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require_relative "setup.rb"
class NanoparticleTest < MiniTest::Test
def setup
Import::Enanomapper.import File.join(File.dirname(__FILE__),"data","enm")
#`mongorestore --db=development #{File.join(File.dirname(__FILE__),"..","dump","production")}`
end
def test_create_model_with_feature_selection
skip
training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
feature = Feature.find_or_create_by(name: "7.99 Toxicity (other) ICP-AES", category: "TOX", unit: "mL/ug(Mg)")
model = Model::LazarRegression.create(feature, training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", :neighbor_algorithm => "nanoparticle_neighbors"})
nanoparticle = training_dataset.nanoparticles[-34]
#p nanoparticle.neighbors
prediction = model.predict nanoparticle
p prediction
#p prediction
refute_nil prediction[:value]
end
def test_create_model
training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
feature = Feature.find_or_create_by(name: "7.99 Toxicity (other) ICP-AES", category: "TOX", unit: "mL/ug(Mg)")
model = Model::LazarRegression.create(feature, training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", :neighbor_algorithm => "nanoparticle_neighbors"})
nanoparticle = training_dataset.nanoparticles[-34]
prediction = model.predict nanoparticle
p prediction
#p prediction
refute_nil prediction[:value]
end
def test_validate_model
training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
feature = Feature.find_or_create_by(name: "7.99 Toxicity (other) ICP-AES", category: "TOX", unit: "mL/ug(Mg)")
#model = Model::LazarRegression.create(feature, training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression", :neighbor_algorithm => "nanoparticle_neighbors"})
model = Model::LazarRegression.create(feature, training_dataset, {:prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average", :neighbor_algorithm => "nanoparticle_neighbors"})
p model
cv = RegressionCrossValidation.create model
p cv
end
def test_export
skip
Dataset.all.each do |d|
puts d.to_csv
end
end
def test_summaries
skip
features = Feature.all.to_a
#p features.collect do |f|
#f if f.category == "TOX"
#end.to_a.flatten.size
toxcounts = {}
pccounts = {}
Nanoparticle.all.each do |np|
np.toxicities.each do |t,v|
toxcounts[t] ||= 0
toxcounts[t] += 1#v.uniq.size
end
np.physchem_descriptors.each do |t,v|
pccounts[t] ||= 0
pccounts[t] += 1#v.uniq.size
end
end
#puts counts.keys.collect{|i| Feature.find(i)}.to_yaml
#pccounts.each{|e,n| p Feature.find(e),n if n > 100}
#p toxcounts.collect{|e,n| Feature.find(e).name if n > 1}.uniq
toxcounts.each{|e,n| p Feature.find(e),n if n > 100}
end
def test_import_ld
skip
dataset_ids = Import::Enanomapper.import_ld
end
end
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