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Makefile
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## Download Datasets
download_data:
git clone https://github.com/NYULibraries/aco-karms/ data/raw_records/aco/
wget -nc -P data/raw_records/umich http://www.lib.umich.edu/files/umich_bib.xml.gz
gunzip data/raw_records/umich/*
for val in {01..43}; do wget -nc -P data/raw_records/loc https://www.loc.gov/cds/downloads/MDSConnect/BooksAll.2016.part$$val.xml.gz; done
gunzip data/raw_records/loc/*
## extract parallel lines
extract_lines:
python3 src/loc_transcribe.py extract
## filter, clean, and split data into train dev test
data_set:
python3 src/loc_transcribe.py preprocess --split
## train full mle learning curve
train_mles:
python3 src/loc_transcribe.py train mle
# prep dataset and create dalma scripts
prep_seq2seq:
python3 src/loc_transcribe.py train seq2seq --prep
# (dalma scripts) trains full learning curve and predicts dev
train_seq2seq:
python3 src/loc_transcribe.py train seq2seq --train
predict_seq2seq_test:
python3 src/loc_transcribe.py predict seq2seq --predict_test -s 1.0
align_seq2seq:
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/dev/seq2seq_size1.0.out predictions_out/mle_morph/dev/mle_morph_size1.0.out
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/dev/seq2seq_size1.0.out predictions_out/mle_simple/dev/mle_simple_size1.0.out
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/dev/seq2seq_size1.0.out predictions_out/morph/dev/morph.out
align_seq2seq_test:
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/test/seq2seq_size1.0.out predictions_out/mle_morph/test/mle_morph_size1.0.out
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/test/seq2seq_size1.0.out predictions_out/mle_simple/test/mle_simple_size1.0.out
python3 src/loc_transcribe.py predict seq2seq -b predictions_out/seq2seq/test/seq2seq_size1.0.out predictions_out/morph/test/morph.out
predict_translit:
python3 src/loc_transcribe.py predict simple dev
python3 src/loc_transcribe.py predict morph dev
predict_translit_test:
python3 src/loc_transcribe.py predict translit_simple test
python3 src/loc_transcribe.py predict translit_morph test
## predict full mle curve
predict_mles:
python3 src/loc_transcribe.py predict mle dev -m models/mle/size1.0.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.5.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.25.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.125.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.0625.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.03125.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size0.015625.tsv -b predictions_out/simple/dev/simple.out
python3 src/loc_transcribe.py predict mle dev -m models/mle/size1.0.tsv -b predictions_out/morph/dev/morph.out
predict_mles_test:
python3 src/loc_transcribe.py predict mle test -m models/mle/size1.0.tsv -b predictions_out/simple/test/simple.out
python3 src/loc_transcribe.py predict mle test -m models/mle/size1.0.tsv -b predictions_out/morph/test/morph.out
## evaluate full mle curve
evaluate:
#eval seq2seq dev aligned
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/dev/seq2seq_size1.0Xmle_morph_size1.0.out data/processed/dev.tsv
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/dev/seq2seq_size1.0Xmle_simple_size1.0.out data/processed/dev.tsv
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/dev/seq2seq_size1.0Xmorph.out data/processed/dev.tsv
#eval mle_morph dev
python3 src/loc_transcribe.py evaluate predictions_out/mle_morph/dev/mle_morph_size1.0.out
#eval morph dev
python3 src/loc_transcribe.py evaluate predictions_out/morph/dev/morph.out
#eval mle_simple curve
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size1.0.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.5.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.25.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.125.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.0625.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.03125.out
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/dev/mle_simple_size0.015625.out
#eval simple dev
python3 src/loc_transcribe.py evaluate predictions_out/simple/dev/simple.out
#eval seq2seq dev curve
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size1.0.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.5.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.25.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.125.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.0625.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.03125.out
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/dev/seq2seq_size0.015625.out
#eval seq2seq test aligned
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/test/seq2seq_size1.0Xmle_morph_size1.0.out data/processed/test.tsv
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/test/seq2seq_size1.0Xmle_simple_size1.0.out data/processed/test.tsv
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/test/seq2seq_size1.0Xmorph.out data/processed/test.tsv
#eval mle_morph test
python3 src/loc_transcribe.py evaluate predictions_out/mle_morph/test/mle_morph_size1.0.out data/processed/test.tsv
#eval morph test
python3 src/loc_transcribe.py evaluate predictions_out/morph/test/morph.out data/processed/test.tsv
#eval simple test
python3 src/loc_transcribe.py evaluate predictions_out/simple/test/simple.out data/processed/test.tsv
#eval seq2seq test
python3 src/loc_transcribe.py evaluate predictions_out/seq2seq/test/seq2seq_size1.0.out data/processed/test.tsv
#eval mle_simple test
python3 src/loc_transcribe.py evaluate predictions_out/mle_simple/test/mle_simple_size1.0.out data/processed/test.tsv
## redo custom part of pipeline
# train_mles
redo: predict_translit predict_mles evaluate
## update requirements
update_requirements:
pipdeptree -f --warn silence | grep -v '[[:space:]]' > requirements.txt
## Delete all compiled Python files
clean:
find . -type f -name "*.py[co]" -delete
find . -type d -name "__pycache__" -delete
## Lint using flake8
lint:
flake8 src
## Upload Data to hpc
sync_data_to_hpc: clean
# --delete
# rsync -av --progress ./ --exclude="data/*/*" --exclude="reports/*" tunnel-dalma:/scratch/fae211/LOC_transcribe --delete
rsync -av --progress ./ --exclude="data/*" --exclude="reports/*" --exclude="MADAMIRA/*" --exclude="predictions_out/*" --exclude="evaluation/*" --exclude="models/*" tunnel-dalma:/scratch/fae211/LOC_transcribe --delete
## Download Data from hpc
sync_data_from_hpc:
# rsync -av --progress --exclude="all_records" tunnel-dalma:/scratch/fae211/LOC_transcribe/data/ ./data/
# rsync -av --progress tunnel-dalma:/scratch/fae211/LOC_transcribe/reports/ ./reports/
rsync -av --progress --exclude="all_records" fae211@dalma.abudhabi.nyu.edu:/scratch/fae211/LOC_transcribe/data/ ./data/
rsync -av --progress fae211@dalma.abudhabi.nyu.edu:/scratch/fae211/LOC_transcribe/reports/ ./reports/
## Set up python interpreter environment
create_env:
ifeq (True,$(HAS_CONDA))
@echo ">>> Detected conda, creating conda environment."
ifeq (3,$(findstring 3,$(PYTHON_INTERPRETER)))
conda create --name $(PROJECT_NAME) python=3.7
else
conda create --name $(PROJECT_NAME) python=2.7
endif
@echo ">>> New conda env created. Activate with:\nsource activate $(PROJECT_NAME)"
else
$(PYTHON_INTERPRETER) -m pip install -q virtualenv virtualenvwrapper
@echo ">>> Installing virtualenvwrapper if not already installed.\nMake sure the following lines are in shell startup file\n\
export WORKON_HOME=$$HOME/.virtualenvs\nexport PROJECT_HOME=$$HOME/Devel\nsource /usr/local/bin/virtualenvwrapper.sh\n"
@bash -c "source `which virtualenvwrapper.sh`;mkvirtualenv $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER)"
@echo ">>> New virtualenv created. Activate with:\nworkon $(PROJECT_NAME)"
endif
#################################################################################
# PROJECT RULES #
#################################################################################
#################################################################################
# Self Documenting Commands #
#################################################################################
.DEFAULT_GOAL := help
# Inspired by <http://marmelab.com/blog/2016/02/29/auto-documented-makefile.html>
# sed script explained:
# /^##/:
# * save line in hold space
# * purge line
# * Loop:
# * append newline + line to hold space
# * go to next line
# * if line starts with doc comment, strip comment character off and loop
# * remove target prerequisites
# * append hold space (+ newline) to line
# * replace newline plus comments by `---`
# * print line
# Separate expressions are necessary because labels cannot be delimited by
# semicolon; see <http://stackoverflow.com/a/11799865/1968>
.PHONY: help
help:
@echo "$$(tput bold)Available rules:$$(tput sgr0)"
@echo
@sed -n -e "/^## / { \
h; \
s/.*//; \
:doc" \
-e "H; \
n; \
s/^## //; \
t doc" \
-e "s/:.*//; \
G; \
s/\\n## /---/; \
s/\\n/ /g; \
p; \
}" ${MAKEFILE_LIST} \
| LC_ALL='C' sort --ignore-case \
| awk -F '---' \
-v ncol=$$(tput cols) \
-v indent=19 \
-v col_on="$$(tput setaf 6)" \
-v col_off="$$(tput sgr0)" \
'{ \
printf "%s%*s%s ", col_on, -indent, $$1, col_off; \
n = split($$2, words, " "); \
line_length = ncol - indent; \
for (i = 1; i <= n; i++) { \
line_length -= length(words[i]) + 1; \
if (line_length <= 0) { \
line_length = ncol - indent - length(words[i]) - 1; \
printf "\n%*s ", -indent, " "; \
} \
printf "%s ", words[i]; \
} \
printf "\n"; \
}' \
| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')