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require 'rubygems'
require 'opentox-ruby'
require 'test/unit'
require "./validate-owl.rb"
class Float
def round_to(x)
(self * 10**x).round.to_f / 10**x
end
end
class LazarTest < Test::Unit::TestCase
def setup
@predictions = []
@models = []
end
def teardown
@predictions.each {|p| p.delete(@@subjectid)}
@models.each {|m| m.delete(@@subjectid)}
end
=begin
=end
def test_create_regression_model
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => @@regression_training_dataset.uri, :subjectid => @@subjectid}).to_s
#puts model_uri
validate_owl model_uri,@@subjectid
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
@models << lazar
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid).to_s
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
assert_equal prediction.value(compound).round_to(3),0.378.round_to(3)
assert_equal prediction.confidence(compound).round_to(3), 0.276.round_to(3)
#assert_equal prediction.value(compound).round_to(4), 0.2847.round_to(4)
#assert_equal prediction.confidence(compound).round_to(4), 0.3223.round_to(4)
assert_equal prediction.neighbors(compound).size, 61
end
def test_create_regression_prop_model
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => @@regression_training_dataset.uri, :subjectid => @@subjectid, :local_svm_kernel => "propositionalized"}).to_s
#puts model_uri
validate_owl model_uri,@@subjectid
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
@models << lazar
assert_equal 219, lazar.features.size
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid).to_s
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
assert_equal prediction.value(compound).round_to(1),0.1.round_to(1)
assert_equal prediction.confidence(compound).round_to(3), 0.276.round_to(3)
#assert_equal prediction.value(compound).round_to(4), 0.2847.round_to(4)
#assert_equal prediction.confidence(compound).round_to(4), 0.3223.round_to(4)
assert_equal prediction.neighbors(compound).size, 61
end
def test_classification_model
# create model
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => @@classification_training_dataset.uri, :subjectid => @@subjectid}).to_s
validate_owl model_uri,@@subjectid
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
@models << lazar
assert_equal lazar.features.size, 52
# single prediction
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
#puts prediction_uri
assert_equal prediction.value(compound), "false"
assert_equal prediction.confidence(compound).round_to(4), 0.3067.round_to(4)
assert_equal prediction.neighbors(compound).size, 14
# dataset activity
compound = OpenTox::Compound.from_smiles("CNN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find prediction_uri, @@subjectid
@predictions << prediction
assert !prediction.measured_activities(compound).empty?
assert_equal prediction.measured_activities(compound).first.to_s, "true"
assert prediction.value(compound).nil?
# dataset prediction
test_dataset = OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
prediction = OpenTox::LazarPrediction.find lazar.run(:dataset_uri => test_dataset.uri, :subjectid => @@subjectid), @@subjectid
@predictions << prediction
assert_equal prediction.compounds.size, 4
compound = OpenTox::Compound.from_smiles "CC(=Nc1ccc2c(c1)Cc1ccccc21)O"
assert_equal prediction.value(compound), nil
assert_equal prediction.measured_activities(compound).first.to_s, "true"
end
def test_classification_svm_model
# create model
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => @@classification_training_dataset.uri, :subjectid => @@subjectid, :prediction_algorithm => "local_svm_classification"}).to_s
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
@models << lazar
assert_equal lazar.features.size, 52
# single prediction
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
assert_equal prediction.value(compound), "false"
assert_equal prediction.confidence(compound).round_to(4), 0.4131.round_to(4)
assert_equal prediction.neighbors(compound).size, 14
# dataset prediction
test_dataset = OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
prediction = OpenTox::LazarPrediction.find lazar.run(:dataset_uri => test_dataset.uri, :subjectid => @@subjectid), @@subjectid
@predictions << prediction
assert_equal prediction.compounds.size, 4
compound = OpenTox::Compound.from_smiles "CC(=Nc1ccc2c(c1)Cc1ccccc21)O"
assert_equal prediction.value(compound), nil
assert_equal prediction.measured_activities(compound).first, true
end
def test_classification_svm_prop_model
# create model
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => @@classification_training_dataset.uri, :subjectid => @@subjectid, :prediction_algorithm => "local_svm_classification", :local_svm_kernel => "propositionalized"}).to_s
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
@models << lazar
assert_equal lazar.features.size, 52
# single prediction
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
assert_equal prediction.value(compound), "false"
assert_equal prediction.confidence(compound).round_to(4), 0.4131.round_to(4)
assert_equal prediction.neighbors(compound).size, 14
# dataset prediction
test_dataset = OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
prediction = OpenTox::LazarPrediction.find lazar.run(:dataset_uri => test_dataset.uri, :subjectid => @@subjectid), @@subjectid
@predictions << prediction
assert_equal prediction.compounds.size, 4
compound = OpenTox::Compound.from_smiles "CC(=Nc1ccc2c(c1)Cc1ccccc21)O"
assert_equal prediction.value(compound), nil
assert_equal prediction.measured_activities(compound).first, true
end
=begin
def test_ambit_classification_model
# create model
dataset_uri = "http://apps.ideaconsult.net:8080/ambit2/dataset/9?max=400"
feature_uri ="http://apps.ideaconsult.net:8080/ambit2/feature/21573"
#model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => dataset_uri, :prediction_feature => feature_uri}).to_s
#lazar = OpenTox::Model::Lazar.find model_uri
model_uri = OpenTox::Algorithm::Lazar.new.run({:dataset_uri => dataset_uri, :prediction_feature => feature_uri, :subjectid => @@subjectid}).to_s
validate_owl model_uri,@@subjectid
lazar = OpenTox::Model::Lazar.find model_uri, @@subjectid
puts lazar.features.size
assert_equal lazar.features.size, 1874
#puts "Model: #{lazar.uri}"
#puts lazar.features.size
# single prediction
compound = OpenTox::Compound.from_smiles("c1ccccc1NN")
#prediction_uri = lazar.run(:compound_uri => compound.uri)
#prediction = OpenTox::LazarPrediction.find(prediction_uri)
prediction_uri = lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
#puts "Prediction: #{prediction.uri}"
#puts prediction.value(compound)
assert_equal prediction.value(compound), "3.0"
#puts @prediction.confidence(compound).round_to(4)
#assert_equal @prediction.confidence(compound).round_to(4), 0.3005.round_to(4)
#assert_equal @prediction.neighbors(compound).size, 15
#@prediction.delete(@@subjectid)
# dataset activity
#compound = OpenTox::Compound.from_smiles("CNN")
#prediction_uri = @lazar.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
#@prediction = OpenTox::LazarPrediction.find prediction_uri, @@subjectid
#assert !@prediction.measured_activities(compound).empty?
#assert_equal @prediction.measured_activities(compound).first, true
#assert @prediction.value(compound).nil?
#@prediction.delete(@@subjectid)
# dataset prediction
#@lazar.delete(@@subjectid)
end
=end
end
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