From db21f62399669be741e4e692278344e16cb39580 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Sun, 9 Aug 2015 13:43:07 +0200
Subject: customized prediction algorithms implemented

---
 test/compound.rb   |  1 +
 test/validation.rb | 36 ++++++++++++++++++++++++++++++++----
 2 files changed, 33 insertions(+), 4 deletions(-)

diff --git a/test/compound.rb b/test/compound.rb
index 2bf0204..d1b1520 100644
--- a/test/compound.rb
+++ b/test/compound.rb
@@ -85,6 +85,7 @@ class CompoundTest < MiniTest::Test
     d.compounds.each do |c|
       refute_nil c.fp4
     end
+    # TODO assert neighbor size
     c = d.compounds[371]
     #p c
     p c.neighbors
diff --git a/test/validation.rb b/test/validation.rb
index 80055f2..9348c6f 100644
--- a/test/validation.rb
+++ b/test/validation.rb
@@ -2,12 +2,40 @@ require_relative "setup.rb"
 
 class ValidationTest < MiniTest::Test
 
+  def test_fminer_crossvalidation
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
+    model = Model::LazarFminerClassification.create dataset#, features
+    cv = ClassificationCrossValidation.create model
+    p cv.accuracy
+    p cv.weighted_accuracy
+    assert cv.accuracy > 0.8
+    assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) larger than unweighted accuracy(#{cv.accuracy}) "
+  end
+
   def test_classification_crossvalidation
     dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
-    features = Algorithm::Fminer.bbrc dataset
-    model = Model::Lazar.create dataset, features
-    cv = CrossValidation.create model
-    p cv
+    model = Model::LazarClassification.create dataset#, features
+    cv = ClassificationCrossValidation.create model
+    p cv.accuracy
+    p cv.weighted_accuracy
+    assert cv.accuracy > 0.7
+    assert cv.weighted_accuracy > cv.accuracy
+  end
+
+  def test_regression_crossvalidation
+    dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
+    #dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.csv"
+    model = Model::LazarRegression.create dataset
+    cv = RegressionCrossValidation.create model
+    p cv.rmse 
+    p cv.weighted_rmse
+    p cv.mae
+    p cv.weighted_mae
+    `inkview #{cv.plot}`
+    assert cv.rmse < 30, "RMSE > 30"
+    assert cv.weighted_rmse < cv.rmse, "Weighted RMSE (#{cv.weighted_rmse}) larger than unweighted RMSE(#{cv.rmse}) "
+    assert cv.mae < 12
+    assert cv.weighted_mae < cv.mae
   end
 
 end
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