Merge pull request #106 from tqchen/master

pull master into unity

R-package/R/predict.xgb.Booster.R

@@ -25,9 +25,13 @@ setClass("xgb.Booster")
 #' @export
 #' 
 setMethod("predict", signature = "xgb.Booster", 
-          definition = function(object, newdata, outputmargin = FALSE, ntreelimit = NULL) {
+          definition = function(object, newdata, missing = NULL, outputmargin = FALSE, ntreelimit = NULL) {
   if (class(newdata) != "xgb.DMatrix") {
-    newdata <- xgb.DMatrix(newdata)
+    if (is.null(missing)) {
+      newdata <- xgb.DMatrix(newdata)
+    } else {
+      newdata <- xgb.DMatrix(newdata, missing = missing)
+    }
   }
   if (is.null(ntreelimit)) {
     ntreelimit <- 0

R-package/R/utils.R

@@ -68,13 +68,17 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
 ## ----the following are low level iteratively function, not needed if
 ## you do not want to use them ---------------------------------------
 # get dmatrix from data, label
-xgb.get.DMatrix <- function(data, label = NULL) {
+xgb.get.DMatrix <- function(data, label = NULL, missing = NULL) {
   inClass <- class(data)
   if (inClass == "dgCMatrix" || inClass == "matrix") {
     if (is.null(label)) {
       stop("xgboost: need label when data is a matrix")
     }
-    dtrain <- xgb.DMatrix(data, label = label)
+    if (is.null(missing)){
+      dtrain <- xgb.DMatrix(data, label = label)
+    } else {
+      dtrain <- xgb.DMatrix(data, label = label, missing = missing)
+    }
   } else {
     if (!is.null(label)) {
       warning("xgboost: label will be ignored.")

R-package/R/xgb.cv.R

@@ -53,7 +53,7 @@
 #'                   "max.depth"=3, "eta"=1, "objective"="binary:logistic")
 #' @export
 #'
-xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
+xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
                    showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, ...) {
   if (typeof(params) != "list") {
     stop("xgb.cv: first argument params must be list")
@@ -61,7 +61,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
   if (nfold <= 1) {
     stop("nfold must be bigger than 1")
   }
-  dtrain <- xgb.get.DMatrix(data, label)
+  if (is.null(missing)) {
+    dtrain <- xgb.get.DMatrix(data, label)
+  } else {
+    dtrain <- xgb.get.DMatrix(data, label, missing)
+  }
   params <- append(params, list(...))
   params <- append(params, list(silent=1))
   for (mc in metrics) {

R-package/R/xgboost.R

@@ -43,9 +43,14 @@
 #' 
 #' @export
 #' 
-xgboost <- function(data = NULL, label = NULL, params = list(), nrounds, 
+xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds, 
                     verbose = 1, ...) {
-  dtrain <- xgb.get.DMatrix(data, label)  
+  if (is.null(missing)) {
+    dtrain <- xgb.get.DMatrix(data, label)
+  } else {
+    dtrain <- xgb.get.DMatrix(data, label, missing)
+  }
+    
   params <- append(params, list(...))
   
   if (verbose > 0) {

R-package/demo/custom_objective.R

@@ -37,3 +37,26 @@ print ('start training with user customized objective')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
 bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
+
+#
+# there can be cases where you want additional information 
+# being considered besides the property of DMatrix you can get by getinfo
+# you can set additional information as attributes if DMatrix
+
+# set label attribute of dtrain to be label, we use label as an example, it can be anything 
+attr(dtrain, 'label') <- getinfo(dtrain, 'label')
+# this is new customized objective, where you can access things you set
+# same thing applies to customized evaluation function
+logregobjattr <- function(preds, dtrain) {
+  # now you can access the attribute in customized function
+  labels <- attr(dtrain, 'label')
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+
+print ('start training with user customized objective, with additional attributes in DMatrix')
+# training with customized objective, we can also do step by step training
+# simply look at xgboost.py's implementation of train
+bst <- xgb.train(param, dtrain, num_round, watchlist, logregobjattr, evalerror)

README.md

@@ -12,8 +12,15 @@ Examples Code: [Learning to use xgboost by examples](demo)
 
 Notes on the Code: [Code Guide](src)
 
+Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
+* This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
+* It present boosted tree learning as formal functional space optimization of defined objective.
+* The model presented is used by xgboost for boosted trees
+
 What's New
 =====
+
+* Thanks to Bing Xu, [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl) allows you to use xgboost from Julia
 * See the updated [demo folder](demo) for feature walkthrough
 * Thanks to Tong He, the new [R package](R-package) is available
 
@@ -26,7 +33,6 @@ Features
 * Speed: XGBoost is very fast
   - IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
 * Layout of gradient boosting algorithm to support user defined objective
-* Python interface, works with numpy and scipy.sparse matrix
 
 Build
 =====

demo/README.md

@@ -8,12 +8,30 @@ This folder contains the all example codes using xgboost.
 Features Walkthrough
 ====
 This is a list of short codes introducing different functionalities of xgboost and its wrapper.
-* Basic walkthrough of wrappers [python](guide-python/basic_walkthrough.py)
-* Cutomize loss function, and evaluation metric [python](guide-python/custom_objective.py)
-* Boosting from existing prediction [python](guide-python/boost_from_prediction.py)
-* Predicting using first n trees [python](guide-python/predict_first_ntree.py)
-* Generalized Linear Model [python](guide-python/generalized_linear_model.py)
-* Cross validation [python](guide-python/cross_validation.py)
+* Basic walkthrough of wrappers 
+  [python](guide-python/basic_walkthrough.py)
+  [R](../R-package/demo/basic_walkthrough.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
+* Cutomize loss function, and evaluation metric
+  [python](guide-python/custom_objective.py)
+  [R](../R-package/demo/custom_objective.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
+* Boosting from existing prediction
+  [python](guide-python/boost_from_prediction.py)
+  [R](../R-package/demo/boost_from_prediction.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
+* Predicting using first n trees 
+  [python](guide-python/predict_first_ntree.py)
+  [R](../R-package/demo/boost_from_prediction.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)  
+* Generalized Linear Model
+  [python](guide-python/generalized_linear_model.py)
+  [R](../R-package/demo/generalized_linear_model.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)  
+* Cross validation
+  [python](guide-python/cross_validation.py)
+  [R](../R-package/demo/cross_validation.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)  
 
 Basic Examples by Tasks
 ====

wrapper/README.md

@@ -7,6 +7,10 @@ Python
 * To make the python module, type ```make``` in the root directory of project
 * Refer also to the walk through example in [demo folder](../demo/guide-python)
 
-R 
+R
 =====
 * See [R-package](../R-package)
+
+Julia
+=====
+* See [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl)

wrapper/xgboost.py

@@ -436,7 +436,11 @@ def train(params, dtrain, num_boost_round = 10, evals = [], obj=None, feval=None
     for i in range(num_boost_round):
         bst.update( dtrain, i, obj )
         if len(evals) != 0:
-            sys.stderr.write(bst.eval_set(evals, i, feval).decode()+'\n')
+            bst_eval_set=bst.eval_set(evals, i, feval)
+            if isinstance(bst_eval_set,str):
+                sys.stderr.write(bst_eval_set+'\n')
+            else:
+                sys.stderr.write(bst_eval_set.decode()+'\n')
     return bst
 
 class CVPack:
@@ -467,7 +471,7 @@ def mknfold(dall, nfold, param, seed, evals=[], fpreproc = None):
             dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
         else:
             tparam = param
-        plst = tparam.items() + [('eval_metric', itm) for itm in evals]
+        plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals]
         ret.append(CVPack(dtrain, dtest, plst))
     return ret
 
@@ -481,12 +485,16 @@ def aggcv(rlist, show_stdv=True):
         arr = line.split()
         assert ret == arr[0]
         for it in arr[1:]:
+            if not isinstance(it,str):
+                it=it.decode()
             k, v  = it.split(':')
             if k not in cvmap:
                 cvmap[k] = []
             cvmap[k].append(float(v))
     for k, v in sorted(cvmap.items(), key = lambda x:x[0]):
         v = np.array(v)
+        if not isinstance(ret,str):
+            ret = ret.decode()
         if show_stdv:
             ret += '\tcv-%s:%f+%f' % (k, np.mean(v), np.std(v))
         else:

wrapper/xgboost_wrapper.cpp

@@ -132,6 +132,7 @@ extern "C"{
                                bst_ulong nrow,
                                bst_ulong ncol,
                                float  missing) {
+    bool nan_missing = std::isnan(missing);
     DMatrixSimple *p_mat = new DMatrixSimple();
     DMatrixSimple &mat = *p_mat;
     mat.info.info.num_row = nrow;
@@ -139,9 +140,13 @@ extern "C"{
     for (bst_ulong i = 0; i < nrow; ++i, data += ncol) {
       bst_ulong nelem = 0;
       for (bst_ulong j = 0; j < ncol; ++j) {
-        if (data[j] != missing) {
-          mat.row_data_.push_back(RowBatch::Entry(j, data[j]));
-          ++nelem;
+        if (std::isnan(data[j])) {
+          utils::Check(nan_missing, "There are NAN in the matrix, however, you did not set missing=NAN");          
+        } else {
+          if (nan_missing || data[j] != missing) {
+            mat.row_data_.push_back(RowBatch::Entry(j, data[j]));
+            ++nelem;
+          }
         }
       }
       mat.row_ptr_.push_back(mat.row_ptr_.back() + nelem);

wrapper/xgboost_wrapper.h

@@ -6,7 +6,6 @@
  * \brief a C style wrapper of xgboost
  *  can be used to create wrapper of other languages
  */
-#include <cstdio>
 #ifdef _MSC_VER
 #define XGB_DLL __declspec(dllexport)
 #else
@@ -15,8 +14,9 @@
 // manually define unsign long
 typedef unsigned long bst_ulong;
 
-
+#ifdef __cplusplus
 extern "C" {
+#endif
   /*!
    * \brief load a data matrix 
    * \return a loaded data matrix
@@ -205,5 +205,7 @@ extern "C" {
    */
   XGB_DLL const char **XGBoosterDumpModel(void *handle, const char *fmap,
                                           bst_ulong *out_len);
+#ifdef __cplusplus
 }
+#endif
 #endif  // XGBOOST_WRAPPER_H_