1 files changed, 177 insertions, 0 deletions
diff --git a/test/model.rb b/test/model.rb
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+++ b/test/model.rb
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+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