@g****m 20
wanglu61 5****1 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 4
  • Total pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.25
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 4
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • DaPraxis (4)
Pull Request Authors
  • dependabot[bot] (4)
  • wanglu61 (1)
Top Labels
Issue Labels
documentation (3) Algorithm (3) Test (2) enhancement (2) Unil Function (1) Visualization/side function (1)
Pull Request Labels
dependencies (4) python (2) ruby (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 12 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 2
  • Total maintainers: 1
pypi.org: md-mtl

An Ensemble Med-Multi-Task Learning Package

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 12 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 19.1%
Dependent repos count: 21.6%
Average: 24.2%
Stargazers count: 27.8%
Downloads: 42.2%
Maintainers (1)
Max_JJ
Last synced: 6 months ago

Dependencies

doc/requirements.txt pypi
  • Sphinx *
  • sphinx-autobuild *
requirements.txt pypi
  • numpy ==1.18.1
  • pandas ==1.0.4
  • plotly ==4.8.1
  • pytest ==5.3.5
  • recommonmark *
  • scikit_learn ==0.22.1
  • scipy ==1.4.1
  • setuptools ==45.2.0
  • sphinxcontrib-napoleon *
  • tqdm ==4.46.1
doc/.deploy_heroku/Gemfile rubygems
  • sinatra ~> 1.4.2
doc/.deploy_heroku/Gemfile.lock rubygems
  • rack 1.5.2
  • rack-protection 1.5.1
  • sinatra 1.4.4
  • tilt 1.4.1
.github/workflows/python-app.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/python-publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite