https://github.com/stsievert/salmon

A tool to collect triplet queries
https://github.com/stsievert/salmon

active-learning crowdsourcing embedding machine-learning triplet-loss triplets

Last synced: 20 days ago
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A tool to collect triplet queries

• Host: GitHub
• URL: https://github.com/stsievert/salmon
• Owner: stsievert
• License: bsd-3-clause
• Created: 2019-11-12T20:32:16.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-06-07T03:48:32.000Z (5 months ago)
• Last Synced: 2024-10-07T18:08:54.970Z (about 1 month ago)
• Topics: active-learning, crowdsourcing, embedding, machine-learning, triplet-loss, triplets
• Language: Python
• Homepage: https://docs.stsievert.com/salmon/
• Size: 119 MB
• Stars: 8
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt
  • Citation: CITATION.cff

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README

        
## Salmon



  








 





 





  



Salmon is a tool for efficiently generating ordinal embeddings. It relies on

"active" machine learning algorithms to choose the most informative queries for

humans to answer.

### Documentation

This documentation is available at these locations:

- **Primary source**: https://docs.stsievert.com/salmon/

- Secondary source: as [a raw PDF][pdf] (and as a [slower loading PDF][blobpdf]).

- Secondary source: as [zipped HTML directory][ziphtml], which requires unzipping the directory

  then opening up `index.html`.

[pdf]:https://github.com/stsievert/salmon/raw/gh-pages/salmon.pdf

[blobpdf]:https://github.com/stsievert/salmon/blob/gh-pages/salmon.pdf

[ziphtml]:https://github.com/stsievert/salmon/archive/refs/heads/gh-pages.zip

Please [file an issue][issue] if you can not access the documentation.

[issue]:https://github.com/stsievert/salmon/issues/new

### Running Salmon offline

Visit the documentation at https://docs.stsievert.com/salmon/offline.html.

Briefly, this should work:

``` shell

$ cd path/to/salmon

$ conda env create -f salmon.lock.yml

$ conda activate salmon

(salmon) $ pip install -e .

```

The documentation online mentions more about how to generate an embedding

offline: https://docs.stsievert.com/salmon/offline.html#generate-embeddings

With this, it's also possible to create a script that uses and imports Salmon:

``` python

from salmon.triplets.samplers import TSTE

import numpy as np

n, d = 85, 2

sampler = TSTE(n=n, d=d)

em_init = np.array([[i, -i] for i in range(n)])

sampler.opt.initialize(embedding=em_init)

queries, scores, meta = sampler.get_queries(num=10_000)

```

This script allows the data scientist to score queries for an embedding they

specify.

[semver]:https://semver.org