1 files changed, 75 insertions, 17 deletions

diff --git a/test/validation-nanoparticle.rb b/test/validation-nanoparticle.rb
index 1742ef2..0c7d355 100644
--- a/test/validation-nanoparticle.rb
+++ b/test/validation-nanoparticle.rb
@@ -5,69 +5,127 @@ class NanoparticleValidationTest  < MiniTest::Test
 
   def setup
     @training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
-    unless @training_dataset
-      Import::Enanomapper.import File.join(File.dirname(__FILE__),"data","enm")
-      @training_dataset = Dataset.where(name: "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
-    end
     @prediction_feature = @training_dataset.features.select{|f| f["name"] == 'log2(Net cell association)'}.first
   end
 
   def test_validate_default_nanoparticle_model
     model = Model::Lazar.create training_dataset: @training_dataset, prediction_feature: @prediction_feature
     cv = CrossValidation.create model
-    p cv
-    p cv.rmse
-    p cv.r_squared
-    #File.open("tmp.pdf","w+"){|f| f.puts cv.correlation_plot}
+    p cv.id
+    #File.open("tmp.pdf","w+"){|f| f.puts cv.correlation_plot format:"pdf"}
     refute_nil cv.r_squared
     refute_nil cv.rmse
   end
 
-  def test_validate_pls_nanoparticle_model
+  def test_validate_pls_pchem_model
     algorithms = {
       :descriptors => {
         :method => "properties",
         :categories => ["P-CHEM"]
       },
       :prediction => {:method => 'Algorithm::Caret.pls' },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
     }
     model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
     assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
     cv = CrossValidation.create model
-    p cv.rmse
-    p cv.r_squared
+    p cv.id
+    #File.open("tmp2.pdf","w+"){|f| f.puts cv.correlation_plot format:"pdf"}
     refute_nil cv.r_squared
     refute_nil cv.rmse
   end
 
-  def test_validate_proteomics_pls_nanoparticle_model
+=begin
+  def test_validate_proteomics_pls_pchem_model
     algorithms = {
       :descriptors => {
         :method => "properties",
         :categories => ["Proteomics"]
       },
       :prediction => {:method => 'Algorithm::Caret.pls' },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
     }
     model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
     assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
     cv = CrossValidation.create model
-    p cv.rmse
-    p cv.r_squared
     refute_nil cv.r_squared
     refute_nil cv.rmse
   end
+=end
 
-  def test_validate_all_default_nanoparticle_model
+  def test_validate_proteomics_pchem_default_model
     algorithms = {
       :descriptors => {
         :method => "properties",
         :categories => ["Proteomics","P-CHEM"]
       },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_model_without_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :feature_selection => nil
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_weighted_average_model_without_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" },
+      :feature_selection => nil
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_model_with_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
     }
     model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
     cv = CrossValidation.create model
-    p cv.rmse
-    p cv.r_squared
     refute_nil cv.r_squared
     refute_nil cv.rmse
   end