neighbors

A package to perform collaborative filtering on emotion datasets.

https://github.com/cosanlab/neighbors

Science Score: 10.0%

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

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CITATION.cff file
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codemeta.json file
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.zenodo.json file
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DOI references
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Academic publication links
✓
Committers with academic emails
1 of 7 committers (14.3%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary

Keywords

collaborative-filtering

Last synced: 6 months ago · JSON representation

Repository

A package to perform collaborative filtering on emotion datasets.

Basic Info

Host: GitHub
Owner: cosanlab
License: mit
Language: Python
Default Branch: master
Homepage: https://cosanlab.github.io/neighbors
Size: 6.13 MB

Statistics

Stars: 10
Watchers: 3
Forks: 9
Open Issues: 14
Releases: 0

Topics

collaborative-filtering

Created over 9 years ago · Last pushed about 2 years ago

Metadata Files

Readme License

README.md

Neighbors

A Python package for collaborative filtering on social datasets

Installation

Pip (official releases): pip install neighbors
Github (bleeding edge): pip install git+https://github.com/cosanlab/neighbors.git

Getting started

The best way to learn how to use the package is by checking out the documentation site which contains usage tutorials as well as API documentation for all package functionality.

Quick Demo Usage

```python
from neighbors.models import NNMFsgd from neighbors.utils createuseritemmatrix, estimate_performance

Assuming data is 3 column pandas df with 'User', 'Item', 'Rating'

convert it to a (possibly sparse) user x item matrix

mat = createuseritem_matrix(df)

Initialize a model

model = NNMF_sgd(mat)

Fit

model.fit()

If data are time-series optionally fit model using dilation

to leverage auto-correlation and improve performance

model.fit(dilatebynsamples=60)

Visualize results

model.plot_predictions()

Estimate algorithm performance using

Repeated refitting with random masking (dense data)

Or cross-validation (sparse data)

groupresults, userresults = estimateperformance(NNMFsgd, mat) ```

Algorithms

Currently supported algorithms include:

Mean - a baseline model
KNN - k-nearest neighbors
NNMF_mult - non-negative matrix factorization trained via multiplicative updating
NNMF_sgd - non-negative matrix factorization trained via stochastic gradient descent

Owner

Name: Computational Social Affective Neuroscience Laboratory
Login: cosanlab
Kind: organization
Location: Hanover, NH

Website: http://cosanlab.com/
Repositories: 8
Profile: https://github.com/cosanlab

GitHub Events

Total

Issues event: 1
Fork event: 1

Last Year

Issues event: 1
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 204
Total Committers: 7
Avg Commits per committer: 29.143
Development Distribution Score (DDS): 0.485

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
ejolly	e**y@g**m	105
Luke Chang	l**g@g**m	67
ljchang	l**g@d**u	16
Eshin Jolly	e****y	10
Nathan Greenstein	n**5@g**m	4
Eric Andrews	e**c@i**m	1
jcheong	j**8@g**m	1

Committer Domains (Top 20 + Academic)

infiniteline.com: 1 dartmouth.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 24
Total pull requests: 21
Average time to close issues: over 1 year
Average time to close pull requests: 15 days
Total issue authors: 6
Total pull request authors: 5
Average comments per issue: 0.38
Average comments per pull request: 0.71
Merged pull requests: 15
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 2
Pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

ljchang (15)
ejolly (4)
jcheong0428 (1)
Marissa-Clark (1)
Ben-FCC (1)
infiniteline (1)

Pull Request Authors

ejolly (9)
ljchang (7)
ngreenstein (3)
jcheong0428 (1)
infiniteline (1)

Top Labels

Issue Labels

enhancement (4) bug (2) docs (1) question (1)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 436 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 2
(may contain duplicates)
Total versions: 2
Total maintainers: 1

pypi.org: neighbors

A Python package for performing collaborative filtering on social and emotion datasets

Homepage: http://github.com/cosanlab/neighbors
Documentation: https://neighbors.readthedocs.io/
License: MIT
Latest release: 0.1.0
published over 4 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 2
Downloads: 415 Last month

Rankings

Downloads: 8.3%

Dependent packages count: 10.0%

Dependent repos count: 11.6%

Average: 12.0%

Forks count: 12.5%

Stargazers count: 17.7%

Maintainers (1)

ejolly

Last synced: 6 months ago

pypi.org: burnt-ends

A Python package containing modular, well-tested, utility and statistical functions handy for scientific computing and analysis.

Homepage: http://github.com/cosanlab/neighbors
Documentation: https://burnt-ends.readthedocs.io/
License: MIT
Latest release: 0.0.1
published about 3 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 21 Last month

Rankings

Dependent packages count: 6.6%

Forks count: 12.8%

Average: 17.0%

Stargazers count: 17.9%

Dependent repos count: 30.6%

Maintainers (1)

ejolly

Last synced: 6 months ago

Dependencies

requirements-dev.txt pypi

black * development
mkdocs * development
mkdocs-jupyter * development
mkdocs-material * development
mkdocstrings * development

requirements.txt pypi

matplotlib *
numba *
numpy *
pandas >=1.0
scipy *
seaborn *