validation statistic fixes

1 files changed, 0 insertions, 145 deletions

diff --git a/test/model-classification.rb b/test/model-classification.rb
deleted file mode 100644
index ca6eb27..0000000
--- a/test/model-classification.rb
+++ /dev/null
@@ -1,145 +0,0 @@
-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][: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]
-    # 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_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 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 
-    #p prediction_dataset
-  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