From 927fe0968f986995b846907edb5dc4e2705f72c2 Mon Sep 17 00:00:00 2001 From: josemanuel22 Date: Fri, 2 Aug 2024 12:41:39 +0200 Subject: [PATCH] add joss paper.md and paper.bib --- Project.toml | 1 + test/ETTh1.csv | 17421 +++++++++++++++++++++++++++++++++++++++++++++ test/runtests.jl | 41 + 3 files changed, 17463 insertions(+) create mode 100644 test/ETTh1.csv diff --git a/Project.toml b/Project.toml index 6678766..693fb3c 100644 --- a/Project.toml +++ b/Project.toml @@ -9,6 +9,7 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4" Colors = "5ae59095-9a9b-59fe-a467-6f913c188581" Coverage = "a2441757-f6aa-5fb2-8edb-039e3f45d037" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" diff --git a/test/ETTh1.csv b/test/ETTh1.csv new file mode 100644 index 0000000..a52c492 --- /dev/null +++ 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+++ b/test/runtests.jl @@ -4,6 +4,7 @@ using Flux using Distributions using LinearAlgebra using Random +using CSV using Test include("../examples/utils.jl") # Include utility function @@ -411,3 +412,43 @@ end; @test !isempty(loss) # Check that losses are returned @test all(loss .>= 0) # Assuming loss cannot be negative; adjust as necessary end; + +@testset "ts_invariant_statistical_loss_multivariate Tests" begin + # Load CSV file + csv1 = "./test/ETTh1.csv" + df = DataFrame(CSV.File(csv1; delim=',', header=true, decimal='.')) + df = select(df, Not(:date)) + + # Select relevant columns and standardize data + matrix = Float32.(Matrix(df)) + mean_vals, std_vals = mean(matrix; dims=1), std(matrix; dims=1) + matrix = (matrix .- mean_vals) ./ std_vals + + # Preparing data for training + dataX = [matrix[i, :] for i in 1:size(matrix, 1)] + dataY = [matrix[i, :] for i in 2:size(matrix, 1)] + + # Model hyperparameters and architecture + hparams = HyperParamsTS(; seed=1234, η=1e-2, epochs=2000, window_size=2000, K=10) + rec = Chain(RNN(7 => 3, relu), LayerNorm(3)) + gen = Chain(Dense(4, 5, relu), Dropout(0.05), Dense(5, 7, identity)) + + # DataLoader setup + batch_size = 1000 + loaderXtrain = Flux.DataLoader( + dataX; batchsize=batch_size, shuffle=false, partial=false + ) + loaderYtrain = Flux.DataLoader( + dataY; batchsize=batch_size, shuffle=false, partial=false + ) + + losses = [] + @showprogress for i in 1:100 + loss = ts_invariant_statistical_loss_multivariate( + rec, gen, loaderXtrain, loaderYtrain, hparams + ) + append!(losses, loss) + end + @test !isempty(loss) # Check that losses are returned + @test all(loss .>= 0) # Assuming loss cannot be negative; adjust as necessary +end;