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#!/usr/bin/env ruby
require_relative "../lib/lazar"
model = Model.new ARGV[0]
ARGV[1] ? folds = ARGV[1].to_i : folds = 10
nr_instances = model.train.size
indices = (0..nr_instances-1).to_a.shuffle
mid = (nr_instances/folds)
start = 0
0.upto(folds-1) do |i|
fork do
# split train data
puts "Creating fold #{i}"
last = start+mid
last = last-1 unless nr_instances%folds > i
test_idxs = indices[start..last] || []
idxs = {
:train => indices-test_idxs,
:test => test_idxs
}
start = last+1
# write training/test data
cv_dir = File.join(File.dirname(ARGV[0]),"crossvalidation",i.to_s)
idxs.each do |t,idx|
file = File.join(cv_dir,t.to_s+".csv")
`mkdir -p #{File.dirname file}`
case t
when :train
File.open(file,"w+") do |f|
f.puts (["Canonical SMILES",model.dependent_variable_name] + model.independent_variable_names).join(",")
idx.collect{|i| model.train[i]}.each do |t|
f.puts t.join(",")
end
end
when :test
File.open(file,"w+") do |f|
f.puts (["Canonical SMILES"] + model.independent_variable_names).join(",")
idx.collect{|i| model.train[i]}.each do |t|
t.delete_at(1)
f.puts t.join(",")
end
end
file = File.join(cv_dir,t.to_s+"-experimental.csv")
File.open(file,"w+") do |f|
f.puts (["Canonical SMILES", model.dependent_variable_name]).join(",")
idx.collect{|i| model.train[i]}.each do |t|
# TODO fix
f.puts t[0..1].join(",")
end
end
end
end
Process.waitall
end
end
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