Persistent methods for GPHIK with TestUnits implemented and tested

FMKGPHyperparameterOptimization.cpp

@@ -374,11 +374,13 @@ void FMKGPHyperparameterOptimization::performOptimization ( GPLikelihoodApprox &
   }
 
   if ( verbose )
+  {
     std::cerr << "Optimal hyperparameter was: " << gplike.getBestParameters() << std::endl;
   
-  std::map<int, Vector> bestAlphas = gplike.getBestAlphas();
-  std::cerr << "length of alpha vectors: " << bestAlphas.size() << std::endl;
-  std::cerr << "alpha vector: " << bestAlphas.begin()->second << std::endl;
+    std::map<int, NICE::Vector> bestAlphas = gplike.getBestAlphas();
+    std::cerr << "length of alpha vectors: " << bestAlphas.size() << std::endl;
+    std::cerr << "alpha vector for first class: " << bestAlphas.begin()->second << std::endl;
+  }
 }
 
 void FMKGPHyperparameterOptimization::transformFeaturesWithOptimalParameters ( const GPLikelihoodApprox & gplike, const uint & parameterVectorSize )
@@ -1173,6 +1175,11 @@ t.start();*/
 void FMKGPHyperparameterOptimization::restore ( std::istream & is, int format )
 {
   bool b_restoreVerbose ( false );
+
+#ifdef B_RESTOREVERBOSE
+  b_restoreVerbose = true;
+#endif
+
   if ( is.good() )
   {
     if ( b_restoreVerbose ) 

GPHIKClassifier.cpp

@@ -431,6 +431,10 @@ void GPHIKClassifier::restore ( std::istream & is, int format )
   this->clear();
   
   bool b_restoreVerbose ( false );
+#ifdef B_RESTOREVERBOSE
+  b_restoreVerbose = true;
+#endif  
+  
   if ( is.good() )
   {
     if ( b_restoreVerbose ) 

parameterizedFunctions/ParameterizedFunction.cpp

@@ -87,7 +87,7 @@ void ParameterizedFunction::store ( std::ostream & os, int format ) const
     os.precision (std::numeric_limits<double>::digits10 + 1);
     
     os << this->createStartTag( "m_parameters" ) << std::endl;
-    os << m_parameters;
+    os << m_parameters << std::endl;
     os << this->createEndTag( "m_parameters" ) << std::endl;   
     
     

progs/toyExample.cpp

@@ -5,14 +5,17 @@
 * @date 19-10-2012
 */
 
+// STL includes
 #include <iostream>
 #include <vector>
 
+// NICE-core includes
 #include <core/basics/Config.h>
 #include <core/basics/Timer.h>
 #include <core/vector/MatrixT.h>
 #include <core/vector/VectorT.h>
 
+// gp-hik-core includes
 #include "gp-hik-core/GPHIKClassifier.h"
 
 using namespace std; //C basics

tests/TestFastHIK.cpp

@@ -32,7 +32,6 @@ bool compareVVector(const NICE::VVector & A, const NICE::VVector & B, const doub
       break;
     } 
     
-//     std::cerr << "itA->size(): " << itA->size() << "itB->size(): " << itB->size() << std::endl;
     for(uint i = 0; (i < itA->size()) && (i < itB->size()); i++)
     {
       if (fabs((*itA)[i] - (*itB)[i]) > tolerance)
@@ -46,7 +45,6 @@ bool compareVVector(const NICE::VVector & A, const NICE::VVector & B, const doub
           break;        
     itA++;
     itB++;
-//     std::cerr << "foo" << std::endl;
   }
   
   return result;
@@ -72,7 +70,7 @@ bool compareLUTs(const double* LUT1, const double* LUT2, const int & size, const
 const bool verbose = false;
 const bool verboseStartEnd = true;
 const bool solveLinWithoutRand = false;
-const uint n = 20;//1500;//1500;//10;
+const uint n = 30;//1500;//1500;//10;
 const uint d = 5;//200;//2;
 const uint numBins = 11;//1001;//1001;
 const uint solveLinMaxIterations = 1000;
@@ -638,13 +636,13 @@ void TestFastHIK::testLinSolve()
   if (verboseStartEnd)
     std::cerr << "================== TestFastHIK::testLinSolve ===================== " << std::endl;
 
-  Quantization q ( numBins );
+  NICE::Quantization q ( numBins );
 
   // data is generated, such that there is no approximation error
-  vector< vector<double> > dataMatrix;
+  std::vector< std::vector<double> > dataMatrix;
   for ( uint i = 0; i < d ; i++ )
   {
-    vector<double> v;
+    std::vector<double> v;
     v.resize(n);
     for ( uint k = 0; k < n; k++ ) {
       if ( drand48() < sparse_prob ) {
@@ -658,28 +656,27 @@ void TestFastHIK::testLinSolve()
   }
   
   if ( verbose ) {
-    cerr << "data matrix: " << endl;
+    std::cerr << "data matrix: " << std::endl;
     printMatrix ( dataMatrix );
-    cerr << endl;
+    std::cerr << std::endl;
   }
 
   double noise = 1.0;
-  FastMinKernel fmk ( dataMatrix, noise );
+  NICE::FastMinKernel fmk ( dataMatrix, noise );
   
-  ParameterizedFunction *pf = new PFAbsExp ( 1.0 );
+  NICE::ParameterizedFunction *pf = new NICE::PFAbsExp ( 1.0 );
   fmk.applyFunctionToFeatureMatrix( pf );
-//   pf->applyFunctionToFeatureMatrix ( fmk.featureMatrix() );
 
-  Vector y ( n );  
+  NICE::Vector y ( n );  
   for ( uint i = 0; i < y.size(); i++ )
     y[i] = sin(i);
   
-  Vector alpha;
-  Vector alphaRandomized;
+  NICE::Vector alpha;
+  NICE::Vector alphaRandomized;
 
   std::cerr << "solveLin with randomization" << std::endl;
   // tic
-  Timer t;
+  NICE::Timer t;
   t.start();
   //let's try to do 10.000 iterations and sample in each iteration 30 examples randomly
   fmk.solveLin(y,alphaRandomized,q,pf,true,solveLinMaxIterations,30);

tests/TestFeatureMatrixT.cpp

@@ -182,7 +182,7 @@ void TestFeatureMatrixT::testMatlabIO()
 #endif //#ifdef NICE_USELIB_MATIO
 
   if (verboseStartEnd)
-    std::cerr << "================== TestFeatureMatrixT::testMatlabIO done===================== " << std::endl;
+    std::cerr << "================== TestFeatureMatrixT::testMatlabIO done ===================== " << std::endl;
   
 }
 

tests/TestGPHIKPersistent.cpp

@@ -0,0 +1,214 @@
+/** 
+ * @file TestGPHIKPersistent.cpp
+ * @brief CppUnit-Testcase to verify that GPHIKClassifier methods herited from Persistent (store and restore) work as desired.
+ * @author Alexander Freytag
+ * @date 21-12-2013
+*/
+
+#ifdef NICE_USELIB_CPPUNIT
+
+// STL includes
+#include <iostream>
+#include <vector>
+
+// NICE-core includes
+#include <core/basics/Config.h>
+#include <core/basics/Timer.h>
+
+// gp-hik-core includes
+#include "gp-hik-core/GPHIKClassifier.h"
+
+#include "TestGPHIKPersistent.h"
+
+using namespace std; //C basics
+using namespace NICE;  // nice-core
+
+const bool verboseStartEnd = true;
+
+
+CPPUNIT_TEST_SUITE_REGISTRATION( TestGPHIKPersistent );
+
+void TestGPHIKPersistent::setUp() {
+}
+
+void TestGPHIKPersistent::tearDown() {
+}
+void TestGPHIKPersistent::testPersistentMethods()
+{
+  
+  if (verboseStartEnd)
+    std::cerr << "================== TestGPHIKPersistent::testPersistentMethods ===================== " << std::endl;  
+  
+  NICE::Config conf;
+  std::string trainData = conf.gS( "main", "trainData", "toyExampleSmallScaleTrain.data" );
+  NICE::GPHIKClassifier * classifier;  
+  
+  //------------- read the training data --------------
+  
+  NICE::Matrix dataTrain;
+  NICE::Vector yBinTrain;
+  NICE::Vector yMultiTrain; 
+
+  std::ifstream ifsTrain ( trainData.c_str() , ios::in );
+
+  if ( ifsTrain.good() )
+  {
+    ifsTrain >> dataTrain;
+    ifsTrain >> yBinTrain;
+    ifsTrain >> yMultiTrain;
+    ifsTrain.close();  
+  }
+  else 
+  {
+    std::cerr << "Unable to read training data from file " << trainData << " -- aborting." << std::endl;
+    CPPUNIT_ASSERT ( ifsTrain.good() );
+  } 
+  
+  //----------------- convert data to sparse data structures ---------
+  std::vector< NICE::SparseVector *> examplesTrain;
+  examplesTrain.resize( dataTrain.rows() );
+  
+  std::vector< NICE::SparseVector *>::iterator exTrainIt = examplesTrain.begin();
+  for (int i = 0; i < (int)dataTrain.rows(); i++, exTrainIt++)
+  {
+    *exTrainIt =  new NICE::SparseVector( dataTrain.getRow(i) );
+  }  
+  
+  // TRAIN CLASSIFIER FROM SCRATCH
+  
+  classifier = new GPHIKClassifier ( &conf );  
+    
+  classifier->train ( examplesTrain , yMultiTrain );
+  
+  
+  // TEST STORING ABILITIES
+  
+  std::string s_destination_save ( "myClassifier.txt" );
+  
+  std::filebuf fbOut;
+  fbOut.open ( s_destination_save.c_str(), ios::out );
+  std::ostream os (&fbOut);
+  //
+  classifier->store( os );
+  //   
+  fbOut.close(); 
+  
+  
+  // TEST RESTORING ABILITIES
+    
+  NICE::GPHIKClassifier * classifierRestored = new GPHIKClassifier;  
+      
+  std::string s_destination_load ( "myClassifier.txt" );
+  
+  std::filebuf fbIn;
+  fbIn.open ( s_destination_load.c_str(), ios::in );
+  std::istream is (&fbIn);
+  //
+  classifierRestored->restore( is );
+  //   
+  fbIn.close();   
+  
+  
+  // TEST both classifiers to produce equal results
+  
+  //------------- read the test data --------------
+  
+  
+  NICE::Matrix dataTest;
+  NICE::Vector yBinTest;
+  NICE::Vector yMultiTest; 
+
+  std::string testData = conf.gS( "main", "testData", "toyExampleTest.data" );  
+  std::ifstream ifsTest ( testData.c_str(), ios::in );
+  if ( ifsTest.good() )
+  {
+    ifsTest >> dataTest;
+    ifsTest >> yBinTest;
+    ifsTest >> yMultiTest;
+    ifsTest.close();  
+  }
+  else 
+  {
+    std::cerr << "Unable to read test data, aborting." << std::endl;
+    CPPUNIT_ASSERT ( ifsTest.good() );
+  }
+  
+  // ------------------------------------------
+  // ------------- PREPARATION --------------
+  // ------------------------------------------   
+  
+  // determine classes known during training and corresponding mapping
+  // thereby allow for non-continous class labels
+  std::set<int> classesKnownTraining = classifier->getKnownClassNumbers();
+  
+  int noClassesKnownTraining ( classesKnownTraining.size() );
+  std::map<int,int> mapClNoToIdxTrain;
+  std::set<int>::const_iterator clTrIt = classesKnownTraining.begin();
+  for ( int i=0; i < noClassesKnownTraining; i++, clTrIt++ )
+      mapClNoToIdxTrain.insert ( std::pair<int,int> ( *clTrIt, i )  );
+  
+  // determine classes known during testing and corresponding mapping
+  // thereby allow for non-continous class labels
+  std::set<int> classesKnownTest;
+  classesKnownTest.clear();
+  
+
+  // determine which classes we have in our label vector
+  // -> MATLAB: myClasses = unique(y);
+  for ( NICE::Vector::const_iterator it = yMultiTest.begin(); it != yMultiTest.end(); it++ )
+  {
+    if ( classesKnownTest.find ( *it ) == classesKnownTest.end() )
+    {
+      classesKnownTest.insert ( *it );
+    }
+  }          
+  
+  int noClassesKnownTest ( classesKnownTest.size() );  
+  std::map<int,int> mapClNoToIdxTest;
+  std::set<int>::const_iterator clTestIt = classesKnownTest.begin();
+  for ( int i=0; i < noClassesKnownTest; i++, clTestIt++ )
+      mapClNoToIdxTest.insert ( std::pair<int,int> ( *clTestIt, i )  ); 
+          
+  
+  NICE::Matrix confusionMatrix         ( noClassesKnownTraining, noClassesKnownTest, 0.0);
+  NICE::Matrix confusionMatrixRestored ( noClassesKnownTraining, noClassesKnownTest, 0.0);
+  
+  int i_loopEnd  ( (int)dataTest.rows() );
+  
+  
+  for (int i = 0; i < i_loopEnd ; i++)
+  {
+    NICE::Vector example ( dataTest.getRow(i) );
+    NICE::SparseVector scores;
+    int result;
+    
+    // classify with trained classifier 
+    classifier->classify( &example, result, scores );
+       
+    
+    confusionMatrix( mapClNoToIdxTrain.find(result)->second, mapClNoToIdxTest.find(yMultiTest[i])->second ) += 1.0;
+
+    // classify with restored classifier 
+    scores.clear();
+    classifierRestored->classify( &example, result, scores );
+    
+    confusionMatrixRestored( mapClNoToIdxTrain.find(result)->second, mapClNoToIdxTest.find(yMultiTest[i])->second ) += 1.0;
+    
+    
+  }  
+    
+  confusionMatrix.normalizeColumnsL1();
+  double arr ( confusionMatrix.trace()/confusionMatrix.cols() );
+
+  confusionMatrixRestored.normalizeColumnsL1();
+  double arrRestored ( confusionMatrixRestored.trace()/confusionMatrixRestored.cols() );
+
+  
+  CPPUNIT_ASSERT_DOUBLES_EQUAL( arr, arrRestored, 1e-8);
+  
+  if (verboseStartEnd)
+    std::cerr << "================== TestGPHIKPersistent::testPersistentMethods done ===================== " << std::endl;  
+  
+}
+
+#endif

tests/TestGPHIKPersistent.h

@@ -0,0 +1,30 @@
+#ifndef _TESTGPHIKPERSISTENT_H
+#define _TESTGPHIKPERSISTENT_H
+
+#include <cppunit/extensions/HelperMacros.h>
+#include <gp-hik-core/GPHIKClassifier.h>
+
+/**
+ * CppUnit-Testcase. 
+ * @brief CppUnit-Testcase to verify that GPHIKClassifier methods herited from Persistent (store and restore) work as desired.
+ * @author Alexander Freytag
+ * @date 21-12-2013
+ */
+class TestGPHIKPersistent : public CppUnit::TestFixture {
+
+    CPPUNIT_TEST_SUITE( TestGPHIKPersistent );
+	 CPPUNIT_TEST(testPersistentMethods);
+      
+    CPPUNIT_TEST_SUITE_END();
+  
+ private:
+ 
+ public:
+    void setUp();
+    void tearDown();
+
+
+    void testPersistentMethods();
+};
+
+#endif // _TESTGPHIKPERSISTENT_H

tests/toyExampleSmallScaleTrain.data

@@ -0,0 +1,64 @@
+60 x 49
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