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Added first portion of Frechet distance calculations
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Shashank Swaminathan committed Jan 14, 2025
1 parent 9542840 commit 3144c82
Showing 1 changed file with 112 additions and 24 deletions.
136 changes: 112 additions & 24 deletions test/test_mhmc.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,49 @@ function quick_setup(agent_ids, agent_conns, nᵩ; testing=true)
return gt_model, ng
end

function generate_agent_wpts(agent_ids, corners = [-5.,5.])
n = length(agent_ids)
agent_coords = Dict([(aid, Any[]) for aid in agent_ids])

bc = [corners[1], corners[1]]
h = corners[2] - corners[1]
w = (h - 2*h/10) / n

for aid in agent_ids
let ag = agent_coords[aid]
push!(ag, copy(bc))
push!(ag, ag[end] + [0., h])
push!(ag, ag[end] + [w/3, 0.])
push!(ag, ag[end] + [0., -h])
push!(ag, ag[end] + [w/3, 0.])
push!(ag, ag[end] + [0., h])
push!(ag, ag[end] + [w/3, 0.])
push!(ag, ag[end] + [0., -h])

bc = ag[end] + [h/10, 0.]
end
end

agent_coords
end

function generate_sample_locs_from_wpts(wpts::Vector, k::Integer, d::Float64,)
let l = length(wpts), start = wpts[(k-1)%l+1], stop = wpts[k%l+1],
d1 = abs(stop[1] - start[1])/d, d2 = abs(stop[2] - start[2])/d
if d1 >= d2
nₛ = max(Integer(round(d1)), 2)
else
nₛ = max(Integer(round(d2)), 2)
end
# println("\nstart: ", start, " | stop: ", stop, " || d1: ", d1, " | d2: ", d2, " | nₛ: ", nₛ)
hcat(range(start[1], stop[1], length=nₛ+1),
range(start[2], stop[2], length=nₛ+1))
end
end

function mul_agent_distrib_KF(run_name::String, nₐ=3, nₛ=100; testing=true, tol=0.1)
space_corners = [-2., 2.] # corner coordinates

agent_ids = ["agent1", "agent2", "agent3", "agent4", "agent5"][1:nₐ]
if nₐ==3
agent_conns = Dict([("agent1", ["agent2"]),
Expand All @@ -56,8 +98,10 @@ function mul_agent_distrib_KF(run_name::String, nₐ=3, nₛ=100; testing=true,
# Arrays of fused information over time per agent (array of arrays)
# fused_info = Dict([(aid, [copy(ng.vertices[aid].agent.information[1])]) for aid in agent_ids])

sz=(1,2) # or make it (1,2)
new_loc = Dict([(agent_ids[j], [0. 0.; -0.5 -0.5] + [0. 0.; j-1 j-1]) for j in eachindex(agent_ids)])
# sz=(1,2) # or make it (1,2)
# new_loc = Dict([(agent_ids[j], [0. 0.; -0.5 -0.5] + [0. 0.; j-1 j-1]) for j in eachindex(agent_ids)])
agent_wpts = generate_agent_wpts(agent_ids, space_corners)
sample_dists = (space_corners[2] - space_corners[1])/20 # ensure distance is small enough for lawnmower pattern
# new_loc = zeros(sz...)
for i in 1:nₛ
print("k: ", i)
Expand All @@ -74,7 +118,8 @@ function mul_agent_distrib_KF(run_name::String, nₐ=3, nₛ=100; testing=true,
# new_loc = 3*rand(sz...)
for aid in agent_ids
# progress_agent_env_filter(ng.vertices[aid].agent, fused_info[aid][end], gt_model[i+1], copy(new_loc[aid]))
progress_agent_env_filter(ng.vertices[aid].agent, gt_model[i+1], copy(new_loc[aid]))
progress_agent_env_filter(ng.vertices[aid].agent, gt_model[i+1],
copy(generate_sample_locs_from_wpts(agent_wpts[aid], i, sample_dists)))
push!(ng.vertices[aid].history, ng.vertices[aid].agent.estimates[i+1].observations.X)
end
println("ϕ: ", gt_model[end].ϕ, " | ̂ϕ: ", ng.vertices["agent1"].agent.estimates[end].estimate.ϕ)
Expand All @@ -92,7 +137,7 @@ function mul_agent_distrib_KF(run_name::String, nₐ=3, nₛ=100; testing=true,

simple_print_results(gt_model, ng)

@save "test/"*run_name*".jld2" gt_model ng
@save "test/res_data/"*run_name*".jld2" gt_model ng
return gt_model, ng
end

Expand All @@ -110,30 +155,73 @@ function simple_print_results(gt_model::Vector{T} where T<:SCRIBEModel, ng::Netw
end
end

function error_map_plots(run_name::String)
@load "test/"*run_name*".jld2" gt ng
function frechet_dist_eval_plot(run_name::String; layout_size=(800,500))
png_name = "test/res_plots/"*run_name*"_frechet_dist.png"

@load "test/res_data/"*run_name*".jld2" gt_model ng

x_range = -2:0.1:2
y_range = -2:0.1:2
y_range = copy(x_range)
err_sq(gt, am, v) = norm(predict_SCRIBEModel(gt, v) - predict_SCRIBEModel(am, v))^2
frechet(gt, am) = sum([err_sq(gt, am, [x, y]) for x in x_range for y in y_range])
agent_ids = collect(keys(ng.edges))
frechet_dists = Dict([(aid, [frechet(gt_model[i], ng.vertices[aid].agent.estimates[i].estimate) for i in 1:length(gt_model)]) for aid in agent_ids])

p1 = plot(size=layout_size, xlabel="Time (in discretized steps)", ylabel="(pseudo-)Frechet distance",
title="Performance of agent learned environment models \ncompared to ground truth over time", margin=(10, :mm))
for aid in agent_ids
plot!(p1, 1:length(gt_model), frechet_dists[aid], label="Agent "*aid[end], lw=2)
end

p2 = plot(size=layout_size, xlabel="Time (in discretized steps)",
title="Close-up of performance of agent learned environment models \nafter gathering initial observations", margin=(10, :mm))
for aid in agent_ids
plot!(p2, 1:length(gt_model), frechet_dists[aid], label="Agent "*aid[end], lw=2, yrange=[0., 1.0])
end
# xlabel!(p2, "Time (in discretized steps)")
# ylabel!(p2, "(pseudo-)Frechet distance, zoomed in")
# title!(p2, "Performance of agent learned environment models \ncompared to ground truth over time")

plot(p1, p2, layout=(1,2), size=(layout_size[1]*2, layout_size[2]))
savefig(png_name);
println("Plot saved at "*png_name)
end

let gt = gt[end], ag1_mod = ng.vertices["agent1"].agent.estimates[end].estimate,
png_name = "test/res_plots/"*run_name*"_err_map_agent1.png"
error_agent1(x::Vector) = norm(predict_SCRIBEModel(gt, x) - predict_SCRIBEModel(ag1_mod, x))

gt_map_vals = [predict_SCRIBEModel(gt, [x,y]) for y in y_range, x in x_range]
err1_map_vals = [error_agent1([x,y]) for y in y_range, x in x_range]

gt_map = heatmap(x_range, y_range, gt_map_vals,
color=:viridis, xlabel="X", ylabel="Y",
title="Ground truth distribution of phenomena intensity")
err1_map = heatmap(x_range, y_range, err1_map_vals,
color=:viridis, xlabel="X", ylabel="Y",
title="Error distribution of \nagent 1's predictions of phenomena intensity")

plot(gt_map, err1_map, layout=(2,1), size=(600,800));
savefig(png_name);
println("Plot saved at "*png_name)
function error_map_plots(run_name::String; layout_size=(1200,1000))
png_name = "test/res_plots/"*run_name*"_err_map.png"

@load "test/res_data/"*run_name*".jld2" gt_model ng
gt = gt_model[end]

num_plots = length(ng.vertices) + 1
needs_padding = num_plots%2==1
layout_num = (2,Integer(ceil(num_plots/2)))

x_range = -2:0.1:2
y_range = -2:0.1:2
gt_map = heatmap(x_range, y_range, [predict_SCRIBEModel(gt, [x,y]) for y in y_range, x in x_range],
color=:viridis, xlabel="X", ylabel="Y",
title="\nGround truth distribution of phenomena intensity")

error_mapf(m, x) = norm(predict_SCRIBEModel(gt, x) - predict_SCRIBEModel(m, x))
error_maps = Dict{String, Any}()
error_mapv = Any[gt_map]

for aid in keys(ng.vertices)
let m = ng.vertices[aid].agent.estimates[end].estimate, h=reduce(vcat, ng.vertices[aid].history)
error_maps[aid] = heatmap(x_range, y_range, [error_mapf(m, [x,y]) for y in y_range, x in x_range],
color=:viridis, xlabel="X", ylabel="Y",
title="Error distribution of \nAgent "*aid[end]*"'s predictions of phenomena intensity")
plot!(error_maps[aid], copy(h[:,1]), copy(h[:,2]),
linestyle=:dash, marker=:xcross, linecolor=:red, label="Sampling path and sites")
push!(error_mapv, error_maps[aid])
end
end

if needs_padding; push!(error_mapv, nothing); end
plot(error_mapv..., layout=layout_num, size=layout_size)
savefig(png_name);
println("Plot saved at "*png_name)
end

# mul_agent_distrib_KF();

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