From 8bff20c7c0d0e9d8b19b1b0bdd47f5073b5cd333 Mon Sep 17 00:00:00 2001
From: pjentsch <pjentsch@uwaterloo.ca>
Date: Fri, 23 Apr 2021 13:14:11 -0400
Subject: [PATCH] simplify graph code a bit, slightly slower but more readable
 I think

---
 .../plots/univariate/I_0_fraction.pdf         | Bin 226155 -> 228573 bytes
 .../plots/univariate/app_user_fraction.pdf    | Bin 263729 -> 264627 bytes
 .../base_transmission_probability.pdf         | Bin 298961 -> 298344 bytes
 .../univariate/immunization_loss_prob.pdf     | Bin 262534 -> 264317 bytes
 .../univariate/notification_parameter.pdf     | Bin 269790 -> 269146 bytes
 .../plots/univariate/recovery_rate.pdf        | Bin 244314 -> 242848 bytes
 .../plots/univariate/\316\263.pdf"            | Bin 210591 -> 209746 bytes
 .../plots/univariate/\316\267.pdf"            | Bin 210445 -> 209678 bytes
 .../plots/univariate/\316\272.pdf"            | Bin 208874 -> 208615 bytes
 .../plots/univariate/\317\200_base.pdf"       | Bin 200480 -> 196225 bytes
 .../plots/univariate/\317\211.pdf"            | Bin 214423 -> 214912 bytes
 .../plots/univariate/\317\211_en.pdf"         | Bin 215347 -> 214988 bytes
 .../src/ABM/mixing_graphs.jl                  |  41 +++++++++---------
 13 files changed, 20 insertions(+), 21 deletions(-)

diff --git a/CovidAlertVaccinationModel/plots/univariate/I_0_fraction.pdf b/CovidAlertVaccinationModel/plots/univariate/I_0_fraction.pdf
index 0af51224faa35367b6122e476b1f7d5d48d6d514..68970db963cdcfa9923dfcd75694cceec9f03d9c 100644
GIT binary patch
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diff --git a/CovidAlertVaccinationModel/plots/univariate/app_user_fraction.pdf b/CovidAlertVaccinationModel/plots/univariate/app_user_fraction.pdf
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diff --git a/CovidAlertVaccinationModel/plots/univariate/base_transmission_probability.pdf b/CovidAlertVaccinationModel/plots/univariate/base_transmission_probability.pdf
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diff --git a/CovidAlertVaccinationModel/plots/univariate/immunization_loss_prob.pdf b/CovidAlertVaccinationModel/plots/univariate/immunization_loss_prob.pdf
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diff --git a/CovidAlertVaccinationModel/plots/univariate/notification_parameter.pdf b/CovidAlertVaccinationModel/plots/univariate/notification_parameter.pdf
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diff --git a/CovidAlertVaccinationModel/plots/univariate/recovery_rate.pdf b/CovidAlertVaccinationModel/plots/univariate/recovery_rate.pdf
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\316\263.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\316\263.pdf"
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\316\267.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\316\267.pdf"
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\316\272.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\316\272.pdf"
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\317\200_base.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\317\200_base.pdf"
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\317\211.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\317\211.pdf"
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diff --git "a/CovidAlertVaccinationModel/plots/univariate/\317\211_en.pdf" "b/CovidAlertVaccinationModel/plots/univariate/\317\211_en.pdf"
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diff --git a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
index 501a7ef..652e5f8 100644
--- a/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
+++ b/CovidAlertVaccinationModel/src/ABM/mixing_graphs.jl
@@ -10,7 +10,6 @@ struct GraphEdge
     b::Int
 end
 
-
 """
 Define a hash on GraphEdge such that ``hash(a,b) = hash(b,a)`` (hash is commutative).
 
@@ -21,12 +20,13 @@ function Base.hash(e::GraphEdge)
 end
 
 """
-Define symmetric (?) edge equality, matches the hash function.
+Define symmetric edge equality, matches the hash function.
 """
 function Base.isequal(e1::GraphEdge,e2::GraphEdge)
     return isequal(minmax(e1.a,e1.b),minmax(e2.a,e2.b))
 end
 
+
 """
     sample_mixing_graph!(mixing_graph::Graph)
 
@@ -36,13 +36,11 @@ Resample all the weights in `mixing_graph`
 function sample_mixing_graph!(mixing_graph)
     mixing_edges = mixing_graph.mixing_edges
     for i in 1:size(mixing_edges.contact_array)[1], j in 1:i  #diagonal            
-            rand!(RNG, mixing_edges.sampler_matrix[i,j],mixing_edges.sample_cache[i,j])
-            for k in 1:length(mixing_edges.contact_array[i,j][1])
-                kth_node_i = mixing_edges.contact_array[i,j][1][k]
-                kth_node_j = mixing_edges.contact_array[i,j][2][k]
-                edge_weight_k = mixing_edges.sample_cache[i,j][k]
-                set!(mixing_edges.weights_dict,GraphEdge(kth_node_i,kth_node_j), edge_weight_k)
-            end
+        rand!(RNG, mixing_edges.sampler_matrix[i,j],mixing_edges.sample_cache[i,j])
+        for k in 1:length(mixing_edges.contact_array[i,j])
+            edge_weight_k = mixing_edges.sample_cache[i,j][k]
+            set!(mixing_edges.weights_dict, mixing_edges.contact_array[i,j][k], edge_weight_k)
+        end
     end
 end
 
@@ -73,12 +71,12 @@ This is the matrix of distributions, from which the edge weights are sampled. Sp
 """
 struct MixingEdges{M}
     total_edges::Int
-    contact_array::Matrix{Tuple{Vector{Int},Vector{Int}}}
+    contact_array::Matrix{Vector{GraphEdge}}
     sample_cache::Matrix{Vector{Int}}
     weights_dict::Dictionary{GraphEdge,UInt8}
     sampler_matrix::M
     function MixingEdges(total_edges,contact_array,sampler_matrix::Matrix{M}) where M<:Sampleable{Univariate, Discrete}
-        sample_cache = map(v-> Vector{Int}(undef,length(v[1])),contact_array)
+        sample_cache = map(v-> Vector{Int}(undef,length(v)),contact_array)
         weights_dict = Dictionary{GraphEdge,UInt8}()
         new{typeof(sampler_matrix)}(total_edges,contact_array,sample_cache,weights_dict,sampler_matrix)
     end
@@ -88,7 +86,7 @@ end
 This function constructs the `MixingEdges` type for rest and WS graphs. Calls `generate_contact_vectors!(..)` to get the degree distributions for a bipartite graph for each pair of demographics, and then adds edges to MixingEdges.contact_array with the Chung-Lu approach. 
 """
 function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_distribution_matrix)
-    contact_array =[(Vector{Int}(),Vector{Int}()) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
+    contact_array =[Vector{GraphEdge}() for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
     tot = 0
     for i in 1:size(mixing_matrix)[1], j in 1:i  #diagonal
         num_degrees_ij = zeros(Int,length(demographic_index_vectors[i]))
@@ -103,7 +101,8 @@ function create_mixing_edges(demographic_index_vectors,mixing_matrix,weights_dis
             sample!(RNG,demographic_index_vectors[j],Weights(num_degrees_ji./m),stubs_j)
             tot += m
         end    
-        contact_array[j,i] = (stubs_i,stubs_j)
+
+        contact_array[j,i] = GraphEdge.(stubs_i,stubs_j)
     end
     return MixingEdges(tot,contact_array,weights_distribution_matrix)
 end
@@ -113,14 +112,13 @@ end
 This function constructs the `MixingEdges` type for the home graphs. Simply adds edges to MixingEdges from the graph `g`, since we already create that in `generate_population`.
 """
 function create_mixing_edges(g::SimpleGraph,demographics,demographic_index_vectors,weights_distribution_matrix) 
-    contact_array = [(Vector{Int}(),Vector{Int}()) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
-    for e in edges(g)    
+    contact_array = [sizehint!(Vector{GraphEdge}(),ne(g)) for i in 1:length(demographic_index_vectors),j in 1:length(demographic_index_vectors)]
+    for e in edges(g)
         i = src(e)
         j = dst(e)
-        push!(contact_array[Int(demographics[j]),Int(demographics[i])][1],i)
-        push!(contact_array[Int(demographics[j]),Int(demographics[i])][2],j)
+        push!(contact_array[Int(demographics[j]),Int(demographics[i])], GraphEdge(i,j))
     end
-    sample_cache = map(v-> Vector{Int}(undef,length(v[1])),contact_array)
+    sample_cache = map(v-> Vector{Int}(undef,length(v)),contact_array)
     return MixingEdges(ne(g),contact_array,weights_distribution_matrix)
 end
 
@@ -202,8 +200,9 @@ Add the edges defined by MixingEdges to the actual graph G. Another big bottlene
 """
 function graph_from_mixing_edges(g,mixing_edges)
     for i in 1:size(mixing_edges.contact_array)[1], j in 1:i  #diagonal
-        for k in 1:length(mixing_edges.contact_array[i,j][1])
-            add_edge!(g,mixing_edges.contact_array[i,j][1][k],mixing_edges.contact_array[i,j][2][k])
+        for k in 1:length(mixing_edges.contact_array[i,j])
+            e = mixing_edges.contact_array[i,j][k]
+            add_edge!(g,e.a,e.b)
         end
     end
 end
@@ -227,7 +226,7 @@ mutable struct WeightedGraph{G,M}
     function WeightedGraph(g::SimpleGraph,demographics,demographic_index_vectors,weights_distribution_matrix)
         mixing_edges = create_mixing_edges(g, demographics, demographic_index_vectors,weights_distribution_matrix)
         weights_dict = Dict{GraphEdge,UInt8}()
-        sample_cache = map(v-> Vector{Int}(undef,length(v[1])),mixing_edges.contact_array)
+        sample_cache = map(v-> Vector{Int}(undef,length(v)),mixing_edges.contact_array)
         return new{typeof(g),typeof(mixing_edges)}(
             g,
             mixing_edges,
-- 
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