model tests separated and cleaned

1 files changed, 0 insertions, 106 deletions

diff --git a/test/model.rb b/test/model.rb
deleted file mode 100644
index 027efe4..0000000
--- a/test/model.rb
+++ /dev/null
@@ -1,106 +0,0 @@
-require_relative "setup.rb"
-
-class ModelTest < MiniTest::Test
-
-  def test_default_regression
-    algorithms = {
-      :descriptors => [ "MP2D" ],
-      :similarity => {
-        :method => "Algorithm::Similarity.tanimoto",
-        :min => 0.1
-      },
-      :prediction => {
-        :method => "Algorithm::Caret.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 => [ "MP2D" ],
-      :similarity => {
-        :method => "Algorithm::Similarity.tanimoto",
-        :min => 0.3
-      },
-      :prediction => {
-        :method => "Algorithm::Regression.weighted_average",
-      },
-      :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 => {
-        :method => "calculate_properties",
-        :features => PhysChem.openbabel_descriptors,
-      },
-      :similarity => {
-        :method => "Algorithm::Similarity.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::Caret.pls", model.algorithms[:prediction][:method]
-    assert_equal "Algorithm::Similarity.cosine", model.algorithms[:similarity][:method]
-    assert_equal 0.1, model.algorithms[:similarity][:min]
-    algorithms[:descriptors].delete :features
-    assert_equal algorithms[:descriptors], model.algorithms[:descriptors]
-    prediction = model.predict training_dataset.substances[10]
-    refute_nil prediction[:value]
-    # TODO test predictin
-  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::Caret.rf", model.algorithms[:prediction][:method]
-    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
-    skip
-  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::Caret.pls", 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
-
-end