PC test w max perc neighborspc2

1 files changed, 146 insertions, 137 deletions

diff --git a/lazar.rb b/lazar.rb
index ae3ef4b..9200fb7 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -63,146 +63,155 @@ class LazarTest < Test::Unit::TestCase
 =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"
+#  ### 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 0.45, @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 0.76, @predictions.first.confidence(@compounds.first).round_to(2)
+     assert_equal 0.59, @predictions.first.value(@compounds.first).round_to(2)
+     assert_equal 24, @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
+#
+#
+#
+#
+#  ### 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