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
  def test_create_regression_model
    create_model :dataset_uri => @@regression_training_dataset.uri
    predict_compound OpenTox::Compound.from_smiles("c1ccccc1NN")
    assert_equal 1.09.round_to(2), @predictions.first.value(@compounds.first).round_to(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

  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.round_to(4), @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 0.2938.round_to(4), @predictions[0].confidence(@compounds[0]).round_to(4)
   assert_equal 0.3952.round_to(4), @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_model
    create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop"
    predict_compound  OpenTox::Compound.from_smiles("c1ccccc1NN")
    assert_equal 0.453, @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_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_regression_mlr_prop_weighted_model
    create_model :dataset_uri => @@regression_training_dataset.uri, :prediction_algorithm => "local_mlr_prop", :nr_hits => "true"
    predict_compound  OpenTox::Compound.from_smiles("c1ccccc1NN")
    assert_equal 0.453, @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} # stdev = 0
   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