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Makefile
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Makefile
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.DEFAULT_GOAL := show-help
SHELL := /bin/bash
PATH := $(PWD)/thirdparty/miniconda/miniconda/bin:$(PATH)
################################################################################
# Utility
################################################################################
## Run the testsuite
test:
@source activate learna && \
pytest . -p no:warnings
## To clean project state
clean: clean-runtime clean-data clean-thirdparty clean-models clean-results clean-analysis
## Remove runtime files
clean-runtime:
find . -name '*.pyc' -exec rm --force {} +
find . -name '__pycache__' -exec rm -rf --force {} +
## Remove data files
clean-data:
rm -rf data/eterna/*.rna
rm -rf data/eterna/raw/*.txt
rm -rf data/eterna/interim/*.txt
rm -rf data/rfam_taneda
rm -rf data/rfam_learn*
## Remove model examples
clean-models:
rm -rf models/example
## Clean results directory
clean-results:
rm -rf results/
clean-plots:
rm -rf results/plots
## Clean analysis directory
clean-analysis:
rm -rf analysis/reproduce_iclr_2019/
## Remove thirdparty installs
clean-thirdparty:
rm -rf thirdparty/miniconda/miniconda
################################################################################
# Setup General
################################################################################
## Download and prepare all datasets
data: data-eterna data-rfam-taneda data-rfam-learn
## Download and make the Eterna100 dataset
data-eterna:
@source activate learna && \
python -m src.data.download_and_build_eterna
./src/data/secondaries_to_single_files.sh data/eterna data/eterna/interim/eterna.txt
## Download and build the Rfam-Taneda dataset
data-rfam-taneda:
@./src/data/download_and_build_rfam_taneda.sh
## Download and build the Rfam-Learn dataset
data-rfam-learn:
@./src/data/download_and_build_rfam_learn.sh
mv data/rfam_learn/test data/rfam_learn_test
mv data/rfam_learn/validation data/rfam_learn_validation
mv data/rfam_learn/train data/rfam_learn_train
rm -rf data/rfam_learn
################################################################################
# Setup LEARNA
################################################################################
## Install all dependencies
requirements:
./thirdparty/miniconda/make_miniconda.sh
conda env create -f environment.yml
################################################################################
# Test Experiment and Example
################################################################################
## Local experiment testing
experiment-test:
@source activate learna && \
python -m src.learna.design_rna \
--mutation_threshold 5 \
--batch_size 126 \
--conv_sizes 17 5 \
--conv_channels 7 18 \
--embedding_size 3 \
--entropy_regularization 6.762991409135427e-05 \
--fc_units 57 \
--learning_rate 0.0005991629320464973 \
--lstm_units 28 \
--num_fc_layers 1 \
--num_lstm_layers 1 \
--reward_exponent 9.33503385734547 \
--state_radius 32 \
--restart_timeout 1800 \
--target_structure_path data/eterna/2.rna \
--timeout 30
################################################################################
# Reproduce Results of LEARNA
################################################################################
## Reproduce LEARNA on <id> (1-100) of Eterna100
reproduce-LEARNA-Eterna-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 86400 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method LEARNA \
--dataset eterna \
--task_id $*
## Reproduce LEARNA on <id> (1-29) of Rfam-Taneda
reproduce-LEARNA-Rfam-Taneda-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method LEARNA \
--dataset rfam_taneda \
--task_id $*
## Reproduce LEARNA-30min on <id> (1-100) of Rfam-Learn-Test
reproduce-LEARNA-Rfam-Learn-Test-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 3600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method LEARNA \
--dataset rfam_learn_test \
--task_id $*
################################################################################
# Reproduce Results of Meta-LEARNA
################################################################################
## Reproduce Meta-LEARNA on <id> (1-100) of Eterna100
reproduce-Meta-LEARNA-Eterna-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 86400 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA \
--dataset eterna \
--task_id $*
## Reproduce Meta-LEARNA on <id> (1-29) of Rfam-Taneda
reproduce-Meta-LEARNA-Rfam-Taneda-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA \
--dataset rfam_taneda \
--task_id $*
## Reproduce Meta-LEARNA on <id> (1-100) of Rfam-Learn-Test
reproduce-Meta-LEARNA-Rfam-Learn-Test-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 3600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA \
--dataset rfam_learn_test \
--task_id $*
################################################################################
# Reproduce Results of Meta-LEARNA-Adapt
################################################################################
## Reproduce Meta-LEARNA-Adapt on <id> (1-100) of Eterna100
reproduce-Meta-LEARNA-Adapt-Eterna-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 86400 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA-Adapt \
--dataset eterna \
--task_id $*
## Reproduce Meta-LEARNA-Adapt on <id> (1-29) of Rfam-Taneda
reproduce-Meta-LEARNA-Adapt-Rfam-Taneda-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA-Adapt \
--dataset rfam_taneda \
--task_id $*
## Reproduce Meta-LEARNA-Adapt on <id> (1-100) of Rfam-Learn-Test
reproduce-Meta-LEARNA-Adapt-Rfam-Learn-Test-%:
@source activate learna && \
python utils/timed_execution.py \
--timeout 3600 \
--data_dir data/ \
--results_dir results/ \
--experiment_group reproduce_iclr_2019 \
--method Meta-LEARNA-Adapt \
--dataset rfam_learn_test \
--task_id $*
################################################################################
# Joint Architecture and Hyperparameter Search
################################################################################
## Run an example for joint Hyperparameter and Architecture Search using BOHB
bohb-example:
@source activate learna && \
python -m src.optimization.bohb \
--min_budget 2 \
--max_budget 8 \
--n_iter 1 \
--n_cores 1 \
--run_id example \
--data_dir data \
--nic_name lo \
--shared_directory results/ \
--mode learna
################################################################################
# Analysis and Visualization
################################################################################
## Analyse experiment group %
analyse:
@source activate learna && \
python -m src.analyse.analyse_experiment_group --experiment_group results/reproduce_iclr_2019 --analysis_dir analysis/reproduce_iclr_2019 --root_sequences_dir data --ci_alpha 0.05
## Plot reproduced results using pgfplots
plots:
rm -rf results/plots/
@source activate learna && \
pdflatex -synctex=1 -interaction=nonstopmode -shell-escape results/plots.tex
mkdir -p results/plots/
mv pgfplots.pdf results/plots/
rm -f pgfplots*
################################################################################
# Help
################################################################################
# From https://drivendata.github.io/cookiecutter-data-science/
show-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=$$(tput cols) \
-v indent=22 \
-v col_on="$$(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')