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 literal 228573 zcmZ^~WmsIz(l#0(0fG~BkU(&k!QCM^1b26WySqb>;2Jz=fZ*=V;DZH+!QE{>viGy! zcc1f}Gk<2)>gwvQs=My)b*)~MO5&1?tV}>8%Iz)CG!iQ*3#q-aH4;BR60?|-nURaV z6Df_9tP1TvqLsa!xRHw)DUCQUkOj!f!VY9(Wn<xDV}Dgpa<VsdH8J~l2T~5wSIeZP zPF8MaPP9mZf=Fg|rhhF1|7!$QPX{wnW-%idBU^imR|7^CX3nJSuL@s~m=#@JY_05G zWww9G|7+l{{GTcQ+rWQRU*~A=YUe`A@?XRKwf6ti{?{n~QDsG9mbEf<Ce`8iJBr?a zb>#Z5j&f#p7A}^gtUx4YNh@0yGbd&d6)|b)&t@LtW+wKgW`DamyS#?$D-z4Ue&+xB z{dM`D+kegf-_!W(vXj~CjQ{cZi<z^%tCPvA)BOCT%#!x6Ew2}^#+fBNT%=T7{zm?9 zW~5Yr9RE@g6Cq_`V&UOtVdDXkGMgCvm-M%nlnN`5>wgv5|E2hk-ao>>)qouTV~_P; 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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, -- GitLab