From def2bcd3f41a36e52b179aa435c5bf29c94dab5e Mon Sep 17 00:00:00 2001
From: Joe Zhao <ztuowen@gmail.com>
Date: Mon, 18 May 2015 22:22:11 +0800
Subject: added openmp directive

---
 CMakeLists.txt       |  2 +-
 model/rankaccu.cpp   | 81 +++++++++++++++++++++-------------------------------
 model/rankaccu.h     |  4 +--
 model/ranksvm.h      |  2 +-
 model/ranksvmtn.cpp  | 26 ++++++++++-------
 model/ranksvmtn.h    |  2 +-
 tools/dataProvider.h | 32 ++++++++++++---------
 train.cpp            | 29 +++++++++----------
 8 files changed, 84 insertions(+), 94 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 180456c..0e356fe 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,7 +6,7 @@ INCLUDE_DIRECTORIES ( "/usr/include/eigen3" )
 
 # Use Random Library for Random Number Generation
 
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fopenmp")
 
 FIND_PACKAGE( Boost COMPONENTS program_options REQUIRED )
 INCLUDE_DIRECTORIES( ${Boost_INCLUDE_DIR})
diff --git a/model/rankaccu.cpp b/model/rankaccu.cpp
index 726f2c8..f998e93 100644
--- a/model/rankaccu.cpp
+++ b/model/rankaccu.cpp
@@ -70,79 +70,64 @@ void rankmerge(int l,int r,vector<int> &C,vector<int> &rank,const vector<double>
     }
 }
 
-void rank_accu(DataList &D,const vector<double> pred)
+void rank_accu(RidList &D,const vector<double> pred)
 {
     unsigned long n = D.getSize();
     vector<int> orig_rank(n),pred_rank(n),C(n);
     vector<double> orig(n);
-    vector<DataEntry*> &dat = D.getData();
     int i,j;
-    for (i=0;i<dat.size();++i)
+    for (i=0;i<D.getSize();++i)
     {
         orig_rank[i]=i;
         pred_rank[i]=i;
-        orig[i]=dat[i]->rank;
+        orig[i]=D.getL(i);
     }
-    int cnt=0;
     double accu_nDCG=0;
     double accu_AP=0;
-    i=j=0;
-    while (i<dat.size())
+    for (j=0; j<D.getSize();j+=D.getqSize())
     {
-        if ((i+1 == dat.size())|| dat[i]->qid!=dat[i+1]->qid)
+        i=j+D.getqSize()-1;
+        double Y=0,Z=0;
+        double AP=0;
+        ranksort(j,i,orig_rank,orig,pred);
+        ranksort(j,i,pred_rank,pred,orig);
+        for (int k = j;k<=i;++k)
         {
-            double Y=0,Z=0;
-            double AP=0;
-            ranksort(j,i,orig_rank,orig,pred);
-            ranksort(j,i,pred_rank,pred,orig);
-            for (int k = j;k<=i;++k)
-            {
-                Z += (pow(2,offset+orig[orig_rank[k]]) - 1)/log2(2+k-j);
-                Y += (pow(2,offset+orig[pred_rank[k]]) - 1)/log2(2+k-j);
-            }
-            accu_nDCG+=Y/Z;
-            rankmerge(j,i,C,orig_rank,pred,orig);
-            for (int k = j+1;k<=i;++k)
-                AP += ((double)C[k])/(k-j);
-            AP=AP*2/(i-j)-1;
-            accu_AP+=AP;
-            j = i+1;
-            ++cnt;
+            Z += (pow(2,offset+orig[orig_rank[k]]) - 1)/log2(2+k-j);
+            Y += (pow(2,offset+orig[pred_rank[k]]) - 1)/log2(2+k-j);
         }
-        ++i;
+        accu_nDCG+=Y/Z;
+        rankmerge(j,i,C,orig_rank,pred,orig);
+        for (int k = j+1;k<=i;++k)
+            AP += ((double)C[k])/(k-j);
+        AP=AP*2/(i-j)-1;
+        accu_AP+=AP;
     }
-    LOG(INFO)<<"over "<< cnt<< " queries. "<<"Average nDGC: "<< accu_nDCG/cnt<< " Average AP: "<<accu_AP/cnt;
+    LOG(INFO)<<"over "<< D.getuSize()<< " queries. "<<"Average nDGC: "<< accu_nDCG/D.getuSize()<< " Average AP: "<<accu_AP/D.getuSize();
 }
 
-void rank_CMC(DataList &D,const std::vector<double> pred,CMC & cmc) {
+void rank_CMC(RidList &D,const std::vector<double> pred,CMC & cmc) {
     unsigned long n = D.getSize();
     vector<int> orig_rank(n),pred_rank(n);
     vector<double> orig(n);
-    vector<DataEntry*> &dat = D.getData();
     int i,j;
-    for (i=0;i<dat.size();++i)
+    for (i=0;i<D.getSize();++i)
     {
         orig_rank[i]=i;
         pred_rank[i]=i;
-        orig[i]=dat[i]->rank;
+        orig[i]=D.getL(i);
     }
-    int cnt=0;
-    i=j=0;
-    while (i<dat.size())
+    for (j=0; j<D.getSize();j+=D.getqSize())
     {
-        if ((i+1 == dat.size())|| dat[i]->qid!=dat[i+1]->qid)
-        {
-            ranksort(j,i,pred_rank,pred,orig);
-            for (int k=j;k<=i;++k)
-                if (orig[pred_rank[k]]>0)
-                {
-                    LOG(INFO)<<"qid:"<<dat[pred_rank[k]]->qid<<"; pred:"<<pred[k]<<"; rank:"<< k-j;
-                    cmc.addEntry(k-j);
-                    break; // account only for the first match;
-                }
-            j = i+1;
-            ++cnt;
-        }
-        ++i;
+        i=j+D.getqSize()-1;
+        ranksort(j,i,pred_rank,pred,orig);
+        for (int k=j;k<=i;++k)
+            if (orig[pred_rank[k]]>0)
+            {
+                LOG(INFO)<<"qid:"<<D.getQid(j)<<"; pred:"<<pred[k]<<"; rank:"<< k-j;
+                cmc.addEntry(k-j);
+                break; // account only for the first match;
+            }
+        j = i+1;
     }
 }
\ No newline at end of file
diff --git a/model/rankaccu.h b/model/rankaccu.h
index bcb8906..832a9f5 100644
--- a/model/rankaccu.h
+++ b/model/rankaccu.h
@@ -36,8 +36,8 @@ public:
     }
 };
 
-void rank_CMC(DataList &D,const std::vector<double> pred,CMC & cmc);
+void rank_CMC(RidList &D,const std::vector<double> pred,CMC & cmc);
 
-void rank_accu(DataList &D,const std::vector<double> pred);
+void rank_accu(RidList &D,const std::vector<double> pred);
 
 #endif //RANKSVM_RANKACCU_H
diff --git a/model/ranksvm.h b/model/ranksvm.h
index 9051343..20bb35a 100644
--- a/model/ranksvm.h
+++ b/model/ranksvm.h
@@ -26,7 +26,7 @@ protected:
     int fsize;
 public:
     virtual int train(RidList &D)=0;
-    virtual int predict(DataList &D,std::vector<double> &res)=0;
+    virtual int predict(RidList &D,std::vector<double> &res)=0;
     // TODO Not sure how to construct this
     //  Possible solution: generate a nxn matrix each row contains the sorted list of ranker result.
     int saveModel(const std::string fname);
diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 1414c81..f904fdd 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -7,7 +7,7 @@ using namespace std;
 using namespace Eigen;
 
 // Main terminating criteria
-const int maxiter = 40; // max iteration count
+const int maxiter = 50; // max iteration count
 const double prec=1e-10; // precision
 // conjugate gradient
 const double cg_prec=1e-10; // precision
@@ -17,14 +17,21 @@ const int ls_maxiter = 10;
 const double line_prec=1e-10; // precision
 const double line_turb=1e-15; // purturbation
 
+void cal_Dw(RidList &D,const VectorXd &w, VectorXd &Dw)
+{
+    int n = D.getSize();
+    #pragma omp parallel for
+    for (int i=0;i<n;++i)
+        Dw(i) = D.getVec(i).dot(w);
+}
+
 int cal_Hs(RidList &D,const vector<int> &rank,const VectorXd &corr,const VectorXd &alpha,const VectorXd s,VectorXd &Hs)
 {
     int n = D.getSize();
     int q = D.getqSize();
     Hs = VectorXd::Zero(s.rows());
     VectorXd Ds(n);
-    for (int i=0;i<n;++i)
-        Ds(i) = D.getVec(i).dot(s);
+    cal_Dw(D,s,Ds);
     VectorXd gamma(n);
     for (int i=0;i<n;)
     {
@@ -153,8 +160,7 @@ int line_search(const VectorXd &w,RidList &D,const VectorXd &corr,const VectorXd
 {
     int n=D.getSize();
     VectorXd Dd(n);
-    for (int i=0;i<n;++i)
-        Dd(i) = D.getVec(i).dot(step);
+    cal_Dw(D,step,Dd);
     VectorXd alpha,beta,yt;
     VectorXd grad;
     VectorXd Hs;
@@ -167,8 +173,7 @@ int line_search(const VectorXd &w,RidList &D,const VectorXd &corr,const VectorXd
     while (1)
     {
         grad=w+t*step;
-        for (int i=0;i<n;++i)
-            Dd(i) = D.getVec(i).dot(grad);
+        cal_Dw(D,grad,Dd);
         cal_alpha_beta(Dd,corr,D,rank,yt,alpha,beta);
         VectorXd tmp = alpha.cwiseProduct(yt)-beta;
         VectorXd res = 0*grad;
@@ -209,8 +214,7 @@ int train_orig(int fsize, RidList &Data,const VectorXd &corr,VectorXd &weight){
     VectorXd alpha,beta;
     while (true)
     {
-        for (int i=0;i<n;++i)
-            dw(i) = Data.getVec(i).dot(weight);
+        cal_Dw(Data,weight,dw);
         cal_alpha_beta(dw,corr,Data,rank,yt,alpha,beta);
         // Generate support vector matrix sv & gradient
         obj = (weight.dot(weight) + C*(alpha.dot(yt.cwiseProduct(yt))-beta.dot(yt)))/2;
@@ -251,9 +255,9 @@ int RSVMTN::train(RidList &D){
     return 0;
 };
 
-int RSVMTN::predict(DataList &D, vector<double> &res){
+int RSVMTN::predict(RidList &D, vector<double> &res){
     res.clear();
     for (int i=0;i<D.getSize();++i)
-        res.push_back(((D.getData()[i])->feature).dot(model.weight));
+        res.push_back(D.getVec(i).dot(model.weight));
     return 0;
 };
\ No newline at end of file
diff --git a/model/ranksvmtn.h b/model/ranksvmtn.h
index c98e581..97579b3 100644
--- a/model/ranksvmtn.h
+++ b/model/ranksvmtn.h
@@ -13,7 +13,7 @@ public:
         return "TN";
     };
     virtual int train(RidList &D);
-    virtual int predict(DataList &D,std::vector<double> &res);
+    virtual int predict(RidList &D,std::vector<double> &res);
 };
 
 #endif
\ No newline at end of file
diff --git a/tools/dataProvider.h b/tools/dataProvider.h
index 586965e..59b989a 100644
--- a/tools/dataProvider.h
+++ b/tools/dataProvider.h
@@ -82,9 +82,23 @@ public:
         else
             uniq.push_back(d);
     }
+    inline DataEntry* getU(int x)
+    {
+        return uniq[x];
+    }
+    inline DataEntry* getO(int x)
+    {
+        return other[x];
+    }
+    inline std::string getQid(int x)
+    {
+        int a,b,n=getqSize();
+        a=x/n;
+        return getU(a)->qid;
+    }
     inline int getqSize()
     {
-        return (int)(uniq.size()+other.size()-1);
+        return (int)other.size();
     }
     inline int getuSize()
     {
@@ -99,24 +113,14 @@ public:
         a=x/n;
         b=x%n;
         Eigen::VectorXd vec;
-        if (b<a)
-            vec=uniq[a]->feature-uniq[b]->feature;
-        else
-        if (b<uniq.size()-1)
-            vec=uniq[a]->feature-uniq[b+1]->feature;
-        else
-            vec=uniq[a]->feature-other[b-uniq.size()+1]->feature;
-        return vec.cwiseAbs();
+        return (uniq[a]->feature-other[b]->feature).cwiseAbs();
     };
     inline double getL(int x){
         int a,b,n=getqSize();
         a=x/n;
         b=x%n;
-        if (b<uniq.size()-1)
-            return -1;
-        else
-            if (std::fabs(other[b-uniq.size()+1]->rank - a) < 1e-5)
-                return 1;
+        if (std::fabs(other[b]->rank - a) < 1e-5)
+            return 1;
         return -1;
     };
 };
diff --git a/train.cpp b/train.cpp
index 039c93a..0b5b4d4 100644
--- a/train.cpp
+++ b/train.cpp
@@ -43,7 +43,7 @@ int predict(DataProvider &dp) {
     rsvm = RSVM::loadModel(vm["model"].as<string>().c_str());
 
     dp.open();
-    DataList D;
+    RidList D;
     vector<double> L;
     CMC cmc;
     LOG(INFO)<<"Prediction started";
@@ -59,24 +59,21 @@ int predict(DataProvider &dp) {
     else
         ot=&cout;
 
-    while (!dp.EOFile())
-    {
-        dp.getDataSet(D);
-        LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
-        rsvm->predict(D,L);
-
-        if (vm.count("validate"))
-        {
-            rank_accu(D,L);
-            if (vm.count("cmc"))
-                rank_CMC(D,L,cmc);
-        }
+    dp.getAllDataSet(D);
+    LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
+    rsvm->predict(D,L);
 
-        if (vm.count("output") && vm.count("predict"))
-            for (int i=0; i<L.size();++i)
-                *ot<<L[i]<<endl;
+    if (vm.count("validate"))
+    {
+        rank_accu(D,L);
+        if (vm.count("cmc"))
+            rank_CMC(D,L,cmc);
     }
 
+    if (vm.count("output") && vm.count("predict"))
+        for (int i=0; i<L.size();++i)
+            *ot<<L[i]<<endl;
+
     LOG(INFO)<<"Finished";
     if (vm.count("cmc"))
     {
-- 
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