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doAll_Baseline_4gram
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#!/bin/bash
PURPOSE=baseline
JUDGECLASS="oldreut"
#CORPLIST=("robust04_0" "robust04_1" "robust04_2" "robust04_3" "robust04_4" "robust04_5")
#CORPLIST=("FBIS" "FT" "FR" "LA")
CORPLIST=("oldreut")
SOFIA="/home/h435zhan/Develop/sofia-ml-read-only/src/sofia-ml"
for CORP in "${CORPLIST[@]}"
do
# if ! [ -e Corpus/"$CORP".tgz ] ; then
# tar -cvzf Corpus/"$CORP".tgz Corpus/"$CORP"/
# fi
pushd Corpus
# if [ ! -e "$CORP".svm.fil ] || [ ! -e "$CORP".df ]; then
./dofast4 "$CORP"
# fi
cp "$CORP".df ../"$CORP".df
cp "$CORP".svm.fil ../"$CORP".svm.fil
popd
while IFS='' read -r line || [[ -n $line ]]; do
IFS=':' read -ra TEXT <<< "$line"
TOPIC="${TEXT[0]}"
QUERY="${TEXT[1]}"
echo "$TOPIC"
echo "$QUERY"
rm -rf result/"$PURPOSE"/"$CORP"/"$TOPIC"/
mkdir -p result/"$PURPOSE"/"$CORP"/
mkdir -p result/dump/"$PURPOSE"/"$CORP"/
rm -rf $TOPIC
mkdir $TOPIC
echo `wc -l < "$CORP".svm.fil` > N
pushd $TOPIC
echo "$QUERY" > "$TOPIC".seed.doc
cut -d' ' -f1 ../$CORP.svm.fil | sed -e 's/.*/& &/' > docfil
cut -d' ' -f1 docfil | cat -n > docfils
touch rel.$TOPIC.fil
#cut -f2 docfil | join - $TOPIC.seed.sorted | cut -d' ' -f2 >> rel.$TOPIC.fil
touch prel.$TOPIC
rm -rf prevalence.rate
touch prevalence.rate
rm -rf rel.rate
touch rel.rate
rm -f new[0-9][0-9].$TOPIC tail[0-9][0-9].$TOPIC self*.$TOPIC gold*.$TOPIC
touch new00.$TOPIC
NDOCS=`cat docfils | wc -l`
NDUN=0
L=1
R=100
export LAMBDA=0.0001
cp $TOPIC.seed.doc ../$TOPIC.seed.doc
popd
./dofeaturesseed4 $TOPIC.seed.doc $TOPIC $CORP
pushd $TOPIC
sed -e 's/[^ ]*/0/' ../$CORP.svm.fil | ../dosplit
sed -e 's/[^ ]*/1/' svm.$TOPIC.seed.doc.fil > $TOPIC.synthetic.seed
for x in 0 1 2 3 4 5 6 7 8 9 ; do
for y in 0 1 2 3 4 5 6 7 8 9 ; do
if [ $NDUN -lt $NDOCS ] ; then
export N=$x$y
cp $TOPIC.synthetic.seed trainset
#cut -f2 docfils | join -v1 - rel.$TOPIC.fil > $TOPIC.allNoRel.docfils
#cut -f1 $TOPIC.allNoRel.docfils | sort -R | head -$R | sort | join - ../svm.fil | sed -e's/[^ ]*/-1/' >> trainset
cut -f2 docfils | sort -R | head -$R | sort | join - ../$CORP.svm.fil | sed -e's/[^ ]*/-1/' >> trainset
cat new[0-9][0-9].$TOPIC > seed
#cut -f2 docfil | join - $TOPIC.clusteringJudged.doc.sorted | cut -d' ' -f2 >> seed
cat seed | sort | join - rel.$TOPIC.fil | sed -e 's/^/1 /' > x
#cat seed | sort | join -v1 - rel.$TOPIC.fil | join -v1 - $TOPIC.clusteringNotRel.doc.sorted | sort -R | head -50000 | sed -e 's/^/-1 /' >> x
cat seed | sort | join -v1 - rel.$TOPIC.fil | sort -R | head -50000 | sed -e 's/^/-1 /' >> x
sort -k2 x | join -12 - ../$CORP.svm.fil | cut -d' ' -f2- | sort -n >> trainset
#Calculate relevant documents prevalence rate in the traning set
RELTRAINDOC=`grep -E "^1\b" trainset | wc -l`
NOTRELTRAINDOC=`grep -E "^-1\b" trainset | wc -l`
PREVALENCERATE=`echo "scale=4; $RELTRAINDOC / ($RELTRAINDOC + $NOTRELTRAINDOC)" | bc`
echo $RELTRAINDOC $NOTRELTRAINDOC $PREVALENCERATE >> prevalence.rate
$SOFIA --learner_type logreg-pegasos --loop_type roc --lambda $LAMBDA --iterations 200000 --training_file trainset --dimensionality 9300000 --model_out svm_model
#/home/user/svmlight/svm_learn trainset
RES=$?
echo $RES
if [ "$RES" -eq "0" ] ; then
for z in svm.test.* ; do
$SOFIA --test_file $z --dimensionality 9300000 --model_in svm_model --results_file pout.$z
#/home/user/svmlight/svm_classify $z svm_model pout.$z
done
else
rm -f pout.svm.test.*
cut -f2 docfils | sort -R | cat -n | sort -k2 | sed -e 's/ */-/' > pout.svm.test.1
fi
cut -f1 pout.svm.test.* | ../fixnum | cat -n | join -o2.2,1.2 -t$'\t' - docfils > inlr.out
sort seed | join -v2 - inlr.out | sort -rn -k2 | cut -d' ' -f1 > new$N.$TOPIC
cat new[0-9][0-9].$TOPIC > x
if [ "$N" != "99" ] ; then
head -$L new$N.$TOPIC > y ; mv y new$N.$TOPIC
fi
#sed -e 's/.*\///' -e 's/.*/"&"/' new$N.$TOPIC | tr '\n' ',' | sed -e 's/^/[/' -e 's/,$/]/' | curl -XPOST -H 'Content-Type:application/json' "$TRSERVER/judge/$LOGIN/$TOPIC" -d @- | tr '}' '\n' | grep 'judgement.:1' | cut -d'"' -f4 | sort | join -o2.2 - docfil >> rel.$TOPIC.fil
# python ../doJudgementMain.py --topic=$TOPIC --judgefile=../judgement/qrels.$JUDGECLASS.list --input=new$N.$TOPIC --output=rel.$TOPIC.Judged.doc.list --memorydumpfile=judge.effort.$TOPIC."$PURPOSE".dump
# rm -rf rel.$TOPIC.Judged.doc.list
# while IFS='' read -r line || [[ -n $line ]]; do
# RELFLAG=`cat ../judgement/qrels.$JUDGECLASS.list | grep "$TOPIC 0 $line 1" | wc -l`
# echo looking up "$TOPIC 0 $line 1" in ../judgement/qrels.$JUDGECLASS.list
# if [ $RELFLAG -gt "0" ] ; then
# echo $line 1 >> rel.$TOPIC.Judged.doc.list
# echo $line 1 >> $TOPIC.record.list
# else
# echo $line 0 >> $TOPIC.record.list
# touch rel.$TOPIC.Judged.doc.list
# fi
# done < new$N.$TOPIC
python ../doJudgementMain.py --topic=$TOPIC --judgefile=../judgement/qrels.$JUDGECLASS.list --input=new$N.$TOPIC --output=rel.$TOPIC.Judged.doc.list --record=$TOPIC.record.list
cat rel.$TOPIC.Judged.doc.list >> rel.$TOPIC.fil
cat rel.$TOPIC.Judged.doc.list > rel.$TOPIC.$N.Judged.doc.list
RELFINDDOC=`wc -l < rel.$TOPIC.Judged.doc.list`
RELRATE=`echo "scale=4; $RELFINDDOC / $L" | bc`
CURRENTREL=`wc -l < rel.$TOPIC.fil`
echo $RELFINDDOC $L $RELRATE $CURRENTREL >> rel.rate
sort rel.$TOPIC.fil | sed -e 's/$/ 1/' > prel.$TOPIC
cut -d' ' -f1 prel.$TOPIC > rel.$TOPIC.fil
NDUN=$((NDUN+L))
L=$((L+(L+9)/10))
fi
done
done
# cp judge.effort.$TOPIC."$PURPOSE".dump ../result/dump/"$PURPOSE"/"$CORP"/judge.effort.$TOPIC."$PURPOSE".dump
rm -rf svm.test.*
popd
mv $TOPIC result/"$PURPOSE"/"$CORP"/$TOPIC
rm $TOPIC.seed.doc
done < "judgement/$CORP.topic.stemming.txt"
rm -rf "$CORP".svm.fil
rm "$CORP".df
rm N
#Generate LSI from tfdf
#python clustering/doLSI.py --input=tfdf_oldreut --output=LSIVector/"$CORP".lsi.dump --mapping=LSIVector/"$CORP".mapping.dump --latent=200 --choice=entropy --normalization=yes
done