[mlpack-git] master: Fix tests to new API. (133f036)

gitdub at big.cc.gt.atl.ga.us gitdub at big.cc.gt.atl.ga.us
Wed Dec 23 11:44:53 EST 2015


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

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/de9cc4b05069e1fa4793d9355f2f595af5ff45d2...6070527af14296cd99739de6c62666cc5d2a2125

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

commit 133f03633a53cbf2925c12083791cf1ec4d64d68
Author: Ryan Curtin <ryan at ratml.org>
Date:   Wed Oct 21 10:04:29 2015 -0400

    Fix tests to new API.


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

133f03633a53cbf2925c12083791cf1ec4d64d68
 src/mlpack/tests/hoeffding_tree_test.cpp | 31 ++++++++++++++-----------------
 src/mlpack/tests/serialization_test.cpp  | 16 ++++++++--------
 2 files changed, 22 insertions(+), 25 deletions(-)

diff --git a/src/mlpack/tests/hoeffding_tree_test.cpp b/src/mlpack/tests/hoeffding_tree_test.cpp
index 2f76f03..d2dcc52 100644
--- a/src/mlpack/tests/hoeffding_tree_test.cpp
+++ b/src/mlpack/tests/hoeffding_tree_test.cpp
@@ -240,7 +240,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitNoSplitTest)
   info.MapString("cat1", 2);
   info.MapString("cat2", 2);
 
-  HoeffdingSplit<> split(3, 2, info, 0.95, 5000);
+  HoeffdingSplit<> split(3, 2, info, 0.95, 5000, 1);
 
   // Feed it samples.
   for (size_t i = 0; i < 1000; ++i)
@@ -271,7 +271,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitEasySplitTest)
   info.MapString("cat1", 0);
   info.MapString("cat0", 1);
 
-  HoeffdingSplit<> split(2, 2, info, 0.95, 5000);
+  HoeffdingSplit<> split(2, 2, info, 0.95, 5000, 1);
 
   // Feed samples from each class.
   for (size_t i = 0; i < 500; ++i)
@@ -299,7 +299,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitProbability1SplitTest)
   info.MapString("cat1", 0);
   info.MapString("cat0", 1);
 
-  HoeffdingSplit<> split(2, 2, info, 1.0, 12000);
+  HoeffdingSplit<> split(2, 2, info, 1.0, 12000, 1);
 
   // Feed samples from each class.
   for (size_t i = 0; i < 5000; ++i)
@@ -327,7 +327,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitAlmostPerfectSplit)
   info.MapString("cat0", 1);
   info.MapString("cat1", 1);
 
-  HoeffdingSplit<> split(2, 2, info, 0.95, 5000);
+  HoeffdingSplit<> split(2, 2, info, 0.95, 5000, 1);
 
   // Feed samples.
   for (size_t i = 0; i < 500; ++i)
@@ -362,7 +362,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitEqualSplitTest)
   info.MapString("cat0", 1);
   info.MapString("cat1", 1);
 
-  HoeffdingSplit<> split(2, 2, info, 0.95, 5000);
+  HoeffdingSplit<> split(2, 2, info, 0.95, 5000, 1);
 
   // Feed samples.
   for (size_t i = 0; i < 500; ++i)
@@ -420,10 +420,10 @@ BOOST_AUTO_TEST_CASE(StreamingDecisionTreeSimpleDatasetTest)
   // Now train two streaming decision trees; one on the whole dataset, and one
   // on streaming data.
   StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      HoeffdingNumericSplit<GiniImpurity, size_t>>, arma::Mat<size_t>>
+      HoeffdingDoubleNumericSplit>, arma::Mat<size_t>>
       batchTree(dataset, info, labels, 3);
   StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      HoeffdingNumericSplit<GiniImpurity, size_t>>, arma::Mat<size_t>>
+      HoeffdingDoubleNumericSplit>, arma::Mat<size_t>>
       streamTree(info, 3, 3);
   for (size_t i = 0; i < 9000; ++i)
     streamTree.Train(dataset.col(i), labels[i]);
@@ -623,11 +623,10 @@ BOOST_AUTO_TEST_CASE(NumericHoeffdingTreeTest)
   // Now train two streaming decision trees; one on the whole dataset, and one
   // on streaming data.
   StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      HoeffdingNumericSplit<GiniImpurity>>, arma::mat>
-      batchTree(dataset, info, labels, 3);
+      HoeffdingDoubleNumericSplit>, arma::mat> batchTree(dataset, info, labels,
+      3);
   StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      HoeffdingNumericSplit<GiniImpurity>>, arma::mat>
-      streamTree(info, 3, 3);
+      HoeffdingDoubleNumericSplit>, arma::mat> streamTree(info, 3, 3);
   for (size_t i = 0; i < 9000; ++i)
     streamTree.Train(dataset.col(i), labels[i]);
 
@@ -694,12 +693,10 @@ BOOST_AUTO_TEST_CASE(BinaryNumericHoeffdingTreeTest)
 
   // Now train two streaming decision trees; one on the whole dataset, and one
   // on streaming data.
-  StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      BinaryNumericSplit<GiniImpurity>>, arma::mat>
-      batchTree(dataset, info, labels, 3);
-  StreamingDecisionTree<HoeffdingSplit<GiniImpurity,
-      BinaryNumericSplit<GiniImpurity>>, arma::mat>
-      streamTree(info, 4, 3);
+  StreamingDecisionTree<HoeffdingSplit<GiniImpurity, BinaryDoubleNumericSplit>,
+      arma::mat> batchTree(dataset, info, labels, 3);
+  StreamingDecisionTree<HoeffdingSplit<GiniImpurity, BinaryDoubleNumericSplit>,
+      arma::mat> streamTree(info, 4, 3);
   for (size_t i = 0; i < 9000; ++i)
     streamTree.Train(dataset.col(i), labels[i]);
 
diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp
index 1c15731..be35e46 100644
--- a/src/mlpack/tests/serialization_test.cpp
+++ b/src/mlpack/tests/serialization_test.cpp
@@ -850,7 +850,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
   data::DatasetInfo info;
   info.MapString("0", 2); // Dimension 1 is categorical.
   info.MapString("1", 2);
-  HoeffdingSplit<> split(5, 2, info, 0.99, 15000);
+  HoeffdingSplit<> split(5, 2, info, 0.99, 15000, 1);
 
   // Train for 2 samples.
   split.Train(arma::vec("0.3 0.4 1 0.6 0.7"), 0);
@@ -858,7 +858,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
 
   data::DatasetInfo wrongInfo;
   wrongInfo.MapString("1", 1);
-  HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10);
+  HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10, 1);
 
   // Force the binarySplit to split.
   data::DatasetInfo binaryInfo;
@@ -866,7 +866,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
   binaryInfo.MapString("cat1", 0);
   binaryInfo.MapString("cat0", 1);
 
-  HoeffdingSplit<> binarySplit(2, 2, info, 0.95, 5000);
+  HoeffdingSplit<> binarySplit(2, 2, info, 0.95, 5000, 1);
 
   // Feed samples from each class.
   for (size_t i = 0; i < 500; ++i)
@@ -875,7 +875,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
     binarySplit.Train(arma::Col<size_t>("1 0"), 1);
   }
 
-  HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000);
+  HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000, 1);
 
   SerializeObjectAll(split, xmlSplit, textSplit, binarySplit);
 
@@ -904,7 +904,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitAfterSplitTest)
   info.MapString("cat1", 0);
   info.MapString("cat0", 1);
 
-  HoeffdingSplit<> split(2, 2, info, 0.95, 5000);
+  HoeffdingSplit<> split(2, 2, info, 0.95, 5000, 1);
 
   // Feed samples from each class.
   for (size_t i = 0; i < 500; ++i)
@@ -916,18 +916,18 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitAfterSplitTest)
 
   data::DatasetInfo wrongInfo;
   wrongInfo.MapString("1", 1);
-  HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10);
+  HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10, 1);
 
   data::DatasetInfo binaryInfo;
   binaryInfo.MapString("0", 2); // Dimension 1 is categorical.
   binaryInfo.MapString("1", 2);
-  HoeffdingSplit<> binarySplit(5, 2, binaryInfo, 0.99, 15000);
+  HoeffdingSplit<> binarySplit(5, 2, binaryInfo, 0.99, 15000, 1);
 
   // Train for 2 samples.
   binarySplit.Train(arma::vec("0.3 0.4 1 0.6 0.7"), 0);
   binarySplit.Train(arma::vec("-0.3 0.0 0 0.7 0.8"), 1);
 
-  HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000);
+  HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000, 1);
 
   SerializeObjectAll(split, xmlSplit, textSplit, binarySplit);
 



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