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classify.sh
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classify.sh
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#!/bin/bash
#Set Script Name variable
SCRIPT="classify.sh"
print_help() {
printf "\nHelp documentation for ${BOLD}$SCRIPT ${NC}\n\n"
printf "The following command line options are recognized:\n"
printf " ${BOLD}-i ${NC}\t-- the input file coded in **UTF-8 without BOM**, containing the corpus for the classification;[here](https://github.com/collab-uniba/Emotion_and_Polarity_SO/wiki/File-format-for-classification-corpus).\n"
printf " ${BOLD}-d ${NC}\t-- the delimiter semicolon or comma used in the csv file.\n"
printf " ${BOLD}-m ${NC}\t-- path to the liblinear model will be used for classification; if you omit this input, the script will use the model trained on Stack Overflow on the emotion you specify\n"
printf " ${BOLD}-o ${NC}\t-- path to the n-grams folder containing UnigramsList.txt and BigramsList.txt used to train the model given in input\n"
printf " ${BOLD}-f ${NC}\t-- if you give the model as input you must specify n-grams path containing UnigramsList.txt and BigramsList.txt used to train the model given in input\n"
printf " ${BOLD}-e ${NC}\t-- the specific emotion for training the model, defined in joy, anger,sadness, love, surprise, fear.\n"
printf " ${BOLD}-p ${NC}\t-- wheter to calculate the feature of politeness, mood and modality.\n"
printf " ${BOLD}-l ${NC}\t-- indicates if the csv given in input has the column named label or not\n"
printf " ${BOLD}-X ${NC}\t -- the maximum amount of heap memory to use (e.g.: `-X 30000m`).\n"
printf " ${BOLD}-h ${NC}\t-- Displays this help message. No further functions are performed.\n\n"
printf "Example: ${BOLD} bash classify.sh -i love.csv -e love -g training_love/liblinear/NoDownSampling/modelLiblinear_0.Rda -l -f training_love/idfs -o training_love/n-grams -d semicolon ${NC}\n\n"
exit 1
}
# redefine an echo function depending on verbose switch
print() {
local level=$1
local code=$GREEN # default is "NOTICE"
if [ "${level}" = 'ERROR' ]; then
code=$RED
elif [ "${level}" = 'INFO' ]; then
code=$CYAN
fi
if [ "${VERBOSE}" == 'on' ]; then
printf "${code}[${level}]${NC}: " | tee -a $LOG
echo $2 | tee -a $LOG
else
printf "${code}[${level}]${NC}: "
echo $2
fi
}
HASLABEL=""
CALCPOLITEIMPOLITEMOODMODALITY=""
MODEL=""
# parse args
while getopts "i:d:e:lf:o:m:hp" FLAG; do
case $FLAG in
i ) INPUT=$OPTARG;;
f ) IDFPATH=$OPTARG;;
o) DICTIONARYPATH=$OPTARG;;
m ) MODEL=$OPTARG;;
l ) HASLABEL="-L";;
e ) EMOTION=$OPTARG
if [ "$EMOTION" != 'anger' ] && [ "$EMOTION" != 'fear' ] && [ "$EMOTION" != 'sadness' ] && [ "$EMOTION" != 'love' ] && [ "$EMOTION" != 'joy' ] && [ "$EMOTION" != 'surprise' ]; then
print "ERROR" "-e option has wrong argument."
exit 2;
fi;;
d ) DELIMITER=$OPTARG
if [ "$DELIMITER" != 'c' ] && [ "$DELIMITER" != 'sc' ] ; then
print "ERROR" "-d option has wrong argument."
exit 2;
fi;;
X ) MAX_HEAP_SIZE=$OPTARG;;
h ) print_help;;
p ) CALCPOLITEIMPOLITEMOODMODALITY="-polmod";;
\? ) #unrecognized option - show help
printf "INFO" "Use $SCRIPT -h to see the help documentation."
exit 2;;
: ) print "ERROR" "Missing option argument for -$OPTARG"
exit 2;;
* ) printf "ERROR" "Unimplemented option: -$OPTARG"
exit 2;;
esac
done
shift $((OPTIND-1)) #This tells getopts to move on to the next argument.
### end getopts code ###
filename=${INPUT##*/}
filename=${filename%.*}
# Create ids file
DELIMITER_CHAR=','
if [ "$DELIMITER" = 'sc' ] ; then
DELIMITER_CHAR=';'
fi;
awk -F "\"*"$DELIMITER_CHAR"\"*" '{print $1}' $INPUT | sed 's/^\"*//' > "classification_$filename""_$EMOTION"/features-ids.csv
if [ "$MODEL" != '' ] ; then
#use the model given as input
cp $MODEL r/Liblinear/
else
if [ "$EMOTION" = 'anger' ] ; then
MODEL=r/Liblinear/SOModels/modelAnger.Rda
elif [ "$EMOTION" = 'joy' ] ; then
MODEL=r/Liblinear/SOModels/modelJoy.Rda
elif [ "$EMOTION" = 'fear' ] ; then
MODEL=r/Liblinear/SOModels/modelFear.Rda
elif [ "$EMOTION" = 'love' ] ; then
MODEL=r/Liblinear/SOModels/modelLove.Rda
elif [ "$EMOTION" = 'sadness' ] ; then
MODEL=r/Liblinear/SOModels/modelSadness.Rda
elif [ "$EMOTION" = 'surprise' ] ; then
MODEL=r/Liblinear/SOModels/modelSurprise.Rda
fi;
cp $MODEL r/Liblinear/
#use the default grams and IDF
DICTIONARYPATH=java/res/n-gramsSO
IDFPATH=java/res/idfsSO
fi;
#: <<'CREATEDOCFORMAT'
# rm -rf "classification_$filename""_$EMOTION"
#Creating the format to give at python files.
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
if [ "$DELIMITER" = 'sc' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex createDocFormat -e $EMOTION
elif [ "$DELIMITER"='c' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex createDocFormat -e $EMOTION
fi;
# CREATEDOCFORMAT
# : <<'MODELPY'
#taking only the file.csv name, deleting path and the extension
#taking the files created for the two python files
cp "classification_$filename""_$EMOTION"/ElaboratedFiles/docs.py python/CalculatePoliteAndImpolite/
cp "classification_$filename""_$EMOTION"/ElaboratedFiles/docs.py python/CalculateMoodModality/
#starting python files for polite , impolite mood and modality extraction
cd python/CalculatePoliteAndImpolite
#alias pj="cd CalculatePoliteAndImpolite/"
python model.py
cd ../..
cp python/CalculatePoliteAndImpolite/textsPoliteAndImpolite.csv "classification_$filename""_$EMOTION"/ElaboratedFiles/
rm python/CalculatePoliteAndImpolite/textsPoliteAndImpolite.csv
# MODELPY
# : <<'MOODMODALITY'
cd python/CalculateMoodModality
python moodAndModality.py
cd ../..
cp python/CalculateMoodModality/textsMoodAndModality.csv "classification_$filename""_$EMOTION"/ElaboratedFiles/
rm python/CalculateMoodModality/textsMoodAndModality.csv
# MOODMODALITY
fi;
# : <<'UNIGRAMSBIGRAMS'
#copy the
mkdir -p "classification_$filename""_$EMOTION"/idfs
mkdir -p "classification_$filename""_$EMOTION"/n-grams
cp $IDFPATH/* "classification_$filename""_$EMOTION"/idfs/
cp $DICTIONARYPATH/* "classification_$filename""_$EMOTION"/n-grams/
#starting Emotion_and_Polarity_SO.jar to extract the features
if [ "$DELIMITER" = 'sc' ] ; then
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -P "classification_$filename""_$EMOTION"/ElaboratedFiles/textsPoliteAndImpolite.csv -M "classification_$filename""_$EMOTION"/ElaboratedFiles/textsMoodAndModality.csv -d ';' $EXTRACTDICTIONARY -t classification -Ex SenPolImpolMoodModality -e $EMOTION
fi;
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex unigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex bigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex unigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex bigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t classification -Ex wordnet $HASLABEL -e $EMOTION
elif [ "$DELIMITER"='c' ] ; then
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -P "classification_$filename""_$EMOTION"/ElaboratedFiles/textsPoliteAndImpolite.csv -M "classification_$filename""_$EMOTION"/ElaboratedFiles/textsMoodAndModality.csv -d ',' $EXTRACTDICTIONARY -t classification -Ex SenPolImpolMoodModality -e $EMOTION
fi;
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex unigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex bigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex unigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex bigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t classification -Ex wordnet $HASLABEL -e $EMOTION
fi;
# UNIGRAMSBIGRAMS
#merging the single features extracted
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
paste -d , "classification_$filename""_$EMOTION"/features-SenPolImpolMoodModality.csv "classification_$filename""_$EMOTION"/features-unigrams_1.csv "classification_$filename""_$EMOTION"/features-unigrams_2.csv "classification_$filename""_$EMOTION"/features-bigrams_1.csv "classification_$filename""_$EMOTION"/features-bigrams_2.csv "classification_$filename""_$EMOTION"/features-wordnet.csv > "classification_$filename""_$EMOTION"/features-$EMOTION.csv
else
paste -d , "classification_$filename""_$EMOTION"/features-ids.csv "classification_$filename""_$EMOTION"/features-unigrams_1.csv "classification_$filename""_$EMOTION"/features-unigrams_2.csv "classification_$filename""_$EMOTION"/features-bigrams_1.csv "classification_$filename""_$EMOTION"/features-bigrams_2.csv "classification_$filename""_$EMOTION"/features-wordnet.csv > "classification_$filename""_$EMOTION"/features-$EMOTION.csv
fi;
#rm "classification_$filename""_$EMOTION"/features-SenPolImpolMoodModality.csv
#rm "classification_$filename""_$EMOTION"/features-unigrams_1.csv
#rm "classification_$filename""_$EMOTION"/features-bigrams_1.csv
#rm "classification_$filename""_$EMOTION"/features-unigrams_2.csv
#rm "classification_$filename""_$EMOTION"/features-bigrams_2.csv
#rm "classification_$filename""_$EMOTION"/features-wordnet.csv
# : <<'END'
#run the R script for classification
#create a folder for the liblinear's generated outputs into the output folder
mv "classification_$filename""_$EMOTION"/features-$EMOTION.csv r/Liblinear/
modelName=${MODEL##*/}
cd r/Liblinear
rm -rf output/Results_$EMOTION
if [ "$HASLABEL" = '-L' ] ; then
Rscript classification.R Results_$EMOTION $modelName features-$EMOTION.csv 1 $EMOTION
else
Rscript classification.R Results_$EMOTION $modelName features-$EMOTION.csv 0 $EMOTION
fi;
cd ../..
mv r/Liblinear/output/Results_$EMOTION/* "classification_$filename""_$EMOTION"/
rm -r r/Liblinear/$modelName
rm -r r/Liblinear/output/Results_$EMOTION
rm -r "classification_$filename""_$EMOTION"/n-grams
rm -r "classification_$filename""_$EMOTION"/idfs
rm -r "classification_$filename""_$EMOTION"/ElaboratedFiles
rm "classification_$filename""_$EMOTION"/features-bigrams_1.csv
rm "classification_$filename""_$EMOTION"/features-bigrams_2.csv
rm "classification_$filename""_$EMOTION"/features-SenPolImpolMoodModality.csv
rm "classification_$filename""_$EMOTION"/features-unigrams_1.csv
rm "classification_$filename""_$EMOTION"/features-unigrams_2.csv
rm "classification_$filename""_$EMOTION"/features-wordnet.csv
# rm "classification_$filename""_$EMOTION"/features-"$EMOTION".csv
# END