check in softmax multiclass

demo/multiclass_classification/train.py

@@ -42,8 +42,9 @@ print ('predicting, classification error=%f' % (sum( int(pred[i]) != test_Y[i] f
 # do the same thing again, but output probabilities
 param['objective'] = 'multi:softprob'
 bst = xgb.train(param, xg_train, num_round, watchlist );
-# get prediction, this is in 1D array, need reshape to (nclass, ndata)
+# Note: this convention has been changed since xgboost-unity
-yprob = bst.predict( xg_test ).reshape( 6, test_Y.shape[0] )
+# get prediction, this is in 1D array, need reshape to (ndata, nclass)
-ylabel = np.argmax( yprob, axis=0)
+yprob = bst.predict( xg_test ).reshape( test_Y.shape[0], 6 )
+ylabel = np.argmax(yprob, axis=1)
 
 print ('predicting, classification error=%f' % (sum( int(ylabel[i]) != test_Y[i] for i in range(len(test_Y))) / float(len(test_Y)) ))

python/xgboost_wrapper.cpp

@@ -86,7 +86,7 @@ extern "C"{
     mat.row_ptr_.resize(nindptr);
     memcpy(&mat.row_ptr_[0], indptr, sizeof(size_t)*nindptr);
     mat.row_data_.resize(nelem);
-    for (size_t i = 0; i < nelem; ++ i) {
+    for (size_t i = 0; i < nelem; ++i) {
       mat.row_data_[i] = SparseBatch::Entry(indices[i], data[i]);
       mat.info.num_col = std::max(mat.info.num_col,
                                   static_cast<size_t>(indices[i]+1));
@@ -137,7 +137,7 @@ extern "C"{
     iter->BeforeFirst();
     utils::Assert(iter->Next(), "slice");
     const SparseBatch &batch = iter->Value();
-    for(size_t i = 0; i < len; ++i) {
+    for (size_t i = 0; i < len; ++i) {
       const int ridx = idxset[i];
       SparseBatch::Inst inst = batch[ridx];
       utils::Check(ridx < batch.size, "slice index exceed number of rows");
@@ -173,11 +173,11 @@ extern "C"{
     pmat->info.weights.resize(len);
     memcpy(&(pmat->info).weights[0], weight, sizeof(float) * len);
   }
-  void XGDMatrixSetGroup(void *handle, const unsigned *group, size_t len){
+  void XGDMatrixSetGroup(void *handle, const unsigned *group, size_t len) {
     DataMatrix *pmat = static_cast<DataMatrix*>(handle);
     pmat->info.group_ptr.resize(len + 1);
     pmat->info.group_ptr[0] = 0;
-    for (size_t i = 0; i < len; ++ i) {
+    for (size_t i = 0; i < len; ++i) {
       pmat->info.group_ptr[i+1] = pmat->info.group_ptr[i]+group[i];
     }
   }
@@ -225,7 +225,8 @@ extern "C"{
     bst->CheckInit(dtr);
     bst->BoostOneIter(*dtr, grad, hess, len);
   }
-  const char* XGBoosterEvalOneIter(void *handle, int iter, void *dmats[], const char *evnames[], size_t len) {
+  const char* XGBoosterEvalOneIter(void *handle, int iter, void *dmats[],
+                                   const char *evnames[], size_t len) {
     Booster *bst = static_cast<Booster*>(handle);
     std::vector<std::string> names;
     std::vector<const DataMatrix*> mats;
@@ -243,13 +244,12 @@ extern "C"{
   void XGBoosterLoadModel(void *handle, const char *fname) {
     static_cast<Booster*>(handle)->LoadModel(fname);
   }
-  void XGBoosterSaveModel( const void *handle, const char *fname) {
+  void XGBoosterSaveModel(const void *handle, const char *fname) {
     static_cast<const Booster*>(handle)->SaveModel(fname);
   }
   const char** XGBoosterDumpModel(void *handle, const char *fmap, size_t *len){
-    using namespace xgboost::utils;
+    utils::FeatMap featmap;
-    FeatMap featmap; 
+    if (strlen(fmap) != 0) {
-    if(strlen(fmap) != 0) {
       featmap.LoadText(fmap);
     }
     return static_cast<Booster*>(handle)->GetModelDump(featmap, false, len);

src/learner/learner-inl.hpp

@@ -79,6 +79,7 @@ class BoostLearner {
     if (!strcmp(name, "silent")) silent = atoi(val);
     if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
     if (!strcmp("seed", name)) random::Seed(atoi(val));
+    if (!strcmp(name, "num_class")) this->SetParam("num_output_group", val);
     if (gbm_ == NULL) {
       if (!strcmp(name, "objective")) name_obj_ = val;
       if (!strcmp(name, "booster")) name_gbm_ = val;

src/learner/objective-inl.hpp

@@ -7,7 +7,9 @@
  */
 #include <vector>
 #include <cmath>
+#include "../data.h"
 #include "./objective.h"
+#include "./helper_utils.h"
 
 namespace xgboost {
 namespace learner {
@@ -133,6 +135,94 @@ class RegLossObj : public IObjFunction{
   float scale_pos_weight;
   LossType loss;
 };
+
+// softmax multi-class classification
+class SoftmaxMultiClassObj : public IObjFunction {
+ public:
+  explicit SoftmaxMultiClassObj(int output_prob)
+      : output_prob(output_prob) {
+    nclass = 0;
+  }
+  virtual ~SoftmaxMultiClassObj(void) {}
+  virtual void SetParam(const char *name, const char *val) {
+    if (!strcmp( "num_class", name )) nclass = atoi(val);
+  }
+  virtual void GetGradient(const std::vector<float>& preds,
+                           const MetaInfo &info,
+                           int iter,
+                           std::vector<bst_gpair> *out_gpair) {
+    utils::Check(nclass != 0, "must set num_class to use softmax");
+    utils::Check(preds.size() == static_cast<size_t>(nclass) * info.labels.size(),
+                 "SoftmaxMultiClassObj: label size and pred size does not match");
+    std::vector<bst_gpair> &gpair = *out_gpair;
+    gpair.resize(preds.size());
+    const unsigned ndata = static_cast<unsigned>(info.labels.size());
+    #pragma omp parallel
+    {
+      std::vector<float> rec(nclass);
+      #pragma omp for schedule(static)
+      for (unsigned j = 0; j < ndata; ++j) {
+        for (int k = 0; k < nclass; ++k) {
+          rec[k] = preds[j * nclass + k];
+        }
+        Softmax(&rec);
+        unsigned label = static_cast<unsigned>(info.labels[j]);
+        utils::Check(label < nclass, "SoftmaxMultiClassObj: label exceed num_class");
+        const float wt = info.GetWeight(j);
+        for (int k = 0; k < nclass; ++k) {
+          float p = rec[k];
+          const float h = 2.0f * p * (1.0f - p) * wt;
+          if (label == k) {
+            gpair[j * nclass + k] = bst_gpair((p - 1.0f) * wt, h);
+          } else {
+            gpair[j * nclass + k] = bst_gpair(p* wt, h);
+          }
+        }
+      }
+    }
+  }
+  virtual void PredTransform(std::vector<float> *io_preds) {
+    this->Transform(io_preds, output_prob);
+  }
+  virtual void EvalTransform(std::vector<float> *io_preds) {
+    this->Transform(io_preds, 0);
+  }
+  virtual const char* DefaultEvalMetric(void) {
+    return "merror";
+  }
+
+ private:
+  inline void Transform(std::vector<float> *io_preds, int prob) {
+    utils::Check(nclass != 0, "must set num_class to use softmax");
+    std::vector<float> &preds = *io_preds;
+    const unsigned ndata = static_cast<unsigned>(preds.size()/nclass);
+    #pragma omp parallel
+    {
+      std::vector<float> rec(nclass);
+      #pragma omp for schedule(static)
+      for (unsigned j = 0; j < ndata; ++j) {
+        for (int k = 0; k < nclass; ++k) {
+          rec[k] = preds[j * nclass + k];
+        }
+        if (prob == 0) {
+          preds[j] = FindMaxIndex(rec);
+        } else {
+          Softmax(&rec);
+          for (int k = 0; k < nclass; ++k) {
+            preds[j * nclass + k] = rec[k];
+          }
+        }
+      }
+    }
+    if (prob == 0) {
+      preds.resize(ndata);
+    }
+  }
+  // data field
+  int nclass;
+  int output_prob;
+};
+
 }  // namespace learner
 }  // namespace xgboost
 #endif  // XGBOOST_LEARNER_OBJECTIVE_INL_HPP_

src/learner/objective.h

@@ -71,6 +71,8 @@ inline IObjFunction* CreateObjFunction(const char *name) {
   if (!strcmp("reg:logistic", name)) return new RegLossObj(LossType::kLogisticNeglik);
   if (!strcmp("binary:logistic", name)) return new RegLossObj(LossType::kLogisticClassify);
   if (!strcmp("binary:logitraw", name)) return new RegLossObj(LossType::kLogisticRaw);
+  if (!strcmp("multi:softmax", name)) return new SoftmaxMultiClassObj(0);
+  if (!strcmp("multi:softprob", name)) return new SoftmaxMultiClassObj(1);
   utils::Error("unknown objective function type: %s", name);
   return NULL;
 }