compound and descriptor tests fixed

4 files changed, 203 insertions, 15 deletions

diff --git a/lib/compound.rb b/lib/compound.rb
index 4d62c53..93cfc03 100644
--- a/lib/compound.rb
+++ b/lib/compound.rb
@@ -77,7 +77,7 @@ module OpenTox
 
     def calculated_physchem descriptors=PhysChem.openbabel_descriptors
       # TODO: speedup java descriptors
-      calculated_ids = descriptors.keys
+      calculated_ids = properties.keys
       # BSON::ObjectId instances are not allowed as keys in a BSON document.
       new_ids = descriptors.collect{|d| d.id.to_s} - calculated_ids
       descs = {}
@@ -90,11 +90,11 @@ module OpenTox
       # avoid recalculating Cdk features with multiple values
       descs.keys.uniq.each do |k|
         descs[k].send(k[0].downcase,k[1],self).each do |n,v|
-          descriptors[algos[n].id.to_s] = v # BSON::ObjectId instances are not allowed as keys in a BSON document.
+          properties[algos[n].id.to_s] = v # BSON::ObjectId instances are not allowed as keys in a BSON document.
         end
       end
       save
-      descriptors.select{|id,v| descriptors.collect{|d| d.id.to_s}.include? id}
+      properties.select{|id,v| descriptors.collect{|d| d.id.to_s}.include? id}
     end
 
     def smarts_match smarts, count=false
diff --git a/test/compound.rb b/test/compound.rb
index c9faa21..c78acb1 100644
--- a/test/compound.rb
+++ b/test/compound.rb
@@ -85,7 +85,13 @@ print c.sdf
       refute_nil c.fingerprint("MP2D")
     end
     c = d.compounds[371]
-    n = c.fingerprint_neighbors({:type => "FP4", :min_sim => 0.7, :dataset_id => d.id, :prediction_feature_id => d.features.first.id })
+    n = c.neighbors(
+      descriptors: {:method => "fingerprint", :type => "FP4"},
+      similarity: {:method => "Algorithm::Similarity.tanimoto", :min => 0.7},
+      dataset_id: d.id,
+      prediction_feature_id: d.features.first.id 
+    )
+
     assert n.size >= 8, "Neighbors size (#{n.size}) should be larger than 7"
   end
 
@@ -118,7 +124,12 @@ print c.sdf
     ].each do |smi|
       c = OpenTox::Compound.from_smiles smi
       types.each do |type|
-        neighbors = c.fingerprint_neighbors({:type => type, :dataset_id => training_dataset.id, :min_sim => min_sim, :prediction_feature_id => training_dataset.features.first.id})
+        neighbors = c.fingerprint_neighbors(
+          descriptors: {:method => "fingerprint",:type => type},
+          dataset_id: training_dataset.id,
+          similarity: {:method => "Algorithm::Similarity.tanimoto", :min => min_sim},
+          prediction_feature_id: training_dataset.features.first.id
+        )
         unless type == "FP2" and smi == "CC(=O)CC(C)C#N" or smi == "C(=O)CC(C)C#N" and (type == "FP2" or type == "MACCS")
           refute_empty neighbors
         end
@@ -197,8 +208,8 @@ print c.sdf
 
   def test_physchem
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C"
-    assert_equal PhysChem::OBDESCRIPTORS.size, c.physchem.size
-    assert_equal PhysChem::OBDESCRIPTORS.size, c.physchem(PhysChem.openbabel_descriptors).size
-    assert_equal PhysChem::unique_descriptors.size, c.physchem(PhysChem.unique_descriptors).size
+    assert_equal PhysChem::OBDESCRIPTORS.size, c.calculated_physchem.size
+    assert_equal PhysChem::OBDESCRIPTORS.size, c.calculated_physchem(PhysChem.openbabel_descriptors).size
+    assert_equal PhysChem::unique_descriptors.size, c.calculated_physchem(PhysChem.unique_descriptors).size
   end
 end
diff --git a/test/descriptor.rb b/test/descriptor.rb
index cd0c1ff..2a5be60 100644
--- a/test/descriptor.rb
+++ b/test/descriptor.rb
@@ -28,34 +28,34 @@ class DescriptorTest < MiniTest::Test
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C#N"
     PhysChem.openbabel_descriptors # required for descriptor initialisation, TODO: move into libs
     PhysChem.find_or_create_by(:name => "Openbabel.logP")
-    result = c.physchem [PhysChem.find_or_create_by(:name => "Openbabel.logP")]
+    result = c.calculated_physchem [PhysChem.find_or_create_by(:name => "Openbabel.logP")]
     assert_equal 1.12518, result.first.last.round(5)
   end
 
   def test_compound_cdk_single
     PhysChem.cdk_descriptors # required for descriptor initialisation, TODO: move into libs
     c = OpenTox::Compound.from_smiles "c1ccccc1"
-    result = c.physchem [PhysChem.find_or_create_by(:name => "Cdk.AtomCount.nAtom")]
+    result = c.calculated_physchem [PhysChem.find_or_create_by(:name => "Cdk.AtomCount.nAtom")]
     assert_equal 12, result.first.last
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C#N"
-    result = c.physchem [PhysChem.find_or_create_by(:name => "Cdk.AtomCount.nAtom")]
+    result = c.calculated_physchem [PhysChem.find_or_create_by(:name => "Cdk.AtomCount.nAtom")]
     assert_equal 17, result.first.last
     c_types = {"Cdk.CarbonTypes.C1SP1"=>1, "Cdk.CarbonTypes.C2SP1"=>0, "Cdk.CarbonTypes.C1SP2"=>0, "Cdk.CarbonTypes.C2SP2"=>1, "Cdk.CarbonTypes.C3SP2"=>0, "Cdk.CarbonTypes.C1SP3"=>2, "Cdk.CarbonTypes.C2SP3"=>1, "Cdk.CarbonTypes.C3SP3"=>1, "Cdk.CarbonTypes.C4SP3"=>0}
     physchem_features = c_types.collect{|t,nr| PhysChem.find_or_create_by(:name => t)}
-    result = c.physchem physchem_features
+    result = c.calculated_physchem physchem_features
     assert_equal [1, 0, 0, 1, 0, 2, 1, 1, 0], result.values
   end
 
   def test_compound_joelib_single
     PhysChem.joelib_descriptors # required for descriptor initialisation, TODO: move into libs
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C#N"
-    result = c.physchem [PhysChem.find_or_create_by(:name => "Joelib.LogP")]
+    result = c.calculated_physchem [PhysChem.find_or_create_by(:name => "Joelib.LogP")]
     assert_equal 2.65908, result.first.last
   end
 
   def test_compound_all
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C#N"
-    result = c.physchem PhysChem.descriptors
+    result = c.calculated_physchem PhysChem.descriptors
     amr = PhysChem.find_or_create_by(:name => "Cdk.ALOGP.AMR", :library => "Cdk")
     sbonds = PhysChem.find_by(:name => "Openbabel.sbonds")
     assert_equal 30.8723, result[amr.id.to_s]
@@ -65,7 +65,7 @@ class DescriptorTest < MiniTest::Test
   def test_compound_descriptor_parameters
     PhysChem.descriptors
     c = OpenTox::Compound.from_smiles "CC(=O)CC(C)C#N"
-    result = c.physchem [ "Openbabel.logP", "Cdk.AtomCount.nAtom", "Joelib.LogP" ].collect{|d| PhysChem.find_or_create_by(:name => d)}
+    result = c.calculated_physchem [ "Openbabel.logP", "Cdk.AtomCount.nAtom", "Joelib.LogP" ].collect{|d| PhysChem.find_or_create_by(:name => d)}
     assert_equal 3, result.size
     result.each do |fid,v|
       feature = Feature.find(fid)
diff --git a/test/model.rb b/test/model.rb
new file mode 100644
index 0000000..563d081
--- /dev/null
+++ b/test/model.rb
@@ -0,0 +1,177 @@
+require_relative "setup.rb"
+
+class ModelTest < MiniTest::Test
+
+  def test_default_regression
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D"
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :prediction => {
+        :method => "Algorithm::Regression.caret",
+        :parameters => "pls",
+      },
+      :feature_selection => nil,
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
+    model = Model::Lazar.create  training_dataset: training_dataset
+    assert_kind_of Model::LazarRegression, model
+    assert_equal algorithms, model.algorithms
+    substance = training_dataset.substances[10]
+    prediction = model.predict substance
+    assert_includes prediction[:prediction_interval][0]..prediction[:prediction_interval][1], prediction[:measurements].median, "This assertion assures that measured values are within the prediction interval. It may fail in 5% of the predictions."
+  end
+
+  def test_regression_parameters
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D"
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.3
+      },
+      :prediction => {
+        :method => "Algorithm::Regression.weighted_average",
+        :parameters => "rf",
+      },
+      :feature_selection => nil,
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
+    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
+    assert_kind_of Model::LazarRegression, model
+    assert_equal "Algorithm::Regression.weighted_average", model.algorithms[:prediction][:method]
+    assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
+    assert_equal algorithms[:similarity][:min], model.algorithms[:similarity][:min]
+    assert_equal algorithms[:prediction][:parameters], model.algorithms[:prediction][:parameters]
+    substance = training_dataset.substances[10]
+    prediction = model.predict substance
+    assert_equal 0.83, prediction[:value].round(2)
+  end
+
+  def test_physchem_regression
+    algorithms = {
+      :descriptors => "physchem",
+      :similarity => {
+        :method => "Algorithm::Similarity.weighted_cosine",
+      }
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini_log10.csv")
+    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
+    assert_kind_of Model::LazarRegression, model
+    assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method]
+    assert_equal "Algorithm::Similarity.weighted_cosine", model.algorithms[:similarity][:method]
+    assert_equal 0.1, model.algorithms[:similarity][:min]
+    assert_equal algorithms[:descriptors], model.algorithms[:descriptors]
+  end
+
+  def test_nanoparticle_default
+    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
+    model = Model::Lazar.create  training_dataset: training_dataset
+    assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method]
+    assert_equal "rf", model.algorithms[:prediction][:parameters]
+    assert_equal "Algorithm::Similarity.weighted_cosine", model.algorithms[:similarity][:method]
+    prediction = model.predict training_dataset.substances[14]
+    assert_includes prediction[:prediction_interval][0]..prediction[:prediction_interval][1], prediction[:measurements].median, "This assertion assures that measured values are within the prediction interval. It may fail in 5% of the predictions."
+
+  end
+
+  def test_nanoparticle_parameters
+  end
+
+  def test_regression_with_feature_selection
+    algorithms = {
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini_log10.csv")
+    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
+    assert_kind_of Model::LazarRegression, model
+    assert_equal "Algorithm::Regression.caret", model.algorithms[:prediction][:method]
+    assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
+    assert_equal 0.1, model.algorithms[:similarity][:min]
+    assert_equal algorithms[:feature_selection][:method], model.algorithms[:feature_selection][:method]
+  end
+
+  def test_caret_parameters
+  end
+
+  def test_default_classification
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => 'MP2D',
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :prediction => {
+        :method => "Algorithm::Classification.weighted_majority_vote",
+      },
+      :feature_selection => nil,
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
+    model = Model::Lazar.create  training_dataset: training_dataset
+    assert_kind_of Model::LazarClassification, model
+    assert_equal algorithms, model.algorithms
+    substance = training_dataset.substances[10]
+    prediction = model.predict substance
+    assert_equal "false", prediction[:value]
+  end
+ 
+  def test_classification_parameters
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => 'MACCS',
+      },
+      :similarity => {
+        :min => 0.4
+      },
+    }
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
+    model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
+    assert_kind_of Model::LazarClassification, model
+    assert_equal "Algorithm::Classification.weighted_majority_vote", model.algorithms[:prediction][:method]
+    assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
+    assert_equal algorithms[:similarity][:min], model.algorithms[:similarity][:min]
+    substance = training_dataset.substances[10]
+    prediction = model.predict substance
+    assert_equal "false", prediction[:value]
+    assert_equal 4, prediction[:neighbors].size
+  end
+
+=begin
+  def test_physchem_description
+    assert_equal 355, PhysChem.descriptors.size
+    assert_equal 15, PhysChem.openbabel_descriptors.size
+    assert_equal 295, PhysChem.cdk_descriptors.size
+    assert_equal 45, PhysChem.joelib_descriptors.size
+    assert_equal 310, PhysChem.unique_descriptors.size
+  end
+
+  def test_physchem
+    assert_equal 355, PhysChem.descriptors.size
+    c = Compound.from_smiles "CC(=O)CC(C)C"
+    logP = PhysChem.find_or_create_by :name => "Openbabel.logP"
+    assert_equal 1.6215, logP.calculate(c)
+    jlogP = PhysChem.find_or_create_by :name => "Joelib.LogP"
+    assert_equal 3.5951, jlogP.calculate(c)
+    alogP = PhysChem.find_or_create_by :name => "Cdk.ALOGP.ALogP"
+    assert_equal 0.35380000000000034, alogP.calculate(c)
+  end
+=end
+
+end