PC tests (lazar.rb)

1 files changed, 155 insertions, 95 deletions

diff --git a/lazar.rb b/lazar.rb
index c440f47..ae3ef4b 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -62,97 +62,157 @@ class LazarTest < Test::Unit::TestCase
 
 =begin
 =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
-#
-#  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
 
+  ### 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 0.453, @predictions.first.confidence(@compounds.first).round_to(3)
-     assert_equal 0.765, @predictions.first.value(@compounds.first).round_to(3)
+     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
+
+
+
+
+  ### 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")
@@ -162,18 +222,18 @@ class LazarTest < Test::Unit::TestCase
 ##    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
+  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