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library(igraph)
library(foreach)
library(doMC)
registerDoMC(cores=4)
setwd('~/Documents/Cascade Project')
load('Raw Data/lcc.RData')
load('Results/hyper-lcc.RData')
load('Results/dag_dat_all.RData')
source('criminal_cascades/R Scripts/temporal.R')
source('criminal_cascades/R Scripts/structural.R')
##### Initialize data
formula = vic ~ sex + race + age + gang.member #+ gang.name
lcc_verts$sex = as.factor(lcc_verts$sex)
lcc_verts$race = as.factor(lcc_verts$race)
lcc_verts$age = as.numeric(lcc_verts$age)
lcc_verts$gang.name = as.factor(lcc_verts$gang.name)
# sum(hyp_lcc_verts$vic)/length(days)
df = data.frame(ir=lcc_verts$ir_no, dem=0, cas=0, comb=0)
alpha = 0.0028
delta = 0.06
days = sort(unique(hyp_lcc_verts$vic.day)) # 70:max(hyp_lcc_verts$vic.day, na.rm=T)
lambdas = c(0,1)#c(0, exp(seq(log(0.0000001), log(.0005), length.out=150)), 1)
nvics = sum(hyp_lcc_verts$vic.day %in% days)
edges_all = dag_dat_all
##### Loop through days
writeLines(c(""), "Results/log.txt")
ptm = proc.time()
correct_rank = foreach (day = days, .combine=rbind) %dopar% {
if (which(day==days) %% 100 == 0){sink("Results/log.txt", append=TRUE);cat(paste("day:",day,"\n"))}
##### Demographics model
vics = match(unique(hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day<day)]),lcc_verts$name)
victims = lcc_verts[,c('vic','sex','race','age','gang.member','gang.name')]
victims$vic[vics] = TRUE
victims$vic[-vics] = FALSE
fit = lm(formula, data=victims)
# fit = glm(formula, data=victims, family=binomial)
# fit = randomForest(formula, data=victims[,1:5], ntree=100)
probs = predict(fit, newdata=lcc_verts, type='response')
##### Cascade Model
edges = edges_all[which(edges_all$t1<day),]
f = temporal(edges$t1, day, alpha)
h = structural(delta,edges$dist)
weights = f*h
ids = edges$to
irs = hyp_lcc_verts$ir_no[ids]
risk = data.frame(id=ids, ir=irs, weight=weights)
risk = risk[order(weights, decreasing=T),]
risk = risk[match(unique(risk$ir),risk$ir),]
# maybe need to change this to reflect new algorithm that accounts for \tilde{p}
##### Combined Model
combined = df#data.frame(ir=attr(probs,'name'), dem=as.numeric(probs), cas=0, comb=0)
combined$dem[match(attr(probs,'name'), df$ir)] = as.numeric(probs)
combined$cas[match(risk$ir, attr(probs,'name'))] = risk$weight
##### Gather results
infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
crday = matrix(nrow=length(infected_irs), ncol=length(lambdas))
for (lambda in lambdas){
combined$comb = lambda*combined$dem + (1-lambda)*combined$cas
c_idx = which(lambdas==lambda)
crday[,c_idx] = rank(-combined$comb,ties.method='average')[match(infected_irs,combined$ir)]
}
return(crday)
}
print(proc.time()-ptm)
# save(correct_rank, file='Results/correct_rank_62815.RData')
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