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KaiWaldrant committed Jun 28, 2024
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## Benchmarking ([docs](https://openproblems.bio/documentation/create_task/create_workflow))

When you are finished with creating your components and datset processor you can create a workflow to benchmark the components. This workflow will be created together with the openproblems team.





<!-- Add to readme
* update _viash.yaml
* update src/api/task_info.yaml
* update scripts/download_resources
-->

<!-- #!/bin/bash
echo "This script is not supposed to be run directly."
echo "Please run the script step-by-step."
exit 1
# sync resources
scripts/download_resources.sh
# create a new component
method_id="my_metric"
method_lang="python" # change this to "r" if need be
common/create_component/create_component -- \
--language "$method_lang" \
--name "$method_id"
# TODO: fill in required fields in src/task/methods/foo/config.vsh.yaml
# TODO: edit src/task/methods/foo/script.py/R
# test the component
viash test src/task/methods/$method_id/config.vsh.yaml
# rebuild the container (only if you change something to the docker platform)
# You can reduce the memory and cpu allotted to jobs in _viash.yaml by modifying .platforms[.type == "nextflow"].config.labels
viash run src/task/methods/$method_id/config.vsh.yaml -- \
---setup cachedbuild ---verbose
# run the method (using parquet as input)
viash run src/task/methods/$method_id/config.vsh.yaml -- \
--de_train "resources/neurips-2023-kaggle/de_train.parquet" \
--id_map "resources/neurips-2023-kaggle/id_map.csv" \
--output "output/prediction.parquet"
# run the method (using h5ad as input)
viash run src/task/methods/$method_id/config.vsh.yaml -- \
--de_train_h5ad "resources/neurips-2023-kaggle/2023-09-12_de_by_cell_type_train.h5ad" \
--id_map "resources/neurips-2023-kaggle/id_map.csv" \
--output "output/prediction.parquet"
# run evaluation metric
viash run src/task/metrics/mean_rowwise_error/config.vsh.yaml -- \
--de_test "resources/neurips-2023-kaggle/de_test.parquet" \
--prediction "output/prediction.parquet" \
--output "output/score.h5ad"
# print score on kaggle test dataset
python -c 'import anndata; print(anndata.read_h5ad("output/score.h5ad").uns)' -->

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