a fixed version

Makefile

@@ -7,7 +7,7 @@ export LDFLAGS= -pthread -lm
 ifeq ($(no_omp),1)
 	export CFLAGS = -Wall -O3 -msse2  -Wno-unknown-pragmas -DDISABLE_OPENMP -funroll-loops 
 else
-	export CFLAGS = -Wall -O3 -msse2 -Wno-unknown-pragmas -fopenmp -funroll-loops 
+	export CFLAGS = -Wall -O3 -msse2 -Wno-unknown-pragmas -fopenmp  -funroll-loops 
 endif
 
 # expose these flags to R CMD SHLIB
@@ -18,11 +18,11 @@ BIN = xgboost
 OBJ = 
 SLIB = wrapper/libxgboostwrapper.so 
 RLIB = wrapper/libxgboostR.so 
-.PHONY: clean all R
+.PHONY: clean all R python
 
 all: $(BIN) wrapper/libxgboostwrapper.so
 R: wrapper/libxgboostR.so
-
+python: wrapper/libxgboostwrapper.so
 xgboost: src/xgboost_main.cpp src/io/io.cpp src/data.h src/tree/*.h src/tree/*.hpp src/gbm/*.h src/gbm/*.hpp src/utils/*.h src/learner/*.h src/learner/*.hpp 
 # now the wrapper takes in two files. io and wrapper part
 wrapper/libxgboostwrapper.so: wrapper/xgboost_wrapper.cpp src/io/io.cpp src/*.h src/*/*.hpp src/*/*.h

src/learner/evaluation-inl.hpp

@@ -110,7 +110,7 @@ struct EvalAMS : public IEvaluator {
   }
   virtual float Eval(const std::vector<float> &preds,
                      const MetaInfo &info) const {
-    const unsigned ndata = static_cast<unsigned>(preds.size());
+    const unsigned ndata = static_cast<unsigned>(info.labels.size());
     utils::Check(info.weights.size() == ndata, "we need weight to evaluate ams");
     std::vector< std::pair<float, unsigned> > rec(ndata);
 
@@ -207,10 +207,13 @@ struct EvalPrecisionRatio : public IEvaluator{
 struct EvalAuc : public IEvaluator {
   virtual float Eval(const std::vector<float> &preds,
                      const MetaInfo &info) const {
-    utils::Check(preds.size() == info.labels.size(), "label size predict size not match");
+
-    std::vector<unsigned> tgptr(2, 0); tgptr[1] = preds.size();
+    utils::Check(info.labels.size() != 0, "label set cannot be empty");
+    utils::Check(preds.size() % info.labels.size() == 0,
+                 "label size predict size not match");
+    std::vector<unsigned> tgptr(2, 0); tgptr[1] = info.labels.size();
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
-    utils::Check(gptr.back() == preds.size(),
+    utils::Check(gptr.back() == info.labels.size(),
                  "EvalAuc: group structure must match number of prediction");
     const unsigned ngroup = static_cast<unsigned>(gptr.size() - 1);
     // sum statictis

src/learner/objective-inl.hpp

@@ -123,7 +123,7 @@ class RegLossObj : public IObjFunction{
       float p = loss.PredTransform(preds[i]);
       float w = info.GetWeight(j);
       if (info.labels[j] == 1.0f) w *= scale_pos_weight;
-      gpair[j] = bst_gpair(loss.FirstOrderGradient(p, info.labels[j]) * w,
+      gpair[i] = bst_gpair(loss.FirstOrderGradient(p, info.labels[j]) * w,
                            loss.SecondOrderGradient(p, info.labels[j]) * w);
     }
   }

src/tree/model.h

@@ -270,6 +270,7 @@ class TreeModel {
     param.num_nodes = param.num_roots;
     nodes.resize(param.num_nodes);
     stats.resize(param.num_nodes);
+    leaf_vector.resize(param.num_nodes * param.size_leaf_vector, 0.0f);
     for (int i = 0; i < param.num_nodes; i ++) {
       nodes[i].set_leaf(0.0f);
       nodes[i].set_parent(-1);

src/tree/param.h

@@ -100,9 +100,9 @@ struct TrainParam{
     double w = CalcWeight(sum_grad, sum_hess);
     double ret = test_grad * w  + 0.5 * (test_hess + reg_lambda) * Sqr(w);
     if (reg_alpha == 0.0f) {
-      return 2.0 * ret;
+      return - 2.0 * ret;
     } else {
-      return 2.0 * (ret + reg_alpha * std::abs(w));
+      return - 2.0 * (ret + reg_alpha * std::abs(w));
     }
   }
   // calculate weight given the statistics
@@ -206,15 +206,15 @@ struct GradStats {
 };
 
 /*! \brief vectorized cv statistics */
-template<int vsize>
+template<unsigned vsize>
 struct CVGradStats : public GradStats {
   // additional statistics
   GradStats train[vsize], valid[vsize];
   // constructor
-  explicit CVGradStats(const TrainParam &param) 
+  explicit CVGradStats(const TrainParam &param) {
-      : GradStats(param) {
     utils::Check(param.size_leaf_vector == vsize,
                  "CVGradStats: vsize must match size_leaf_vector");
+    this->Clear();
   }
   /*! \brief check if necessary information is ready */
   inline static void CheckInfo(const BoosterInfo &info) {
@@ -224,7 +224,7 @@ struct CVGradStats : public GradStats {
   /*! \brief clear the statistics */
   inline void Clear(void) {
     GradStats::Clear();
-    for (int i = 0; i < vsize; ++i) {
+    for (unsigned i = 0; i < vsize; ++i) {
       train[i].Clear(); valid[i].Clear();
     }
   }
@@ -233,7 +233,7 @@ struct CVGradStats : public GradStats {
                   bst_uint ridx) {
     GradStats::Add(gpair[ridx].grad, gpair[ridx].hess);
     const size_t step = info.fold_index.size();
-    for (int i = 0; i < vsize; ++i) {
+    for (unsigned i = 0; i < vsize; ++i) {
       const bst_gpair &b = gpair[(i + 1) * step + ridx];
       if (info.fold_index[ridx] == i) {
         valid[i].Add(b.grad, b.hess);
@@ -245,18 +245,18 @@ struct CVGradStats : public GradStats {
   /*! \brief calculate gain of the solution */
   inline double CalcGain(const TrainParam &param) const {
     double ret = 0.0;
-    for (int i = 0; i < vsize; ++i) {
+    for (unsigned i = 0; i < vsize; ++i) {
       ret += param.CalcGain(train[i].sum_grad,
                             train[i].sum_hess,
                             vsize * valid[i].sum_grad,
                             vsize * valid[i].sum_hess);      
     }
-    return ret;
+    return ret / vsize;
   }
   /*! \brief add statistics to the data */
   inline void Add(const CVGradStats &b) {
     GradStats::Add(b);
-    for (int i = 0; i < vsize; ++i) {
+    for (unsigned i = 0; i < vsize; ++i) {
       train[i].Add(b.train[i]);
       valid[i].Add(b.valid[i]);
     }

src/tree/updater_colmaker-inl.hpp

@@ -82,7 +82,6 @@ class ColMaker: public IUpdater<FMatrix> {
                         RegTree *p_tree) {
       this->InitData(gpair, fmat, info.root_index, *p_tree);
       this->InitNewNode(qexpand, gpair, fmat, info, *p_tree);
-
       for (int depth = 0; depth < param.max_depth; ++depth) {
         this->FindSplit(depth, this->qexpand, gpair, fmat, info, p_tree);
         this->ResetPosition(this->qexpand, fmat, *p_tree);