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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 = []
@compounds = []
@files = []
@dump_dir = FileUtils.mkdir_p File.join(File.dirname(__FILE__),"dump",File.basename(__FILE__,".rb"))
FileUtils.mkdir_p File.join(File.dirname(__FILE__),"reference",File.basename(__FILE__,".rb"))
end
def dump(object,file)
@files << file
FileUtils.mkdir_p File.dirname(file)
File.open(file,"w+"){|f| f.puts object.to_yaml}
end
def create_model(params)
params[:subjectid] = @@subjectid
model_uri = OpenTox::Algorithm::Lazar.new.run(params).to_s
@model = OpenTox::Model::Lazar.find model_uri, @@subjectid
dump @model, File.join(@dump_dir,caller[0][/`.*'/][1..-2],"model")+".yaml"
end
def predict_compound(compound)
@compounds << compound
prediction_uri = @model.run(:compound_uri => compound.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
dump prediction, File.join(@dump_dir,caller[0][/`.*'/][1..-2],"compound_prediction")+@compounds.size.to_s+".yaml"
end
def predict_dataset(dataset)
prediction_uri = @model.run(:dataset_uri => dataset.uri, :subjectid => @@subjectid)
prediction = OpenTox::LazarPrediction.find(prediction_uri, @@subjectid)
@predictions << prediction
dump prediction, File.join(@dump_dir,caller[0][/`.*'/][1..-2],"dataset_prediction")+".yaml"
end
def cleanup # executed only when assertions succeed (teardown is called even when assertions fail)
@files.each do |f|
reference = f.sub(/dump/,"reference")
FileUtils.mkdir_p File.dirname(reference)
FileUtils.cp f, reference
FileUtils.rm f
end
#@predictions.each do |dataset|
# dataset.delete(@@subjectid)
#end
#@model.delete(@@subjectid)
end
=begin
=end
# ### Regression
# #
# # Nominal / Ordinal Features
# def test_regression_mlr_prop_model
# create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 0.45, @predictions.first.confidence(@compounds.first).round_to(2)
# assert_equal 0.62, @predictions.first.value(@compounds.first).round_to(2)
# assert_equal 253, @predictions.first.neighbors(@compounds.first).size
# assert_equal 131, @model.features.size
# end
#
# def test_create_regression_model
# create_model :dataset_uri => @@regression_training_dataset.uri
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_in_delta @predictions.first.value(@compounds.first), 0.15, 0.2
# assert_equal 0.453.round_to(3), @predictions.first.confidence(@compounds.first).round_to(3)
# assert_equal 253, @predictions.first.neighbors(@compounds.first).size
# cleanup
# end
#
# def test_create_regression_prop_model
# create_model :dataset_uri => @@regression_training_dataset.uri, :local_svm_kernel => "propositionalized"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 0.453.round_to(3), @predictions.first.confidence(@compounds.first).round_to(3)
# assert_equal 253, @predictions.first.neighbors(@compounds.first).size
# assert_equal 131, @model.features.size
# cleanup
# end
#
# # Numeric Features
# def test_create_regression_prop_pc_model
# create_model :dataset_uri => @@regression_training_dataset.uri, :local_svm_kernel => "propositionalized", :pc_type => "electronic"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_in_delta @predictions.first.value(@compounds.first), 0.53, 0.1
# assert_equal 0.453.round_to(3), @predictions.first.confidence(@compounds.first).round_to(3)
# assert_equal 253, @predictions.first.neighbors(@compounds.first).size
# assert_equal 131, @model.features.size
# cleanup
# end
#
# def test_regression_mlr_prop_pc_model
# create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop", :pc_type => "electronic"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 0.45, @predictions.first.confidence(@compounds.first).round_to(2)
# assert_equal 0.76, @predictions.first.value(@compounds.first).round_to(2)
# assert_equal 253, @predictions.first.neighbors(@compounds.first).size
# assert_equal 131, @model.features.size
# end
def test_regression_mlr_prop_pc_model_max_n
create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop", :pc_type => "electronic", :max_perc_neighbors => "5"
predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
assert_equal 0.76, @predictions.first.confidence(@compounds.first).round_to(2)
assert_equal 0.57, @predictions.first.value(@compounds.first).round_to(2)
assert_equal 24, @predictions.first.neighbors(@compounds.first).size
assert_equal 131, @model.features.size
end
#
#
#
#
# ### Classification
#
# ## Nominal / Ordinal Features
#
# def test_classification_model
# create_model :dataset_uri => @@classification_training_dataset.uri
# # single prediction
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# # dataset activity
# predict_compound OpenTox::Compound.from_smiles("CNN")
# # dataset prediction
# predict_dataset OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
# # assertions
# # single prediction
# assert_equal "false", @predictions[0].value(@compounds[0])
# assert_equal 0.2938.round_to(4), @predictions[0].confidence(@compounds[0]).round_to(4)
# assert_equal 16, @predictions[0].neighbors(@compounds[0]).size
# # dataset activity
# assert !@predictions[1].measured_activities(@compounds[1]).empty?
# assert_equal "true", @predictions[1].measured_activities(@compounds[1]).first.to_s
# assert @predictions[1].value(@compounds[1]).nil?
# # dataset prediction
# c = OpenTox::Compound.from_smiles("CC(=Nc1ccc2c(c1)Cc1ccccc21)O")
# assert_equal nil, @predictions[2].value(c)
# assert_equal "true", @predictions[2].measured_activities(c).first.to_s
# c = OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal "false", @predictions[2].value(c)
# assert_equal 0.2938.round_to(4) , @predictions[2].confidence(c).round_to(4)
# # model
# assert_equal 41, @model.features.size
# cleanup
# end
#
#
# def test_classification_svm_model
# create_model :dataset_uri => @@classification_training_dataset.uri, :prediction_algorithm => "local_svm_classification"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# predict_dataset OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
#
# assert_equal "false", @predictions[0].value(@compounds[0])
# assert_equal 0.3952, @predictions[0].confidence(@compounds[0]).round_to(4)
# assert_equal 16, @predictions[0].neighbors(@compounds[0]).size
#
# c = OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 4, @predictions[1].compounds.size
# assert_equal "false", @predictions[1].value(c)
#
# assert_equal 41, @model.features.size
# cleanup
# end
#
# def test_classification_svm_prop_model
# create_model :dataset_uri => @@classification_training_dataset.uri, :prediction_algorithm => "local_svm_classification", :local_svm_kernel => "propositionalized"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# predict_dataset OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
#
# assert_equal "false", @predictions[0].value(@compounds[0])
# assert_equal 0.3952, @predictions[0].confidence(@compounds[0]).round_to(4)
# assert_equal 16, @predictions[0].neighbors(@compounds[0]).size
#
# c = OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 4, @predictions[1].compounds.size
# assert_equal "false", @predictions[1].value(c)
#
# assert_equal 41, @model.features.size
# cleanup
# end
#
#
# ## Numeric Features
#
# def test_classification_svm_prop_pc_model
# create_model :dataset_uri => @@classification_training_dataset.uri, :prediction_algorithm => "local_svm_classification", :local_svm_kernel => "propositionalized", :pc_type => "electronic"
# predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
# predict_dataset OpenTox::Dataset.create_from_csv_file("data/multicolumn.csv", @@subjectid)
#
# assert_equal "false", @predictions[0].value(@compounds[0])
# assert_equal 0.3952, @predictions[0].confidence(@compounds[0]).round_to(4)
# assert_equal 16, @predictions[0].neighbors(@compounds[0]).size
#
# c = OpenTox::Compound.from_smiles("c1ccccc1NN")
# assert_equal 4, @predictions[1].compounds.size
# assert_equal "false", @predictions[1].value(c)
#
# assert_equal 41, @model.features.size
# cleanup
# end
## def test_regression_mlr_prop_conf_stdev
## create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop", :conf_stdev => "true"
## predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
## assert_equal 0.154, @predictions.first.confidence(@compounds.first).round_to(3)
## assert_equal 0.265, @predictions.first.value(@compounds.first).round_to(3)
## assert_equal 253, @predictions.first.neighbors(@compounds.first).size
## assert_equal 131, @model.features.size
## end
def test_conf_stdev
params = {:sims => [0.6,0.72,0.8], :acts => [1,1,1], :neighbors => [1,1,1], :conf_stdev => true}
params2 = {:sims => [0.6,0.7,0.8], :acts => [3.4,2,0.6], :neighbors => [1,1,1,1], :conf_stdev => true } # stev ~ 1.4
params3 = {:sims => [0.6,0.7,0.8], :acts => [1,1,1], :neighbors => [1,1,1], }
params4 = {:sims => [0.6,0.7,0.8], :acts => [3.4,2,0.6], :neighbors => [1,1,1] }
2.times {
assert_in_delta OpenTox::Algorithm::Neighbors::get_confidence(params), 0.72, 0.0001
assert_in_delta OpenTox::Algorithm::Neighbors::get_confidence(params2), 0.172617874759125, 0.0001
assert_in_delta OpenTox::Algorithm::Neighbors::get_confidence(params3), 0.7, 0.0001
assert_in_delta OpenTox::Algorithm::Neighbors::get_confidence(params4), 0.7, 0.0001
}
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
|
