validations fixed (all models were executed with default parameters)

6 files changed, 167 insertions, 28 deletions

diff --git a/test/compound.rb b/test/compound.rb
index 6a3c696..b33a643 100644
--- a/test/compound.rb
+++ b/test/compound.rb
@@ -134,4 +134,16 @@ print c.sdf
       end
     end
   end
+
+  def test_mna
+    c = OpenTox::Compound.from_smiles "N#[N+]C1=CC=CC=C1.F[B-](F)(F)F"
+    p c.mna 4
+  end
+
+  def test_mpd
+    c = OpenTox::Compound.from_smiles "N#[N+]C1=CC=CC=C1.F[B-](F)(F)F"
+    assert 13, c.mpd.size
+    assert 7, c.mpd.uniq.size
+    assert_equal c.mpd, c.openbabel_fingerprint("mpd")
+  end
 end
diff --git a/test/dataset.rb b/test/dataset.rb
index 84be547..752073e 100644
--- a/test/dataset.rb
+++ b/test/dataset.rb
@@ -202,5 +202,15 @@ class DatasetTest < MiniTest::Test
     assert_equal 0.00323, d2.data_entries[5][0]
   end
 
+  def test_scaled_dataset
+    original_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini.csv")
+    scaled_dataset = original_dataset.scale
+    scaled_dataset.data_entries.each_with_index do |row,i|
+      row.each_with_index do |value,j|
+        assert_equal original_dataset.data_entries[i][j].round(4), scaled_dataset.original_value(value,j).round(4) if value # ignore nils
+      end
+    end
+  end
+
 end
 
diff --git a/test/experiment.rb b/test/experiment.rb
index 4b54768..76a0498 100644
--- a/test/experiment.rb
+++ b/test/experiment.rb
@@ -63,21 +63,26 @@ class ExperimentTest < MiniTest::Test
   end
 
   def test_regression_fingerprints
+=begin
     datasets = [
-      "LOAEL_mmol_corrected_smiles.csv"
+      "EPAFHM.medi.csv",
+      #"LOAEL_mmol_corrected_smiles.csv"
     ]
     min_sims = [0.3,0.7]
-    types = ["FP2","FP3","FP4","MACCS"]
+    #min_sims = [0.7]
+    #types = ["FP2","FP3","FP4","MACCS","mpd"]
+    types = ["mpd","FP3"]
     experiment = Experiment.create(
-      :name => "Fminer vs fingerprint classification for datasets #{datasets}.",
+      :name => "Fingerprint regression with different types for datasets #{datasets}.",
       :dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
     )
     types.each do |type|
       min_sims.each do |min_sim|
         experiment.model_settings << {
-          :algorithm => "OpenTox::Model::LazarRegression",
+          :model_algorithm => "OpenTox::Model::LazarRegression",
+          :prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
           :neighbor_algorithm => "fingerprint_neighbors",
-          :neighbor_algorithm_parameter => {
+          :neighbor_algorithm_parameters => {
             :type => type,
             :min_sim => min_sim,
           }
@@ -85,7 +90,53 @@ class ExperimentTest < MiniTest::Test
       end
     end
     experiment.run
-    p experiment.report
+=end
+#=begin
+    experiment = Experiment.find '56029cb92b72ed673d000000'
+#=end
+    p experiment.id
+    experiment.results.each do |dataset,result|
+      result.each do |r|
+        params = Model::Lazar.find(r["model_id"])[:neighbor_algorithm_parameters]
+        RepeatedCrossValidation.find(r["repeated_crossvalidation_id"]).crossvalidations.each do |cv|
+          cv.validation_ids.each do |vid|
+            model_params = Model::Lazar.find(Validation.find(vid).model_id)[:neighbor_algorithm_parameters]
+            assert_equal params[:type], model_params[:type]
+            assert_equal params[:min_sim], model_params[:min_sim]
+            refute_equal params[:training_dataset_id], model_params[:training_dataset_id]
+          end
+        end
+      end
+    end
+    puts experiment.report.to_yaml
+    p experiment.summary
+  end
 
+  def test_mpd_fingerprints
+=begin
+    datasets = [
+      "EPAFHM.medi.csv",
+    ]
+    types = ["FP2","mpd"]
+    experiment = Experiment.create(
+      :name => "FP2 vs mpd fingerprint regression for datasets #{datasets}.",
+      :dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
+    )
+    types.each do |type|
+    experiment.model_settings << {
+      :algorithm => "OpenTox::Model::LazarRegression",
+      :neighbor_algorithm => "fingerprint_neighbors",
+      :neighbor_algorithm_parameter => {
+        :type => type,
+        :min_sim => 0.7,
+      }
+    }
+    end
+    experiment.run
+    p experiment.id
+=end
+    experiment = Experiment.find '55ffd0c02b72ed123c000000'
+    p experiment
+    puts experiment.report.to_yaml
   end
 end
diff --git a/test/lazar-physchem-short.rb b/test/lazar-physchem-short.rb
index e74a4b9..59d8112 100644
--- a/test/lazar-physchem-short.rb
+++ b/test/lazar-physchem-short.rb
@@ -2,27 +2,29 @@ require_relative "setup.rb"
 
 class LazarPhyschemDescriptorTest < MiniTest::Test
   def test_epafhm
-    skip "Physchem Regression not yet implemented."
-    # check available descriptors
-    @descriptors = OpenTox::Algorithm::Descriptor::DESCRIPTORS.keys
-    assert_equal 111,@descriptors.size,"wrong number of physchem descriptors"
-    @descriptor_values = OpenTox::Algorithm::Descriptor::DESCRIPTOR_VALUES
 
-    # select descriptors for test
-    @num_features_offset = 0
-    @descriptors.keep_if{|x| x=~/^Openbabel\./}
-    @descriptors.delete("Openbabel.L5") # TODO Openbabel.L5 does not work, investigate!!!
-    puts "Descriptors: #{@descriptors}"
+    @descriptors = OpenTox::Algorithm::Descriptor::OBDESCRIPTORS.keys
+    refute_empty @descriptors
 
     # UPLOAD DATA
     training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
-    puts "Dataset: "+training_dataset.id
-#    feature_dataset = Algorithm::Descriptor.physchem training_dataset, @descriptors
-    model = Model::LazarRegression.create training_dataset#, feature_dataset
-    #p model
+    feature_dataset = Algorithm::Descriptor.physchem training_dataset, @descriptors
+    scaled_feature_dataset = feature_dataset.scale
+    model = Model::LazarRegression.create training_dataset
+    model.neighbor_algorithm = "physchem_neighbors"
+    model.neighbor_algorithm_parameters = {
+      :feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.physchem",
+      :descriptors => @descriptors,
+      :feature_dataset_id => scaled_feature_dataset.id,
+      :min_sim => 0.3
+    }
+    model.save
     compound = Compound.from_smiles "CC(C)(C)CN"
     prediction = model.predict compound
-    p prediction
-
+    refute_nil prediction[:value]
+    refute_nil prediction[:confidence]
+    prediction[:neighbors].each do |line|
+      assert_operator line[1], :>, 0.3
+    end
   end
 end
diff --git a/test/lazar-regression.rb b/test/lazar-regression.rb
index cc7f356..8b2d473 100644
--- a/test/lazar-regression.rb
+++ b/test/lazar-regression.rb
@@ -13,6 +13,16 @@ class LazarRegressionTest < MiniTest::Test
     assert_equal 1, prediction[:neighbors].size
   end
 
+  def test_mpd_fingerprints
+    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
+    model = Model::LazarRegression.create training_dataset
+    model.neighbor_algorithm_parameters[:type] = "mpd"
+    compound = Compound.from_smiles "CCCSCCSCC"
+    prediction = model.predict compound
+    assert_equal 0.04, prediction[:value].round(2)
+    assert_equal 1, prediction[:neighbors].size
+  end
+
   def test_local_linear_regression
     skip
     training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
diff --git a/test/validation.rb b/test/validation.rb
index dfa2c81..9717ccc 100644
--- a/test/validation.rb
+++ b/test/validation.rb
@@ -20,10 +20,25 @@ class ValidationTest < MiniTest::Test
   end
 
   def test_regression_crossvalidation
-    #dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
-    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.csv"
-    model = Model::LazarRegression.create dataset
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
+    #dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.csv"
+    params = {
+      :prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
+      :neighbor_algorithm => "fingerprint_neighbors",
+      :neighbor_algorithm_parameters => {
+        :type => "MACCS",
+        :min_sim => 0.7,
+      }
+    }
+    model = Model::LazarRegression.create dataset, params
     cv = RegressionCrossValidation.create model
+    cv.validation_ids.each do |vid|
+      model = Model::Lazar.find(Validation.find(vid).model_id)
+      assert_equal params[:neighbor_algorithm_parameters][:type], model[:neighbor_algorithm_parameters][:type]
+      assert_equal params[:neighbor_algorithm_parameters][:min_sim], model[:neighbor_algorithm_parameters][:min_sim]
+      refute_equal params[:neighbor_algorithm_parameters][:training_dataset_id], model[:neighbor_algorithm_parameters][:training_dataset_id]
+    end
+
     #`inkview #{cv.plot}`
     #puts JSON.pretty_generate(cv.misclassifications)#.collect{|l| l.join ", "}.join "\n"
     #`inkview #{cv.plot}`
@@ -37,12 +52,51 @@ class ValidationTest < MiniTest::Test
     dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
     model = Model::LazarClassification.create dataset
     repeated_cv = RepeatedCrossValidation.create model
-    p repeated_cv
     repeated_cv.crossvalidations.each do |cv|
-      p cv
-      p cv.accuracy
       assert cv.accuracy > 0.7
     end
   end
 
+  def test_crossvalidation_parameters
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
+    params = {
+      :neighbor_algorithm_parameters => {
+        :min_sim => 0.3,
+        :type => "FP3"
+      }
+    }
+    model = Model::LazarClassification.create dataset, params
+    model.save
+    cv = ClassificationCrossValidation.create model
+    params = model.neighbor_algorithm_parameters
+    params = Hash[params.map{ |k, v| [k.to_s, v] }] # convert symbols to string
+    cv.validations.each do |validation|
+      assert_equal params, validation.model.neighbor_algorithm_parameters
+    end
+  end
+
+  def test_physchem_regression_crossvalidation
+
+    @descriptors = OpenTox::Algorithm::Descriptor::OBDESCRIPTORS.keys
+    refute_empty @descriptors
+
+    # UPLOAD DATA
+    training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
+    feature_dataset = Algorithm::Descriptor.physchem training_dataset, @descriptors
+    feature_dataset.save
+    scaled_feature_dataset = feature_dataset.scale
+    scaled_feature_dataset.save
+    model = Model::LazarRegression.create training_dataset
+    model.neighbor_algorithm = "physchem_neighbors"
+    model.neighbor_algorithm_parameters = {
+      :feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.physchem",
+      :descriptors => @descriptors,
+      :feature_dataset_id => scaled_feature_dataset.id,
+      :min_sim => 0.3
+    }
+    model.save
+    cv = RegressionCrossValidation.create model
+    p cv
+  end
+
 end