small change

demo/rank/runexp.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-# map the data to features. For convenience we only use 7 original attributes and encode them as features in a trivial way 
-python mapfeat.py
-# split train and test
-python mknfold.py machine.txt 1
-# training and output the models
-../../xgboost machine.conf
-# output predictions of test data
-../../xgboost machine.conf task=pred model_in=0002.model
-# print the boosters of 0002.model in dump.raw.txt
-../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
-# print the boosters of 0002.model in dump.nice.txt with feature map
-../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
-
-# cat the result
-cat dump.nice.txt

demo/rank/toy.conf

@@ -0,0 +1,35 @@
+# General Parameters, see comment for each definition
+# choose the tree booster, 0: tree, 1: linear
+booster_type = 0 
+# this is the only difference with classification, use 0: linear regression
+# when labels are in [0,1] we can also use 1: logistic regression
+loss_type = 0
+
+objective="rank:pairwise"
+#objective="rank:softmax"
+#objective="lambdarank:map"
+#objective="lambdarank:ndcg"
+
+# Tree Booster Parameters
+# step size shrinkage
+bst:eta = 1.0 
+# minimum loss reduction required to make a further partition
+bst:gamma = 1.0 
+# minimum sum of instance weight(hessian) needed in a child
+bst:min_child_weight = 1 
+# maximum depth of a tree
+bst:max_depth = 3 
+
+# Task parameters
+# the number of round to do boosting
+num_round = 2
+# 0 means do not save any model except the final round model
+save_period = 0 
+# The path of training data
+data = "toy.train" 
+# The path of validation data, used to monitor training process, here [test] sets name of the validation set
+eval[test] = "toy.eval" 
+# The path of test data 
+test:data = "toy.test"      
+
+