From 9d17895ab9e8cd31e0f32e8e622e13612ea5ff77 Mon Sep 17 00:00:00 2001
From: "helma@in-silico.ch" <helma@in-silico.ch>
Date: Fri, 12 Oct 2018 21:58:36 +0200
Subject: validation statistic fixes

---
 test/regression-validation.rb | 91 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 test/regression-validation.rb

(limited to 'test/regression-validation.rb')

diff --git a/test/regression-validation.rb b/test/regression-validation.rb
new file mode 100644
index 0000000..44162c0
--- /dev/null
+++ b/test/regression-validation.rb
@@ -0,0 +1,91 @@
+require_relative "setup.rb"
+
+class ValidationRegressionTest < MiniTest::Test
+  include OpenTox::Validation
+
+  # defaults
+  
+  def test_default_regression_crossvalidation
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM_log10.csv"
+    model = Model::Lazar.create training_dataset: dataset
+    cv = RegressionCrossValidation.create model
+    assert cv.rmse[:all] < 1.5, "RMSE #{cv.rmse[:all]} should be smaller than 1.5, this may occur due to unfavorable training/test set splits"
+    assert cv.mae[:all] < 1.1, "MAE #{cv.mae[:all]} should be smaller than 1.1, this may occur due to unfavorable training/test set splits"
+    assert cv.within_prediction_interval[:all]/cv.nr_predictions[:all] > 0.8, "Only #{(100*cv.within_prediction_interval[:all]/cv.nr_predictions[:all]).round(2)}% of measurement within prediction interval. This may occur due to unfavorable training/test set splits"
+  end
+
+  # parameters
+  
+  def test_regression_crossvalidation_params
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi_log10.csv"
+    algorithms = {
+      :prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" },
+      :descriptors => { :type => "MACCS", },
+      :similarity => {:min => 0.7}
+    }
+    model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms
+    assert_equal algorithms[:descriptors][:type], model.algorithms[:descriptors][:type]
+    cv = RegressionCrossValidation.create model
+    cv.validation_ids.each do |vid|
+      model = Model::Lazar.find(Validation.find(vid).model_id)
+      assert_equal algorithms[:descriptors][:type], model.algorithms[:descriptors][:type]
+      assert_equal algorithms[:similarity][:min], model.algorithms[:similarity][:min]
+      refute_nil model.training_dataset_id
+      refute_equal dataset.id, model.training_dataset_id
+    end
+
+    refute_nil cv.rmse[:all]
+    refute_nil cv.mae[:all]
+  end
+
+  def test_physchem_regression_crossvalidation
+    training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
+    model = Model::Lazar.create training_dataset:training_dataset
+    cv = RegressionCrossValidation.create model
+    refute_nil cv.rmse[:all]
+    refute_nil cv.mae[:all]
+  end
+
+  # LOO
+
+  def test_regression_loo_validation
+    dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
+    model = Model::Lazar.create training_dataset: dataset
+    loo = RegressionLeaveOneOut.create model
+    assert loo.r_squared[:all] > 0.34, "R^2 (#{loo.r_squared[:all]}) should be larger than 0.034"
+  end
+
+  def test_regression_loo_validation_with_feature_selection
+    dataset = OpenTox::Dataset.from_csv_file File.join(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: dataset, algorithms: algorithms
+    assert_raises OpenTox::BadRequestError do
+      loo = RegressionLeaveOneOut.create model
+    end
+  end
+
+  # repeated CV
+
+  def test_repeated_crossvalidation
+    dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi_log10.csv")
+    model = Model::Lazar.create training_dataset: dataset
+    repeated_cv = RepeatedCrossValidation.create model
+    repeated_cv.crossvalidations.each do |cv|
+      assert cv.r_squared[:all] > 0.34, "R^2 (#{cv.r_squared[:all]}) should be larger than 0.034"
+      assert cv.rmse[:all] < 1.5, "RMSE (#{cv.rmse[:all]}) should be smaller than 0.5"
+    end
+  end
+
+end
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