rank pass toy

demo/rank/README

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+Demonstrating how to use XGBoost accomplish regression tasks on computer hardware dataset https://archive.ics.uci.edu/ml/datasets/Computer+Hardware
+
+Run: ./runexp.sh
+
+Format of input: LIBSVM format
+
+Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
+  - Feature id must be from 0 to number of features, in sorted order.
+  - i means this feature is binary indicator feature
+  - q means this feature is a quantitative value, such as age, time, can be missing
+  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
+
+Explainations: https://github.com/tqchen/xgboost/wiki/Regression

demo/rank/runexp.sh

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+#!/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.eval

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+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.eval.group

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+2
+3

demo/rank/toy.test

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+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.test.group

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+2
+3

demo/rank/toy.train

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+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.train.group

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+2
+3