Merge remote-tracking branch 'upstream/master'

R-package/R/utils.R

@@ -15,21 +15,29 @@ xgb.setinfo <- function(dmat, name, info) {
     stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix")
   }
   if (name == "label") {
+    if (length(info)!=xgb.numrow(dmat))
+      stop("The length of labels must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info), 
           PACKAGE = "xgboost")
     return(TRUE)
   }
   if (name == "weight") {
+    if (length(info)!=xgb.numrow(dmat))
+      stop("The length of weights must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info), 
           PACKAGE = "xgboost")
     return(TRUE)
   }
   if (name == "base_margin") {
+    if (length(info)!=xgb.numrow(dmat))
+      stop("The length of base margin must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info), 
           PACKAGE = "xgboost")
     return(TRUE)
   }
   if (name == "group") {
+    if (length(info)!=xgb.numrow(dmat))
+      stop("The length of groups must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info), 
           PACKAGE = "xgboost")
     return(TRUE)

R-package/src/xgboost_R.cpp

@@ -293,7 +293,9 @@ extern "C" {
     const char *raw = XGBoosterGetModelRaw(R_ExternalPtrAddr(handle), &olen);
     _WrapperEnd();
     SEXP ret = PROTECT(allocVector(RAWSXP, olen));
-    memcpy(RAW(ret), raw, olen);
+    if (olen != 0) {
+      memcpy(RAW(ret), raw, olen);
+    }
     UNPROTECT(1);    
     return ret;
   }

src/learner/evaluation-inl.hpp

@@ -27,8 +27,9 @@ struct EvalEWiseBase : public IEvaluator {
                      const MetaInfo &info,
                      bool distributed) const {
     utils::Check(info.labels.size() != 0, "label set cannot be empty");
-    utils::Check(preds.size() % info.labels.size() == 0,
-                 "label and prediction size not match");
+    utils::Check(preds.size() == info.labels.size(),
+                 "label and prediction size not match"\
+                 "hint: use merror or mlogloss for multi-class classification");
 
     const bst_omp_uint ndata = static_cast<bst_omp_uint>(info.labels.size());
 
@@ -50,7 +51,6 @@ struct EvalEWiseBase : public IEvaluator {
    *   get evaluation result from one row 
    * \param label label of current instance
    * \param pred prediction value of current instance
-   * \param weight weight of current instance
    */
   inline static float EvalRow(float label, float pred);
   /*! 
@@ -98,15 +98,84 @@ struct EvalError : public EvalEWiseBase<EvalError> {
   }
 };
 
+/*! 
+ * \brief base class of multi-class evaluation
+ * \tparam Derived the name of subclass
+ */
+template<typename Derived>
+struct EvalMClassBase : public IEvaluator {
+  virtual float Eval(const std::vector<float> &preds,
+                     const MetaInfo &info,
+                     bool distributed) const {
+    utils::Check(info.labels.size() != 0, "label set cannot be empty");
+    utils::Check(preds.size() % info.labels.size() == 0,
+                 "label and prediction size not match");
+    const size_t nclass = preds.size() / info.labels.size();
+    const bst_omp_uint ndata = static_cast<bst_omp_uint>(info.labels.size());
+    
+    float sum = 0.0, wsum = 0.0;
+    #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
+      const float wt = info.GetWeight(i);
+      sum += Derived::EvalRow(info.labels[i],
+                              BeginPtr(preds) + i * nclass,
+                              nclass) * wt;
+      wsum += wt;
+    }
+    float dat[2]; dat[0] = sum, dat[1] = wsum;
+    if (distributed) {
+      rabit::Allreduce<rabit::op::Sum>(dat, 2);
+    }
+    return Derived::GetFinal(dat[0], dat[1]);
+  }
+  /*! 
+   * \brief to be implemented by subclass, 
+   *   get evaluation result from one row 
+   * \param label label of current instance
+   * \param pred prediction value of current instance 
+   * \param nclass number of class in the prediction
+   */
+  inline static float EvalRow(float label,
+                              const float *pred,
+                              size_t nclass);
+  /*! 
+   * \brief to be overide by subclas, final trasnformation 
+   * \param esum the sum statistics returned by EvalRow
+   * \param wsum sum of weight
+   */
+  inline static float GetFinal(float esum, float wsum) {
+    return esum / wsum;
+  }
+};
 /*! \brief match error */
-struct EvalMatchError : public EvalEWiseBase<EvalMatchError> {
+struct EvalMatchError : public EvalMClassBase<EvalMatchError> {
   virtual const char *Name(void) const {
     return "merror";
   }
-  inline static float EvalRow(float label, float pred) {
-    return static_cast<int>(pred) != static_cast<int>(label);
+  inline static float EvalRow(float label,
+                              const float *pred,
+                              size_t nclass) {
+    return FindMaxIndex(pred, nclass) != static_cast<int>(label);
   }
 };
+/*! \brief match error */
+struct EvalMultiLogLoss : public EvalMClassBase<EvalMultiLogLoss> {
+  virtual const char *Name(void) const {
+    return "mlogloss";
+  }
+  inline static float EvalRow(float label,
+                              const float *pred,
+                              size_t nclass) {
+    const float eps = 1e-16f;
+    size_t k = static_cast<size_t>(label);
+    utils::Check(k < nclass, "mlogloss: label must be in [0, num_class)");
+    if (pred[k] > eps) {
+      return -std::log(pred[k]);
+    } else {
+      return -std::log(eps);
+    }
+  } 
+};
 
 /*! \brief ctest */
 struct EvalCTest: public IEvaluator {

src/learner/evaluation.h

@@ -45,6 +45,7 @@ inline IEvaluator* CreateEvaluator(const char *name) {
   if (!strcmp(name, "error")) return new EvalError();
   if (!strcmp(name, "merror")) return new EvalMatchError();
   if (!strcmp(name, "logloss")) return new EvalLogLoss();
+  if (!strcmp(name, "mlogloss")) return new EvalMultiLogLoss();
   if (!strcmp(name, "auc")) return new EvalAuc();
   if (!strncmp(name, "ams@", 4)) return new EvalAMS(name);
   if (!strncmp(name, "pre@", 4)) return new EvalPrecision(name);

src/learner/helper_utils.h

@@ -27,21 +27,28 @@ inline static void Softmax(std::vector<float>* p_rec) {
     rec[i] /= static_cast<float>(wsum);
   }
 }
-// simple helper function to do softmax
-inline static int FindMaxIndex(const std::vector<float>& rec) {
+
+inline static int FindMaxIndex(const float  *rec, size_t size) {
   size_t mxid = 0;
-  for (size_t i = 1; i < rec.size(); ++i) {
-    if (rec[i] > rec[mxid] + 1e-6f) {
+  for (size_t i = 1; i < size; ++i) {
+    if (rec[i] > rec[mxid]) {
       mxid = i;
     }
   }
   return static_cast<int>(mxid);
 }
 
+// simple helper function to do softmax
+inline static int FindMaxIndex(const std::vector<float>& rec) {
+  return FindMaxIndex(BeginPtr(rec), rec.size());
+}
+
+
 inline static bool CmpFirst(const std::pair<float, unsigned> &a,
                             const std::pair<float, unsigned> &b) {
   return a.first > b.first;
 }
+
 inline static bool CmpSecond(const std::pair<float, unsigned> &a,
                              const std::pair<float, unsigned> &b) {
   return a.second > b.second;

src/learner/objective-inl.hpp

@@ -225,7 +225,7 @@ class SoftmaxMultiClassObj : public IObjFunction {
     this->Transform(io_preds, output_prob);
   }
   virtual void EvalTransform(std::vector<float> *io_preds) {
-    this->Transform(io_preds, 0);
+    this->Transform(io_preds, 1);
   }
   virtual const char* DefaultEvalMetric(void) const {
     return "merror";

src/tree/param.h

@@ -50,6 +50,7 @@ struct TrainParam{
   /*! \brief constructor */
   TrainParam(void) {
     learning_rate = 0.3f;
+    min_split_loss = 0.0f;
     min_child_weight = 1.0f;
     max_depth = 6;
     reg_lambda = 1.0f;