validation statistic fixes

1 files changed, 128 insertions, 0 deletions

diff --git a/test/classification-model.rb b/test/classification-model.rb
new file mode 100644
index 0000000..b94b5e6
--- /dev/null
+++ b/test/classification-model.rb
@@ -0,0 +1,128 @@
+require_relative "setup.rb"
+
+class LazarClassificationTest < MiniTest::Test
+
+  def test_classification_default
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D"
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.5
+      },
+      :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
+    [ {
+      :compound => OpenTox::Compound.from_inchi("InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H"),
+      :prediction => "false",
+    },{
+      :compound => OpenTox::Compound.from_smiles("c1ccccc1NN"),
+      :prediction => "false",
+    } ].each do |example|
+      prediction = model.predict example[:compound]
+      p example[:compound]
+      p prediction
+      #assert_equal example[:prediction], prediction[:value]
+    end
+
+    compound = Compound.from_smiles "CCO"
+    prediction = model.predict compound
+    assert_equal "true", prediction[:value]
+    assert_equal ["false"], prediction[:measurements]
+
+    # make a dataset prediction
+    compound_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini_log10.csv")
+    prediction_dataset = model.predict compound_dataset
+    assert_equal compound_dataset.compounds, prediction_dataset.compounds
+
+    cid = prediction_dataset.compounds[7].id.to_s
+    assert_equal "Could not find similar substances with experimental data in the training dataset.", prediction_dataset.predictions[cid][:warnings][0]
+    expectations = ["Cannot create prediction: Only one similar compound in the training set.",
+    "Could not find similar substances with experimental data in the training dataset."]
+    prediction_dataset.predictions.each do |cid,pred|
+      assert_includes expectations, pred[:warnings][0] if pred[:value].nil?
+    end
+    cid = Compound.from_smiles("CCOC(=O)N").id.to_s
+    assert_match "excluded", prediction_dataset.predictions[cid][:info]
+  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
+
+  def test_dataset_prediction
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
+    model = Model::Lazar.create training_dataset: training_dataset
+    result = model.predict training_dataset
+    assert_kind_of Dataset, result
+    assert 3, result.features.size
+    assert 8, result.compounds.size
+    assert_equal ["true"], result.values(result.compounds.first, result.features[0])
+    assert_equal [0.65], result.values(result.compounds.first, result.features[1])
+    assert_equal [0], result.values(result.compounds.first, result.features[2]) # classification returns nil, check if 
+  end
+
+  def test_carcinogenicity_rf_classification
+    skip "Caret rf may run into a (endless?) loop for some compounds."
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/multi_cell_call.csv"
+    algorithms = {
+      :prediction => {
+        :method => "Algorithm::Caret.rf",
+      },
+    }
+    model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms
+    substance = Compound.from_smiles "[O-]S(=O)(=O)[O-].[Mn+2].O"
+    prediction = model.predict substance
+    p prediction
+    
+  end
+
+  def test_rf_classification
+    skip "Caret rf may run into a (endless?) loop for some compounds."
+    algorithms = {
+      :prediction => {
+        :method => "Algorithm::Caret.rf",
+      },
+    }
+    training_dataset = Dataset.from_sdf_file File.join(DATA_DIR,"cas_4337.sdf")
+    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
+    #p model.id.to_s
+    #model = Model::Lazar.find "5bbb4c0cca626909f6c8a924"
+    assert_kind_of Model::LazarClassification, model
+    assert_equal algorithms[:prediction][:method], model.algorithms["prediction"]["method"]
+    substance = Compound.from_smiles "Clc1ccc(cc1)C(=O)c1ccc(cc1)OC(C(=O)O)(C)C"
+    prediction = model.predict substance
+    assert_equal  51, prediction[:neighbors].size
+    assert_equal "nonmutagen", prediction[:value]
+    assert_equal 0.1, prediction[:probabilities]["mutagen"].round(1)
+    assert_equal 0.9, prediction[:probabilities]["nonmutagen"].round(1)
+  end
+
+end