From 160e75e696452ac61e651664ac56d16ce1c9c4b6 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Thu, 13 Oct 2016 19:17:03 +0200
Subject: model tests separated and cleaned

---
 test/validation-nanoparticle.rb | 74 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 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..3692515
--- /dev/null
+++ b/test/validation-nanoparticle.rb
@@ -0,0 +1,74 @@
+require_relative "setup.rb"
+
+class NanoparticleTest  < 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
+    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
+    @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.rmse
+    p cv.r_squared
+    #File.open("tmp.pdf","w+"){|f| f.puts cv.correlation_plot}
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_validate_pls_nanoparticle_model
+    algorithms = {
+      :descriptors => { :types => ["P-CHEM"] },
+      :prediction => {:parameters => 'pls' },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    assert_equal "pls", model.algorithms[:prediction][:parameters]
+    assert_equal "Algorithm::Caret.regression", model.algorithms[:prediction][:method]
+    cv = CrossValidation.create model
+    p cv.rmse
+    p cv.r_squared
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_validate_proteomics_pls_nanoparticle_model
+    algorithms = {
+      :descriptors => { :types => ["Proteomics"] },
+      :prediction => { :parameters => 'pls' }
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    assert_equal "pls", model.algorithms[:prediction][:parameters]
+    assert_equal "Algorithm::Caret.regression", model.algorithms[:prediction][:method]
+    cv = CrossValidation.create model
+    p cv.rmse
+    p cv.r_squared
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+  def test_validate_all_default_nanoparticle_model
+    algorithms = {
+      :descriptors => {
+        :types => ["Proteomics","P-CHEM"]
+      },
+    }
+    model = Model::Lazar.create prediction_feature: @prediction_feature, training_dataset: @training_dataset, algorithms: algorithms
+    cv = CrossValidation.create model
+    p cv.rmse
+    p cv.r_squared
+    refute_nil cv.r_squared
+    refute_nil cv.rmse
+  end
+
+
+  def test_import_ld
+    skip # Ambit JSON-LD export defunct
+    dataset_ids = Import::Enanomapper.import_ld
+  end
+end
-- 
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