Beispiel #1
0
double TAGMFast::LikelihoodForRow(const int UID, const TIntFltH& FU) {
  double L = 0.0;
  TFltV HOSumFV; //adjust for Fv of v hold out
  if (HOVIDSV[UID].Len() > 0) {
    HOSumFV.Gen(SumFV.Len());
    
    for (int e = 0; e < HOVIDSV[UID].Len(); e++) {
      for (int c = 0; c < SumFV.Len(); c++) {
        HOSumFV[c] += GetCom(HOVIDSV[UID][e], c);
      }
    }
  }

  TUNGraph::TNodeI NI = G->GetNI(UID);
  if (DoParallel && NI.GetDeg() > 10) {
#pragma omp parallel for schedule(static, 1)
    for (int e = 0; e < NI.GetDeg(); e++) {
      int v = NI.GetNbrNId(e);
      if (v == UID) { continue; }
      if (HOVIDSV[UID].IsKey(v)) { continue; }
      double LU = log (1.0 - Prediction(FU, F[v])) + NegWgt * DotProduct(FU, F[v]);
#pragma omp atomic
      L += LU;
    }
    for (TIntFltH::TIter HI = FU.BegI(); HI < FU.EndI(); HI++) {
      double HOSum = HOVIDSV[UID].Len() > 0?  HOSumFV[HI.GetKey()].Val: 0.0;//subtract Hold out pairs only if hold out pairs exist
      double LU = NegWgt * (SumFV[HI.GetKey()] - HOSum - GetCom(UID, HI.GetKey())) * HI.GetDat();
      L -= LU;
    }
  } else {
    for (int e = 0; e < NI.GetDeg(); e++) {
      int v = NI.GetNbrNId(e);
      if (v == UID) { continue; }
      if (HOVIDSV[UID].IsKey(v)) { continue; }
      L += log (1.0 - Prediction(FU, F[v])) + NegWgt * DotProduct(FU, F[v]);
    }
    for (TIntFltH::TIter HI = FU.BegI(); HI < FU.EndI(); HI++) {
      double HOSum = HOVIDSV[UID].Len() > 0?  HOSumFV[HI.GetKey()].Val: 0.0;//subtract Hold out pairs only if hold out pairs exist
      L -= NegWgt * (SumFV[HI.GetKey()] - HOSum - GetCom(UID, HI.GetKey())) * HI.GetDat();
    }
  }
  //add regularization
  if (RegCoef > 0.0) { //L1
    L -= RegCoef * Sum(FU);
  }
  if (RegCoef < 0.0) { //L2
    L += RegCoef * Norm2(FU);
  }

  return L;
}
Beispiel #2
0
int TAGMFast::MLEGradAscentParallel(const double& Thres, const int& MaxIter, const int ChunkNum, const int ChunkSize, const TStr PlotNm, const double StepAlpha, const double StepBeta) {
  //parallel
  time_t InitTime = time(NULL);
  uint64 StartTm = TSecTm::GetCurTm().GetAbsSecs();
  TExeTm ExeTm, CheckTm;
  double PrevL = Likelihood(true);
  TIntFltPrV IterLV;
  int PrevIter = 0;
  int iter = 0;
  TIntV NIdxV(F.Len(), 0);
  for (int i = 0; i < F.Len(); i++) { NIdxV.Add(i); }
  TIntV NIDOPTV(F.Len()); //check if a node needs optimization or not 1: does not require optimization
  NIDOPTV.PutAll(0);
  TVec<TIntFltH> NewF(ChunkNum * ChunkSize);
  TIntV NewNIDV(ChunkNum * ChunkSize);
  for (iter = 0; iter < MaxIter; iter++) {
    NIdxV.Clr(false);
    for (int i = 0; i < F.Len(); i++) { 
      if (NIDOPTV[i] == 0) {  NIdxV.Add(i); }
    }
    IAssert (NIdxV.Len() <= F.Len());
    NIdxV.Shuffle(Rnd);
    // compute gradient for chunk of nodes
#pragma omp parallel for schedule(static, 1)
    for (int TIdx = 0; TIdx < ChunkNum; TIdx++) {
      TIntFltH GradV;
      for (int ui = TIdx * ChunkSize; ui < (TIdx + 1) * ChunkSize; ui++) {
        NewNIDV[ui] = -1;
        if (ui > NIdxV.Len()) { continue; }
        int u = NIdxV[ui]; //
        //find set of candidate c (we only need to consider c to which a neighbor of u belongs to)
        TUNGraph::TNodeI UI = G->GetNI(u);
        TIntSet CIDSet(5 * UI.GetDeg());
        TIntFltH CurFU = F[u];
        for (int e = 0; e < UI.GetDeg(); e++) {
          if (HOVIDSV[u].IsKey(UI.GetNbrNId(e))) { continue; }
          TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)];
          for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) {
            CIDSet.AddKey(CI.GetKey());
          }
        }
        if (CIDSet.Empty()) { 
          CurFU.Clr();
        }
        else {
          for (TIntFltH::TIter CI = CurFU.BegI(); CI < CurFU.EndI(); CI++) { //remove the community membership which U does not share with its neighbors
            if (! CIDSet.IsKey(CI.GetKey())) {
              CurFU.DelIfKey(CI.GetKey());
            }
          }
          GradientForRow(u, GradV, CIDSet);
          if (Norm2(GradV) < 1e-4) { NIDOPTV[u] = 1; continue; }
          double LearnRate = GetStepSizeByLineSearch(u, GradV, GradV, StepAlpha, StepBeta, 5);
          if (LearnRate <= 1e-5) { NewNIDV[ui] = -2; continue; }
          for (int ci = 0; ci < GradV.Len(); ci++) {
            int CID = GradV.GetKey(ci);
            double Change = LearnRate * GradV.GetDat(CID);
            double NewFuc = CurFU.IsKey(CID)? CurFU.GetDat(CID) + Change : Change;
            if (NewFuc <= 0.0) {
              CurFU.DelIfKey(CID);
            } else {
              CurFU.AddDat(CID) = NewFuc;
            }
          }
          CurFU.Defrag();
        }
        //store changes
        NewF[ui] = CurFU;
        NewNIDV[ui] = u;
      }
    }
    int NumNoChangeGrad = 0;
    int NumNoChangeStepSize = 0;
    for (int ui = 0; ui < NewNIDV.Len(); ui++) {
      int NewNID = NewNIDV[ui];
      if (NewNID == -1) { NumNoChangeGrad++; continue; }
      if (NewNID == -2) { NumNoChangeStepSize++; continue; }
      for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) {
        SumFV[CI.GetKey()] -= CI.GetDat();
      }
    }
#pragma omp parallel for
    for (int ui = 0; ui < NewNIDV.Len(); ui++) {
      int NewNID = NewNIDV[ui];
      if (NewNID < 0) { continue; }
      F[NewNID] = NewF[ui];
    }
    for (int ui = 0; ui < NewNIDV.Len(); ui++) {
      int NewNID = NewNIDV[ui];
      if (NewNID < 0) { continue; }
      for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) {
        SumFV[CI.GetKey()] += CI.GetDat();
      }
    }
    // update the nodes who are optimal
    for (int ui = 0; ui < NewNIDV.Len(); ui++) {
      int NewNID = NewNIDV[ui];
      if (NewNID < 0) { continue; }
      TUNGraph::TNodeI UI = G->GetNI(NewNID);
      NIDOPTV[NewNID] = 0;
      for (int e = 0; e < UI.GetDeg(); e++) {
        NIDOPTV[UI.GetNbrNId(e)] = 0;
      }
    }
    int OPTCnt = 0;
    for (int i = 0; i < NIDOPTV.Len(); i++) { if (NIDOPTV[i] == 1) { OPTCnt++; } }
    if (! PlotNm.Empty()) {
      printf("\r%d iterations [%s] %d secs", iter * ChunkSize * ChunkNum, ExeTm.GetTmStr(), int(TSecTm::GetCurTm().GetAbsSecs() - StartTm));
      if (PrevL > TFlt::Mn) { printf(" (%f) %d g %d s %d OPT", PrevL, NumNoChangeGrad, NumNoChangeStepSize, OPTCnt); }
      fflush(stdout);
    }
    if ((iter - PrevIter) * ChunkSize * ChunkNum >= G->GetNodes()) {
      PrevIter = iter;
      double CurL = Likelihood(true);
      IterLV.Add(TIntFltPr(iter * ChunkSize * ChunkNum, CurL));
      printf("\r%d iterations, Likelihood: %f, Diff: %f [%d secs]", iter, CurL,  CurL - PrevL, int(time(NULL) - InitTime));
       fflush(stdout);
      if (CurL - PrevL <= Thres * fabs(PrevL)) { 
        break;
      }
      else {
        PrevL = CurL;
      }
    }
  }
  if (! PlotNm.Empty()) {
    printf("\nMLE completed with %d iterations(%s secs)\n", iter, int(TSecTm::GetCurTm().GetAbsSecs() - StartTm));
    TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");[]
  } else {
Beispiel #3
0
int TAGMFast::MLEGradAscent(const double& Thres, const int& MaxIter, const TStr PlotNm, const double StepAlpha, const double StepBeta) {
  time_t InitTime = time(NULL);
  TExeTm ExeTm, CheckTm;
  int iter = 0, PrevIter = 0;
  TIntFltPrV IterLV;
  TUNGraph::TNodeI UI;
  double PrevL = TFlt::Mn, CurL = 0.0;
  TIntV NIdxV(F.Len(), 0);
  for (int i = 0; i < F.Len(); i++) { NIdxV.Add(i); }
  IAssert(NIdxV.Len() == F.Len());
  TIntFltH GradV;
  while(iter < MaxIter) {
    NIdxV.Shuffle(Rnd);
    for (int ui = 0; ui < F.Len(); ui++, iter++) {
      int u = NIdxV[ui]; //
      //find set of candidate c (we only need to consider c to which a neighbor of u belongs to)
      UI = G->GetNI(u);
      TIntSet CIDSet(5 * UI.GetDeg());
      for (int e = 0; e < UI.GetDeg(); e++) {
        if (HOVIDSV[u].IsKey(UI.GetNbrNId(e))) { continue; }
        TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)];
        for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) {
          CIDSet.AddKey(CI.GetKey());
        }
      }
      for (TIntFltH::TIter CI = F[u].BegI(); CI < F[u].EndI(); CI++) { //remove the community membership which U does not share with its neighbors
        if (! CIDSet.IsKey(CI.GetKey())) {
          DelCom(u, CI.GetKey());
        }
      }
      if (CIDSet.Empty()) { continue; }
      GradientForRow(u, GradV, CIDSet);
      if (Norm2(GradV) < 1e-4) { continue; }
      double LearnRate = GetStepSizeByLineSearch(u, GradV, GradV, StepAlpha, StepBeta);
      if (LearnRate == 0.0) { continue; }
      for (int ci = 0; ci < GradV.Len(); ci++) {
        int CID = GradV.GetKey(ci);
        double Change = LearnRate * GradV.GetDat(CID);
        double NewFuc = GetCom(u, CID) + Change;
        if (NewFuc <= 0.0) {
          DelCom(u, CID);
        } else {
          AddCom(u, CID, NewFuc);
        }
      }
      if (! PlotNm.Empty() && (iter + 1) % G->GetNodes() == 0) {
        IterLV.Add(TIntFltPr(iter, Likelihood(false)));
      }
    }
    printf("\r%d iterations (%f) [%lu sec]", iter, CurL, time(NULL) - InitTime);
    fflush(stdout);
    if (iter - PrevIter >= 2 * G->GetNodes() && iter > 10000) {
      PrevIter = iter;
      CurL = Likelihood();
      if (PrevL > TFlt::Mn && ! PlotNm.Empty()) {
        printf("\r%d iterations, Likelihood: %f, Diff: %f", iter, CurL,  CurL - PrevL);
      }
      fflush(stdout);
      if (CurL - PrevL <= Thres * fabs(PrevL)) { break; }
      else { PrevL = CurL; }
    }
    
  }
  printf("\n");
  printf("MLE for Lambda completed with %d iterations(%s)\n", iter, ExeTm.GetTmStr());
  if (! PlotNm.Empty()) {
    TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");
  }
  return iter;
}
Beispiel #4
0
/// Newton method: DEPRECATED
int TAGMFast::MLENewton(const double& Thres, const int& MaxIter, const TStr PlotNm) {
  TExeTm ExeTm;
  int iter = 0, PrevIter = 0;
  TIntFltPrV IterLV;
  double PrevL = TFlt::Mn, CurL;
  TUNGraph::TNodeI UI;
  TIntV NIdxV;
  G->GetNIdV(NIdxV);
  int CID, UID, NewtonIter;
  double Fuc, PrevFuc, Grad, H;
  while(iter < MaxIter) {
    NIdxV.Shuffle(Rnd);
    for (int ui = 0; ui < F.Len(); ui++, iter++) {
      if (! PlotNm.Empty() && iter % G->GetNodes() == 0) {
        IterLV.Add(TIntFltPr(iter, Likelihood(false)));
      }
      UID = NIdxV[ui];
      //find set of candidate c (we only need to consider c to which a neighbor of u belongs to)
      TIntSet CIDSet;
      UI = G->GetNI(UID);
      if (UI.GetDeg() == 0) { //if the node is isolated, clear its membership and skip
        if (! F[UID].Empty()) { F[UID].Clr(); }
        continue;
      }
      for (int e = 0; e < UI.GetDeg(); e++) {
        if (HOVIDSV[UID].IsKey(UI.GetNbrNId(e))) { continue; }
        TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)];
        for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) {
          CIDSet.AddKey(CI.GetKey());
        }
      }
      for (TIntFltH::TIter CI = F[UID].BegI(); CI < F[UID].EndI(); CI++) { //remove the community membership which U does not share with its neighbors
        if (! CIDSet.IsKey(CI.GetKey())) {
          DelCom(UID, CI.GetKey());
        }
      }
      if (CIDSet.Empty()) { continue; }
      for (TIntSet::TIter CI = CIDSet.BegI(); CI < CIDSet.EndI(); CI++) {
        CID = CI.GetKey();
        //optimize for UID, CID
        //compute constants
        TFltV AlphaKV(UI.GetDeg());
        for (int e = 0; e < UI.GetDeg(); e++) {
          if (HOVIDSV[UID].IsKey(UI.GetNbrNId(e))) { continue; }
          AlphaKV[e] = (1 - PNoCom) * exp(- DotProduct(UID, UI.GetNbrNId(e)) + GetCom(UI.GetNbrNId(e), CID) * GetCom(UID, CID));
          IAssertR(AlphaKV[e] <= 1.0, TStr::Fmt("AlphaKV=%f, %f, %f", AlphaKV[e].Val, PNoCom.Val, GetCom(UI.GetNbrNId(e), CID)));
        }
        Fuc = GetCom(UID, CID);
        PrevFuc = Fuc;
        Grad = GradientForOneVar(AlphaKV, UID, CID, Fuc), H = 0.0;
        if (Grad <= 1e-3 && Grad >= -0.1) { continue; }
        NewtonIter = 0;
        while (NewtonIter++ < 10) {
          Grad = GradientForOneVar(AlphaKV, UID, CID, Fuc), H = 0.0;
          H = HessianForOneVar(AlphaKV, UID, CID, Fuc);
          if (Fuc == 0.0 && Grad <= 0.0) { Grad = 0.0; }
          if (fabs(Grad) < 1e-3) { break; }
          if (H == 0.0) { Fuc = 0.0; break; }
          double NewtonStep = - Grad / H;
          if (NewtonStep < -0.5) { NewtonStep = - 0.5; }
          Fuc += NewtonStep;
          if (Fuc < 0.0) { Fuc = 0.0; }
        }
        if (Fuc == 0.0) {
          DelCom(UID, CID);
        }
        else {
          AddCom(UID, CID, Fuc);
        }
      }
    }
    if (iter - PrevIter >= 2 * G->GetNodes() && iter > 10000) {
      PrevIter = iter;
      CurL = Likelihood();
      if (PrevL > TFlt::Mn && ! PlotNm.Empty()) {
        printf("\r%d iterations, Likelihood: %f, Diff: %f", iter, CurL,  CurL - PrevL);
      }
      fflush(stdout);
      if (CurL - PrevL <= Thres * fabs(PrevL)) { break; }
      else { PrevL = CurL; }
    }
    
  }
  if (! PlotNm.Empty()) {
    printf("\nMLE for Lambda completed with %d iterations(%s)\n", iter, ExeTm.GetTmStr());
    TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");
  }
  return iter;
}
Beispiel #5
0
int main(int argc, char* argv[])
{
  TEnv Env(argc, argv);
  TStr PrefixPath = Env.GetArgs() > 1 ? Env.GetArg(1) : TStr("");

  double ts1 = Tick();
  TTableContext Context;
  TVec<PTable> NodeTblV = TVec<PTable>();
  TVec<PTable> EdgeTblV = TVec<PTable>();
  Schema NodeSchema = Schema();
  Schema EdgeSchema = Schema();
  LoadFlickrTables(PrefixPath, Context, NodeTblV, NodeSchema, EdgeTblV, EdgeSchema);

  double ts2 = Tick();

  int ExpectedSz = 0;
  for (TVec<PTable>::TIter it = NodeTblV.BegI(); it < NodeTblV.EndI(); it++) {
    PTable Table = *it;
    ExpectedSz += Table->GetNumRows();
  }

  THash<TStr, TInt> Hash(ExpectedSz);
  TStrV OriNIdV(ExpectedSz);

  MergeNodeTables(NodeTblV, NodeSchema, Hash, OriNIdV);
  PTable EdgeTable = MergeEdgeTables(EdgeTblV, EdgeSchema, Hash, Context);

  double ts3 = Tick();
  TStrV V;
  TStrV VE;
  VE.Add(EdgeSchema.GetVal(2).GetVal1());
  PNEANet Graph = TSnap::ToNetwork<PNEANet>(EdgeTable, EdgeSchema.GetVal(0).GetVal1(), EdgeSchema.GetVal(1).GetVal1(),
						V, V, VE, aaLast);
  double ts4 = Tick();

  //int nExps = 1;
  int nExps = 40;
  TIntFltH PageRankResults;
  for (int i = 0; i < nExps; i++) {
    PageRankResults = TIntFltH(ExpectedSz);
#ifdef USE_OPENMP
    TSnap::GetWeightedPageRankMP2(Graph, PageRankResults, EdgeSchema.GetVal(2).GetVal1(), 0.849999999999998, 0.0001, 10);
#else
    TSnap::GetWeightedPageRank(Graph, PageRankResults, EdgeSchema.GetVal(2).GetVal1(), 0.849999999999998, 0.0001, 10);
#endif
  }
  double ts5 = Tick();

  PSOut ResultOut = TFOut::New(PrefixPath + TStr("page-rank-results.tsv"));
  for (TIntFltH::TIter it = PageRankResults.BegI(); it < PageRankResults.EndI(); it++) {
    ResultOut->PutStrFmtLn("%s\t%f9", OriNIdV[it.GetKey()].CStr(), it.GetDat().Val);
  }
  double ts6 = Tick();

  bool isPar = false;
#ifdef USE_OPENMP
  isPar = true;
#endif

//  PSOut FeaturesOut = TFOut::New(PrefixPath + "features.txt");
//  FeaturesOut->PutStrFmtLn("Photo %d", PPhotoTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Users %d", PUserTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Tags %d", PTagTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Comments %d", PCommentTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Locations %d", PLocationTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Photo - Owner %d", PPhotoOwnerTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Photo - Comment %d", PPhotoCommentTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Photo - Location %d", PPhotoLocationTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Comment - User %d", PCommentUserTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Comment - User %d", PCommentUserTbl->GetNumRows().Val);
////  FeaturesOut->PutStrFmtLn("Photo - Tagger %d", PPhotoTaggerTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Tagger - Tag %d", PTaggerTagTbl->GetNumRows().Val);
//  FeaturesOut->PutStrFmtLn("Total number of nodes = %d", Graph->GetNodes());
//  FeaturesOut->PutStrFmtLn("Total number of edges = %d", Graph->GetEdges());

  PSOut TimeOut = TFOut::New(PrefixPath + TStr("time.txt"), true);
  TimeOut->PutStrFmtLn("Experiment Weighted - %s - %s", PrefixPath.CStr(), (isPar ? "Parallel" : "Sequential"));
  TimeOut->PutStrFmtLn("Input Time = %f", GetCPUTimeUsage(ts1, ts2));
  TimeOut->PutStrFmtLn("Preprocessing Time = %f", GetCPUTimeUsage(ts2, ts3));
  TimeOut->PutStrFmtLn("Conversion Time = %f", GetCPUTimeUsage(ts3, ts4));
  TimeOut->PutStrFmtLn("Computing Time = %f", GetCPUTimeUsage(ts4, ts5)/nExps);
  TimeOut->PutStrFmtLn("Output Time = %f", GetCPUTimeUsage(ts5, ts6));

  return 0;
}