validation statistic fixes

1 files changed, 0 insertions, 171 deletions

diff --git a/test/model-regression.rb b/test/model-regression.rb
deleted file mode 100644
index 5903e88..0000000
--- a/test/model-regression.rb
+++ /dev/null
@@ -1,171 +0,0 @@
-require_relative "setup.rb"
-
-class LazarRegressionTest < MiniTest::Test
-
-  def test_default_regression
-    algorithms = {
-      :descriptors => {
-        :method => "fingerprint",
-        :type => "MP2D"
-      },
-      :similarity => {
-        :method => "Algorithm::Similarity.tanimoto",
-        :min => 0.5
-      },
-      :prediction => {
-        :method => "Algorithm::Caret.rf",
-      },
-      :feature_selection => nil,
-    }
-    training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM_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[145]
-    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."
-    substance = Compound.from_smiles "c1ccc(cc1)Oc1ccccc1"
-    prediction = model.predict substance
-    refute_nil prediction[:value]
-    refute_nil prediction[:prediction_interval]
-    refute_empty prediction[:neighbors]
-  end
-
-  def test_weighted_average
-    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
-    algorithms = {
-      :similarity => {
-        :min => 0
-      },
-      :prediction => {
-        :method => "Algorithm::Regression.weighted_average",
-      },
-    }
-    model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
-    compound = Compound.from_smiles "CC(C)(C)CN"
-    prediction = model.predict compound
-    assert_equal -0.86, prediction[:value].round(2)
-    assert_equal model.substance_ids.size, prediction[:neighbors].size
-  end
-
-  def test_mpd_fingerprints
-    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
-    algorithms = {
-      :descriptors => {
-        :method => "fingerprint",
-        :type => "MP2D"
-      },
-    }
-    model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
-    compound = Compound.from_smiles "CCCSCCSCC"
-    prediction = model.predict compound
-    assert_equal 3, prediction[:neighbors].size
-    assert prediction[:value].round(2) > 1.37, "Prediction value (#{prediction[:value].round(2)}) should be larger than 1.37."
-  end
-
-  def test_local_physchem_regression
-    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
-    algorithms = {
-      :descriptors => {
-        :method => "calculate_properties",
-        :features => PhysChem.openbabel_descriptors,
-      },
-      :similarity => {
-        :method => "Algorithm::Similarity.weighted_cosine",
-        :min => 0.5
-      },
-    }
-    model = Model::Lazar.create(training_dataset:training_dataset, algorithms:algorithms)
-    compound = Compound.from_smiles "NC(=O)OCCC"
-    prediction = model.predict compound
-    refute_nil prediction[:value]
-  end
-
-  def test_local_physchem_regression_with_feature_selection
-    training_dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
-    algorithms = {
-      :descriptors => {
-        :method => "calculate_properties",
-        :features => PhysChem.openbabel_descriptors,
-      },
-      :similarity => {
-        :method => "Algorithm::Similarity.weighted_cosine",
-        :min => 0.5
-      },
-      :feature_selection => {
-        :method => "Algorithm::FeatureSelection.correlation_filter",
-      },
-    }
-    model = Model::Lazar.create(training_dataset:training_dataset, algorithms:algorithms)
-    compound = Compound.from_smiles "NC(=O)OCCC"
-    prediction = model.predict compound
-    refute_nil prediction[:value]
-  end
-
-  def test_unweighted_cosine_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.medi_log10.csv")
-    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
-    assert_kind_of Model::LazarRegression, model
-    assert_equal "Algorithm::Caret.rf", model.algorithms[:prediction][:method]
-    assert_equal "Algorithm::Similarity.cosine", model.algorithms[:similarity][:method]
-    assert_equal 0.5, 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]
-  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_log10.csv")
-    model = Model::Lazar.create  training_dataset: training_dataset, algorithms: algorithms
-    assert_kind_of Model::LazarRegression, model
-    assert_equal "Algorithm::Caret.rf", model.algorithms[:prediction][:method]
-    assert_equal "Algorithm::Similarity.tanimoto", model.algorithms[:similarity][:method]
-    assert_equal 0.5, model.algorithms[:similarity][:min]
-    assert_equal algorithms[:feature_selection][:method], model.algorithms[:feature_selection][:method]
-    prediction = model.predict training_dataset.substances[145]
-    refute_nil prediction[:value]
-  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",
-      },
-      :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
-
-end