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| 1 | import logging |
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| 2 | from model import distance |
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| 3 | from model import query |
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| 4 | from neartag import NearestByTag |
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| 5 | from config import tag_retriever, user_tags_retriever, user_retriever |
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| 6 | |
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| 7 | def get(user, k=10): |
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| 8 | # see how to order by and get only k firts elements |
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| 9 | res = distance.select().execute(user=user).fetchall() |
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| 10 | res = [(el[2], el[1]) for el in res] |
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| 11 | res.sort() |
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| 12 | return res |
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| 13 | |
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| 14 | def compute(): |
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| 15 | # tags |
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| 16 | tags = [res[0] for res in query(tag_retriever)] |
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| 17 | users = [res[0] for res in query(user_retriever)] |
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| 18 | |
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| 19 | # will get the tags, the users, compute |
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| 20 | # the distance, then save them |
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| 21 | solver = NearestByTag(tags) |
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| 22 | |
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| 23 | for user in users: |
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| 24 | user_tags = [res[0] for res in query(user_tags_retriever % user)] |
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| 25 | solver.add_user(user, user_tags) |
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| 26 | |
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| 27 | # now gets the computed results, and save them |
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| 28 | distance.delete().execute() |
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| 29 | |
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| 30 | for user in users: |
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| 31 | neighbours = solver.neighbours(user) |
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| 32 | for value, name in neighbours: |
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| 33 | distance.insert().execute(user=user, neighbour=name, |
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| 34 | distance=value) |
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| 35 | |
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| 36 | |
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| 37 | |
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