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dataSets.mk
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dataSets.mk
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# Makefile for obtaining and preparing data sets
allData := mnist covtype URLRep ER movielens
################ begin generic stuff #########
VPATH+=$(testCodeDir)
WGET ?= wget -nv -N --no-use-server-timestamps --no-check-certificate
.PHONY: getData prepData archive eraseData
# archive file name can be specified on the command line as ARF=<filename>
archive:
cd $(dataDir)
find . -name '*prep' -print0 | tar -cjhv --null -f $(ARF) -T -
getData: $(dataDir)
cd $(dataDir)
$(WGET) $(URL)
fName=`basename $(URL)` ; tar xjvmf $$fName
# If a URL is specified, then simply download all the data
# pre-prepped. Checked-in check-sums should be used to guard against
# something missing or corrupted in the archive.
allDataTargets := $(addsuffix .prep,$(allData))
ifdef URL
prepData: getData
else
prepData: $(allDataTargets)
endif
@echo "finished preparing all data"
%.prep: $(dataDir)/%.dir/prep ;
# allDataDirs := $(addprefix $(dataDir)/,$(addsuffix .dir,$(allData)))
eraseData:
-rm -r $(dataDir)
@echo "finished erasing all data"
################ end generic stuff #########
#OCR
OCR.prep: $(dataDir)/OCR.dir/train.prep $(dataDir)/OCR.dir/test.prep ;
$(dataDir)/OCR.dir/train.prep: $(dataDir)/OCR.dir/test.prep ;
$(dataDir)/OCR.dir/test.prep: $(dataDir)/OCR.dir/letter.data.gz $(dataDir)/OCR.dir/letter.names
dir=$(dir $@) ;\
cd $$dir ;\
$(testCodeDir)/ocr2vw.py letter.data.gz letter.names train.prep test.prep
$(dataDir)/OCR.dir/letter.data.gz:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) http://ai.stanford.edu/~btaskar/ocr/letter.data.gz
$(dataDir)/OCR.dir/letter.names:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) http://ai.stanford.edu/~btaskar/ocr/letter.names
#movielens
movielens.prep: $(dataDir)/movielens.dir/train.prep ;
$(dataDir)/movielens.dir/train.prep: $(dataDir)/movielens.dir/test.prep ;
cd $(dataDir)/movielens.dir/ ;\
perl -ne 'BEGIN { srand 8675309; }; \
1; print join "\t", rand (), $$_;' \
pre.train.vw | sort -k1 | \
cut -f2- > train.prep
$(dataDir)/movielens.dir/test.prep: $(dataDir)/movielens.dir/ml-1m.zip
cd $(dataDir)/movielens.dir/ ;\
unzip -ou ml-1m.zip ;\
$(testCodeDir)/movielensRatings2vw.pl pre.train.vw test.prep ml-1m/ratings.dat
$(dataDir)/movielens.dir/ml-1m.zip:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) http://files.grouplens.org/datasets/movielens/ml-1m.zip
#ER
ER.prep: $(dataDir)/ER.dir/train.prep $(dataDir)/ER.dir/test.prep ;
$(dataDir)/ER.dir/train.prep: $(dataDir)/ER.dir/ER_train.vw
cd $(dataDir)/ER.dir/ ;\
ln -sf ER_train.vw train.prep
$(dataDir)/ER.dir/test.prep: $(dataDir)/ER.dir/ER_test.vw
cd $(dataDir)/ER.dir/ ;\
ln -sf ER_test.vw test.prep
$(dataDir)/ER.dir/ER_train.vw: $(dataDir)/ER.dir/ER_test.vw ;
touch $@
$(dataDir)/ER.dir/ER_test.vw: $(dataDir)/ER.dir/er.zip
cd $(dataDir)/ER.dir/ ;\
unzip -ou er.zip ;\
touch ER_test.vw
$(dataDir)/ER.dir/er.zip:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) http://web.engr.illinois.edu/~kchang10/data/er.zip
# URLRep
$(dataDir)/URLRep.dir/prep: $(dataDir)/URLRep.dir/url_svmlight.tar.gz URLRep.munge.sh
export testCodeDir=$(testCodeDir) ;\
cd $(dataDir)/URLRep.dir/ ;\
$(testCodeDir)/URLRep.munge.sh url_svmlight.tar.gz > prep
$(dataDir)/URLRep.dir/url_svmlight.tar.gz:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) https://archive.ics.uci.edu/ml/machine-learning-databases/url/url_svmlight.tar.gz
# COVERTYPE
$(dataDir)/covtype.dir/prep: $(dataDir)/covtype.dir/covtype.data.gz covtype.munge.sh
export testCodeDir=$(testCodeDir) ;\
cd $(dataDir)/covtype.dir/ ;\
$(testCodeDir)/covtype.munge.sh covtype.data.gz > prep
$(dataDir)/covtype.dir/covtype.data.gz:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
$(WGET) https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz
## MNIST
# override implicit %.prep rule
mnist.prep: $(dataDir)/mnist.dir/train.prep $(dataDir)/mnist.dir/test.prep ;
$(dataDir)/mnist.dir/train.prep: mnist.extractfeatures mnist.extract-labels.pl shuffle.pl $(dataDir)/mnist.dir/train-labels-idx1-ubyte.gz $(dataDir)/mnist.dir/train-images-idx3-ubyte.gz mnist.munge.sh
export testCodeDir=$(testCodeDir) ;\
cd $(dataDir)/mnist.dir/ ;\
$(testCodeDir)/mnist.munge.sh train-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz \
| $(testCodeDir)/shuffle.pl > train.prep
$(dataDir)/mnist.dir/test.prep: mnist.munge.sh mnist.extractfeatures mnist.extract-labels.pl $(dataDir)/mnist.dir/t10k-labels-idx1-ubyte.gz $(dataDir)/mnist.dir/t10k-images-idx3-ubyte.gz
export testCodeDir=$(testCodeDir) ;\
cd $(dataDir)/mnist.dir/ ;\
$(testCodeDir)/mnist.munge.sh t10k-labels-idx1-ubyte.gz t10k-images-idx3-ubyte.gz > test.prep
mnist.extractfeatures: mnist.extractfeatures.cpp
cd $(testCodeDir)/ ;\
g++ -O3 -Wall $^ -o $@
$(dataDir)/mnist.dir/%.gz:
dir=$(dir $@) ;\
mkdir -p $$dir ;\
cd $$dir ;\
fileName=`basename $@` ;\
$(WGET) http://yann.lecun.com/exdb/mnist/$$fileName