library(igraph)
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)

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(lcc_verts$vic)#sum(hyp_lcc_verts$vic.day %in% days)
correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
edges_all = dag_dat_all

##### Loop through days
ptm = proc.time()
for (day in days){
  if (which(day==days) %% 100 == 0) print(day)
  
  ##### 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
#   glm.fit = glm(formula, data=victims, family=binomial)
  glm.fit = lm(formula, data=victims)
  glm.probs = predict(glm.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 = data.frame(ir=attr(glm.probs,'name'), dem=as.numeric(glm.probs), cas=0, comb=0)
  combined$cas[match(risk$ir, attr(glm.probs,'name'))] = risk$weight
  
  ##### Gather results
  infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
  for (lambda in lambdas){
    combined$comb = lambda*combined$dem + (1-lambda)*combined$cas
    c_idx = which(lambdas==lambda)
    r_idx = head(which(is.na(correct_rank[,c_idx])),length(infected_irs))
    # !! order should be first: rank of (3,5,5,7) should be (1,2,2,4), may need to do n-rank
    correct_rank[r_idx,c_idx] = match(infected_irs, combined$ir[order(combined$comb, decreasing=T)])
    # maybe should also mark down vic/nonvic status of each?
  }
  
}
print(proc.time()-ptm)