library(igraph)
setwd('~/Documents/Cascade Project')
load('Raw Data/lcc.RData')
load('Results/hyper-lcc.RData')
load('Results/dag_dat_all.RData')
source('Scripts/temporal.R')
source('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)

##### Loop through days
alpha = 1/100
gamma = 0.18
days = 70:max(hyp_lcc_verts$vic.day, na.rm=T)
lambdas = 0#c(0, exp(seq(log(0.0000001), log(.0005), length.out=150)), 1)
nvics = sum(hyp_lcc_verts$vic.day %in% days)
correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
# correct_rank1 = correct_rank2 = correct_rank3 = c()
edges_all = dag_dat_all[dag_dat_all$dist<2,]
ptm = proc.time()
for (day in days){
  if (day %% 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(gamma,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$comb = combined$cas#lambda*combined$dem + (1-lambda)*(1-combined$dem)*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)


##### Plot results
hist(correct_rank3,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
     xlab='Risk Ranking of Victims',main='')
hist(correct_rank1,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
legend("topright", c("Demographics Model", "Cascade Model"), 
       fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))

counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
                  colSums(correct_rank<(vcount(lcc)/200))*100/nvics,
                  colSums(correct_rank<(vcount(lcc)/100))*100/nvics),
                nrow=3, byrow=T)
plot(lambdas,counts[1,],log='x',type='l')

correct_rank1 = correct_rank[,length(lambdas)]
correct_rank2 = correct_rank[,1]
correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))]
counts = matrix(c(sum(correct_rank1<(vcount(lcc)*0.001)),
               sum(correct_rank1<(vcount(lcc)*0.005)),
               sum(correct_rank1<(vcount(lcc)*0.01)),
               sum(correct_rank2<(vcount(lcc)*0.001)),
               sum(correct_rank2<(vcount(lcc)*0.005)),
               sum(correct_rank2<(vcount(lcc)*0.01)),
               sum(correct_rank3<(vcount(lcc)*0.001)),
               sum(correct_rank3<(vcount(lcc)*0.005)),
               sum(correct_rank3<(vcount(lcc)*0.01))),
               nrow=3, byrow=T)
counts = counts*100/nvics
barplot(counts, 
        xlab="Size of High-Risk Population",
        ylab="Percent of Victims Predicted",
        names.arg=c('0.1%','0.5%','1%'),ylim=c(0,max(counts)*1.1),
        col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
        beside=TRUE)
legend("topleft", inset=0.05, 
       c("Demographics Model", "Cascade Model", "Combined Model"), 
       fill=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)))
box(which='plot')
par(new=T)
counts = counts/(100/nvics)
barplot(counts, 
        ylim=c(0,max(counts)*1.1),
        col=c(rgb(0,0,1,0),rgb(1,0,0,0),rgb(0,1,0,0)),
        beside=TRUE)
axis(side = 4)
mtext(side = 4, line = 3, "Number of Victims Predicted")

popsizes = c(0.1, 0.5, 1)
plot(popsizes,counts[1,],type='l',ylim=c(0,max(counts)))
lines(popsizes,counts[2,])
lines(popsizes,counts[3,])
lines(c(0,1),c(0,1))

#### Precision-Recall Curve
plot(ecdf(correct_rank1),col='red',xlim=c(0,vcount(lcc)),lwd=2)
plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T)
plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T)
legend("bottomright", inset=0.05, 
       c("Demographics Model", "Cascade Model", "Combined Model"), 
       fill=c('red','darkblue','darkgreen'))
lines(c(0,vcount(lcc)),c(0,1))