Data synth #560
Data synth #560
Conversation
| for _ in range(num_calls): | ||
| if len(non_affiliated_people) > 1: | ||
| caller, callee = non_affiliated_people.sample(n=2, replace=False)['phone_number'].values | ||
| self.add_call_log(call_logs_df, caller, callee, start_date) |
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it can be nice to, ahead of time, determine some overlapping social networks, vs random connections
checkout igraph's sbm & forest fire game generators, that can dictate who connects to who for N samples
e.g., given N users and intent for E relns, I think you can get [(a,b), (x, y), ...] from one of igraph's generators, and then turn those into call logs generator calls
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Good idea will look into this
| leader_calls = int(num_affiliated_calls * leader_call_percentage) | ||
| gang_calls = num_affiliated_calls - leader_calls | ||
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| # Generate intra-gang calls |
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neato
i think this is similar to above... maybe for each gang:
- generate a general social network within the gang
- inject the leader or hierarchy or cell structure you want
- add some random overlap between the gang, other gangs, and unaffiliated
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Yea was thinking about how to do this wrt the intra-gang call logs , for example only a leader would likely call to another gangs leaders
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| return call_logs_df | ||
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| def generate_affiliated_call_logs( |
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is this modeling phenomena like burner phones vs regular? i'm not sure how that'd look
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Oh no, this is gang affiliated call logs versus regular, burner would be interesting, not sure how to model that though , maybe like similar profiles like someone calls x,y,z numbers on main, but then realized he slipped up and switches to a burner and calls same numbers?
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yeah i think you'd want the distributions to mirror
so like your simulation secretly tracks who has what burner when, and has burners call one another most of the time, and occasionally the slipup of calling a main
| df = pd.DataFrame(records) | ||
| return df | ||
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| def generate_call_logs( |
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should there be a notion of tracked phone numbers?
- most foolks slooowly rotate the main phone
- some folks quickly rotate their burners, and are primarily used for calling burners? something like that
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(and burners are / aren't associated with people, e.g., sometimes known, sometimes not?)
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Yea was thinking maybe I do something similar to the whereabouts where it has a date tied to the person at that address
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yeah, similar to the social network, i think either:
- prepick a distribution/network/etc ahead of time, and follow that
- do a per-person/community timeline 'simulation'
i think we want to guarantee that burners are active only for a period of time before retiring, so i can imagine some sort of simulation:
ACTION_CALL_BURNER=0
ACTION_CALL_MAIN=1
ACTION_DESTROY_BURNER=2
ACTION_NEW_BURNER=3
ACTION_REPLACE_PHONE=4
person_to_burners : Dict[PersonId, List[BurnerId]]
person_to_main_phone : Dict[PersonId, PhoneId]
for tick:
person = pick(person_to_burners.keys())
switch random():
case ACTION_CALL_BURNER:
...
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a bit hard to see without viz etc but:
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see comment on injecting social networks, vs picking random pairs that'll come out funny looking wrt graph
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i didn't quite follow the split between people, entities (addresses, phones, ...), and records (call logs, criminal, ....) linking them together, maybe easier to see
Yea this was the next step was to figure this part out |
initial PR for people data synthesis