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/validation-nanoparticle.rb~ | 133 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 133 insertions(+)
 create mode 100644 test/validation-nanoparticle.rb~

(limited to 'test/validation-nanoparticle.rb~')

diff --git a/test/validation-nanoparticle.rb~ b/test/validation-nanoparticle.rb~
new file mode 100644
index 0000000..0c7d355
--- /dev/null
+++ b/test/validation-nanoparticle.rb~
@@ -0,0 +1,133 @@
+require_relative "setup.rb"
+
+class NanoparticleValidationTest  < MiniTest::Test
+  include OpenTox::Validation
+
+  def setup
+    @training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
+    @prediction_feature = @training_dataset.features.select{|f| f["name"] == 'log2(Net cell association)'}.first
+  end
+
+  def test_validate_default_nanoparticle_model
+    model = Model::Lazar.create training_dataset: @training_dataset, prediction_feature: @prediction_feature
+    cv = CrossValidation.create model
+    p cv.id
+    #File.open("tmp.pdf","w+"){|f| f.puts cv.correlation_plot format:"pdf"}
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_validate_pls_pchem_model
+    algorithms = {
+      :descriptors => {
+        :method => "properties",
+        :categories => ["P-CHEM"]
+      },
+      :prediction => {:method => 'Algorithm::Caret.pls' },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
+    cv = CrossValidation.create model
+    p cv.id
+    #File.open("tmp2.pdf","w+"){|f| f.puts cv.correlation_plot format:"pdf"}
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+=begin
+  def test_validate_proteomics_pls_pchem_model
+    algorithms = {
+      :descriptors => {
+        :method => "properties",
+        :categories => ["Proteomics"]
+      },
+      :prediction => {:method => 'Algorithm::Caret.pls' },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    assert_equal "Algorithm::Caret.pls", model.algorithms[:prediction][:method]
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+=end
+
+  def test_validate_proteomics_pchem_default_model
+    algorithms = {
+      :descriptors => {
+        :method => "properties",
+        :categories => ["Proteomics","P-CHEM"]
+      },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_model_without_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :feature_selection => nil
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_weighted_average_model_without_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :prediction => { :method => "OpenTox::Algorithm::Regression.weighted_average" },
+      :feature_selection => nil
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_nanoparticle_fingerprint_model_with_feature_selection
+    algorithms = {
+      :descriptors => {
+        :method => "fingerprint",
+        :type => "MP2D",
+      },
+      :similarity => {
+        :method => "Algorithm::Similarity.tanimoto",
+        :min => 0.1
+      },
+      :feature_selection => {
+        :method => "Algorithm::FeatureSelection.correlation_filter",
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+end
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