test script

test.jl

@@ -0,0 +1,32 @@
+include("lib/NodeUtils.jl")
+include("lib/delhi.jl")
+include("lib/utils.jl")
+using .NodeUtils
+using .Delhi
+using JLD2, Statistics, DataFrames
+using Interpolations, Observables
+using Random
+
+
+rng = MersenneTwister(123)
+if isfile("data.jld2")
+    @load "data.jld2" train_df test_df scaling
+end
+
+features = [:meantemp, :humidity, :wind_speed, :meanpressure]
+units = ["Celsius", "g/m³ of water", "km/h", "hPa"]
+feature_names = ["Mean temperature", "Humidity", "Wind speed", "Mean pressure"]
+
+
+data = vcat(train_df, test_df)
+# Functions to interpolate when calculating the MSE
+interpolators = [LinearInterpolation(data.t, data[!, col]) for col in names(data)]
+# NODE parameters
+const obs_grid = 4:4:20 # we train on an increasing amount of the first k obs
+const maxiters = 150
+const lr = 5e-3
+const N_steps = 100 # number of points in prediction over the full time range
+_, θ_new, init_state = NodeUtils.neural_ode(train_df.t, length(features))
+t_grid = range(minimum(data.t), maximum(data.t), length=N_steps) |> collect
+θs = Observable(θ_new)
+θs[], state = NodeUtils.train(Vector(train_df[!,:t]), Matrix(train_df[!,features]), obs_grid, lr, rng, θs; maxiters=maxiters);