JsonGrinder.jl
JsonGrinder is a collection of routines that facilitates conversion of JSON documents into structures used by Mill.jl project.
Motivation
Imagine that you want to train a classifier on data looking like this
{
"ind1": 1,
"inda": 0,
"logp": 6.01,
"lumo": -2.184,
"mutagenic": 1,
"atoms": [
{
"element": "c",
"atom_type": 22,
"charge": -0.118,
"bonds": [
{
"bond_type": 7,
"element": "c",
"atom_type": 22,
"charge": -0.118
},
{
"bond_type": 1,
"element": "h",
"atom_type": 3,
"charge": 0.141
},
{
"bond_type": 7,
"element": "c",
"atom_type": 22,
"charge": -0.118
}
]
},
⋮
{
"element": "c",
"atom_type": 27,
"charge": 0.012,
"bonds": [
{
"bond_type": 7,
"element": "c",
"atom_type": 22,
"charge": -0.118
},
{
"bond_type": 7,
"element": "c",
"atom_type": 27,
"charge": -0.089
},
{
"bond_type": 7,
"element": "c",
"atom_type": 22,
"charge": -0.118
}
]
}
]
},
and the task is to predict the value in key mutagenic (in this sample it's 1) from the rest of the JSON.
With most machine learning libraries assuming your data being stored as tensors of a fixed dimension, or a sequence, you will have a bad time. Contrary, JsonGrider.jl assumes your data to be stored in a flexible JSON format and tries to automate most labor using reasonable default, but it still gives you an option to control and tweak almost everything. JsonGrinder.jl is built on top of Mill.jl which itself is built on top of Flux.jl (we do not reinvent the wheel). Although JsonGrinder was designed for JSON files, you can easily adapt it to XML, Protocol Buffers, MessagePack, and other similar structures
There are four steps to create a classifier once you load the data.
- Create a schema of JSON files (using
sch = JsonGrinder.schema). - Create an extractor converting JSONs to Mill structures (
extractor = suggestextractor(sch))). Schemaschfrom previous step is very helpful, as it helps to identify, how to convert nodes (Dict,Array) to (Mill.ProductNodeandMill.BagNode) and how to convert values in leaves to (Float32,Vector{Float32},String,Categorical). - Extract your JSON files into Mill structures using extractor
extractbatch(extractor, samples) - Create a model for your JSONs, which can be easily done by (using
model = reflectinmodel(sch, extractor,...)) - Use your favourite methods to train the model, it is 100% compatible with
Flux.jltooling.
The first three steps are handled by JsonGrinder.jl, the fourth step by Mill.jl and the fourth by a combination of Mill.jl and Flux.jl.
Authors see the biggest advantage in the model being hierarchical and reflecting the JSON structure. Thanks to Mill.jl, it can handle missing values at all levels.
Example
Our idealized workflow is demonstrated in examples/mutagenesis.jldetermining mutagenicity on Salmonella typhimurium and it looks as follows (for many datasets which fits in memory it's sufficient just to change the key with labels (labelkey) and names of files to use the example on them):
using Flux, MLDataPattern, Mill, JsonGrinder, JSON, Statistics, IterTools, StatsBase, ThreadTools
using JsonGrinder: suggestextractor, ExtractDict
using Mill: reflectinmodel
samples = Vector{Dict}(open(JSON.parse, "../data/mutagenesis/data.json"))
metadata = open(JSON.parse, "data/mutagenesis/meta.json")
labelkey = metadata["label"]
val_num = metadata["val_samples"]
test_num = metadata["test_samples"]
minibatchsize = 100
iterations = 1_000
neurons = 20 # neurons per layer
targets = map(i -> i[labelkey], samples)
foreach(i -> delete!(i, labelkey), samples)
train_indices = 1:length(samples)-val_num-test_num
val_indices = length(samples)-val_num-test_num+1:length(samples)-test_num
test_indices = length(samples)-test_num+1:length(samples)
#####
# Create the schema and extractor
#####
sch = JsonGrinder.schema(samples)
extractor = suggestextractor(sch)
#####
# Convert samples to Mill structure and extract targets
#####
data = tmap(extractor, samples)
labelnames = unique(targets)
#####
# Create the model
#####
model = reflectinmodel(sch, extractor,
k -> Dense(k, neurons, relu),
d -> meanmax_aggregation(d),
fsm = Dict("" => k -> Dense(k, length(labelnames))),
)
#####
# Train the model
#####
function minibatch()
idx = sample(1:length(data[train_indices]), minibatchsize, replace = false)
reduce(catobs, data[idx]), Flux.onehotbatch(targets[idx], labelnames)
end
accuracy(x,y) = mean(labelnames[Flux.onecold(model(x).data)] .== y)
trainset = reduce(catobs, data[train_indices])
valset = reduce(catobs, data[val_indices])
testset = reduce(catobs, data[test_indices])
cb = () -> begin
train_acc = accuracy(trainset, targets[train_indices])
val_acc = accuracy(valset, targets[val_indices])
test_acc = accuracy(testset, targets[test_indices])
println("accuracy: train = $train_acc, val = $val_acc, test = $test_acc")
end
ps = Flux.params(model)
loss = (x,y) -> Flux.logitcrossentropy(model(x).data, y)
Flux.Optimise.train!(loss, ps, repeatedly(minibatch, iterations), ADAM(), cb = Flux.throttle(cb, 2))
###############################################################
# Classify test set
###############################################################
probs = softmax(model(testset).data)
o = Flux.onecold(probs)
pred_classes = labelnames[o]
print(mean(pred_classes .== targets[test_indices]))
# we see the accuracy is around 79% on test set
#predicted classes for first 10 samples
print(pred_classes)
#gt classes for first 10 samples
print(ground_truth_classes)
# probabilities of first 10 samples
print(probs)A walkthrough of the example
Here we include libraries and load the data.
using Flux, MLDataPattern, Mill, JsonGrinder, JSON, Statistics, IterTools, StatsBase, ThreadTools
using JsonGrinder: suggestextractor, ExtractDict
using Mill: reflectinmodel
samples = Vector{Dict}(open(JSON.parse, "../../data/mutagenesis/data.json"))188-element Array{Dict,1}:
Dict{String,Any}("ind1" => 1,"lumo" => -1.246,"inda" => 0,"logp" => 4.23,"mutagenic" => 1,"atoms" => Any[Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => -0.117), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.142), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.013), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => -0.087)],"charge" => -0.087), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.087), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.013), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.013), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143)],"charge" => -0.117), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => 0.013)],"charge" => -0.117), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.143), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.143), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.087), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142)],"charge" => -0.117), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143)],"charge" => -0.117) … Dict{String,Any}("element" => "n","atom_type" => 38,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.388), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.388)],"charge" => 0.812), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.812)],"charge" => -0.388), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.812)],"charge" => -0.388), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => -0.087), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142)],"charge" => -0.117), Dict{String,Any}("element" => "c","atom_type" => 195,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.087), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => 0.013)],"charge" => -0.087), Dict{String,Any}("element" => "c","atom_type" => 195,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => -0.087), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.013), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 195,"charge" => 0.013), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.117), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142)],"charge" => -0.117), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.142), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.143), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.117)],"charge" => 0.142)])
Dict{String,Any}("ind1" => 1,"lumo" => -1.387,"inda" => 0,"logp" => 4.62,"mutagenic" => 1,"atoms" => Any[Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "n","atom_type" => 38,"charge" => 0.812), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.118)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.118), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.168)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 29,"charge" => 0.011), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142)],"charge" => -0.118), Dict{String,Any}("element" => "c","atom_type" => 29,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.118), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 29,"charge" => 0.011)],"charge" => 0.011), Dict{String,Any}("element" => "c","atom_type" => 10,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.061), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.061), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.119)],"charge" => 0.011), Dict{String,Any}("element" => "n","atom_type" => 38,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.388), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.388)],"charge" => 0.812), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.812)],"charge" => -0.388), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.812)],"charge" => -0.388), Dict{String,Any}("element" => "c","atom_type" => 10,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.061), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.061), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.061), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.168)],"charge" => 0.011), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.119)],"charge" => 0.142) … Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011)],"charge" => 0.061), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011)],"charge" => 0.061), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011)],"charge" => 0.061), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 29,"charge" => 0.011)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 29,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 29,"charge" => 0.011), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119)],"charge" => 0.011), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 29,"charge" => 0.011), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.118)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142)],"charge" => -0.118), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 29,"charge" => 0.011), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.142), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.168)],"charge" => -0.119), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.119), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 10,"charge" => 0.011)],"charge" => -0.168)])
Dict{String,Any}("ind1" => 0,"lumo" => -1.034,"inda" => 0,"logp" => 2.68,"mutagenic" => 0,"atoms" => Any[Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.128), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.132), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.128)],"charge" => -0.128), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.128)],"charge" => 0.132), Dict{String,Any}("element" => "c","atom_type" => 29,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.128), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.128), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 29,"charge" => 0.002)],"charge" => 0.002), 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Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => 0.266)],"charge" => -0.524), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "n","atom_type" => 35,"charge" => -0.524), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.145)],"charge" => 0.266), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => 0.266), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.145), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.015)],"charge" => -0.115), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.145), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => 0.266)],"charge" => 0.145), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.145), Dict{String,Any}("element" => "n","atom_type" => 38,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.384), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.384), Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.816), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.816)],"charge" => -0.384), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.816)],"charge" => -0.384), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.015), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.145)],"charge" => -0.115), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.015)],"charge" => 0.015), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.015), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.015), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.015), Dict{String,Any}("bond_type" => 1,"element" => "n","atom_type" => 38,"charge" => 0.816), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => -0.115), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.115), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.145)],"charge" => -0.115), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.145), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.145), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.115)],"charge" => 0.145)])
Dict{String,Any}("ind1" => 1,"lumo" => -2.159,"inda" => 0,"logp" => 2.72,"mutagenic" => 1,"atoms" => Any[Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => -0.116), Dict{String,Any}("element" => "c","atom_type" => 26,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 21,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => -0.086), Dict{String,Any}("element" => "c","atom_type" => 21,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 21,"charge" => -0.086), Dict{String,Any}("bond_type" => 1,"element" => "n","atom_type" => 38,"charge" => 0.813), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086)],"charge" => 0.014), Dict{String,Any}("element" => "c","atom_type" => 21,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 21,"charge" => 0.014), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.114)],"charge" => -0.086), Dict{String,Any}("element" => "n","atom_type" => 38,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 21,"charge" => 0.014), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.387), Dict{String,Any}("bond_type" => 2,"element" => "o","atom_type" => 40,"charge" => -0.387)],"charge" => 0.813), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.813)],"charge" => -0.387), Dict{String,Any}("element" => "o","atom_type" => 40,"bonds" => Any[Dict{String,Any}("bond_type" => 2,"element" => "n","atom_type" => 38,"charge" => 0.813)],"charge" => -0.387), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.143), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.143), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.143) … Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.144), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.143), Dict{String,Any}("element" => "h","atom_type" => 3,"bonds" => Any[Dict{String,Any}("bond_type" => 1,"element" => "c","atom_type" => 21,"charge" => -0.086)],"charge" => 0.114), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => 0.014), Dict{String,Any}("element" => "c","atom_type" => 27,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.014), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086)],"charge" => -0.086), Dict{String,Any}("element" => "c","atom_type" => 26,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 21,"charge" => 0.014), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => -0.086), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143)],"charge" => -0.116), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 27,"charge" => 0.014), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => -0.116), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.144), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116)],"charge" => -0.116), Dict{String,Any}("element" => "c","atom_type" => 22,"bonds" => Any[Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 22,"charge" => -0.116), Dict{String,Any}("bond_type" => 7,"element" => "c","atom_type" => 26,"charge" => -0.086), Dict{String,Any}("bond_type" => 1,"element" => "h","atom_type" => 3,"charge" => 0.143)],"charge" => -0.116)])we load metadata, which store which class is to be predicted and how many samples to be used for validation and for testing
metadata = open(JSON.parse, "../../data/mutagenesis/meta.json")
val_num = metadata["val_samples"]
test_num = metadata["test_samples"]
minibatchsize = 100
iterations = 1_000
neurons = 20 # neurons per layer
labelkey = metadata["label"]"mutagenic"
we see that label to be predicted is mutagenic.
We create labels and remove them from data, such that we do not use them as features. We also prepare indices of train, test and validation data here.
targets = map(i -> i[labelkey], samples)
foreach(i -> delete!(i, labelkey), samples)
train_indices = 1:length(samples)-val_num-test_num
val_indices = length(samples)-val_num-test_num+1:length(samples)-test_num
test_indices = length(samples)-test_num+1:length(samples)145:188
We create the schema of data
sch = JsonGrinder.schema(samples)[Dict] (updated = 188) ├── atoms: [List] (updated = 188) │ └── [Dict] (updated = 4893) │ ⋮ ├─── ind1: [Scalar - Int64], 2 unique values, updated = 188 ├─── inda: [Scalar - Int64], 2 unique values, updated = 188 ├─── logp: [Scalar - Float64,Int64], 107 unique values, updated = 188 └─── lumo: [Scalar - Float64], 177 unique values, updated = 188
Then we use it to create the extractor converting jsons to Mill structure. The suggestextractor is executed below with default setting, but it allows you heavy customization.
extractor = suggestextractor(sch)Dict ├── atoms: Array of │ └── Dict │ ⋮ ├─── ind1: Categorical d = 3 ├─── inda: Categorical d = 3 ├─── logp: Float32 └─── lumo: Float32
We convert jsons to mill data samples.
data = tmap(extractor, samples)
labelnames = unique(targets)2-element Array{Int64,1}:
1
0We create the model reflecting structure of the data
model = reflectinmodel(sch, extractor,
k -> Dense(k, neurons, relu),
d -> meanmax_aggregation(d),
fsm = Dict("" => k -> Dense(k, length(labelnames))),
)ProductModel … ↦ ArrayModel(Dense(62, 2)) ├── atoms: BagModel … ↦ ⟨SegmentedMean(20), SegmentedMax(20)⟩ ↦ ArrayModel(Dense(41, 20, relu)) │ └── ProductModel … ↦ ArrayModel(Dense(61, 20, relu)) │ ⋮ ├─── ind1: ArrayModel(Dense(3, 20, relu)) ⋮ └─── lumo: ArrayModel(identity)
individual arguments of reflectinmodel are explained in Mill.jl documentation
Lastly, we define few handy functions.
function minibatch()
idx = sample(1:length(data[train_indices]), minibatchsize, replace = false)
reduce(catobs, data[idx]), Flux.onehotbatch(targets[idx], labelnames)
end
accuracy(x,y) = mean(labelnames[Flux.onecold(model(x).data)] .== y)
trainset = reduce(catobs, data[train_indices])
valset = reduce(catobs, data[val_indices])
testset = reduce(catobs, data[test_indices])
cb = () -> begin
train_acc = accuracy(trainset, targets[train_indices])
val_acc = accuracy(valset, targets[val_indices])
test_acc = accuracy(testset, targets[test_indices])
println("accuracy: train = $train_acc, val = $val_acc, test = $test_acc")
end
ps = Flux.params(model)
loss = (x,y) -> Flux.logitcrossentropy(model(x).data, y)#13 (generic function with 1 method)
and then we can start trining
julia> Flux.Optimise.train!(loss, ps, repeatedly(minibatch, iterations), ADAM(), cb = Flux.throttle(cb, 2))
accuracy: train = 0.61, val = 0.7727272727272727, test = 0.6818181818181818
accuracy: train = 0.61, val = 0.7727272727272727, test = 0.6818181818181818
accuracy: train = 0.65, val = 0.7727272727272727, test = 0.7045454545454546
accuracy: train = 0.73, val = 0.75, test = 0.8409090909090909
accuracy: train = 0.74, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.81, val = 0.75, test = 0.8863636363636364
accuracy: train = 0.84, val = 0.6818181818181818, test = 0.7954545454545454
accuracy: train = 0.9, val = 0.75, test = 0.8181818181818182
accuracy: train = 0.92, val = 0.7954545454545454, test = 0.8409090909090909
accuracy: train = 0.94, val = 0.7954545454545454, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.93, val = 0.7272727272727273, test = 0.8409090909090909
accuracy: train = 0.94, val = 0.7272727272727273, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.94, val = 0.75, test = 0.8409090909090909
accuracy: train = 0.94, val = 0.75, test = 0.8409090909090909
accuracy: train = 0.94, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.95, val = 0.75, test = 0.8636363636363636
accuracy: train = 0.96, val = 0.7045454545454546, test = 0.8181818181818182
accuracy: train = 0.96, val = 0.7045454545454546, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.75, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.75, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.75, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.7045454545454546, test = 0.8181818181818182
accuracy: train = 0.97, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7272727272727273, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.7272727272727273, test = 0.7954545454545454
accuracy: train = 0.96, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.7045454545454546, test = 0.8181818181818182
accuracy: train = 0.97, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7727272727272727, test = 0.7727272727272727
accuracy: train = 0.98, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.7727272727272727
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.75, test = 0.7727272727272727
accuracy: train = 0.96, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.7727272727272727
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.75, test = 0.7954545454545454
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.97, val = 0.7727272727272727, test = 0.8181818181818182
accuracy: train = 0.98, val = 0.7727272727272727, test = 0.8181818181818182We can see the accuracy rising and obtaining over 98% on training set quite quickly, and on validation and test set we get over 70%.
Last part is inference on test data
julia> probs = softmax(model(testset).data)
2×44 Array{Float32,2}:
1.0 1.0 0.747354 … 0.902423 0.135826 1.0
4.76709f-12 2.69321f-7 0.252646 0.0975767 0.864174 5.2728f-11
julia> o = Flux.onecold(probs)
44-element Array{Int64,1}:
1
1
1
2
1
1
1
1
1
1
⋮
2
1
1
1
1
1
1
2
1
julia> pred_classes = labelnames[o]
44-element Array{Int64,1}:
1
1
1
0
1
1
1
1
1
1
⋮
0
1
1
1
1
1
1
0
1
julia> print(mean(pred_classes .== targets[test_indices]))
0.8181818181818182pred_classes contains the predictions for our test set.
We can look at individual samples. For instance, first sample in te test_samples[2] is
{
"ind1": 1,
"inda": 0,
"logp": 3.92,
"lumo": -3.406,
"mutagenic": 1,
"atoms": [
{
"element": "c",
"atom_type": 22,
"charge": -0.109,
"bonds": [
{
"bond_type": 1,
"element": "h",
"atom_type": 3,
"charge": 0.151
},
...
{
"bond_type": 7,
"element": "c",
"atom_type": 22,
"charge": -0.109
}
]
}
...
]
}and the corresponding classification is
julia> pred_classes[1]
1if you want to see the probability distribution, it can be obtained by applying softmax to the output of the network.
julia> softmax(model(testset[1]).data)
2×1 Array{Float32,2}:
1.0
4.767093f-12so we can see that the probability that given sample is mutagenetic is almost 1.
This concludes a simple classifier for JSON data.
But keep in mind the framework is general and given its ability to embed hierarchical data into fixed-size vectors, it can be used for classification, regression, and various other ML tasks.