other minor changes

.gitignore

@@ -1,3 +1,11 @@
+################################################################################
+#                                   Custom                                     #
+################################################################################
+
+data/times
+*.h5
+
+
 ################################################################################
 #                          DrWatson Project Structure                          #
 ################################################################################

scripts/experiments_mill/hmil_classifier.jl

@@ -45,7 +45,8 @@ function sample_params()
     return (mdim=mdim, activation=activation, aggregation=aggregation, nlayers=nlayers)
 end
 
-loss(model, x, y) = Flux.logitcrossentropy(model(x), y)
+loss(model, x, y) = Flux.crossentropy(model(x), y)
+# loss(model, x, y) = Flux.logitcrossentropy(model(x), y)
 
 """
 	fit(data, parameters)

test/lhco.jl

@@ -0,0 +1,60 @@
+using Test
+using GroupAD
+using PyCall
+using Mill
+
+"""
+    load_lhco(dataset = "events_anomalydetection_v2.h5")
+	
+This function loads the LHCO2020 dataset (the R&D version for now)
+and processes it to get a Mill.jl datasets of normal and anomalous
+samples.
+
+Note: PyCall.jl must be installed, Python/3.8 loaded with pandas,
+tables packages installed. If this version is used, the path to
+Python must be `/mnt/appl/software/Python/3.8.6-GCCcore-10.2.0/bin/python`.
+"""
+function load_lhco_from_pandas(dataset = "events_anomalydetection_v2.h5")
+	file = joinpath(get_lhco_datapath(), dataset)
+	if occursin("Python/3.8.6-GCCcore-10.2.0", read(`which python`, String))
+    	pd = pyimport("pandas")
+	end
+
+    data = Array{Float32}[]
+    labels = Int[]
+
+    for i in 0:100000:1100000
+        df_test = pd.read_hdf(file, start=i, stop=i+100000)
+        data_array = df_test[:values]
+
+        for row in eachrow(data_array)#[1:100000, :])
+            label = row[end] |> Int
+            push!(labels, label)
+            zeroix = findfirst(x -> x == 0.0, row) |> Int
+            d = row[1:zeroix-1]
+            al = zeros(Float32, 3, length(d)÷3)
+            al[1,:] = d[1:3:end]
+            al[2,:] = d[2:3:end]
+            al[3,:] = d[3:3:end]
+            push!(data, al)
+        end
+    end
+
+    obs0 = labels .== 0
+    obs1 = labels .== 1
+
+    ls0 = size.(data[obs0], 2)
+    ls1 = size.(data[obs1], 2)
+
+    bagids1 = Mill.length2bags(ls1)
+    bagids0 = Mill.length2bags(ls0)
+
+    return (
+        normal = BagNode(ArrayNode(hcat(data[obs0]...)), bagids0),
+        anomaly = BagNode(ArrayNode(hcat(data[obs1]...)), bagids1)
+    )
+end
+
+@testset "LHCO data" begin
+    @test 1 == 1
+end