[mlpack] 41/207: Add tests for first part of decision trees (the gains).

[Barak A. Pearlmutter]

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bap pushed a commit to branch master
in repository mlpack.

commit d25083f6799cc52a41c190d2e9b8b93647eaee2d
Author: Ryan Curtin <ryan at ratml.org>
Date:   Tue Jan 17 14:20:24 2017 -0500

    Add tests for first part of decision trees (the gains).
---
 src/mlpack/tests/CMakeLists.txt         |   1 +
 src/mlpack/tests/decision_tree_test.cpp | 184 ++++++++++++++++++++++++++++++++
 2 files changed, 185 insertions(+)

diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt
index cd83ade..1b5bf40 100644
--- a/src/mlpack/tests/CMakeLists.txt
+++ b/src/mlpack/tests/CMakeLists.txt
@@ -11,6 +11,7 @@ add_executable(mlpack_test
   cli_test.cpp
   cosine_tree_test.cpp
   decision_stump_test.cpp
+  decision_tree_test.cpp
   det_test.cpp
   distribution_test.cpp
   drusilla_select_test.cpp
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
new file mode 100644
index 0000000..df4e961
--- /dev/null
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -0,0 +1,184 @@
+/**
+ * @file decision_tree_test.cpp
+ * @author Ryan Curtin
+ *
+ * Tests for the DecisionTree class and related classes.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#include <mlpack/core.hpp>
+#include <mlpack/methods/decision_tree/decision_tree.hpp>
+#include <mlpack/methods/decision_tree/information_gain.hpp>
+#include <mlpack/methods/decision_tree/gini_gain.hpp>
+
+#include <boost/test/unit_test.hpp>
+#include "test_tools.hpp"
+#include "serialization.hpp"
+
+using namespace mlpack;
+using namespace mlpack::tree;
+
+BOOST_AUTO_TEST_SUITE(DecisionTreeTest);
+
+/**
+ * Make sure the Gini gain is zero when the labels are perfect.
+ */
+BOOST_AUTO_TEST_CASE(GiniGainPerfectTest)
+{
+  arma::Row<size_t> labels;
+  labels.zeros(10);
+
+  // Test that it's perfect regardless of number of classes.
+  for (size_t c = 1; c < 10; ++c)
+    BOOST_REQUIRE_SMALL(GiniGain::Evaluate(labels, c), 1e-5);
+}
+
+/**
+ * Make sure the Gini gain is -0.5 when the class split between two classes
+ * is even.
+ */
+BOOST_AUTO_TEST_CASE(GiniGainEvenSplitTest)
+{
+  arma::Row<size_t> labels(10);
+  for (size_t i = 0; i < 5; ++i)
+    labels[i] = 0;
+  for (size_t i = 5; i < 10; ++i)
+    labels[i] = 1;
+
+  // Test that it's -0.5 regardless of the number of classes.
+  for (size_t c = 2; c < 10; ++c)
+    BOOST_REQUIRE_CLOSE(GiniGain::Evaluate(labels, c), -0.5, 1e-5);
+}
+
+/**
+ * The Gini gain of an empty vector is 0.
+ */
+BOOST_AUTO_TEST_CASE(GiniGainEmptyTest)
+{
+  // Test across some numbers of classes.
+  arma::Row<size_t> labels;
+  for (size_t c = 1; c < 10; ++c)
+    BOOST_REQUIRE_SMALL(GiniGain::Evaluate(labels, c), 1e-5);
+}
+
+/**
+ * The Gini gain is -(1 - 1/k) for k classes evenly split.
+ */
+BOOST_AUTO_TEST_CASE(GiniGainEvenSplitManyClassTest)
+{
+  // Try with many different classes.
+  for (size_t c = 2; c < 30; ++c)
+  {
+    arma::Row<size_t> labels(c);
+    for (size_t i = 0; i < c; ++i)
+      labels[i] = i;
+
+    // Calculate Gini gain and make sure it is correct.
+    BOOST_REQUIRE_CLOSE(GiniGain::Evaluate(labels, c), -(1.0 - 1.0 / c), 1e-5);
+  }
+}
+
+/**
+ * The Gini gain should not be sensitive to the number of points.
+ */
+BOOST_AUTO_TEST_CASE(GiniGainManyPoints)
+{
+  for (size_t i = 1; i < 20; ++i)
+  {
+    const size_t numPoints = 100 * i;
+    arma::Row<size_t> labels(numPoints);
+    for (size_t j = 0; j < numPoints / 2; ++j)
+      labels[j] = 0;
+    for (size_t j = numPoints / 2; j < numPoints; ++j)
+      labels[j] = 1;
+
+    BOOST_REQUIRE_CLOSE(GiniGain::Evaluate(labels, 2), -0.5, 1e-5);
+  }
+}
+
+/**
+ * The information gain should be zero when the labels are perfect.
+ */
+BOOST_AUTO_TEST_CASE(InformationGainPerfectTest)
+{
+  arma::Row<size_t> labels;
+  labels.zeros(10);
+
+  // Test that it's perfect regardless of number of classes.
+  for (size_t c = 1; c < 10; ++c)
+    BOOST_REQUIRE_SMALL(InformationGain::Evaluate(labels, c), 1e-5);
+}
+
+/**
+ * If we have an even split, the information gain should be -1.
+ */
+BOOST_AUTO_TEST_CASE(InformationGainEvenSplitTest)
+{
+  arma::Row<size_t> labels(10);
+  for (size_t i = 0; i < 5; ++i)
+    labels[i] = 0;
+  for (size_t i = 5; i < 10; ++i)
+    labels[i] = 1;
+
+  // Test that it's -1 regardless of the number of classes.
+  for (size_t c = 2; c < 10; ++c)
+    BOOST_REQUIRE_CLOSE(InformationGain::Evaluate(labels, c), -1.0, 1e-5);
+}
+
+/**
+ * The information gain of an empty vector is 0.
+ */
+BOOST_AUTO_TEST_CASE(InformationGainEmptyTest)
+{
+  arma::Row<size_t> labels;
+  for (size_t c = 1; c < 10; ++c)
+    BOOST_REQUIRE_SMALL(InformationGain::Evaluate(labels, c), 1e-5);
+}
+
+/**
+ * The information gain is log2(1/k) when splitting equal classes.
+ */
+BOOST_AUTO_TEST_CASE(InformationGainEvenSplitManyClassTest)
+{
+  // Try with many different numbers of classes.
+  for (size_t c = 2; c < 30; ++c)
+  {
+    arma::Row<size_t> labels(c);
+    for (size_t i = 0; i < c; ++i)
+      labels[i] = i;
+
+    // Calculate information gain and make sure it is correct.
+    BOOST_REQUIRE_CLOSE(InformationGain::Evaluate(labels, c),
+        std::log2(1.0 / c), 1e-5);
+  }
+}
+
+/**
+ * The information gain should not be sensitive to the number of points.
+ */
+BOOST_AUTO_TEST_CASE(InformationGainManyPoints)
+{
+  for (size_t i = 1; i < 20; ++i)
+  {
+    const size_t numPoints = 100 * i;
+    arma::Row<size_t> labels(numPoints);
+    for (size_t j = 0; j < numPoints / 2; ++j)
+      labels[j] = 0;
+    for (size_t j = numPoints / 2; j < numPoints; ++j)
+      labels[j] = 1;
+
+    BOOST_REQUIRE_CLOSE(InformationGain::Evaluate(labels, 2), -1.0, 1e-5);
+  }
+}
+
+/**
+- aux split info is empty
+- basic construction test
+- build on sparse test on dense
+- efficacy test
+
+*/
+BOOST_AUTO_TEST_SUITE_END();

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