[mlpack-git] master, mlpack-1.0.x: Another test to make sure the correct splitting attribute is used. (30216b2)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branches: master,mlpack-1.0.x
Link       : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40

>---------------------------------------------------------------

commit 30216b2f2871d1b98b7713fd6ecadee0d2633b37
Author: Ryan Curtin <ryan at ratml.org>
Date:   Wed Jul 9 16:49:59 2014 +0000

    Another test to make sure the correct splitting attribute is used.


>---------------------------------------------------------------

30216b2f2871d1b98b7713fd6ecadee0d2633b37
 src/mlpack/tests/decision_stump_test.cpp | 123 +++++++++++++++++++++++++++----
 1 file changed, 110 insertions(+), 13 deletions(-)

diff --git a/src/mlpack/tests/decision_stump_test.cpp b/src/mlpack/tests/decision_stump_test.cpp
index d6e4791..1fec0bc 100644
--- a/src/mlpack/tests/decision_stump_test.cpp
+++ b/src/mlpack/tests/decision_stump_test.cpp
@@ -13,6 +13,7 @@
 using namespace mlpack;
 using namespace mlpack::decision_stump;
 using namespace arma;
+using namespace mlpack::distribution;
 
 BOOST_AUTO_TEST_SUITE(DecisionStumpTest);
 
@@ -47,24 +48,28 @@ BOOST_AUTO_TEST_CASE(OneClass)
     BOOST_CHECK_EQUAL(predictedLabels(i), 1);
 
 }
-/*
-This tests whether the entropy is being correctly calculated by
-checking the correct value of the splitting column value. 
-This test is for an inpBucketSize of 4 and the correct value of 
-the splitCol is 1. 
-*/
+
+/**
+ * This tests whether the entropy is being correctly calculated by checking the
+ * correct value of the splitting column value.  This test is for an
+ * inpBucketSize of 4 and the correct value of the splitting attribute is 0.
+ */
 BOOST_AUTO_TEST_CASE(CorrectAttributeChosen)
 {
   const size_t numClasses = 2;
   const size_t inpBucketSize = 4;
 
+  // This dataset comes from Chapter 6 of the book "Data Mining: Concepts,
+  // Models, Methods, and Algorithms" (2nd Edition) by Mehmed Kantardzic.  It is
+  // found on page 176 (and a description of the correct splitting attribute is
+  // given below that).
   mat trainingData;
-  trainingData << 0 << 0 << 0 << 0 << 0 << 1 << 1 << 1 << 1
-               << 2  << 2  << 2  << 2  << 2 << endr
+  trainingData << 0  << 0  << 0  << 0  << 0  << 1  << 1  << 1  << 1
+               << 2  << 2  << 2  << 2  << 2  << endr
                << 70 << 90 << 85 << 95 << 70 << 90 << 78 << 65 << 75
-               << 80  << 70  << 80  << 80  << 96 << endr
-               << 1 << 1 << 0 << 0 << 0 << 1 << 0 << 1 << 0
-               << 1  << 1  << 0  << 0  << 0 << endr;
+               << 80 << 70 << 80 << 80 << 96 << endr
+               << 1  << 1  << 0  << 0  << 0  << 1  << 0  << 1  << 0
+               << 1  << 1  << 0  << 0  << 0  << endr;
 
   // No need to normalize labels here.
   Mat<size_t> labelsIn;
@@ -74,8 +79,7 @@ BOOST_AUTO_TEST_CASE(CorrectAttributeChosen)
   DecisionStump<> ds(trainingData, labelsIn.row(0), numClasses, inpBucketSize);
 
   // Only need to check the value of the splitting column, no need of classification.
-
-  BOOST_CHECK_EQUAL(ds.splitCol,1);
+  BOOST_CHECK_EQUAL(ds.SplitAttribute(), 0);
 }
 
 /**
@@ -210,4 +214,97 @@ BOOST_AUTO_TEST_CASE(MultiClassSplit)
   BOOST_CHECK_EQUAL(predictedLabels(0, 7), 2);
 }
 
+/**
+ * This tests that the decision stump can learn a good split on a dataset with
+ * four dimensions that have progressing levels of separation.
+ */
+BOOST_AUTO_TEST_CASE(DimensionSelectionTest)
+{
+  const size_t numClasses = 2;
+  const size_t inpBucketSize = 25;
+
+  arma::mat dataset(4, 5000);
+
+  // The most separable dimension.
+  GaussianDistribution g1("-5", "1");
+  GaussianDistribution g2("5", "1");
+
+  for (size_t i = 0; i < 2500; ++i)
+  {
+    arma::vec tmp = g1.Random();
+    dataset(1, i) = tmp[0];
+  }
+  for (size_t i = 2500; i < 5000; ++i)
+  {
+    arma::vec tmp = g2.Random();
+    dataset(1, i) = tmp[0];
+  }
+
+  g1 = GaussianDistribution("-3", "1");
+  g2 = GaussianDistribution("3", "1");
+
+  for (size_t i = 0; i < 2500; ++i)
+  {
+    arma::vec tmp = g1.Random();
+    dataset(3, i) = tmp[0];
+  }
+  for (size_t i = 2500; i < 5000; ++i)
+  {
+    arma::vec tmp = g2.Random();
+    dataset(3, i) = tmp[0];
+  }
+
+  g1 = GaussianDistribution("-1", "1");
+  g2 = GaussianDistribution("1", "1");
+
+  for (size_t i = 0; i < 2500; ++i)
+  {
+    arma::vec tmp = g1.Random();
+    dataset(0, i) = tmp[0];
+  }
+  for (size_t i = 2500; i < 5000; ++i)
+  {
+    arma::vec tmp = g2.Random();
+    dataset(0, i) = tmp[0];
+  }
+
+  // Not separable at all.
+  g1 = GaussianDistribution("0", "1");
+  g2 = GaussianDistribution("0", "1");
+
+  for (size_t i = 0; i < 2500; ++i)
+  {
+    arma::vec tmp = g1.Random();
+    dataset(2, i) = tmp[0];
+  }
+  for (size_t i = 2500; i < 5000; ++i)
+  {
+    arma::vec tmp = g2.Random();
+    dataset(2, i) = tmp[0];
+  }
+
+  // Generate the labels.
+  arma::Row<size_t> labels(5000);
+  for (size_t i = 0; i < 2500; ++i)
+    labels[i] = 0;
+  for (size_t i = 2500; i < 5000; ++i)
+    labels[i] = 1;
+
+  // Now create a decision stump.
+  DecisionStump<> ds(dataset, labels, numClasses, inpBucketSize);
+
+  // Make sure it split on the dimension that is most separable.
+  BOOST_REQUIRE_EQUAL(ds.SplitAttribute(), 1);
+
+  // Make sure every bin below -1 classifies as label 0, and every bin above 1
+  // classifies as label 1 (What happens in [-1, 1] isn't that big a deal.).
+  for (size_t i = 0; i < ds.Split().n_elem; ++i)
+  {
+    if (ds.Split()[i] <= -3.0)
+      BOOST_REQUIRE_EQUAL(ds.BinLabels()[i], 0);
+    else if (ds.Split()[i] >= 3.0)
+      BOOST_REQUIRE_EQUAL(ds.BinLabels()[i], 1);
+  }
+}
+
 BOOST_AUTO_TEST_SUITE_END();