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File Structure
- each training set will have it's own
teamsvecs.pkl
file that can be accessed through the following file path: - the file structure is identical to that in the OpeNTF github repository
\---output
| \---{domain} # e.g. dblp
| \---{training dataset} # e.g. toy.dblp.v12.json
| teamsvecs.pkl
Teamsvecs Structure
Teamsvecs.pkl
{
'skill': {spare matrix of n_teams x n_skills},
'member': {sparse matrix of n_teams x n_members},
'loc': {sparse matrix of n_teams x n_locs} // optional
}
skill
- every team in the entire dataset is represented as a row
- every skill in the entire dataset is represented as a column
- each row represents the skills that everyone on the team possesses
member
- every team in the entire dataset is represented as a row
- every member in the entire dataset is represented as a column
- each row represents all the members for a the given team
loc
- every geolocation in the entire dataset is represented as a row
- every skill in the entire dataset is represented as a column
- each row represents all the geolocations for a given team
- NOTE: will be set to
None
if the dataset does not include geolocation
Full Sparse Matrix
- 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)
Example
- team 1 includes members m1 and m2, skills of s2 and s4, and has the geolocation of l2.
- team 2 includes members m2 and m3, skills of s1 and s2, and has the geolocation of l1.
s1 | s2 | s3 | s4 | m1 | m2 | m3 | l1 | l2 |
---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 |
1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 |
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