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https://github.com/VowpalWabbit/vowpal_wabbit

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

https://github.com/VowpalWabbit/vowpal_wabbit

Science Score: 36.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    15 of 328 committers (4.6%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.5%) to scientific vocabulary

Keywords

active-learning c-plus-plus contextual-bandits cpp learning-to-search machine-learning online-learning reinforcement-learning

Keywords from Contributors

distributed deep-neural-networks tensor gbrt transformation agents gbdt gbm autograd parallel
Last synced: 6 months ago · JSON representation

Repository

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Basic Info
  • Host: GitHub
  • Owner: VowpalWabbit
  • License: other
  • Language: C++
  • Default Branch: master
  • Homepage: https://vowpalwabbit.org
  • Size: 157 MB
Statistics
  • Stars: 8,600
  • Watchers: 347
  • Forks: 1,933
  • Open Issues: 142
  • Releases: 30
Topics
active-learning c-plus-plus contextual-bandits cpp learning-to-search machine-learning online-learning reinforcement-learning
Created over 16 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Authors

README.md

Vowpal Wabbit

Linux build status Windows build status

codecov Total Alerts

This is the Vowpal Wabbit fast online learning code.

Why Vowpal Wabbit?

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.

  • Input Format. The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.
  • Speed. The learning algorithm is fast -- similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.
  • Scalability. This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.
  • Feature Interaction. Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it's handled.

Visit the wiki to learn more.

Getting Started

For the most up to date instructions for getting started on Windows, MacOS or Linux please see the wiki. This includes:

Owner

  • Name: Vowpal Wabbit
  • Login: VowpalWabbit
  • Kind: organization

GitHub Events

Total
  • Issues event: 11
  • Watch event: 124
  • Issue comment event: 18
  • Push event: 4
  • Pull request event: 3
  • Fork event: 20
Last Year
  • Issues event: 11
  • Watch event: 124
  • Issue comment event: 18
  • Push event: 4
  • Pull request event: 3
  • Fork event: 20

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 8,331
  • Total Committers: 328
  • Avg Commits per committer: 25.399
  • Development Distribution Score (DDS): 0.841
Past Year
  • Commits: 4
  • Committers: 2
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.25
Top Committers
Name Email Commits
John Langford jl@h****t 1,327
Jack Gerrits j****s 1,130
Markus Cozowicz e****r@e****t 670
Luong Hoang l****g@l****m 545
Hal Daume III me@h****e 529
ariel faigon g****9@y****m 249
Griffin Bassman g****n@g****m 247
Jon Morra j****a@e****m 217
olgavrou o****u@g****m 168
Eduardo Salinas e****s@m****m 157
Rajan Chari r****s@m****m 150
John Langford jl@n****) 143
Alexander Trufanov t****n@g****m 117
Paul Mineiro p****b@m****m 111
U-NORTHAMERICA\jcl j****l@J****m 86
Rajan Chari r****i@y****m 79
ataymano@microsoft.com A****v 78
Kai-Wei Chang k****c@g****m 76
Dan M m****d@m****m 70
Alexey Taymanov 4****o 66
ariel faigon a****t@y****m 60
John Langford jl@j****) 57
Aarti Bagul a****5@g****m 57
Jacob Alber j****r@m****m 57
Zhen Qin Z****n@e****m 53
John Langford jl@h****) 52
Martin Popel p****l@u****z 51
Vaclav Petricek v****k@e****m 44
Alekh Agarwal a****l@g****m 44
sidsen s****1@g****m 42
and 298 more...
Committer Domains (Top 20 + Academic)
microsoft.com: 23 eharmony.com: 4 twitter.com: 3 research-mm5.corp.sp1.yahoo.com: 2 nanigans.com: 2 seanet.com: 2 cmu.edu: 2 hunch.net: 2 yendor.com: 2 booking.com: 2 braid.(none): 1 fticonsulting.com: 1 niruc-m6600.redmond.corp.microsoft.com: 1 enversion.dk: 1 research-mm6.corp.sp1.yahoo.com: 1 gramhagen.com: 1 starbuilt.newyork.corp.yahoo.com: 1 dmp-analytics-1.loc.wz.facetz.net: 1 datarobot.com: 1 niruc1.redmond.corp.microsoft.com: 1 itbhu.ac.in: 1 dsub00.umiacs.umd.edu: 1 psu.edu: 1 ieee.org: 1 cs.cmu.edu: 1 cern.ch: 1 eecs.berkeley.edu: 1 n01.clsp.jhu.edu: 1 login.clsp.jhu.edu: 1 a01.clsp.jhu.edu: 1 andrew.cmu.edu: 1 cs.ucsd.edu: 1 cs.stanford.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 67
  • Total pull requests: 188
  • Average time to close issues: about 1 month
  • Average time to close pull requests: about 1 month
  • Total issue authors: 48
  • Total pull request authors: 34
  • Average comments per issue: 2.93
  • Average comments per pull request: 0.39
  • Merged pull requests: 136
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 12
  • Pull requests: 6
  • Average time to close issues: about 2 months
  • Average time to close pull requests: N/A
  • Issue authors: 11
  • Pull request authors: 5
  • Average comments per issue: 0.75
  • Average comments per pull request: 0.5
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ataymano (7)
  • jackgerrits (6)
  • bassmang (3)
  • suming (3)
  • FabianKaiser (2)
  • olgavrou (2)
  • arielf (2)
  • fantauzzi (2)
  • hugovk (1)
  • NegatioN (1)
  • paulusm (1)
  • LIMHARRY (1)
  • marcospassos (1)
  • money8203 (1)
  • samipak458 (1)
Pull Request Authors
  • bassmang (41)
  • jackgerrits (35)
  • olgavrou (25)
  • lalo (15)
  • michiboo (13)
  • lokitoth (12)
  • ataymano (9)
  • peterychang (5)
  • rajan-chari (5)
  • cheng-tan (4)
  • byronxu99 (4)
  • Abinash-bit (3)
  • Sharvani2002 (2)
  • mrucker (2)
  • gogo2464 (2)
Top Labels
Issue Labels
Bug (35) Feature Request (10) Documentation (8) rlos2023 (3) Question (2) Technical debt (1) Unintuitive Behavior (1) Build Issue (1) Lang: Python (1) Help wanted (1) Good First Issue (1)
Pull Request Labels

Dependencies

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