Merge branch 'master' of github.com:Thibauth/criminal_cascades

1 files changed, 23 insertions, 16 deletions

diff --git a/R Scripts/predict-victims.R b/R Scripts/predict-victims.R
index 470815d..2bda7e2 100644
--- a/R Scripts/predict-victims.R
+++ b/R Scripts/predict-victims.R
@@ -1,4 +1,7 @@
 library(igraph)
+library(foreach)
+library(doMC)
+registerDoMC(cores=4)
 setwd('~/Documents/Cascade Project')
 load('Raw Data/lcc.RData')
 load('Results/hyper-lcc.RData')
@@ -7,34 +10,36 @@ 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
+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(lcc_verts$vic)#sum(hyp_lcc_verts$vic.day %in% days)
-correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
+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()
-for (day in days){
-  if (which(day==days) %% 100 == 0) print(day)
-  
+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
-#   glm.fit = glm(formula, data=victims, family=binomial)
-  glm.fit = lm(formula, data=victims)
-  glm.probs = predict(glm.fit, newdata=lcc_verts, type='response')
+  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),]
@@ -49,19 +54,21 @@ for (day in days){
 # 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$weight
+  combined = df#data.frame(ir=attr(probs,'name'), dem=as.numeric(probs), cas=0, comb=0)
+  combined$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$cas
     c_idx = which(lambdas==lambda)
-    r_idx = head(which(is.na(correct_rank[,c_idx])),length(infected_irs))
-    # !! order should be first: rank of (3,5,5,7) should be (1,2,2,4), may need to do n-rank
-    correct_rank[r_idx,c_idx] = match(infected_irs, combined$ir[order(combined$comb, decreasing=T)])
-    # maybe should also mark down vic/nonvic status of each?
+    crday[,c_idx] = rank(-combined$comb,ties.method='average')[match(infected_irs,combined$ir)]
   }
-  
+
+  return(crday)
 }
 print(proc.time()-ptm)
+
+# save(correct_rank, file='Results/correct_rank_62815.RData') 
\ No newline at end of file