79 lines
3.9 KiB
Makefile
79 lines
3.9 KiB
Makefile
SHELL=/bin/bash
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# no not delete intermediate files
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.SECONDARY:
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# the directory where the challenge will be created
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output_directory=...
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# let's define which files are necessary, other files will be created if needed;
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# we'll compress the input files with xz and leave `expected.tsv` files uncompressed
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# (but you could decide otherwise)
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all: $(output_directory)/train/in.tsv.xz $(output_directory)/train/expected.tsv \
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$(output_directory)/dev-0/in.tsv.xz $(output_directory)/dev-0/expected.tsv \
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$(output_directory)/test-A/in.tsv.xz $(output_directory)/test-A/expected.tsv \
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$(output_directory)/README.md \
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$(output_directory)/in-header.tsv \
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$(output_directory)/out-header.tsv
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# always validate the challenge
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geval --validate --expected-directory $(output_directory)
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# we need to replace the default README.md, we assume that it
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# is kept as challenge-readme.md in the repo with this Makefile;
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# note that the title from README.md will be taken as the title of the challenge
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# and the first paragraph — as a short description
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$(output_directory)/README.md: challenge-readme.md $(output_directory)/config.txt
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cp $< $@
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# prepare header files (see above section on headers)
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$(output_directory)/in-header.tsv: in-header.tsv $(output_directory)/config.txt
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cp $< $@
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$(output_directory)/out-header.tsv: out-header.tsv $(output_directory)/config.txt
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cp $< $@
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$(output_directory)/config.txt:
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mkdir -p $(output_directory)
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geval --init --expected-directory $(output_directory) --metric MAIN_METRIC --metric AUXILIARY_METRIC --precision N --gonito-host https://some.gonito.host.net
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# `geval --init` will generate a toy challenge for a given metric(s)
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# ... but we remove the `in/expected.tsv` files just in case
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# (we will overwrite this with our data anyway)
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rm -f $(output_directory)/{train,dev-0,test-A}/{in,expected}.tsv
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rm $(output_directory)/{README.md,in-header.tsv,out-header.tsv}
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# a "total" TSV containing all the data, we'll split it later
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all-data.tsv.xz: prepare.py some-other-files
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# the data are generated using your script, let's say prepare.py and
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# some other files (of course, it depends on your task);
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# the file will be compressed with xz
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./prepare.py some-other-files | xz > $@
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# and now the challenge files, note that they will depend on config.txt so that
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# the challenge skeleton is generated first
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# The best way to split data into train, dev-0 and test-A set is to do it in a random,
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# but _stable_ manner, the set into which an item is assigned should depend on the MD5 sum
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# of some field in the input data (a field unlikely to change). Let's assume
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# that you created a script `filter.py` that takes as an argument a regular expression that will be applied
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# to the MD5 sum (written in the hexadecimal format).
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$(output_directory)/train/in.tsv.xz $(output_directory)/train/expected.tsv: all-data.tsv.xz filter.py $(output_directory)/config.txt
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# 1. xzcat for decompression
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# 2. ./filter.py will select 14/16=7/8 of items in a stable random manner
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# 3. tee >(...) is Bash magic to fork the ouptut into two streams
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# 4. cut will select the columns
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# 5. xz will compress it back
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xzcat $< | ./filter.py '[0-9abcd]$' | tee >(cut -f 1 > $(output_directory)/train/expected.tsv) | cut -f 2- | xz > $(output_directory)/train/in.tsv.xz
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$(output_directory)/dev-0/in.tsv.xz $(output_directory)/dev-0/expected.tsv: all-data.tsv.xz filter.py $(output_directory)/config.txt
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# 1/16 of items goes to dev-0 set
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xzcat $< | ./filter.py 'e$' | tee >(cut -f 1 > $(output_directory)/dev-0/expected.tsv) | cut -f 2- | xz > $(output_directory)/dev-0/in.tsv.xz
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$(output_directory)/test-A/in.tsv.xz $(output_directory)/test-A/expected.tsv: all-data.tsv.xz filter.py $(output_directory)/config.txt
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# (other) 1/16 of items goes to test-A set
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xzcat $< | ./filter.py 'f$' | tee >(cut -f 1 > $(output_directory)/test-A/expected.tsv) | cut -f 2- | xz > $(output_directory)/test-A/in.tsv.xz
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# wiping out the challenge, if you are desperate
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clean:
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rm -rf $(output_directory)
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