From ad7ec6a1e33f69557fe64371581d5f42a65ecaa8 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Thu, 13 Oct 2016 17:34:31 +0200
Subject: classification fixed

---
 test/model-classification.rb | 106 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)
 create mode 100644 test/model-classification.rb

(limited to 'test/model-classification.rb')

diff --git a/test/model-classification.rb b/test/model-classification.rb
new file mode 100644
index 0000000..1424f6a
--- /dev/null
+++ b/test/model-classification.rb
@@ -0,0 +1,106 @@
+require_relative "setup.rb"
+
+class LazarClassificationTest < MiniTest::Test
+
+  def test_classification_default
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D"
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :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]
+    [ {
+      :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]
+      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][:warning]
+    prediction_dataset.predictions.each do |cid,pred|
+      assert_equal "Could not find similar substances with experimental data in the training dataset.", pred[:warning] if pred[:value].nil?
+    end
+    cid = Compound.from_smiles("CCOC(=O)N").id.to_s
+    assert_match "excluded", prediction_dataset.predictions[cid][:warning]
+    # cleanup
+    [training_dataset,model,compound_dataset,prediction_dataset].each{|o| o.delete}
+  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_kazius
+    t = Time.now
+    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"kazius.csv")
+    t = Time.now
+    model = Model::Lazar.create training_dataset: training_dataset
+    t = Time.now
+    2.times do
+      compound = Compound.from_smiles("Clc1ccccc1NN")
+      prediction = model.predict compound
+      assert_equal "1", prediction[:value]
+    end
+    training_dataset.delete
+  end
+
+  def test_caret_classification
+    skip
+  end
+
+  def test_fingerprint_chisq_feature_selection
+    skip
+  end
+
+  def test_physchem_classification
+    skip
+  end
+end
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