Worked on synthetic data recovery so we can tell how high the actual

infector is ranked among all potential parents. Cleaned up code for the
predicting victims benchmarking test.

6 files changed, 177 insertions, 137 deletions

diff --git a/R Scripts/benchmarking.R b/R Scripts/benchmarking.R
deleted file mode 100644
index 1cf99e0..0000000
--- a/R Scripts/benchmarking.R
+++ /dev/null
@@ -1,130 +0,0 @@
-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))
diff --git a/R Scripts/find-cascades.R b/R Scripts/find-cascades.R
index 0a69c5c..7673909 100644
--- a/R Scripts/find-cascades.R
+++ b/R Scripts/find-cascades.R
@@ -9,14 +9,14 @@ source('criminal_cascades/R Scripts/structural.R')
 vic_ids = which(V(hyp_lcc)$vic==TRUE)
 
 #### Initialize model parameters
-alpha = 1/21
+alpha = 0.041
 # gamma = 0.3
-delta = 0.001
-beta = 0.00796964464237
+delta = 0.1
+# beta = 0.00796964464237
 
 ##### Get weights
 # find max n days where infection possible given alpha
-edges = dag_dat_vics
+edges = dag_dat_test[!is.na(dag_dat_test$t2),]
 # edges = edges[(edges$t2-edges$t1)<300,]
 
 p_t = temporal(edges$t1, edges$t2, alpha)
@@ -31,7 +31,7 @@ weights = p/p_tilde
 # probs = as.numeric(lm.probs)
 # betas = probs[match(hyp_lcc_verts$ir_no[edges$to],names)]
 # betas = 0.055
-thresh = beta/(1-beta)
+# thresh = beta/(1-beta)
 
 realized = c()
 # edges = edges[weights>thresh,]
@@ -51,6 +51,7 @@ for (u in vic_ids){
   }
   realized = c(realized, max_edge)
 }
+edges[realized,c('from','to')]
 #   if (length(Ei)>0){
 #     max_edge = Ei[which.max(weights[Ei])] # how to deal with ties????
 #     realized = c(realized, max_edge)
diff --git a/R Scripts/find-parents.R b/R Scripts/find-parents.R
new file mode 100644
index 0000000..3ec8809
--- /dev/null
+++ b/R Scripts/find-parents.R
@@ -0,0 +1,40 @@
+# library(igraph)
+# setwd("~/Documents/Cascade Project/")
+# load('Results/hyper-lcc.RData')
+# load('Results/dag_dat_vics.RData')
+# source('criminal_cascades/R Scripts/temporal.R')
+# source('criminal_cascades/R Scripts/structural.R')
+
+##### Initialize parameters based on what ml2 found
+alpha = 0.061
+delta = 0.082
+
+##### Get weights
+edges = dag_dat_test[!is.na(dag_dat_test$t2),]
+
+dt = edges$t2 - edges$t1
+p_t = exp(-alpha*dt) * (exp(alpha)-1)
+p_s = structural(delta, edges$dist)
+p = p_s * p_t
+p_tilde = 1 - p_s + p_s * exp(-alpha*dt)
+weights = p/p_tilde
+edges$weight = weights
+
+##### Find most likely parents
+parents = data.frame(vic=0,Npars=0,par_rank=0)
+vics = setdiff(vic_ids,seeds)
+for (u in vics){
+  u_parents = edges[edges$to==u,]
+  u_parents = u_parents[order(u_parents$weight,decreasing=T),]
+  Nparents = dim(u_parents)[1]
+  infector = V(g)$infector[u]
+  infectorID = which(u_parents$from==infector)
+  parents[which(vics==u),] = c(u, Nparents, infectorID)
+}
+
+##### Get some summary statistics on how well 
+median(parents$par_rank[parents$Npars>9])
+median(parents$par_rank[parents$Npars>99])
+
+
+
diff --git a/R Scripts/generate-network.R b/R Scripts/generate-network.R
index dc4a4f8..3b40969 100644
--- a/R Scripts/generate-network.R
+++ b/R Scripts/generate-network.R
@@ -9,7 +9,6 @@ delta = 0.15
 # lmbda = 1/10
 t_max = 1000
 
-# g = watts.strogatz.game(1, 100, 3, 0.25)
 N = 5000
 g = forest.fire.game(nodes=N, fw.prob=0.3, ambs=1, directed=F)
 plot(g, vertex.size=5, vertex.label=NA)
@@ -31,7 +30,7 @@ for (day in 1:t_max){
     infected = setdiff(infected,seeds) # don't try to infect seeds
     inf.days = day + ceiling(alpha*rexp(length(infected),alpha))
     V(g)$vic[infected] = TRUE
-    infects = (inf.days < V(g)$vic.day[infected]) %in% c(NA,T)
+    infects = (inf.days <= V(g)$vic.day[infected]) %in% c(NA,T)
     V(g)$vic.day[infected[infects]] = inf.days[infects]
     V(g)$infector[infected[infects]] = vic
   }
@@ -76,3 +75,5 @@ infectors = cbind(setdiff(vic_ids,seeds),
                   V(g)$infector[setdiff(vic_ids,seeds)],
                   recovered$infector[recovered$victim %in% setdiff(vic_ids,seeds)])
 mean(infectors[,2]==infectors[,3])
+
+dag_dat_test[dag_dat_test$to==4984,]
\ No newline at end of file
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
new file mode 100644
index 0000000..8a93667
--- /dev/null
+++ b/R Scripts/predict-victims-plots.R
@@ -0,0 +1,61 @@
+##### 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))
diff --git a/R Scripts/predict-victims.R b/R Scripts/predict-victims.R
new file mode 100644
index 0000000..470815d
--- /dev/null
+++ b/R Scripts/predict-victims.R
@@ -0,0 +1,67 @@
+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)