rename

multi-node/hadoop/run_binary_classification_on_hadoop.sh

@@ -1,43 +0,0 @@
-#!/bin/bash
-
-if [ "$#" -lt 2 ];
-then
-    echo "Usage: <nworkers> <path_in_HDFS>"
-    exit -1
-fi
-
-curDir=`pwd`
-dataDir=../../demo/binary_classification
-trainFile=$dataDir/agaricus.txt.train
-input=$2
-output=$2/model
-
-# generate the training file if it doesnot exist
-if [ ! -f "$trainFile" ];
-then 
-  echo "Generating training file:"
-  cd $dataDir
-  # map feature using indicator encoding, also produce featmap.txt
-  python mapfeat.py
-  # split train and test
-  python mknfold.py agaricus.txt 1
-  cd $curDir
-fi
-
-hadoop fs -mkdir $input
-hadoop fs -put $trainFile $input
-#hadoop fs -rm -skipTrash -r $output
-
-# training and output the models
-python ../../rabit/tracker/rabit_hadoop.py -n 3 -i $input/agaricus.txt.train -o $output -f $dataDir/mushroom.hadoop.conf \
-    --jobname xgboost_hadoop ../../xgboost mushroom.hadoop.conf dsplit=row num_round=3 data=stdin model_out=stdout
-
-# get the final model file
-hadoop fs -get $output/part-00000 ./final.model
-# output prediction task=pred 
-../../xgboost $dataDir/mushroom.hadoop.conf task=pred model_in=./final.model
-# print the boosters of 00002.model in dump.raw.txt
-../../xgboost $dataDir/mushroom.hadoop.conf task=dump model_in=final.model name_dump=dump.raw.txt
-# use the feature map in printing for better visualization
-../../xgboost $dataDir/mushroom.hadoop.conf task=dump model_in=final.model fmap=$dataDir/featmap.txt name_dump=dump.nice.txt
-cat dump.nice.txt