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README.md
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---
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language:
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- en
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tags:
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- machine-learning
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- neural-team-formation
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pretty_name: 'OpeNTF: An Open-Source Neural Team Formation Benchmark Library'
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---
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# File Structure
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- each training set will have it's own `teamsvecs.pkl` file that can be accessed through the following file path:
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- the file structure is identical to that in the OpeNTF github repository
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```
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\---output
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| \---{domain} # e.g. dblp
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| \---{training dataset} # e.g. toy.dblp.v12.json
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| teamsvecs.pkl
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```
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# Teamsvecs Structure
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## Teamsvecs.pkl
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```
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{
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'skill': {spare matrix of n_teams x n_skills},
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'member': {sparse matrix of n_teams x n_members},
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'loc': {sparse matrix of n_teams x n_locs} // optional
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}
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```
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### skill
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- every team in the entire dataset is represented as a row
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- every skill in the entire dataset is represented as a column
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- **each row represents the skills that everyone on the team possesses**
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### member
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- every team in the entire dataset is represented as a row
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- every member in the entire dataset is represented as a column
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- **each row represents all the members for a the given team**
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### loc
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- every geolocation in the entire dataset is represented as a row
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- every skill in the entire dataset is represented as a column
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- **each row represents all the geolocations for a given team**
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- NOTE: will be set to `None` if the dataset does not include geolocation
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## Full Sparse Matrix
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- the three sparse matrices from above can be aligned side by side to form a sparse matrix of `n_teams x (n_skills + n_members + n_locs)`
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### Example
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- team 1 includes members m1 and m2, skills of s2 and s4, and has the geolocation of l2.
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- team 2 includes members m2 and m3, skills of s1 and s2, and has the geolocation of l1.
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| s1 | s2 | s3 | s4 | m1 | m2 | m3 | l1 | l2 |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 |
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| 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 |
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# Links
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- [OpeNTF GitHub Repository](https://github.com/fani-lab/OpeNTF)
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